Multiple Access

Multiple access techniques aim to efficiently share limited communication resources among multiple devices, a crucial challenge in distributed computing and the Internet of Things. Current research heavily focuses on optimizing federated learning over wireless networks, employing over-the-air computation and various multiple access schemes (e.g., TDMA, random access, NOMA) to aggregate data efficiently while mitigating interference and noise. This involves developing novel algorithms like deep reinforcement learning for resource allocation and robust gradient aggregation methods to improve model training accuracy and convergence speed. These advancements are vital for enabling large-scale distributed applications, particularly in areas like edge intelligence and the Metaverse, by reducing communication bottlenecks and enhancing system efficiency.

Papers

October 30, 2024

October 29, 2024

Generative AI Enabled Matching for 6G Multiple Access
Xudong Wang, Hongyang Du, Dusit Niyato, Lijie Zhou, Lei Feng, Zhixiang Yang, Fanqin Zhou, Wenjing Li
Generative Artificial Intelligence Multiple Access GenAI Model Matching Strategy

September 23, 2024

Robust Federated Learning Over the Air: Combating Heavy-Tailed Noise with Median Anchored Clipping
Jiaxing Li, Zihan Chen, Kai Fong Ernest Chong, Bikramjit Das, Tony Q. S. Quek, Howard H. Yang
Edge Learning Multiple Access Heavy Tailed Noise Adaptive Clipping

July 16, 2024

Scalable and Reliable Over-the-Air Federated Edge Learning
Maximilian Egger, Christoph Hofmeister, Cem Kaya, Rawad Bitar, Antonia Wachter-Zeh
Potential Scalability Federated Edge Learning Air Federated Learning Air Computation Multiple Access

April 3, 2024

Optimal Batch Allocation for Wireless Federated Learning
Jaeyoung Song, Sang-Woon Jeon
Federated Learning Random Access Multiple Access Optimal Batch

February 26, 2024

Multiple Access in the Era of Distributed Computing and Edge Intelligence
Nikos G. Evgenidis, Nikos A. Mitsiou, Vasiliki I. Koutsioumpa, Sotiris A. Tegos, Panagiotis D. Diamantoulakis, George K. Karagiannidis
New Era Wireless Network Edge Intelligence Multiple Access Multi Access Edge Computing

February 4, 2024

Interference-Aware Emergent Random Access Protocol for Downlink LEO Satellite Networks
Chang-Yong Lim, Jihong Park, Jinho Choi, Ju-Hyung Lee, Daesub Oh, Heewook Kim
Multi Agent Deep Reinforcement Learning Emergent Communication Multiple Access Low Earth Orbit Satellite Protocol Learning

September 18, 2023

Self-Sustaining Multiple Access with Continual Deep Reinforcement Learning for Dynamic Metaverse Applications
Hamidreza Mazandarani, Masoud Shokrnezhad, Tarik Taleb, Richard Li
Continual LEArning Continual Reinforcement Learning Multi Channel Multiple Access Double Deep Q Learning Metaverse System Adaptive AI

August 28, 2023

Deep Reinforcement Learning for Uplink Scheduling in NOMA-URLLC Networks
Benoît-Marie Robaglia, Marceau Coupechoux, Dimitrios Tsilimantos
Deep Reinforcement Learning Non Orthogonal Multiple Access Uplink Control Multiple Access

August 11, 2023

Wireless Federated $k$-Means Clustering with Non-coherent Over-the-Air Computation
Alphan Sahin
Federated Learning K Mean K$ Mean Air Computation Multiple Access

July 7, 2023

Federated Learning over a Wireless Network: Distributed User Selection through Random Access
Chen Sun, Shiyao Ma, Ce Zheng, Songtao Wu, Tao Cui, Lingjuan Lyu
Preference Feedback Wireless Network Random Access Multiple Access Radio Resource Management User Selection

July 3, 2023

Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions
Bingnan Xiao, Xichen Yu, Wei Ni, Xin Wang, H. Vincent Poor
Future Direction Open Challenge Edge Network Air Federated Learning Chaotic Regime Multiple Access Air Aggregation

March 29, 2023

FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA
Abdelaziz Salama, Syed Ali Zaidi, Des McLernon, Mohammed M. H. Qazzaz
Machine Learning High Efficiency Network Dynamic Multiple Access Internet of Medical Thing Wireless Node

December 13, 2022

Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO
Houssem Sifaou, Geoffrey Ye Li
Air Federated Learning Air Computation Multiple Access Channel Correlation

November 17, 2022

Distributed Deep Joint Source-Channel Coding over a Multiple Access Channel
Selim F. Yilmaz, Can Karamanli, Deniz Gunduz
Image Transmission Joint Source Channel Coding Non Orthogonal Multiple Access Multiple Access Orthogonal Multiple Access

October 20, 2022

A Survey on Over-the-Air Computation
Alphan Sahin, Rui Yang
Timely Survey Computation Method Air Computation Multiple Access Wireless Channel

October 5, 2022

Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations
Jialing Liao, Zheng Chen, Erik G. Larsson
Differential Privacy PRIvacy Leakage Additive Perturbation Air Federated Learning Waveform Domain Multiple Access

August 15, 2022

Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation
Liang Li, Chenpei Huang, Dian Shi, Hao Wang, Xiangwei Zhou, Minglei Shu, Miao Pan
Energy Policy Research Air Computation Multiple Access Federated Learning Convergence

July 14, 2022

ASUMAN: Age Sense Updating Multiple Access in Networks
Purbesh Mitra, Sennur Ulukus
Network Programming Speech Based Age Gossip Algorithm Multiple Access Poisson Process Dynamic Update to Data Ratio

Multiple Access

Papers

Communication-Efficient Federated Learning over Wireless Channels via Gradient Sketching

NetworkGym: Reinforcement Learning Environments for Multi-Access Traffic Management in Network Simulation

Generative AI Enabled Matching for 6G Multiple Access

Robust Federated Learning Over the Air: Combating Heavy-Tailed Noise with Median Anchored Clipping

Scalable and Reliable Over-the-Air Federated Edge Learning

Optimal Batch Allocation for Wireless Federated Learning

Multiple Access in the Era of Distributed Computing and Edge Intelligence

Interference-Aware Emergent Random Access Protocol for Downlink LEO Satellite Networks

Self-Sustaining Multiple Access with Continual Deep Reinforcement Learning for Dynamic Metaverse Applications

Deep Reinforcement Learning for Uplink Scheduling in NOMA-URLLC Networks

Wireless Federated $k$-Means Clustering with Non-coherent Over-the-Air Computation

Federated Learning over a Wireless Network: Distributed User Selection through Random Access

Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions

FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA

Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO

Distributed Deep Joint Source-Channel Coding over a Multiple Access Channel

A Survey on Over-the-Air Computation

Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations

Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation

ASUMAN: Age Sense Updating Multiple Access in Networks