Tootfinder

Repeated-and-Offset QPSK for DFT-s-OFDM in Satellite Access
Renaud-Alexandre Pitaval
https://arxiv.org/abs/2510.11445 https://arxiv.org/pdf/2510.11445

Repeated-and-Offset QPSK for DFT-s-OFDM in Satellite Access
Motivated by the convergence of terrestrial cellular networks with satellite networks, we consider an adaptation of offset quadrature phase shift keying (OQPSK), used with single-carrier waveform in traditional satellite systems, to discrete Fourier transform spread (DFT-s-) orthogonal frequency-division multiplexed (OFDM) waveform employed in the uplink of terrestrial systems. We introduce a new order-one constellation modulation, termed repeated-and-offset QPSK (RO-QPSK), derive its basic pro…

Tempological Control of Network Dynamics
Yuanzhao Zhang, Sean P. Cornelius
https://arxiv.org/abs/2510.10926 https://arxiv.org/pdf/2510.10926

Tempological Control of Network Dynamics
Feedback control is an effective strategy for stabilizing a desired state and has been widely adopted in maintaining the stability of systems such as flying birds and power grids. By default, this framework requires continuous control input to offset deviations from the desired state, which can be invasive and cost a considerable amount of energy. Here, we introduce tempological (temporal + topological) control -- a novel, noninvasive strategy that harnesses the inherent flexibility of time-var…

FedAPTA: Federated Multi-task Learning in Computing Power Networks with Adaptive Layer-wise Pruning and Task-aware Aggregation
Yachao Yuan, Zhen Yu, Jin Wang, Zhipeng Cheng, Jianhua Hu
https://arxiv.org/abs/2508.02230

FedAPTA: Federated Multi-task Learning in Computing Power Networks with Adaptive Layer-wise Pruning and Task-aware Aggregation
Federated Learning (FL) has shown considerable promise in Computing Power Networks (CPNs) for privacy protection, efficient data utilization, and dynamic collaboration. Although it offers practical benefits, applying FL in CPNs continues to encounter a major obstacle, i.e., multi-task deployment. However, existing work mainly focuses on mitigating FL's computation and communication overhead of a single task while overlooking the computing resource wastage issue of heterogeneous devices across m…

Sharpness of Minima in Deep Matrix Factorization: Exact Expressions
Anil Kamber, Rahul Parhi
https://arxiv.org/abs/2509.25783 https://arxiv.org/pdf/2509.25…

Sharpness of Minima in Deep Matrix Factorization: Exact Expressions
Understanding the geometry of the loss landscape near a minimum is key to explaining the implicit bias of gradient-based methods in non-convex optimization problems such as deep neural network training and deep matrix factorization. A central quantity to characterize this geometry is the maximum eigenvalue of the Hessian of the loss, which measures the sharpness of the landscape. Currently, its precise role has been obfuscated because no exact expressions for this sharpness measure were known i…

COMET: Co-Optimization of a CNN Model using Efficient-Hardware OBC Techniques
Boyang Chen, Mohd Tasleem Khan, George Goussetis, Mathini Sellathurai, Yuan Ding, Jo\~ao F. C. Mota
https://arxiv.org/abs/2510.03516

COMET: Co-Optimization of a CNN Model using Efficient-Hardware OBC Techniques
Convolutional Neural Networks (CNNs) are highly effective for computer vision and pattern recognition tasks; however, their computational intensity and reliance on hardware such as FPGAs pose challenges for deployment on low-power edge devices. In this work, we present COMET, a framework of CNN designs that employ efficient hardware offset-binary coding (OBC) techniques to enable co-optimization of performance and resource utilization. The approach formulates CNN inference with OBC representati…

Blind Source Separation-Enabled Joint Communication and Sensing in IBFD MIMO Systems
Siyao Li, Conrad Prisby, Thomas Yang
https://arxiv.org/abs/2508.20409 https://

Blind Source Separation-Enabled Joint Communication and Sensing in IBFD MIMO Systems
This paper addresses the challenge of joint communication and sensing (JCAS) in next-generation wireless networks, with an emphasis on in-band full-duplex (IBFD) multiple-input multiple-output (MIMO) systems. Traditionally, self-interference (SI) in IBFD systems is a major obstacle to recovering the signal of interest (SOI). Under the JCAS paradigm, however, this high-power SI signal presents an opportunity for efficient sensing. Since each transceiver node has access to the original SI signal,…