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Multiple Access in the Era of Distributed Computing and Edge Intelligence
This paper focuses on the latest research and innovations in fundamental next-generation multiple access (NGMA) techniques and the coexistence with other key technologies for the sixth generation (6G) of wireless networks.
Wireless Quantized Federated Learning: A Joint Computation and Communication Design
The impact of the quantization error on the convergence time is evaluated and the trade-off among model accuracy and timely execution is revealed.
Wireless Federated Learning (WFL) for 6G Networks -- Part II: The Compute-then-Transmit NOMA Paradigm
As it has been discussed in the first part of this work, the utilization of advanced multiple access protocols and the joint optimization of the communication and computing resources can facilitate the reduction of delay for wireless federated learning (WFL), which is of paramount importance for the efficient integration of WFL in the sixth generation of wireless networks (6G).
Wireless Federated Learning (WFL) for 6G Networks -- Part I: Research Challenges and Future Trends
Conventional machine learning techniques are conducted in a centralized manner.
On the Distribution of the Sum of Double-Nakagami-m Random Vectors and Application in Randomly Reconfigurable Surfaces
To facilitate the performance analysis of a RRS-assisted system, first, we present novel closed-form expressions for the probability density function, the cumulative distribution function, the moments, and the characteristic function of the distribution of the sum of double-Nakagami-m random vectors, whose amplitudes follow the double-Nakagami-m distribution, i. e., the distribution of the product of two Nakagami-m random variables, and phases follow the circular uniform distribution.
Information Theory Signal Processing Information Theory Applications
