Tootfinder

Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G
Chao Wang, Kai-Kit Wong, Zan Li, Liang Jin, Chan-Byoung Chae
https://arxiv.org/abs/2506.14288

Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G
The Fluid Antenna System (FAS), which enables flexible Multiple-Input Multiple-Output (MIMO) communications, introduces new spatial degrees of freedom for next-generation wireless networks. Unlike traditional MIMO, FAS involves joint port selection and precoder design, a combinatorial NP-hard optimization problem. Moreover, fully leveraging FAS requires acquiring Channel State Information (CSI) across its ports, a challenge exacerbated by the system's near-continuous reconfigurability. These fa…

Semantic Communications in 6G: Coexistence, Multiple Access, and Satellite Networks
Ishtiaque Ahmed, Yingzhuo Sun, Jingwen Fu, Alper Kose, Leila Musavian, Ming Xiao, Berna Ozbek
https://arxiv.org/abs/2506.11779

Semantic Communications in 6G: Coexistence, Multiple Access, and Satellite Networks
The exponential growth of wireless users and bandwidth constraints necessitates innovative communication paradigms for next-generation networks. Semantic Communication (SemCom) emerges as a promising solution by transmitting extracted meaning rather than raw bits, enhancing spectral efficiency and enabling intelligent resource allocation. This paper explores the integration of SemCom with conventional Bit-based Communication (BitCom) in heterogeneous networks, highlighting key challenges and op…

Theoretical Foundations of Waste Factor and Waste Figure with Applications to Fixed Wireless Access and Relay Systems
Nurullah Sevim, Mostafa Ibrahim, Sabit Ekin, Theodore S. Rappaport
https://arxiv.org/abs/2506.08414

Theoretical Foundations of Waste Factor and Waste Figure with Applications to Fixed Wireless Access and Relay Systems
The exponential rise in energy consumption across wireless communication systems, particularly in anticipation of next-generation wireless systems, necessitates rigorous frameworks for evaluating and optimizing energy efficiency. This paper revisits and expands the concept of the Waste Factor (W), or Waste Figure (WF) in decibel scale, as a unifying metric that captures both utilized and wasted power in cascaded communication systems. Building upon its foundation in system-level power modeling,…

Diffusion-RL for Scalable Resource Allocation for 6G Networks
Salar Nouri, Mojdeh Karbalaee Motalleb, Vahid Shah-Mansouri
https://arxiv.org/abs/2506.07880 …

Diffusion-RL for Scalable Resource Allocation for 6G Networks
This paper presents a novel approach to resource allocation in Open Radio Access Networks (O-RAN), leveraging a Generative AI technique with network slicing to address the diverse demands of 5G and 6G service types such as Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and Massive Machine-Type Communications (mMTC). Additionally, we provide a comprehensive analysis and comparison of machine learning (ML) techniques for resource allocation within O-RAN, eval…

Quantum correlations in molecular cavity optomechanics
E. Kongkui Berinyuy, D. R. Kenigoule Massembele, P. Djorwe, R. Altuijri, M. R. Eid, A. -H. Abdel-Aty, S. G. Nana Engo
https://arxiv.org/abs/2506.07277

Quantum correlations in molecular cavity optomechanics
In quantum information, quantum correlations--particularly entanglement, EPR steering, and quantum discord--stand as interesting resources, powering breakthroughs from secure communication and quantum teleportation to efficient quantum computation. Here, we unveil a theoretical framework for generating and controlling these vital quantum phenomena within a double-cavity molecular optomechanical (McOM) system. This innovative approach leverages strong interactions between confined optical fields…

Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis
Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad, Aditya Sant, David J. Love, Christopher G. Brinton
https://arxiv.org/abs/2506.00374

Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis
In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML systems for wireless communications is the availability of realistic wireless channel datasets, which are extremely resource-intensive to produce. To this end, the generation of realistic wireless channels plays a key role in the subsequent design of effective ML algorithms for wireless communication systems. Generativ…

Frequency Resource Management in 6G User-Centric CFmMIMO: A Hybrid Reinforcement Learning and Metaheuristic Approach
Selina Cheggour, Valeria Loscri
https://arxiv.org/abs/2505.22443

Frequency Resource Management in 6G User-Centric CFmMIMO: A Hybrid Reinforcement Learning and Metaheuristic Approach
As sixth-generation (6G) networks continue to evolve, AI-driven solutions are playing a crucial role in enabling more efficient and adaptive resource management in wireless communication. One of the key innovations in 6G is user-centric cell-free massive Multiple-Input Multiple-Output (UC-CFmMIMO), a paradigm that eliminates traditional cell boundaries and enhances network performance by dynamically assigning access points (APs) to users. This approach is particularly well-suited for vehicular …

MudiNet: Task-guided Disentangled Representation Learning for 5G Indoor Multipath-assisted Positioning
Ye Tian, Xueting Xu, Ao Peng
https://arxiv.org/abs/2506.04024

MudiNet: Task-guided Disentangled Representation Learning for 5G Indoor Multipath-assisted Positioning
In the fifth-generation communication system (5G), multipath-assisted positioning (MAP) has emerged as a promising approach. With the enhancement of signal resolution, multipath component (MPC) are no longer regarded as noise but rather as valuable information that can contribute to positioning. However, existing research often treats reflective surfaces as ideal reflectors, while being powerless in the face of indistinguishable multipath caused by diffuse reflectors. This study approaches diff…

Plant Bioelectric Early Warning Systems: A Five-Year Investigation into Human-Plant Electromagnetic Communication
Peter A. Gloor
https://arxiv.org/abs/2506.04132

Plant Bioelectric Early Warning Systems: A Five-Year Investigation into Human-Plant Electromagnetic Communication
We present a comprehensive investigation into plant bioelectric responses to human presence and emotional states, building on five years of systematic research. Using custom-built plant sensors and machine learning classification, we demonstrate that plants generate distinct bioelectric signals correlating with human proximity, emotional states, and physiological conditions. A deep learning model based on ResNet50 architecture achieved 97% accuracy in classifying human emotional states through …