Tootfinder

Cooperative Base Station Assignment and Resource Allocation for 6G ISAC Network
Jiajia Liao, Luping Xiang, Shida Zhong, Lixia Xiao, Haochen Liu, Kun Yang
https://arxiv.org/abs/2509.10240

Cooperative Base Station Assignment and Resource Allocation for 6G ISAC Network
In the upcoming 6G networks, integrated sensing and communications (ISAC) will be able to provide a performance boost in both perception and wireless connectivity. This paper considers a multiple base station (BS) architecture to support the comprehensive services of data transmission and multi-target sensing. In this context, a cooperative BS assignment and resource allocation (CBARA) strategy is proposed in this paper, aiming at jointly optimizing the communication and sensing (C&S) performan…

Byzantine-Resilient Decentralized Online Resource Allocation
Runhua Wang, Qing Ling, Hoi-To Wai, Zhi Tian
https://arxiv.org/abs/2508.08658 https://arxiv.or…

Byzantine-Resilient Decentralized Online Resource Allocation
In this paper, we investigate the problem of decentralized online resource allocation in the presence of Byzantine attacks. In this problem setting, some agents may be compromised due to external manipulations or internal failures, causing them to behave maliciously and disrupt the resource allocation process by sending incorrect messages to their neighbors. Given the non-consensual nature of the resource allocation problem, we formulate it under a primal-dual optimization framework, where the …

Online Allocation with Concave, Diminishing-Returns Objectives
Kalen Patton
https://arxiv.org/abs/2510.11266 https://arxiv.org/pdf/2510.11266

Online Allocation with Concave, Diminishing-Returns Objectives
Online resource allocation problems are central challenges in economics and computer science, modeling situations in which $n$ items arriving one at a time must each be immediately allocated among $m$ agents. In such problems, our objective is to maximize a monotone reward function $f(\mathbf{x})$ over the allocation vector $\mathbf{x} = (x_{ij})_{i, j}$, which describes the amount of each item given to each agent. In settings where $f$ is concave and has "diminishing returns" (monotone decreas…

CPU-Limits kill Performance: Time to rethink Resource Control
Chirag Shetty, Sarthak Chakraborty, Hubertus Franke, Larisa Shwartz, Chandra Narayanaswami, Indranil Gupta, Saurabh Jha
https://arxiv.org/abs/2510.10747

CPU-Limits kill Performance: Time to rethink Resource Control
Research in compute resource management for cloud-native applications is dominated by the problem of setting optimal CPU limits -- a fundamental OS mechanism that strictly restricts a container's CPU usage to its specified CPU-limits . Rightsizing and autoscaling works have innovated on allocation/scaling policies assuming the ubiquity and necessity of CPU-limits . We question this. Practical experiences of cloud users indicate that CPU-limits harms application performance and costs more than i…

A Framework for Distributed Resource Allocation in Quantum Networks
Nitish K. Panigrahy, Leonardo Bacciottini, C. V. Hollot, Emily A. Van Milligen, Matheus Guedes de Andrade, Nageswara S. V. Rao, Gayane Vardoyan, Don Towsley
https://arxiv.org/abs/2510.09371

A Framework for Distributed Resource Allocation in Quantum Networks
We introduce a distributed resource allocation framework for the Quantum Internet that relies on feedback-based, fully decentralized coordination to serve multiple co-existing applications. We develop quantum network control algorithms under the mathematical framework of Quantum Network Utility Maximization (QNUM), where utility functions quantify network performance by mapping entanglement rate and quality into a joint optimization objective. We then introduce QPrimal-Dual, a decentralized, sc…

LLMs for Resource Allocation: A Participatory Budgeting Approach to Inferring Preferences
Sankarshan Damle, Boi Faltings
https://arxiv.org/abs/2508.06060 https://

LLMs for Resource Allocation: A Participatory Budgeting Approach to Inferring Preferences
Large Language Models (LLMs) are increasingly expected to handle complex decision-making tasks, yet their ability to perform structured resource allocation remains underexplored. Evaluating their reasoning is also difficult due to data contamination and the static nature of existing benchmarks. We present a dual-purpose framework leveraging Participatory Budgeting (PB) both as (i) a practical setting for LLM-based resource allocation and (ii) an adaptive benchmark for evaluating their reasoning…

Toward Automated Hypervisor Scenario Generation Based on VM Workload Profiling for Resource-Constrained Environments
Hyunwoo Kim, Jaeseong Lee, Sunpyo Hong, Changmin Han
https://arxiv.org/abs/2508.08952

Toward Automated Hypervisor Scenario Generation Based on VM Workload Profiling for Resource-Constrained Environments
In the automotive industry, the rise of software-defined vehicles (SDVs) has driven a shift toward virtualization-based architectures that consolidate diverse automotive workloads on a shared hardware platform. To support this evolution, chipset vendors provide board support packages (BSPs), hypervisor setups, and resource allocation guidelines. However, adapting these static configurations to varying system requirements and workloads remain a significant challenge for Tier 1 integr…

Who pays the RENT? Implications of Spatial Inequality for Prediction-Based Allocation Policies
Tasfia Mashiat, Patrick J. Fowler, Sanmay Das
https://arxiv.org/abs/2508.08573 htt…

Who pays the RENT? Implications of Spatial Inequality for Prediction-Based Allocation Policies
AI-powered scarce resource allocation policies rely on predictions to target either specific individuals (e.g., high-risk) or settings (e.g., neighborhoods). Recent research on individual-level targeting demonstrates conflicting results; some models show that targeting is not useful when inequality is high, while other work demonstrates potential benefits. To study and reconcile this apparent discrepancy, we develop a stylized framework based on the Mallows model to understand how the spatial d…

HiSTM: Hierarchical Spatiotemporal Mamba for Cellular Traffic Forecasting
Zineddine Bettouche, Khalid Ali, Andreas Fischer, Andreas Kassler
https://arxiv.org/abs/2508.09184 http…

HiSTM: Hierarchical Spatiotemporal Mamba for Cellular Traffic Forecasting
Cellular traffic forecasting is essential for network planning, resource allocation, or load-balancing traffic across cells. However, accurate forecasting is difficult due to intricate spatial and temporal patterns that exist due to the mobility of users. Existing AI-based traffic forecasting models often trade-off accuracy and computational efficiency. We present Hierarchical SpatioTemporal Mamba (HiSTM), which combines a dual spatial encoder with a Mamba-based temporal module and attention me…

Fair Resource Allocation for Fleet Intelligence
Oguzhan Baser, Kaan Kale, Po-han Li, Sandeep Chinchali
https://arxiv.org/abs/2509.03353 https://arxiv.org/p…

Fair Resource Allocation for Fleet Intelligence
Resource allocation is crucial for the performance optimization of cloud-assisted multi-agent intelligence. Traditional methods often overlook agents' diverse computational capabilities and complex operating environments, leading to inefficient and unfair resource distribution. To address this, we open-sourced Fair-Synergy, an algorithmic framework that utilizes the concave relationship between the agents' accuracy and the system resources to ensure fair resource allocation across fleet intelli…

On the Convergence of a Noisy Gradient Method for Non-convex Distributed Resource Allocation: Saddle Point Escape
Lei Qin, Ye Pu
https://arxiv.org/abs/2508.06922 https://…

On the Convergence of a Noisy Gradient Method for Non-convex Distributed Resource Allocation: Saddle Point Escape
This paper considers a class of distributed resource allocation problems where each agent privately holds a smooth, potentially non-convex local objective, subject to a globally coupled equality constraint. Built upon the existing method, Laplacian-weighted Gradient Descent, we propose to add random perturbations to the gradient iteration to enable efficient escape from saddle points and achieve second-order convergence guarantees. We show that, with a sufficiently small fixed step size, the it…

Joint Optimization of Computation Offloading and Resource Allocation in ISAC-assisted SAGIN-based IoT
Sooyeob Jung, Seongah Jeong, Jinkyu Kang
https://arxiv.org/abs/2509.08238 h…

Joint Optimization of Computation Offloading and Resource Allocation in ISAC-assisted SAGIN-based IoT
In this letters, an energy-efficient integrated sensing and communication (ISAC) for space-air-ground integrated network (SAGIN)-based Internet of Things (IoT) systems is proposed to facilitate wide coverage and real-time 6G services. For processing a sizable data collected at a IoT device, a hybrid edge computing scheme is applied with the cloudlets mounted at autonomous aerial vehicle (AAV) and low earth orbit (LEO) satellite, where the AAV with multiple antennas performs uplink sensing of th…

Full-Dimensional Beamforming for Multi-User MIMO-OFDM ISAC for Low-Altitude UAV with Zero Sensing Resource Allocation
Zhiwen Zhou
https://arxiv.org/abs/2508.06428 https://

Full-Dimensional Beamforming for Multi-User MIMO-OFDM ISAC for Low-Altitude UAV with Zero Sensing Resource Allocation
Low-altitude unmanned aerial vehicles (UAVs) are expected to play an important role for low-altitude economy with a wide range of applications like precise agriculture, aerial delivery and surveillance. Integrated sensing and communication (ISAC) is a key technology to enable the large-scale deployment and routine usage of UAVs by providing both communication and sensing services efficiently. For UAV ISAC systems, as UAV often acts as both a communication user equipment (UE) and a sensing targe…

Mechanism design and equilibrium analysis of smart contract mediated resource allocation
Jinho Cha, Justin Yoo, Eunchan Daniel Cha, Emily Yoo, Caedon Geoffrey, Hyoshin Song
https://arxiv.org/abs/2510.05504

Mechanism design and equilibrium analysis of smart contract mediated resource allocation
Decentralized coordination and digital contracting are becoming critical in complex industrial ecosystems, yet existing approaches often rely on ad hoc heuristics or purely technical blockchain implementations without a rigorous economic foundation. This study develops a mechanism design framework for smart contract-based resource allocation that explicitly embeds efficiency and fairness in decentralized coordination. We establish the existence and uniqueness of contract equilibria, extending c…

Joint Scheduling and Resource Allocation in mmWave IAB Networks Using Deep RL
Maryam Abbasalizadeh, Sashank Narain
https://arxiv.org/abs/2508.07604 https://

Joint Scheduling and Resource Allocation in mmWave IAB Networks Using Deep RL
Integrated Access and Backhaul (IAB) is critical for dense 5G and beyond deployments, especially in mmWave bands where fiber backhaul is infeasible. We propose a novel Deep Reinforcement Learning (DRL) framework for joint link scheduling and resource slicing in dynamic, interference-prone IAB networks. Our method integrates a greedy Double Deep Q-Network (DDQN) scheduler to activate access and backhaul links based on traffic and topology, with a multi-agent DDQN allocator for bandwidth and ante…

DREAMS: Decentralized Resource Allocation and Service Management across the Compute Continuum Using Service Affinity
Hai Dinh-Tuan, Tien Hung Nguyen, Sanjeet Raj Pandey
https://arxiv.org/abs/2509.07497

DREAMS: Decentralized Resource Allocation and Service Management across the Compute Continuum Using Service Affinity
Modern manufacturing systems require adaptive computing infrastructures that can respond to highly dynamic workloads and increasingly customized production demands. The compute continuum emerges as a promising solution, enabling flexible deployment of microservices across distributed, heterogeneous domains. However, this paradigm also requires a novel approach to resource allocation and service placement, as traditional centralized solutions struggle to scale effectively, suffer from latency bo…

Using AI to Optimize Patient Transfer and Resource Utilization During Mass-Casualty Incidents: A Simulation Platform
Zhaoxun "Lorenz" Liu, Wagner H. Souza, Jay Han, Amin Madani
https://arxiv.org/abs/2509.08756

Using AI to Optimize Patient Transfer and Resource Utilization During Mass-Casualty Incidents: A Simulation Platform
Mass casualty incidents (MCIs) overwhelm healthcare systems and demand rapid, accurate patient-hospital allocation decisions under extreme pressure. Here, we developed and validated a deep reinforcement learning-based decision-support AI agent to optimize patient transfer decisions during simulated MCIs by balancing patient acuity levels, specialized care requirements, hospital capacities, and transport logistics. To integrate this AI agent, we developed MasTER, a web-accessible command dashboa…

Dynamic Solutions for Hybrid Quantum-HPC Resource Allocation
Roberto Rocco, Simone Rizzo, Matteo Barbieri, Gabriella Bettonte, Elisabetta Boella, Fulvio Ganz, Sergio Iserte, Antonio J. Pe\~na, Petter Sand\r{a}s, Alberto Scionti, Olivier Terzo, Chiara Vercellino, Giacomo Vitali, Paolo Viviani, Jonathan Frassineti, Sara Marzella, Daniele Ottaviani, Iacopo Colonnelli, Daniele Gregori

Dynamic Solutions for Hybrid Quantum-HPC Resource Allocation
The integration of quantum computers within classical High-Performance Computing (HPC) infrastructures is receiving increasing attention, with the former expected to serve as accelerators for specific computational tasks. However, combining HPC and quantum computers presents significant technical challenges, including resource allocation. This paper presents a novel malleability-based approach, alongside a workflow-based strategy, to optimize resource utilization in hybrid HPC-quantum workloads…

XGBoost meets INLA: a two-stage spatio-temporal forecasting of wildfires in Portugal
Chenglei Hu, Regina Baltazar Bispo, H{\aa}vard Rue, Carlos C. DaCamara, Ben Swallow, Daniela Castro-Camilo
https://arxiv.org/abs/2508.09896

XGBoost meets INLA: a two-stage spatio-temporal forecasting of wildfires in Portugal
Wildfires pose a major threat to Portugal, with an average of over 115,000 hectares burned annually in the 45-year period of 1980-2024. Beyond a high number of ignitions, the country has experienced devastating mega-fires, such as those in 2017. Accurate forecasting of wildfire occurrence and burned areas is therefore essential for effective firefighting resource allocation and emergency preparedness. In this study, we present a novel two-stage ensemble approach that extends the widely used lat…

Online Multiple Resource Allocation Problems with Departures via the Primal-Dual Approach
Yusuf Amidu, Khaled Elbassioni, Adriana F. Gabor
https://arxiv.org/abs/2510.04737 https…

Online Multiple Resource Allocation Problems with Departures via the Primal-Dual Approach
In this paper we propose primal-dual algorithms for different variants of the online resource allocation problem with departures. In the basic variant, requests (items) arrive over time to a set of resources (knapsacks) and upon arrival, the duration of time a request may occupy a resource, the demand and reward if the request can be granted, become known. %We assume that the duration of stay of a request may depend on the resource. %and that resources may have different capacity sizes. The goa…

Reducing Cartel Violence: The Mexican Dilemma Between Social and Security Spending
Rafael Prieto-Curiel, Dieter Grass, Stefan Wrzaczek, Gian Maria Campedelli, Gernot Tragler, Gustav Feichtinger
https://arxiv.org/abs/2508.06509

Reducing Cartel Violence: The Mexican Dilemma Between Social and Security Spending
Organised crime in Mexico threatens societal stability and public safety, driving pervasive violence and economic disruption. Despite security investments and social programs designed in part to reduce involvement in crime, cartel power and violence continue to persist. This study evaluates existing policies and introduces a novel framework using optimal control theory to analyse cartel dynamics. Specifically, by modelling resource allocation between security measures and social programs, we id…

Auctioning Future Services in Edge Networks with Moving Vehicles: N-Step Look-Ahead Contracts for Sustainable Resource Provision
Ziqi Ling, Minghui Liwang, Xianbin Wang, Seyyedali Hosseinalipour, Zhipeng Cheng, Sai Zou, Wei Ni, Xiaoyu Xia
https://arxiv.org/abs/2510.07333

Auctioning Future Services in Edge Networks with Moving Vehicles: N-Step Look-Ahead Contracts for Sustainable Resource Provision
Timely resource allocation in edge-assisted vehicular networks is essential for compute-intensive services such as autonomous driving and navigation. However, vehicle mobility leads to spatio-temporal unpredictability of resource demands, while real-time double auctions incur significant latency. To address these challenges, we propose a look-ahead contract-based auction framework that shifts decision-making from runtime to planning time. Our approach establishes N-step service contracts betwee…

DORA: Dynamic O-RAN Resource Allocation for Multi-Slice 5G Networks
Alireza Ebrahimi Dorcheh, Tolunay Seyfi, Fatemeh Afghah
https://arxiv.org/abs/2509.07242 https://

DORA: Dynamic O-RAN Resource Allocation for Multi-Slice 5G Networks
The fifth generation (5G) of wireless networks must simultaneously support heterogeneous service categories, including Ultra-Reliable Low-Latency Communications (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine-Type Communications (mMTC), each with distinct Quality of Service (QoS) requirements. Meeting these demands under limited spectrum resources requires adaptive and standards-compliant radio resource management. We present DORA (Dynamic O-RAN Resource Allocation), a deep reinf…

Optimal Distortion-Aware Multi-User Power Allocation for Massive MIMO Networks
Siddarth Marwaha, Pawel Kryszkiewicz, Eduard Jorswieck
https://arxiv.org/abs/2509.06491 https://…

Optimal Distortion-Aware Multi-User Power Allocation for Massive MIMO Networks
Real-world wireless transmitter front-ends exhibit certain nonlinear behavior, e.g., signal clipping by a Power Amplifier (PA). Although many resource allocation solutions do not consider this for simplicity, it leads to inaccurate results or a reduced number of degrees of freedom, not achieving the global performance. In this work, we propose an optimal PA distortion-aware power allocation strategy in a downlink orthogonal frequency division multiplex (OFDM) based massive multiple-input multip…

Cooperative Flexibility Exchange: Fair and Comfort-Aware Decentralized Resource Allocation
Rabiya Khalid, Evangelos Pournaras
https://arxiv.org/abs/2510.04192 https://

Cooperative Flexibility Exchange: Fair and Comfort-Aware Decentralized Resource Allocation
The growing electricity demand and increased use of smart appliances are placing new pressures on power grids, making efficient energy management more important than ever. The existing energy management systems often prioritize system efficiency (balanced energy demand and supply) at the expense of user comfort. This paper addresses this gap by proposing a novel decentralized multi-agent coordination-based demand-side management system. The proposed system enables individual agents to coordinat…

Toward Energy and Location-Aware Resource Allocation in Next Generation Networks
Mandar Datar (CEA-LETI), Mattia Merluzzi (CEA-LETI)
https://arxiv.org/abs/2508.05109 https://

Toward Energy and Location-Aware Resource Allocation in Next Generation Networks
Wireless networks are evolving from radio resource providers to complex systems that also involve computing, with the latter being distributed across edge and cloud facilities. Also, their optimization is shifting more and more from a performance to a value-oriented paradigm. The two aspects shall be balanced continuously, to maximize the utilities of Services Providers (SPs), users quality of experience and fairness, while meeting global constraints in terms of energy consumption and carbon fo…

To Trust or Not to Trust: On Calibration in ML-based Resource Allocation for Wireless Networks
Rashika Raina, Nidhi Simmons, David E. Simmons, Michel Daoud Yacoub, Trung Q. Duong
https://arxiv.org/abs/2507.17494

To Trust or Not to Trust: On Calibration in ML-based Resource Allocation for Wireless Networks
In next-generation communications and networks, machine learning (ML) models are expected to deliver not only accurate predictions but also well-calibrated confidence scores that reflect the true likelihood of correct decisions. This paper studies the calibration performance of an ML-based outage predictor within a single-user, multi-resource allocation framework. We first establish key theoretical properties of this system's outage probability (OP) under perfect calibration. Importantly, we sh…

Crosslisted article(s) found for cs.SI. https://arxiv.org/list/cs.SI/new
[1/1]:
- Multi-Topic Projected Opinion Dynamics for Resource Allocation
Prashil Wankhede, Nirabhra Mandal, Sonia Mart\'inez, Pavankumar Tallapragada

QoS-Aware Integrated Sensing, Communication, and Control with Movable Antenna
Yike Wang, Zhike Wu, Jiang Chen, Chunjie Wang, Xuhui Zhang, Yanyan Shen
https://arxiv.org/abs/2508.07799

QoS-Aware Integrated Sensing, Communication, and Control with Movable Antenna
Integrated sensing, communication, and control (ISCC) has emerged as a key enabler for low-altitude wireless networks with enhanced adaptability through resource allocation co-design and intelligent environment awareness. However, dynamic interference and channel attenuation constrain the potential of the ISCC system. To address this challenge, we propose a novel movable antenna-empowered ISCC system. An achievable data rate maximization problem is formulated while guaranteeing the sensing and …

Distributed Resource Selection for Self-Organising Cloud-Edge Systems
Quentin Renau, Amjad Ullah, Emma Hart
https://arxiv.org/abs/2510.08228 https://arxiv.…

Distributed Resource Selection for Self-Organising Cloud-Edge Systems
This paper presents a distributed resource selection mechanism for diverse cloud-edge environments, enabling dynamic and context-aware allocation of resources to meet the demands of complex distributed applications. By distributing the decision-making process, our approach ensures efficiency, scalability, and resilience in highly dynamic cloud-edge environments where centralised coordination becomes a bottleneck. The proposed mechanism aims to function as a core component of a broader, distribu…

The Online Submodular Cover Problem
Anupam Gupta, Roie Levin
https://arxiv.org/abs/2510.08883 https://arxiv.org/pdf/2510.08883

The Online Submodular Cover Problem
In the submodular cover problem, we are given a monotone submodular function $f$, and we want to pick the min-cost set $S$ such that $f(S) = f(N)$. Motivated by problems in network monitoring and resource allocation, we consider the submodular cover problem in an online setting. As a concrete example, suppose at each time $t$, a nonnegative monotone submodular function $g_t$ is given to us. We define $f^{(t)} = \sum_{s \leq t} g_s$ as the sum of all functions seen so far. We need to maintain a …

New Features We Find Exciting in the #Kubernetes 1.34 Release
https://metalbear.co/blog/kubernetes-1-34/

Distributed Constraint-coupled Resource Allocation: Anytime Feasibility and Violation Robustness
Wenwen Wu, Shanying Zhu, Cailian Chen, Xinping Guan
https://arxiv.org/abs/2508.02164

Distributed Constraint-coupled Resource Allocation: Anytime Feasibility and Violation Robustness
This paper considers distributed resource allocation problems (DRAPs) with a coupled constraint for real-time systems. Based on primal-dual methods, we adopt a control perspective for optimization algorithm design by synthesizing a safe feedback controller using control barrier functions to enforce constraint satisfaction. On this basis, a distributed anytime-feasible resource allocation (DanyRA) algorithm is proposed. It is shown that DanyRA algorithm converges to the exact optimal solution of…

Resource Allocation and Beamforming in FIM-Assisted BS and STAR-BD-RIS-Aided NOMA: A Meta-Learning Approach
Armin Farhadi, Maryam Cheraghy, Qingqing Wu, Eduard Jorswieck
https://arxiv.org/abs/2509.05692

Resource Allocation and Beamforming in FIM-Assisted BS and STAR-BD-RIS-Aided NOMA: A Meta-Learning Approach
This study explores a flexible intelligent metasurface (FIM)-based wireless communication system that integrates simultaneously transmitting and reflecting beyond diagonal reconfigurable intelligent surfaces (STAR-BD-RIS) with non-orthogonal multiple access (NOMA). The system features a multi-antenna FIM-assisted base station (BS) aided by dual-sector BD-RIS. The FIM consists of cost-effective radiating elements that can independently emit signals and dynamically adjust their vertical positions…

A Task Equalization Allocation Algorithm Incorporating Blocking Estimation and Resource Similarity Analysis for Vehicle Control Real-Time Systems
Qianlong Duan, Bide Hao, Fan Zhou, Chen Fei, Shichun Yang
https://arxiv.org/abs/2509.14086

A Task Equalization Allocation Algorithm Incorporating Blocking Estimation and Resource Similarity Analysis for Vehicle Control Real-Time Systems
In multi-core real-time vehicle control systems, synchronization blocking and resource contention pose critical challenges due to increasing task parallelism and shared resource access. These issues significantly degrade system schedulability and real-time performance, as traditional task allocation algorithms often overlook blocking impacts, leading to high scheduling failure rates under heavy loads. To address this, we propose the BR-WFD algorithm, which integrates blocking time estimation an…

Unsupervised Learning based Element Resource Allocation for Reconfigurable Intelligent Surfaces in mmWave Network
Pujitha Mamillapalli, Yoghitha Ramamoorthi, Abhinav Kumar, Tomoki Murakami, Tomoaki Ogawa, Yasushi Takatori
https://arxiv.org/abs/2509.03241

Unsupervised Learning based Element Resource Allocation for Reconfigurable Intelligent Surfaces in mmWave Network
The increasing demand for high data rates and seamless connectivity in wireless systems has sparked significant interest in reconfigurable intelligent surfaces (RIS) and artificial intelligence-based wireless applications. RIS typically comprises passive reflective antenna elements that control the wireless propagation environment by adequately tuning the phase of the reflective elements. The allocation of RIS elements to multipleuser equipment (UEs) is crucial for efficiently utilizing RIS. In…

Joint Routing, Resource Allocation, and Energy Optimization for Integrated Access and Backhaul with Open RAN
Reshma Prasad, Maxime Elkael, Gabriele Gemmi, Osama M. Bushnaq, Debashisha Mishra, Prasanna Raut, Jennifer Simonjan, Michele Polese, Tommaso Melodia
https://arxiv.org/abs/2509.05467

Joint Routing, Resource Allocation, and Energy Optimization for Integrated Access and Backhaul with Open RAN
As networks evolve towards 6G, Mobile Network Operators (MNOs) must accommodate diverse requirements and at the same time manage rising energy consumption. Integrated Access and Backhaul (IAB) networks facilitate dense cellular deployments with reduced infrastructure complexity. However, the multi-hop wireless backhauling in IAB networks necessitates proper routing and resource allocation decisions to meet the performance requirements. At the same time, cell densification makes energy optimizat…

Artificial Intelligence for Cost-Aware Resource Prediction in Big Data Pipelines
Harshit Goyal
https://arxiv.org/abs/2510.05127 https://arxiv.org/pdf/2510.…

Artificial Intelligence for Cost-Aware Resource Prediction in Big Data Pipelines
Efficient resource allocation is a key challenge in modern cloud computing. Over-provisioning leads to unnecessary costs, while under-provisioning risks performance degradation and SLA violations. This work presents an artificial intelligence approach to predict resource utilization in big data pipelines using Random Forest regression. We preprocess the Google Borg cluster traces to clean, transform, and extract relevant features (CPU, memory, usage distributions). The model achieves high predi…

Ensemble of Pathology Foundation Models for MIDOG 2025 Track 2: Atypical Mitosis Classification
Mieko Ochi, Bae Yuan
https://arxiv.org/abs/2509.02591 https://

Ensemble of Pathology Foundation Models for MIDOG 2025 Track 2: Atypical Mitosis Classification
Mitotic figures are classified into typical and atypical variants, with atypical counts correlating strongly with tumor aggressiveness. Accurate differentiation is therefore essential for patient prognostication and resource allocation, yet remains challenging even for expert pathologists. Here, we leveraged Pathology Foundation Models (PFMs) pre-trained on large histopathology datasets and applied parameter-efficient fine-tuning via low-rank adaptation. During training, we employ a fisheye tra…

Demo: Secure Edge Server for Network Slicing and Resource Allocation in Open RAN
Adhwaa Alchaab, Ayman Younis, Dario Pompili
https://arxiv.org/abs/2507.11499

Demo: Secure Edge Server for Network Slicing and Resource Allocation in Open RAN
Next-Generation Radio Access Networks (NGRAN) aim to support diverse vertical applications with strict security, latency, and Service-Level Agreement (SLA) requirements. These demands introduce challenges in securing the infrastructure, allocating resources dynamically, and enabling real-time reconfiguration. This demo presents SnSRIC, a secure and intelligent network slicing framework that mitigates a range of Distributed Denial-of-Service (DDoS) attacks in Open RAN environments. SnSRIC incorp…

Feasibility Guaranteed Learning-to-Optimize in Wireless Communication Resource Allocation
Hanwen Zhang, Haijian Sun
https://arxiv.org/abs/2509.02417 https://

Feasibility Guaranteed Learning-to-Optimize in Wireless Communication Resource Allocation
The emergence of 6G wireless communication enables massive edge device access and supports real-time intelligent services such as the Internet of things (IoT) and vehicle-to-everything (V2X). However, the surge in edge devices connectivity renders wireless resource allocation (RA) tasks as large-scale constrained optimization problems, whereas the stringent real-time requirement poses significant computational challenge for traditional algorithms. To address the challenge, feasibility guarantee…

A Heuristic Method for Simplified Resource Allocation based on Comparative Advantage in Wireless Access Systems
Lin Cheng, Bernardo A. Huberman
https://arxiv.org/abs/2508.01824 …

A Heuristic Method for Simplified Resource Allocation based on Comparative Advantage in Wireless Access Systems
This paper presents a heuristic method for simplifying resource allocation in access systems, leveraging the concept of comparative advantage to reduce computational complexity while maintaining near-optimal performance. Using power-division non-orthogonal multiple access (PD-NOMA) as an example, we demonstrate how this approach mitigates the challenge of power allocation in multi-cell networks. Our method reduces the search space for optimization, significantly decreasing computational overhea…

Effective resource allocation to combat invasions of the spotted lanternfly (Lycorma delicatula) and similar pests
Daniel Str\"ombom, Julianna Hoitt, Jinrong Hu, Swati Pandey, Elizabeth Batchelar
https://arxiv.org/abs/2508.01117

Effective resource allocation to combat invasions of the spotted lanternfly (Lycorma delicatula) and similar pests
The spotted lanternfly is rapidly establishing itself as a major insect pest with global implications. Despite significant management efforts, its spread continues in invaded regions, and refined management strategies are required. A recent study introduced a model that generalized the results of empirical control efficacy studies by incorporating population dynamics and incomplete delivery. In particular, a generalized population growth formula was derived, providing the minimum proportion of …

Prioritizing Latency with Profit: A DRL-Based Admission Control for 5G Network Slices
Proggya Chakraborty, Aaquib Asrar, Jayasree Sengupta, Sipra Das Bit
https://arxiv.org/abs/2510.08769

Prioritizing Latency with Profit: A DRL-Based Admission Control for 5G Network Slices
5G networks enable diverse services such as eMBB, URLLC, and mMTC through network slicing, necessitating intelligent admission control and resource allocation to meet stringent QoS requirements while maximizing Network Service Provider (NSP) profits. However, existing Deep Reinforcement Learning (DRL) frameworks focus primarily on profit optimization without explicitly accounting for service delay, potentially leading to QoS violations for latency-sensitive slices. Moreover, commonly used epsil…

Crosslisted article(s) found for q-fin.GN. https://arxiv.org/list/q-fin.GN/new
[1/1]:
- Mechanism design and equilibrium analysis of smart contract mediated resource allocation
Jinho Cha, Justin Yoo, Eunchan Daniel Cha, Emily Yoo, Caedon Geoffrey, Hyoshin Song

Adaptive KV-Cache Compression without Manually Setting Budget
Chenxia Tang, Jianchun Liu, Hongli Xu, Liusheng Huang
https://arxiv.org/abs/2509.03136 https://

Adaptive KV-Cache Compression without Manually Setting Budget
Large language models (LLMs) inference relies heavily on KV-caches to accelerate autoregressive decoding, but the resulting memory footprint grows rapidly with sequence length, posing significant efficiency challenges. Current KV-cache compression methods suffer from a Procrustes' bed problem: they force diverse workloads into fixed compression ratios, leading to suboptimal resource allocation and inference performance. To this end, we present GVote, an adaptive KV-cache compression scheme that…

Asynchronous and Stochastic Distributed Resource Allocation
Qiang Li, Michal Yemini, Hoi-To Wai
https://arxiv.org/abs/2509.01172 https://arxiv.org/pdf/2509…

Asynchronous and Stochastic Distributed Resource Allocation
This work proposes and studies the distributed resource allocation problem in asynchronous and stochastic settings. We consider a distributed system with multiple workers and a coordinating server with heterogeneous computation and communication times. We explore an approximate stochastic primal-dual approach with the aim of 1) adhering to the resource budget constraints, 2) allowing for the asynchronicity between the workers and the server, and 3) relying on the locally available stochastic gr…

Automated Constraint Specification for Job Scheduling by Regulating Generative Model with Domain-Specific Representation
Yu-Zhe Shi, Qiao Xu, Yanjia Li, Mingchen Liu, Huamin Qu, Lecheng Ruan, Qining Wang
https://arxiv.org/abs/2510.02679

Automated Constraint Specification for Job Scheduling by Regulating Generative Model with Domain-Specific Representation
Advanced Planning and Scheduling (APS) systems have become indispensable for modern manufacturing operations, enabling optimized resource allocation and production efficiency in increasingly complex and dynamic environments. While algorithms for solving abstracted scheduling problems have been extensively investigated, the critical prerequisite of specifying manufacturing requirements into formal constraints remains manual and labor-intensive. Although recent advances of generative models, part…

DYNAMIX: RL-based Adaptive Batch Size Optimization in Distributed Machine Learning Systems
Yuanjun Dai, Keqiang He, An Wang
https://arxiv.org/abs/2510.08522 https://

DYNAMIX: RL-based Adaptive Batch Size Optimization in Distributed Machine Learning Systems
Existing batch size selection approaches in dis- tributed machine learning rely on static allocation or simplistic heuristics that fail to adapt to heterogeneous, dynamic computing environments. We present DYNAMIX, a reinforcement learning framework that formulates batch size optimization as a sequen- tial decision-making problem using Proximal Policy Optimiza- tion (PPO). Our approach employs a multi-dimensional state representation encompassing network-level metrics, system-level resource uti…

Radio Map Assisted Routing and Predictive Resource Allocation over Dynamic Low Altitude Networks
Bowen Li, Junting Chen
https://arxiv.org/abs/2507.19111 https://

Radio Map Assisted Routing and Predictive Resource Allocation over Dynamic Low Altitude Networks
Dynamic low altitude networks offer significant potential for efficient and reliable data transport via unmanned aerial vehicles (UAVs) relays which usually operate with predetermined trajectories. However, it is challenging to optimize the data routing and resource allocation due to the time-varying topology and the need to control interference with terrestrial systems. Traditional schemes rely on time-expanded graphs with uniform and fine time subdivisions, making them impractical for interfe…

AutoMaAS: Self-Evolving Multi-Agent Architecture Search for Large Language Models
Bo Ma, Hang Li, ZeHua Hu, XiaoFan Gui, LuYao Liu, Simon Liu
https://arxiv.org/abs/2510.02669 ht…

AutoMaAS: Self-Evolving Multi-Agent Architecture Search for Large Language Models
Multi-agent systems powered by large language models have demonstrated remarkable capabilities across diverse domains, yet existing automated design approaches seek monolithic solutions that fail to adapt resource allocation based on query complexity and domain requirements. This paper introduces AutoMaAS, a self-evolving multi-agent architecture search framework that leverages neural architecture search principles to automatically discover optimal agent configurations through dynamic operator …

Dynamic distributed decision-making for resilient resource reallocation in disrupted manufacturing systems
Mingjie Bi, Ilya Kovalenko, Dawn M. Tilbury, Kira Barton
https://arxiv.org/abs/2507.19043

Dynamic distributed decision-making for resilient resource reallocation in disrupted manufacturing systems
The COVID-19 pandemic brings many unexpected disruptions, such as frequently shifting markets and limited human workforce, to manufacturers. To stay competitive, flexible and real-time manufacturing decision-making strategies are needed to deal with such highly dynamic manufacturing environments. One essential problem is dynamic resource allocation to complete production tasks, especially when a resource disruption (e.g., machine breakdown) occurs. Though multi-agent methods have been proposed …

Spiking Neural Networks for Resource Allocation in UAV-Enabled Wireless Networks
Vasileios Kouvakis, Stylianos E. Trevlakis, Ioannis Arapakis, Alexandros-Apostolos A. Boulogeorgos
https://arxiv.org/abs/2508.03279

Spiking Neural Networks for Resource Allocation in UAV-Enabled Wireless Networks
This work presents a new spiking neural network (SNN)-based approach for user equipment-base station (UE-BS) association in non-terrestrial networks (NTNs). With the introduction of UAV's in wireless networks, the system architecture becomes heterogeneous, resulting in the need for dynamic and efficient management to avoid congestion and sustain overall performance. The presented framework compares two SNN-based optimization strategies. Specifically, a top-down centralized approach with complet…

Sequential Fair Allocation With Replenishments: A Little Envy Goes An Exponentially Long Way
Chido Onyeze, Sean R. Sinclair, Chamsi Hssaine, Siddhartha Banerjee
https://arxiv.org/abs/2508.21753

Sequential Fair Allocation With Replenishments: A Little Envy Goes An Exponentially Long Way
We study the trade-off between envy and inefficiency in repeated resource allocation settings with stochastic replenishments, motivated by real-world systems such as food banks and medical supply chains. Specifically, we consider a model in which a decision-maker faced with stochastic demand and resource donations must trade off between an equitable and efficient allocation of resources over an infinite horizon. The decision-maker has access to storage with fixed capacity $M$, and incurs effici…

Resource-Splitting Games with Tullock-Based Lossy Contests
Marko Maljkovic, Gustav Nilsson, Nikolas Geroliminis
https://arxiv.org/abs/2507.13853 https://…

Resource-Splitting Games with Tullock-Based Lossy Contests
This paper introduces a novel class of multi-stage resource allocation games that model real-world scenarios in which profitability depends on the balance between supply and demand, and where higher resource investment leads to greater returns. Our proposed framework, which incorporates the notion of profit loss due to insufficient player participation, gives rise to a Tullock-like functional form of the stage payoff structure when weighted fair proportional resource allocation is applied. We e…

Towards Energy-Efficient Serverless Computing with Hardware Isolation
Natalie Carl, Tobias Pfandzelter, David Bermbach
https://arxiv.org/abs/2510.08180 https://

Towards Energy-Efficient Serverless Computing with Hardware Isolation
Serverless computing provides just-in-time infrastructure provisioning with rapid elasticity and a finely-grained pricing model. As full control of resource allocation is in the hands of the cloud provider and applications only consume resources when they actually perform work, we believe that serverless computing is uniquely positioned to maximize energy efficiency. However, the focus of current serverless platforms is to run hundreds or thousands of serverless functions from different tenan…

Performance Evaluation and Threat Mitigation in Large-scale 5G Core Deployment
Rodrigo Moreira, Larissa F. Rodrigues Moreira, Fl\'avio de Oliveira Silva
https://arxiv.org/abs/2507.17850

Performance Evaluation and Threat Mitigation in Large-scale 5G Core Deployment
The deployment of large-scale software-based 5G core functions presents significant challenges due to their reliance on optimized and intelligent resource provisioning for their services. Many studies have focused on analyzing the impact of resource allocation for complex deployments using mathematical models, queue theories, or even Artificial Intelligence (AI). This paper elucidates the effects of chaotic workloads, generated by Distributed Denial of Service (DDoS) on different Network Functi…

A Black-Box Approach for Exogenous Replenishment in Online Resource Allocation
Suho Kang, Ziyang Liu, Rajan Udwani
https://arxiv.org/abs/2507.14812 https:/…

A Black-Box Approach for Exogenous Replenishment in Online Resource Allocation
In a typical online resource allocation problem, we start with a fixed inventory of resources and make online allocation decisions in response to resource requests that arrive sequentially over a finite horizon. We consider settings where the inventory is replenished over time according to an unknown exogenous process. We introduce black-box methods that extend any existing algorithm, originally designed without considering replenishment, into one that works with an arbitrary (adversarial or st…

Enhancing Connectivity for Emergency Vehicles Through UAV Trajectory and Resource Allocation Optimization
S. Fatemeh Bozorgi, S. Mohammad Razavizadeh, Mohsen Rezaee
https://arxiv.org/abs/2509.26067

Enhancing Connectivity for Emergency Vehicles Through UAV Trajectory and Resource Allocation Optimization
Effective communication for emergency vehicles - such as ambulances and fire trucks - is essential to support their operations in various traffic and environmental conditions. In this context, this paper investigates a vehicular communication system assisted by an Unmanned Aerial Vehicle (UAV), which adjusts its trajectory and resource allocation according to communication needs. The system classifies vehicles into two groups to address their varying service requirements: emergency vehicles, wh…

Optimizing Task Scheduling in Fog Computing with Deadline Awareness
Mohammad Sadegh Sirjani, Somayeh Sobati-Moghadam
https://arxiv.org/abs/2509.07378 https://

Optimizing Task Scheduling in Fog Computing with Deadline Awareness
The rise of Internet of Things (IoT) devices has led to the development of numerous applications that require quick responses and low latency. Fog computing has emerged as a solution for processing these IoT applications, but it faces challenges such as resource allocation and job scheduling. Therefore, it is crucial to determine how to assign and schedule tasks on Fog nodes. A well-designed job scheduling algorithm can help decrease energy usage and improve response times for application reque…

Resource Allocation under Stochastic Demands using Shrinking Horizon Optimization
Alexandros E. Tzikas, Nazim Kemal Ure, Mansur Arief, Mykel J. Kochenderfer, Stephen P. Boyd
https://arxiv.org/abs/2509.25412

Resource Allocation under Stochastic Demands using Shrinking Horizon Optimization
We consider the problem of optimally allocating a limited number of resources across time to maximize revenue under stochastic demands. This formulation is relevant in various areas of control, such as supply chain, ticket revenue maximization, healthcare operations, and energy allocation in power grids. We propose a bisection method to solve the static optimization problem and extend our approach to a shrinking horizon algorithm for the sequential problem. The shrinking horizon algorithm compu…

RSU-Assisted Resource Allocation for Collaborative Perception
Guowei Liu, Le Liang, Chongtao Guo, Hao Ye, Shi Jin
https://arxiv.org/abs/2509.17691 https://…

RSU-Assisted Resource Allocation for Collaborative Perception
As a pivotal technology for autonomous driving, collaborative perception enables vehicular agents to exchange perceptual data through vehicle-to-everything (V2X) communications, thereby enhancing perception accuracy of all collaborators. However, existing collaborative perception frameworks often assume ample communication resources, which is usually impractical in real-world vehicular networks. To address this challenge, this paper investigates the problem of communication resource allocation …

AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networks
Ahmet Melih Ince, Ayse Elif Canbilen, Halim Yanikomeroglu
https://arxiv.org/abs/2508.00011 h…

AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networks
Sixth-generation (6G) networks are designed to meet the hyper-reliable and low-latency communication (HRLLC) requirements of safety-critical applications such as autonomous driving. Integrating non-terrestrial networks (NTN) into the 6G infrastructure brings redundancy to the network, ensuring continuity of communications even under extreme conditions. In particular, high-altitude platform stations (HAPS) stand out for their wide coverage and low latency advantages, supporting communication rel…

Coordinated Decentralized Resource Optimization for Cell-Free ISAC Systems
Mehdi Zafari, Rang Liu, A. Lee Swindlehurst
https://arxiv.org/abs/2508.01044 https://

Coordinated Decentralized Resource Optimization for Cell-Free ISAC Systems
Integrated Sensing and Communication (ISAC) is emerging as a key enabler for 6G wireless networks, allowing the joint use of spectrum and infrastructure for both communication and sensing. While prior ISAC solutions have addressed resource optimization, including power allocation, beamforming, and waveform design, they often rely on centralized architectures with full network knowledge, limiting their scalability in distributed systems. In this paper, we propose two coordinated decentralized op…

OPTIC-ER: A Reinforcement Learning Framework for Real-Time Emergency Response and Equitable Resource Allocation in Underserved African Communities
Mary Tonwe
https://arxiv.org/abs/2508.12943

OPTIC-ER: A Reinforcement Learning Framework for Real-Time Emergency Response and Equitable Resource Allocation in Underserved African Communities
Public service systems in many African regions suffer from delayed emergency response and spatial inequity, causing avoidable suffering. This paper introduces OPTIC-ER, a reinforcement learning (RL) framework for real-time, adaptive, and equitable emergency response. OPTIC-ER uses an attention-guided actor-critic architecture to manage the complexity of dispatch environments. Its key innovations are a Context-Rich State Vector, encoding action sub-optimality, and a Precision Reward Function, wh…

Energy Efficient Trajectory Control and Resource Allocation in Multi-UAV-assisted MEC via Deep Reinforcement Learning
Saichao Liu, Geng Sun, Chuang Zhang, Xuejie Liu, Jiacheng Wang, Changyuan Zhao, Dusit Niyato
https://arxiv.org/abs/2508.00261

Energy Efficient Trajectory Control and Resource Allocation in Multi-UAV-assisted MEC via Deep Reinforcement Learning
Mobile edge computing (MEC) is a promising technique to improve the computational capacity of smart devices (SDs) in Internet of Things (IoT). However, the performance of MEC is restricted due to its fixed location and limited service scope. Hence, we investigate an unmanned aerial vehicle (UAV)-assisted MEC system, where multiple UAVs are dispatched and each UAV can simultaneously provide computing service for multiple SDs. To improve the performance of system, we formulated a UAV-based trajec…

Multibeam High Throughput Satellite: Hardware Foundation, Resource Allocation, and Precoding
Rui Chen, Wen-Xuan Long, Bing-Qian Wang, Yuan He, Rui-Jin Sun, Nan Cheng, Gan Zheng, Dusit Niyato
https://arxiv.org/abs/2508.00800

Multibeam High Throughput Satellite: Hardware Foundation, Resource Allocation, and Precoding
With its wide coverage and uninterrupted service, satellite communication is a critical technology for next-generation 6G communications. High throughput satellite (HTS) systems, utilizing multipoint beam and frequency multiplexing techniques, enable satellite communication capacity of up to Tbps to meet the growing traffic demand. Therefore, it is imperative to review the-state-of-the-art of multibeam HTS systems and identify their associated challenges and perspectives. Firstly, we summarize …

Resource Allocation for Mutualistic Symbiotic Radio with Hybrid Active-Passive Communications
Hong Guo, Yinghui Ye, Haijian Sun, Liqin Shi, Rose Qingyang Hu
https://arxiv.org/abs/2509.14567

Resource Allocation for Mutualistic Symbiotic Radio with Hybrid Active-Passive Communications
Mutualistic SR is a communication paradigm that offers high spectrum efficiency and low power consumption, where the SU transmits information by modulating and backscattering the PT's signal, enabling shared use of spectrum and power with PT. In return, the PT's performance can be enhanced by SU's backscattered signal, forming a mutualistic relationship. However, the low modulation rate causes extremely inferior transmission rates for SUs. To improve the SU transmission rate, this paper propose…

Stochastic Black Start Resource Allocation to Enable Dynamic Formation of Networked Microgrids and DER-aided Restoration
Cong Bai, Salish Maharjan, Han Wang, Zhaoyu Wang
https://arxiv.org/abs/2508.13306

Stochastic Black Start Resource Allocation to Enable Dynamic Formation of Networked Microgrids and DER-aided Restoration
Extended outages in distributed systems (DSs) dominated by distributed energy resources (DERs) require innovative strategies to efficiently and securely deploy black start (BS) resources. To address the need, this paper proposes a two-stage stochastic resource allocation method within synchronizing dynamic microgrids (MGs) for black start (SDMG-BS), enabling risk-averse and adaptive restoration across various scenarios while ensuring frequency security. Virtual synchronous generator (VSG)-contr…

Energy-Efficient Joint Offloading and Resource Allocation for Deadline-Constrained Tasks in Multi-Access Edge Computing
Chuanchao Gao, Arvind Easwaran
https://arxiv.org/abs/2509.11162

Energy-Efficient Joint Offloading and Resource Allocation for Deadline-Constrained Tasks in Multi-Access Edge Computing
This paper addresses the deadline-constrained task offloading and resource allocation problem in multi-access edge computing. We aim to determine where each task is offloaded and processed, as well as corresponding communication and computation resource allocations, to maximize the total saved energy for IoT devices, while considering task deadline and system resource constraints. Especially, our system allows each task to be offloaded to one of its accessible access points (APs) and processed …

Alignment Games
Pedro Cesar Lopes Gerum, Thomas Lidbetter
https://arxiv.org/abs/2509.04509 https://arxiv.org/pdf/2509.04509

Alignment Games
This paper introduces alignment games, a new class of zero-sum games modeling strategic interventions where effectiveness depends on alignment with an underlying hidden state. Motivated by operational problems in medical diagnostics, economic sanctions, and resource allocation, this framework features two players, a Hider and a Searcher, who choose subsets of a given space. Payoffs are determined by their misalignment (symmetric difference), explicitly modeling the trade-off between commission …

minPIC: Towards Optimal Power Allocation in Multi-User Interference Channels
Sagnik Bhattacharya, Abhiram Rao Gorle, John M. Cioffi
https://arxiv.org/abs/2509.02797 https://

minPIC: Towards Optimal Power Allocation in Multi-User Interference Channels
6G envisions massive cell-free networks with spatially nested multiple access (MAC) and broadcast (BC) channels without centralized coordination. This makes optimal resource allocation across power, subcarriers, and decoding orders crucial for interference channels (ICs), where neither transmitters nor receivers can cooperate. Current orthogonal multiple access (OMA) methods, as well as non-orthogonal (NOMA) and rate-splitting (RSMA) schemes, rely on fixed heuristics for interference management…

Bayesian Surrogates for Risk-Aware Pre-Assessment of Aging Bridge Portfolios
Sophia V. Kuhn, Rafael Bischof, Marius Weber, Antoine Binggeli, Michael A. Kraus, Walter Kaufmann, Fernando P\'erez-Cruz
https://arxiv.org/abs/2509.25031

Bayesian Surrogates for Risk-Aware Pre-Assessment of Aging Bridge Portfolios
Aging infrastructure portfolios pose a critical resource allocation challenge: deciding which structures require intervention and which can safely remain in service. Structural assessments must balance the trade-off between cheaper, conservative analysis methods and accurate but costly simulations that do not scale portfolio-wide. We propose Bayesian neural network (BNN) surrogates for rapid structural pre-assessment of worldwide common bridge types, such as reinforced concrete frame bridges. T…

Virne: A Comprehensive Benchmark for Deep RL-based Network Resource Allocation in NFV
Tianfu Wang, Liwei Deng, Xi Chen, Junyang Wang, Huiguo He, Leilei Ding, Wei Wu, Qilin Fan, Hui Xiong
https://arxiv.org/abs/2507.19234

Virne: A Comprehensive Benchmark for Deep RL-based Network Resource Allocation in NFV
Resource allocation (RA) is critical to efficient service deployment in Network Function Virtualization (NFV), a transformative networking paradigm. Recently, deep Reinforcement Learning (RL)-based methods have been showing promising potential to address this complexity. However, the lack of a systematic benchmarking framework and thorough analysis hinders the exploration of emerging networks and the development of more robust algorithms while causing inconsistent evaluation. In this paper, we …

FlashResearch: Real-time Agent Orchestration for Efficient Deep Research
Lunyiu Nie, Nedim Lipka, Ryan A. Rossi, Swarat Chaudhuri
https://arxiv.org/abs/2510.05145 https://

FlashResearch: Real-time Agent Orchestration for Efficient Deep Research
Deep research agents, which synthesize information across diverse sources, are significantly constrained by their sequential reasoning processes. This architectural bottleneck results in high latency, poor runtime adaptability, and inefficient resource allocation, making them impractical for interactive applications. To overcome this, we introduce FlashResearch, a novel framework for efficient deep research that transforms sequential processing into parallel, runtime orchestration by dynamicall…

Robust Resource Allocation for Pinching-Antenna Systems under Imperfect CSI
Ming Zeng, Xianbin Wang, Yuanwei Liu, Zhiguo Ding, George K. Karagiannidis, H. Vincent Poor
https://arxiv.org/abs/2507.12582

Robust Resource Allocation for Pinching-Antenna Systems under Imperfect CSI
Pinching-antenna technology has lately showcased its promising capability for reconfiguring wireless propagation environments, especially in high-frequency communication systems like millimeter-wave and terahertz bands. By dynamically placing the antenna over a dielectric waveguide, line-of-sight (LoS) connections can be made to significantly improve system performance. Although recent research have illustrated the advantages of pinching-antenna-assisted designs, they mainly presuppose complete…

Replaced article(s) found for cs.DS. https://arxiv.org/list/cs.DS/new
[1/1]:
- Online Resource Allocation with Cancellations
Farbod Ekbatani, Yiding Feng, Rad Niazadeh

Large Language Model-Based Task Offloading and Resource Allocation for Digital Twin Edge Computing Networks
Qiong Wu, Yu Xie, Pingyi Fan, Dong Qin, Kezhi Wang, Nan Cheng, Khaled B. Letaief
https://arxiv.org/abs/2507.19050

Large Language Model-Based Task Offloading and Resource Allocation for Digital Twin Edge Computing Networks
In this paper, we propose a general digital twin edge computing network comprising multiple vehicles and a server. Each vehicle generates multiple computing tasks within a time slot, leading to queuing challenges when offloading tasks to the server. The study investigates task offloading strategies, queue stability, and resource allocation. Lyapunov optimization is employed to transform long-term constraints into tractable short-term decisions. To solve the resulting problem, an in-context lear…

Replaced article(s) found for eess.SY. https://arxiv.org/list/eess.SY/new
[1/1]:
- Distributed Feedback-Feedforward Algorithms for Time-Varying Resource Allocation
Yiqiao Xu, Tengyang Gong, Zhengtao Ding, Alessandra Parisio

Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training
Oleksiy Ostapenko, Charles Guille-Escuret, Luke Kumar, Max Tian, Denis Kocetkov, Gopeshh Subbaraj, Raymond Li, Joel Lamy-Poirier, Sebastien Paquet, Torsten Scholak
https://arxiv.org/abs/2507.22250

Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training
We introduce a framework for optimizing domain-specific dataset construction in foundation model training. Specifically, we seek a cost-efficient way to estimate the quality of data sources (e.g. synthetically generated or filtered web data, etc.) in order to make optimal decisions about resource allocation for data sourcing from these sources for the stage two pre-training phase, aka annealing, with the goal of specializing a generalist pre-trained model to specific domains. Our approach exten…

Resource Allocation in Cooperative Mid-band/THz Networks in the Presence of Mobility
Mohammad Amin Saeidi, Hina Tabassum
https://arxiv.org/abs/2509.23065 https://

Resource Allocation in Cooperative Mid-band/THz Networks in the Presence of Mobility
This paper develops a comprehensive framework to investigate and optimize the downlink performance of cooperative multi-band networks (MBNs) operating on upper mid-band (UMB) and terahertz (THz) frequencies, where base stations (BSs) in each band cooperatively serve users. The framework captures sophisticated features such as near-field channel modeling, fully and partially connected antenna architectures, and users' mobility. First, we consider joint user association and hybrid beamforming opt…

MOHAF: A Multi-Objective Hierarchical Auction Framework for Scalable and Fair Resource Allocation in IoT Ecosystems
Kushagra Agrawal, Polat Goktas, Anjan Bandopadhyay, Debolina Ghosh, Junali Jasmine Jena, Mahendra Kumar Gourisaria
https://arxiv.org/abs/2508.14830

MOHAF: A Multi-Objective Hierarchical Auction Framework for Scalable and Fair Resource Allocation in IoT Ecosystems
The rapid growth of Internet of Things (IoT) ecosystems has intensified the challenge of efficiently allocating heterogeneous resources in highly dynamic, distributed environments. Conventional centralized mechanisms and single-objective auction models, focusing solely on metrics such as cost minimization or revenue maximization, struggle to deliver balanced system performance. This paper proposes the Multi-Objective Hierarchical Auction Framework (MOHAF), a distributed resource allocation mech…

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial
Bisheng Wei, Ruihong Jiang, Ruichen Zhang, Yinqiu Liu, Dusit Niyato, Yaohua Sun, Yang Lu, Yonghui Li, Shiwen Mao, Chau Yuen, Marco Di Renzo, Mugen Peng
https://arxiv.org/abs/2509.05946

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial
The rapid advancement toward sixth-generation (6G) wireless networks has significantly intensified the complexity and scale of optimization problems, including resource allocation and trajectory design, often formulated as combinatorial problems in large discrete decision spaces. However, traditional optimization methods, such as heuristics and deep reinforcement learning (DRL), struggle to meet the demanding requirements of real-time adaptability, scalability, and dynamic handling of user inte…

Exploring the potential resource integration under passenger-freight shared mobility: collaborative optimization of multi-type bus scheduling and dynamic vehicle capacity allocation for urban-rural bus routes
Jiabin Wu, Zijian Huang, Linhong Wang, Yiming Bie, Yuting Ji, Jun Gong
https://arxiv.org/abs/2509.20017

Exploring the potential resource integration under passenger-freight shared mobility: collaborative optimization of multi-type bus scheduling and dynamic vehicle capacity allocation for urban-rural bus routes
Under the global background of developing urban-rural travel patterns, traditional urban-rural public transport systems are generally faced with the serious challenges of passenger loss and operating deficit, leading to a reduction in the bus frequency and service reliability. In order to break the vicious circle of demand decline-supply shrinkage, passenger-freight shared mobility (PFSM), an innovative operation mode, can achieve synergies between urban-rural logistics and public transport ser…

Finite-Time Guarantees for Multi-Agent Combinatorial Bandits with Nonstationary Rewards
Katherine B. Adams, Justin J. Boutilier, Qinyang He, Yonatan Mintz
https://arxiv.org/abs/2508.20923

Finite-Time Guarantees for Multi-Agent Combinatorial Bandits with Nonstationary Rewards
We study a sequential resource allocation problem where a decision maker selects subsets of agents at each period to maximize overall outcomes without prior knowledge of individual-level effects. Our framework applies to settings such as community health interventions, targeted digital advertising, and workforce retention programs, where intervention effects evolve dynamically. Agents may exhibit habituation (diminished response from frequent selection) or recovery (enhanced response from infre…

EcoFL: Resource Allocation for Energy-Efficient Federated Learning in Multi-RAT ORAN Networks
Abdelaziz Salama, Mohammed M. H. Qazzaz, Syed Danial Ali Shah, Maryam Hafeez, Syed Ali Zaidi, Hamed Ahmadi
https://arxiv.org/abs/2507.21698

EcoFL: Resource Allocation for Energy-Efficient Federated Learning in Multi-RAT ORAN Networks
Federated Learning (FL) enables distributed model training on edge devices while preserving data privacy. However, FL deployments in wireless networks face significant challenges, including communication overhead, unreliable connectivity, and high energy consumption, particularly in dynamic environments. This paper proposes EcoFL, an integrated FL framework that leverages the Open Radio Access Network (ORAN) architecture with multiple Radio Access Technologies (RATs) to enhance communication ef…

DRR-MDPF: A Queue Management Strategy Based on Dynamic Resource Allocation and Markov Decision Process in Named Data Networking (NDN)
Fatemeh Roshanzadeh, Hamid Barati, Ali Barati
https://arxiv.org/abs/2508.20272

DRR-MDPF: A Queue Management Strategy Based on Dynamic Resource Allocation and Markov Decision Process in Named Data Networking (NDN)
Named Data Networking (NDN) represents a transformative shift in network architecture, prioritizing content names over host addresses to enhance data dissemination. Efficient queue and resource management are critical to NDN performance, especially under dynamic and high-traffic conditions. This paper introduces DRR-MDPF, a novel hybrid strategy that integrates the Markov Decision Process Forwarding (MDPF) model with the Deficit Round Robin (DRR) algorithm. MDPF enables routers to intelligently…

Energy-Aware Resource Allocation for Multi-Operator Cell-Free Massive MIMO in V-CRAN Architectures
Derya Nurcan-Atceken, \"Ozlem Tu\u{g}fe Demir, Aysegul Altin-Kayhan, Emil Bj\"ornson, Cicek Cavdar, Bulent Tavli
https://arxiv.org/abs/2507.21644

Energy-Aware Resource Allocation for Multi-Operator Cell-Free Massive MIMO in V-CRAN Architectures
Cell-free massive multiple-input multiple-output (MIMO) implemented in virtualized cloud radio access networks (V-CRAN) has emerged as a promising architecture to enhance spectral efficiency (SE), network flexibility, and energy efficiency (EE) in next-generation wireless systems. In this work, we develop a holistic optimization framework for the efficient deployment of cell-free massive MIMO in V-CRAN with multiple mobile network operators (MNOs). Specifically, we formulate a set of mixed-inte…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[7/10]:
- Online Resource Allocation with Average Budget Constraints
Ruicheng Ao, Hongyu Chen, David Simchi-Levi, Feng Zhu

Autonomous Resource Management in Microservice Systems via Reinforcement Learning
Yujun Zou, Nia Qi, Yingnan Deng, Zhihao Xue, Ming Gong, Wuyang Zhang
https://arxiv.org/abs/2507.12879

Autonomous Resource Management in Microservice Systems via Reinforcement Learning
This paper proposes a reinforcement learning-based method for microservice resource scheduling and optimization, aiming to address issues such as uneven resource allocation, high latency, and insufficient throughput in traditional microservice architectures. In microservice systems, as the number of services and the load increase, efficiently scheduling and allocating resources such as computing power, memory, and storage becomes a critical research challenge. To address this, the paper employs…

Markov Modeling for Licensed and Unlicensed Band Allocation in Underlay and Overlay D2D
Po-Heng Chou, Yen-Ting Liu, Wei-Chang Chen, Walid Saad
https://arxiv.org/abs/2509.23218 h…

Markov Modeling for Licensed and Unlicensed Band Allocation in Underlay and Overlay D2D
In this paper, a novel analytical model for resource allocation is proposed for a device-to-device (D2D) assisted cellular network. The proposed model can be applied to underlay and overlay D2D systems for sharing licensed bands and offloading cellular traffic. The developed model also takes into account the problem of unlicensed band sharing with Wi-Fi systems. In the proposed model, a global system state reflects the interaction among D2D, conventional cellular, and Wi-Fi packets. Under the s…

A Multiobjective Mathematical Model for Optimal Irrigation Water Allocation
Nahid Sultana, M. M. Rizvi, G. M. Wali Ullah
https://arxiv.org/abs/2509.19629 https://

A Multiobjective Mathematical Model for Optimal Irrigation Water Allocation
Water resource management is vital for sustainable irrigation and food security under growing climatic and economic pressures. In this study, we develop two single-objective optimization models for irrigation planning, one that maximizes net benefit and the other that minimizes environmental flow deficiency, and benchmark them against previous works. We then extend the analysis to a multiobjective linear programming formulation solved through different approaches to evaluate trade-offs. Numeric…

NEXUS: Efficient and Scalable Multi-Cell mmWave Baseband Processing with Heterogeneous Compute
Zhenzhou Qi, Chung-Hsuan Tung, Zhihui Gao, Tingjun Chen
https://arxiv.org/abs/2509.04625

NEXUS: Efficient and Scalable Multi-Cell mmWave Baseband Processing with Heterogeneous Compute
The rapid adoption of 5G New Radio (NR), particularly in the millimeter-wave (mmWave) spectrum, imposes stringent demands on the flexibility, scalability, and efficiency of baseband processing. While virtualized Radio Access Networks (vRANs) enable dynamic spectrum sharing across cells, compute resource allocation for baseband processing, especially in multi-cell deployments with heterogeneous workloads, remains underexplored. In this paper, we present NEXUS, the first system to realize real-ti…

Task Offloading and Resource Allocation for MEC-assisted Consumer Internet of Vehicle Systems
Yanheng Liu, Dalin Li, Hao Wu, Zemin Sun, Weihong Qin, Jun Li, Hongyang Du, Geng Sun
https://arxiv.org/abs/2508.15795

Task Offloading and Resource Allocation for MEC-assisted Consumer Internet of Vehicle Systems
Mobile edge computing (MEC)-assisted internet of vehicle (IoV) is emerging as a promising paradigm to provide computing services for vehicles. However, meeting the computing-sensitive and computation-intensive demands of vehicles poses several challenges, including the discrepancy between the limited resource provision and stringent computing requirement, the difficulty in capturing and integrating the intricate features of the MEC-assisted IoV system into the problem formulation, and the need …

Waterfilling at the Edge: Optimal Percentile Resource Allocation via Risk-Averse Reduction
Gokberk Yaylali, Ahmad Ali Khan, Dionysios S. Kalogerias
https://arxiv.org/abs/2507.10838

Waterfilling at the Edge: Optimal Percentile Resource Allocation via Risk-Averse Reduction
We address deterministic resource allocation in point-to-point multi-terminal AWGN channels without inter-terminal interference, with particular focus on optimizing quantile transmission rates for cell-edge terminal service. Classical utility-based approaches -- such as minimum rate, sumrate, and proportional fairness -- are either overconservative, or inappropriate, or do not provide a rigorous and/or interpretable foundation for fair rate optimization at the edge. To overcome these challenges…

Combining Static and Dynamic Traffic with Delay Guarantees in Time-Sensitive Networking
Lisa Maile, Kai-Steffen Hielscher, Reinhard German
https://arxiv.org/abs/2508.18883 https…

Combining Static and Dynamic Traffic with Delay Guarantees in Time-Sensitive Networking
To support reliable and low-latency communication, Time-Sensitive Networking introduced protocols and interfaces for resource allocation in Ethernet. However, the implementation of these allocation algorithms has not yet been covered by the standards. Our work focuses on deadline-guaranteeing resource allocation for networks with static and dynamic traffic. To achieve this, we combine offline network optimization heuristics with online admission control and, thus, allow for new flow registratio…

Beam-Hopping Pattern Design for Grant-Free Random Access in LEO Satellite Communications
Seunghyeon Jeon, Seonjung Kim, Gyeongrae Im, Yo-Seb Jeon
https://arxiv.org/abs/2508.03391

Beam-Hopping Pattern Design for Grant-Free Random Access in LEO Satellite Communications
Increasing demand for massive device connectivity in underserved regions drives the development of advanced low Earth orbit (LEO) satellite communication systems. Beam-hopping LEO systems without connection establishment provide a promising solution for achieving both demand-aware resource allocation and low access latency. This paper investigates beam-hopping pattern design for the grant-free random access systems to dynamically allocate satellite resources according to traffic demands across …

Evaluating UORA-Based Polling Mechanism for Latency-Sensitive Uplink Traffic in Wi-Fi Networks
Douglas Dziedzorm Agbeve, Andrey Belogaev, Chris Blondia, Jeroen Famaey
https://arxiv.org/abs/2510.04731

Evaluating UORA-Based Polling Mechanism for Latency-Sensitive Uplink Traffic in Wi-Fi Networks
IEEE 802.11ax (Wi-Fi 6) introduced Orthogonal Frequency Division Multiple Access (OFDMA), which enables simultaneous transmissions through centralized resource allocation. However, effective uplink scheduling requires the Access Point (AP) to identify which stations (STAs) have data to transmit. This typically necessitates polling for buffer status reports, a process that becomes increasingly inefficient and unscalable with growing device density. In this paper, we study how the Uplink OFDMA-ba…

Optimizing Network Performance and Resource Allocation in HAPS-UAV Integrated Sensing and Communication Systems for 6G
Parisa Kanani, Mohammad Javad Omidi, Mahmoud Modarres-Hashemi, Halim Yanikomeroglu
https://arxiv.org/abs/2507.14310

Optimizing Network Performance and Resource Allocation in HAPS-UAV Integrated Sensing and Communication Systems for 6G
This paper proposes an innovative approach by leveraging uncrewed aerial vehicles (UAVs) as base stations (BSs) and a high-altitude platform station (HAPS) as the central processing unit (CPU) in an integrated sensing and communication (ISAC) system for 6G networks. We explore the challenges, applications, and advantages of ISAC systems in next-generation networks, highlighting the significance of optimizing position and power control. Our approach integrates HAPS and UAVs to enhance wireless c…

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Flexible Base Station Sleeping and Resource Allocation for Green Uplink Fully-Decoupled RAN
Yu Sun, Haibo Zhou, Kai Yu, Yunting Xu, Bo Qian, Lin X. Cai