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Learning Decentralized Power Control in Cell-Free Massive MIMO Networks
This paper studies learning-based decentralized power control methods for cell-free massive multiple-input multiple-output (MIMO) systems where a central processor (CP) controls access points (APs) through fronthaul coordination.
Robust Design of Rate-Splitting Multiple Access With Imperfect CSI for Cell-Free MIMO Systems
Rate-Splitting Multiple Access (RSMA) for multi-user downlink operates by splitting the message for each user equipment (UE) into a private message and a set of common messages, which are simultaneously transmitted by means of superposition coding.
Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks
An efficient learning solution is proposed which constructs a DNN to produce a low-dimensional representation of optimal beamforming and quantization strategies.
Optimizing Over-the-Air Computation in IRS-Aided C-RAN Systems
Over-the-air computation (AirComp) is an efficient solution to enable federated learning on wireless channels.
Signal Processing Information Theory Information Theory
Convergence Analysis of Optimization Algorithms
The regret bound of an optimization algorithms is one of the basic criteria for evaluating the performance of the given algorithm.
