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Multiobjective Optimisation and Machine Learning based Resource Allocation for 5G Massive MIMO Systems
- Author(s)
- SHRUTI SHARMA
- Advisor
- Wonsik Yoon
- Department
- 일반대학원 전자공학과
- Publisher
- The Graduate School, Ajou University
- Publication Year
- 2022-08
- Language
- eng
- Alternative Abstract
- Most of the existing works studies on fifth generation (5G) wireless communication are mainly focused on the quality of service (QoS) requirements, e.g., throughput, energy efficiency (EE), etc., but the situation becomes more complex in a multiobjective environment where several conflicting variables are present. This thesis aims to investigate and optimize the EE of massive multiple-input multiple-output (MIMO) communication network systems using a multiobjective optimization approach based on machine learning (ML)/artificial intelligence (AI) tools. Motivated by this fact, the main focus of this thesis is on solving multiobjective optimization problems (MOOP) in a 5G network. Various resource allocation strategies were used in this work to optimize the system performance of massive MIMO network. The multiobjective trade-offs between EE and spectral efficiency (SE) were investigated by Tchebycheff scalarization, joint selection of antenna and user scheduling (JASUS), switch on-off strategy and various multiobjective reinforcement learning (MORL) applied to remote radio heads (RRHs) based networks. The performance of promising 5G communication technologies cloud-radio access network (C-RAN), large scale-distributed antenna system (L-DAS), distributed and co-located MIMO systems operating under certain constraints were also verified in each case. Such analyses have hands-on significance for 5G technology that requires low latency, small power consumption, and/or the ability to simultaneously support a huge number of users. Recently, C-RAN has been considered a promising technology for 5G communication networks. The radio frequency signals from located antennas are collected by radio remote heads (RRHs) and transferred to the baseband units (BBUs) pool through front haul links. Such a centralized architecture facilitates global optimization of joint baseband processing unit (BPU) and resource allocation for every RRHs and users. For this purpose, this thesis utilizes novel schemes for energy efficient optimization of resources of 5G technologies in full-duplex (FD)-enabled massive MIMO networks. This thesis firstly extends the concept of FD communication network, where existing works have focused on optimization. In addition, this thesis also explores the use of machine learning tools such as multi-agent reinforcement learning (RL) and deep reinforcement learning (DRL) for solving MOOP in a multi-agent scenario. This thesis verified that by simultaneous transmitting and receiving, FD-enabled MIMO has the potential to significantly improve the SE of the half-duplex (HD) communication networks in MIMO networks. The proposed MOOP algorithms to study the EE-SE trade-off of the networks allow both uplink and downlink power savings, leading to an overall EE improvement in the wireless link. The findings of this thesis include enhancement in EE, capacity, QoS, and throughput of the given network using the ML and MOOP algorithms applied to the 5G MIMO systems.
- Fulltext
- Appears in Collections:
- Graduate School of Ajou University > Department of Electronic Engineering > 4. Theses(Ph.D)
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