Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA
in the Indoor Environment via Multi-Agent Reinforcement Learning
- URL: http://arxiv.org/abs/2311.08708v2
- Date: Fri, 17 Nov 2023 03:12:12 GMT
- Title: Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA
in the Indoor Environment via Multi-Agent Reinforcement Learning
- Authors: Yu Min Park, Yan Kyaw Tun, Choong Seon Hong
- Abstract summary: NOMA allows multiple users to share the same resources, which improves the spectral efficiency of the system.
STAR-RISs can achieve improved coverage, increased spectral efficiency, and enhanced communication reliability.
However, STAR-RISs must simultaneously optimize the amplitude and phase shift corresponding to reflection and transmission.
- Score: 15.95700167294255
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The development of 6G/B5G wireless networks, which have requirements that go
beyond current 5G networks, is gaining interest from academia and industry.
However, to increase 6G/B5G network quality, conventional cellular networks
that rely on terrestrial base stations are constrained geographically and
economically. Meanwhile, NOMA allows multiple users to share the same
resources, which improves the spectral efficiency of the system and has the
advantage of supporting a larger number of users. Additionally, by
intelligently manipulating the phase and amplitude of both the reflected and
transmitted signals, STAR-RISs can achieve improved coverage, increased
spectral efficiency, and enhanced communication reliability. However, STAR-RISs
must simultaneously optimize the amplitude and phase shift corresponding to
reflection and transmission, which makes the existing terrestrial networks more
complicated and is considered a major challenging issue. Motivated by the
above, we study the joint user pairing for NOMA and beamforming design of
Multi-STAR-RISs in an indoor environment. Then, we formulate the optimization
problem with the objective of maximizing the total throughput of MUs by jointly
optimizing the decoding order, user pairing, active beamforming, and passive
beamforming. However, the formulated problem is a MINLP. To address this
challenge, we first introduce the decoding order for NOMA networks. Next, we
decompose the original problem into two subproblems, namely: 1) MU pairing and
2) Beamforming optimization under the optimal decoding order. For the first
subproblem, we employ correlation-based K-means clustering to solve the user
pairing problem. Then, to jointly deal with beamforming vector optimizations,
we propose MAPPO, which can make quick decisions in the given environment owing
to its low complexity.
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