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RIS-Aided RSMA Improves the Latency vs. Energy Trade-off in the Finite Block Length MIMO Downlink

Mohammad Soleymani, Bruno Clerckx, Robert Schober, Lajos Hanzo

Abstract

We simultaneously minimize the latency and improve energy efficiency (EE) of the multi-user multiple-input multiple-output (MU-MIMO) rate splitting multiple access (RSMA) downlink, aided by a reconfigurable intelligent surface (RIS). Our results show that RSMA improves the EE and may reduce the delay to 13\% of that of spatial division multiple access (SDMA). Moreover, RIS and RSMA support each other synergistically, while an RIS operating without RSMA provides limited benefits in terms of latency and cannot effectively mitigate interference. {Furthermore, increasing the RIS size amplifies the gains of RSMA more significantly than those of SDMA, without altering the fundamental EE-latency trade-offs.} Results also show that latency increases with more stringent reliability requirements, and RSMA yields more significant gains under such conditions, making it eminently suitable for energy-efficient ultra-reliable low-latency communication (URLLC) scenarios.

RIS-Aided RSMA Improves the Latency vs. Energy Trade-off in the Finite Block Length MIMO Downlink

Abstract

We simultaneously minimize the latency and improve energy efficiency (EE) of the multi-user multiple-input multiple-output (MU-MIMO) rate splitting multiple access (RSMA) downlink, aided by a reconfigurable intelligent surface (RIS). Our results show that RSMA improves the EE and may reduce the delay to 13\% of that of spatial division multiple access (SDMA). Moreover, RIS and RSMA support each other synergistically, while an RIS operating without RSMA provides limited benefits in terms of latency and cannot effectively mitigate interference. {Furthermore, increasing the RIS size amplifies the gains of RSMA more significantly than those of SDMA, without altering the fundamental EE-latency trade-offs.} Results also show that latency increases with more stringent reliability requirements, and RSMA yields more significant gains under such conditions, making it eminently suitable for energy-efficient ultra-reliable low-latency communication (URLLC) scenarios.
Paper Structure (17 sections, 2 theorems, 21 equations, 11 figures, 1 table)

This paper contains 17 sections, 2 theorems, 21 equations, 11 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Motivations and Contributions
  4. Paper Outline and Notations
  5. System Model
  6. Signal Model and Rate Expressions
  7. Problem Statement
  8. Proposed Solution
  9. Updating Beamforming Matrices
  10. Updating RIS Scattering Matrix
  11. Computational Complexity Analysis
  12. Beamforming Update
  13. RIS Scattering Matrix Update
  14. Overall Complexity
  15. Comparison with SDMA
  16. ...and 2 more sections

Key Result

Lemma 1

All feasible $\{{\bf \Upsilon}\}$ satisfy the inequalities where we have: where $\bar{\bf T}_{kj}=\bar{\bf H}_{k}\bar{\bf \Upsilon}_{j}\bar{\bf \Upsilon}_{j}^H\bar{\bf H}_{k}^H$, $\bar{\bf S}_{ck}=\bar{\bf H}_k\bar{\bf \Upsilon}\bar{\bf \Upsilon}^H\bar{\bf H}_k^H$, $\bar{\bf H}_k={\bf H}_k({\bf \Psi}^{(i-1)} )$, $\bar{\bf \Upsilon}={\bf \Upsilon}^{(i-1)}$, and $\bar{\bf \

Figures (11)

  • Figure 1: System model of the MU-MIMO RIS-aided downlink.
  • Figure 2: Network topology for numerical evaluations.
  • Figure 3: Latency-EE region ($N_{{BS}}=N_{u}=3$, and $K=4$).
  • Figure 4: Average min-max delay versus $P$ ($N_{{BS}}=N_{u}=2$).
  • Figure 5: Delay reductions by employing RSMA and RIS versus $P$ ($N_{{BS}}=N_{u}=2$).
  • ...and 6 more figures

Theorems & Definitions (3)

  • Remark 1: Discussion on the reliability constraint
  • Lemma 1: soleymani2024rate
  • Lemma 2: soleymani2024rate