Massive multiple access in future wireless networks: dynamic architectures and machine learning-based design
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Date
2021-06
Authors
Al-Eryani, Yasser
Al-Eryani, Yasser
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Abstract
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.
Description
Keywords
Wireless communication, diversity techniques, cell-free massive MIMO networks, mmwaves, machine learning, deep reinforcement learning
Citation
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).)
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).)