[Paper Review] Modeling and Coverage Analysis for RIS-aided NOMA Transmissions in Heterogeneous Networks

Ziyi Xie, Wenqiang Yi+3 more|arXiv (Cornell University)|Apr 27, 2021

Advanced Wireless Communication Technologies34 references4 citations

TL;DR

This paper proposes a stochastic geometry-based analytical framework for Reconfigurable Intelligent Surface (RIS)-aided Non-Orthogonal Multiple Access (NOMA) in heterogeneous networks (HetNets), modeling RISs, base stations (BSs), and user equipments (UEs) as Poisson point processes. It derives closed-form expressions for link distribution, user association, and SINR/rate coverage probabilities, showing that RIS deployment significantly improves rate coverage and that an optimal RIS-BS density ratio exists, with RIS length not being the decisive factor for coverage gain.

ABSTRACT

Reconfigurable intelligent surface (RIS) has been regarded as a promising tool to strengthen the quality of signal transmissions in non-orthogonal multiple access (NOMA) networks. This article introduces a heterogeneous network (HetNet) structure into RIS-aided NOMA multi-cell networks. A practical user equipment (UE) association scheme for maximizing the average received power is adopted. To evaluate system performance, we provide a stochastic geometry based analytical framework, where the locations of RISs, base stations (BSs), and UEs are modeled as homogeneous Poisson point processes (PPPs). Based on this framework, we first derive the closed-form probability density function (PDF) to characterize the distribution of the reflective links created by RISs. Then, both the exact expressions and upper/lower bounds of UE association probability are calculated. Lastly, the analytical expressions of the signal-to-interference-plus-noise-ratio (SINR) and rate coverage probability are deduced. Additionally, to investigate the impact of RISs on system coverage, the asymptotic expressions of two coverage probabilities are derived. The theoretical results show that RIS length is not the decisive factor for coverage improvement. Numerical results demonstrate that the proposed RIS HetNet structure brings significant enhancement in rate coverage. Moreover, there exists an optimal combination of RISs and BSs deployment densities to maximize coverage probability.

Motivation & Objective

• To address the challenge of poor coverage in high-frequency wireless networks, especially due to blockages and weak channel conditions.
• To integrate RIS-aided NOMA into heterogeneous networks (HetNets) to enhance spectral efficiency and energy efficiency.
• To develop a tractable analytical model using stochastic geometry to evaluate system performance under random deployments of RISs, BSs, and UEs.
• To derive closed-form expressions for key performance metrics such as user association probability, SINR, and rate coverage probability.
• To identify optimal deployment strategies for RISs and BSs that maximize network coverage.

Proposed method

• Models the locations of RISs, BSs, and UEs as independent homogeneous Poisson point processes (PPPs) for stochastic tractability.
• Derives the closed-form probability density function (PDF) for the distance distribution of RIS-reflected links using integral transformations and hypergeometric functions.
• Proposes a practical user association scheme based on maximizing average received power, distinguishing between direct LoS and RIS-reflected links.
• Develops exact and bounded expressions for the probability of user association to either direct BSs or RIS-aided links, considering path loss and shadowing.
• Derives analytical expressions for the signal-to-interference-plus-noise ratio (SINR) and rate coverage probability under both direct and RIS-assisted transmission modes.
• Uses asymptotic analysis to study coverage performance as RIS size approaches infinity, showing that interference from RISs dominates in the large-RIS regime.

Experimental results

Research questions

• RQ1 How does the deployment of RISs affect the user association probability in a RIS-aided NOMA HetNet?
• RQ2 What is the impact of RIS size and deployment density on the SINR and rate coverage probability?
• RQ3 Can a closed-form analytical framework be established for RIS-aided NOMA in HetNets using stochastic geometry?
• RQ4 What is the optimal trade-off between RIS and BS deployment densities to maximize system coverage?
• RQ5 Does RIS length significantly influence coverage improvement, or are other factors more critical?

Key findings

• The proposed RIS-aided NOMA HetNet structure achieves significant improvements in rate coverage probability compared to conventional networks.
• An optimal combination of RIS and BS deployment densities exists that maximizes coverage probability, indicating a non-monotonic performance trade-off.
• RIS length is not the decisive factor for coverage gain; instead, deployment density and channel conditions play more critical roles.
• As RIS size increases, the coverage probability converges to a limit determined by interference from RISs, with noise becoming negligible.
• The asymptotic coverage probability in the large-RIS regime is derived in closed form, showing a dependence on the RIS-BS and RIS-UE link path loss exponents and shadowing parameters.
• The analytical framework successfully captures the impact of RIS deployment on system performance, validated through numerical results showing strong agreement with theoretical derivations.