A POMDP framework for antenna selection and user scheduling in multi-user massive MIMO systems

We use a partially observable Markov decision process (POMDP) framework to design a resource allocation policy for downlink transmit beamforming at a multi-antenna BS that is equipped with a massive number of antennas and only a limited number of RF chains. Considering that channels evolve according to a Markov process and that only partial CSI is available, we use a POMDP framework for antenna selection with the aim to maximize the expected long-term data rate. To avoid the high computational complexity of the value iteration algorithm, we focus on the myopic policy to design a simple yet optimal algorithm. We prove that in the case of a positively correlated two-state Markov channel model, the myopic policy is optimal for antenna selection (for both in massive MISO and MU-MIMO systems) for any number of RF chains. Based on this finding, for general fading channels, we propose to quantize each channel into two levels and apply the myopic policy for antenna selection. Our simulation results show that using this two-level channel quantization for antenna selection results in only a small loss in performance, as compared to the antenna selection technique which use full CSI without quantization. We then utilize a POMDP framework to formulate the joint antenna selection and user scheduling (JASUS) problem for a BS, equipped with a limited number of RF chains that is to serve a large number of single-antenna users in a cell. To do so, we assume that the users are served in a frame, where each frame contains of a finite number of time slots. At the beginning of each frame, given that only partial CSI is available, the BS schedules each user to a time slot, and selects a subset of antennas to serve the scheduled users at that time slot. Considering a positively correlated two-state channel model, we prove the optimality of the myopic policy for our JASUS problem. For Rayleigh fading channels, we devise a low-complexity JASUS algorithm for massive MU-MIMO systems.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Sharifi, Sara

Advisor dc:contributor.advisor

• ShahbazPanahi, Shahram

• Antenna selection
• Joint antenna selection and user scheduling
• Massive MIMO
• Partially observable Markov decision process (POMDP)
• Myopic policy

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1450

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1450