Massive multiple access in future wireless networks: dynamic architectures and machine learning-based design

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Al-Eryani, Yasser
Al-Eryani, Yasser
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During the last few decades, wireless communication technologies and services have radically changed the way we live and interact at the personal, social, local and global levels. Such changes were mainly driven by the continuous emergence of innovative wireless communication services and products. These services and products represents a direct upshot of enduring research outcomes within the area. Nevertheless, the blessing of such innovation was accompanied by extremely high demands in forms of data traffic, per-user transmission rate, minimum transmission delay and in the number of wireless devices per unit area. Tackling these issues through cellular network densification was faced by many technical issues related to high interference levels, tedious user scheduling processes, and complicated network resource allocation algorithms. Trying to address these imperative technical issues in future wireless networks, this thesis develops several innovative enabling techniques for massive wireless multiple access. Specifically, we commence this work by introducing a new concept of partial spectrum overlapping among active users equipment (UEs). The proposed scheme represents a trade-off between fully orthogonal multiple access schemes (e.g. time division multiple access [TDMA], frequency division multiple access (FDMA) and orthogonal frequency division multiple access (OFDMA)) and that of non-orthogonal multiple access (NOMA). Second, we develop several innovative dynamic cell-free network architectures that support massive wireless connectivity through adaptive access points (APs)/base stations (BSs) coordination and/or cooperation. The proposed network models are then evaluated under different state-of-the-art enabling wireless techniques such as millimeter wave (mmWave) channel links and massive multiple-input multiple-output (mMIMO) systems. Furthermore, the performance of the proposed architectures is investigated through the derivation of several closed-form expressions of exact and/or asymptotic performance metrics (example, probability of outage, asymptotic outage, instantaneous rate and outage-capacity). Finally, for practical control and monitoring of the proposed access techniques and network models, we develop several low-complexity deep reinforcement learning (DRL)-based modeling frameworks that can efficiently learn the solution of several combinatorial optimization problems related to network partitioning (clustering) and uplink/downlink beamforming. This is achieved through innovative nested DRL designs that utilizes continuous and discrete deep neural networks (DNN) agents based on the nature of the problem. Several operating scenarios of the proposed techniques are evaluated through extensive Monte-Carlo simulations (Matlab and Python) with practical parameters and assumptions.
Wireless communication, diversity techniques, cell-free massive MIMO networks, mmwaves, machine learning, deep reinforcement learning
Y. Al-Eryani, M. Akrout and E. Hossain, "Multiple Access in Cell-Free Networks: Outage Performance, Dynamic Clustering, and Deep Reinforcement Learning-Based Design," in IEEE Journal on Selected Areas in Communications, vol. 39, no. 4, pp. 1028-1042, April 2021, doi: 10.1109/JSAC.2020.3018825.
Y. Al-Eryani, E. Hossain and D. I. Kim, "Generalized Coordinated Multipoint (GCoMP)-Enabled NOMA: Outage, Capacity, and Power Allocation," in IEEE Transactions on Communications, vol. 67, no. 11, pp. 7923-7936, Nov. 2019, doi: 10.1109/TCOMM.2019.2931971.
Y. Al-Eryani and E. Hossain, "The D-OMA Method for Massive Multiple Access in 6G: Performance, Security, and Challenges," in IEEE Vehicular Technology Magazine, vol. 14, no. 3, pp. 92-99, Sept. 2019, doi: 10.1109/MVT.2019.2919279.
Y. Al-Eryani and E. Hossain, "Self-Organizing mmWave MIMO Cell-Free Networks With Hybrid Beamforming: A Hierarchical DRL-Based Design," in IEEE Commun. (Submitted).)