Spatial Resource Allocation in Massive MIMO Communication : From Cellular to Cell-Free [book]

Trinh Van Chien
2020 Linköping studies in science and technology, Dissertations   unpublished
Printed in Sweden by LiU-Tryck, Linköping 2020 investigates the use of deep learning for power control optimization in Massive MIMO. We formulate the joint data and pilot power optimization for maximum sum SE in multi-cell Massive MIMO systems, which is a non-convex problem. We propose a new optimization algorithm, inspired by the weighted MMSE approach, to obtain a stationary point in polynomial time. We then use this algorithm together with deep learning to train a convolutional neural
more » ... ional neural network to perform the joint data and pilot power control in sub-millisecond runtime. The solution is suitable for online optimization. iv Finally, the fifth part of this thesis considers a large-scale distributed antenna system that serves the users by coherent joint transmission called Cell-free Massive MIMO. For a given user set, only a subset of the access points (APs) is likely needed to satisfy the users' performance demands. To find a flexible and energy-efficient implementation, we minimize the total power consumption at the APs in the DL, considering both the hardwareconsumed and transmit powers, where APs can be turned off to reduce the former part. Even though this is a non-convex optimization problem, a globally optimal solution is obtained by solving a mixed-integer second-order cone program (SOCP). We also propose low-complexity algorithms that exploit group-sparsity or received power strength in the problem formulation.
doi:10.3384/diss.diva-162582 fatcat:mb5bpqsbkndqxg6dyoppqt7x44