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Enhancing xURLLC with RSMA-Assisted Massive-MIMO Networks: Performance Analysis and Optimization

Yuang Chen, Hancheng Lu, Chenwu Zhang, Yansha Deng, Arumugam Nallanathan

TL;DR

The work tackles the design of xURLLC over RSMA-assisted massive-MIMO networks under imperfect CSIT and finite blocklength, aiming to maximize total ETR. It develops an RSMA-mMIMO-xURLLC architecture, derives closed-form SINRs for common and private streams, and formulates a jointly constrained optimization that accounts for pilot overhead, blocklength, and decode-probability constraints. By decomposing the problem into three subproblems—power allocation, rate-splitting, and transmit antenna selection—the authors propose a low-complexity JPRT algorithm that iteratively optimizes these aspects and demonstrates convergence to a local optimum. Numerical results show substantial gains over NOMA and SDMA in ETR, reliability, and scalability, underscoring RSMA’s potential to meet stringent xURLLC requirements in 6G networks.

Abstract

Massive interconnection has sparked people's envisioning for next-generation ultra-reliable and low-latency communications (xURLLC), prompting the design of customized next-generation advanced transceivers (NGAT). Rate-splitting multiple access (RSMA) has emerged as a pivotal technology for NGAT design, given its robustness to imperfect channel state information (CSI) and resilience to quality of service (QoS). Additionally, xURLLC urgently appeals to large-scale access techniques, thus massive multiple-input multiple-output (mMIMO) is anticipated to integrate with RSMA to enhance xURLLC. In this paper, we develop an innovative RSMA-assisted massive-MIMO xURLLC (RSMA-mMIMO-xURLLC) network architecture tailored to accommodate xURLLC's critical QoS constraints in finite blocklength (FBL) regimes. Leveraging uplink pilot training under imperfect CSI at the transmitter, we estimate channel gains and customize linear precoders for efficient downlink short-packet data transmission. Subsequently, we formulate a joint rate-splitting, beamforming, and transmit antenna selection optimization problem to maximize the total effective transmission rate (ETR). Addressing this multi-variable coupled non-convex problem, we decompose it into three corresponding subproblems and propose a low-complexity joint iterative algorithm for efficient optimization. Extensive simulations substantiate that compared with non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), the developed architecture improves the total ETR by 15.3% and 41.91%, respectively, as well as accommodates larger-scale access.

Enhancing xURLLC with RSMA-Assisted Massive-MIMO Networks: Performance Analysis and Optimization

TL;DR

The work tackles the design of xURLLC over RSMA-assisted massive-MIMO networks under imperfect CSIT and finite blocklength, aiming to maximize total ETR. It develops an RSMA-mMIMO-xURLLC architecture, derives closed-form SINRs for common and private streams, and formulates a jointly constrained optimization that accounts for pilot overhead, blocklength, and decode-probability constraints. By decomposing the problem into three subproblems—power allocation, rate-splitting, and transmit antenna selection—the authors propose a low-complexity JPRT algorithm that iteratively optimizes these aspects and demonstrates convergence to a local optimum. Numerical results show substantial gains over NOMA and SDMA in ETR, reliability, and scalability, underscoring RSMA’s potential to meet stringent xURLLC requirements in 6G networks.

Abstract

Massive interconnection has sparked people's envisioning for next-generation ultra-reliable and low-latency communications (xURLLC), prompting the design of customized next-generation advanced transceivers (NGAT). Rate-splitting multiple access (RSMA) has emerged as a pivotal technology for NGAT design, given its robustness to imperfect channel state information (CSI) and resilience to quality of service (QoS). Additionally, xURLLC urgently appeals to large-scale access techniques, thus massive multiple-input multiple-output (mMIMO) is anticipated to integrate with RSMA to enhance xURLLC. In this paper, we develop an innovative RSMA-assisted massive-MIMO xURLLC (RSMA-mMIMO-xURLLC) network architecture tailored to accommodate xURLLC's critical QoS constraints in finite blocklength (FBL) regimes. Leveraging uplink pilot training under imperfect CSI at the transmitter, we estimate channel gains and customize linear precoders for efficient downlink short-packet data transmission. Subsequently, we formulate a joint rate-splitting, beamforming, and transmit antenna selection optimization problem to maximize the total effective transmission rate (ETR). Addressing this multi-variable coupled non-convex problem, we decompose it into three corresponding subproblems and propose a low-complexity joint iterative algorithm for efficient optimization. Extensive simulations substantiate that compared with non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), the developed architecture improves the total ETR by 15.3% and 41.91%, respectively, as well as accommodates larger-scale access.
Paper Structure (28 sections, 8 theorems, 79 equations, 11 figures, 1 table, 2 algorithms)

This paper contains 28 sections, 8 theorems, 79 equations, 11 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. Research Motivations and Challenges
  4. Related Work
  5. Main Contributions
  6. RSMA-mMIMO-xURLLC Network Architecture
  7. Critical QoS Provisioning Requirements for xURLLC
  8. Uplink Pilot Training for Channel Estimations
  9. Precoder Design for Beamforming
  10. Downlink Short-Packet Data Transmission
  11. Problem Formulation And Solution
  12. Problem Formulation
  13. Problem Reformulation
  14. Optimality Analysis
  15. Subproblem I: Power Allocation Optimization
  16. ...and 13 more sections

Key Result

Lemma 1

Assuming receivers are uniformly distributed within the cell with an inner radius of $R_{min}$ and an outer radius of $R_{max}$. The BS is located at a height $\tilde{h}$, satisfying $\tilde{h} \ll R_{min}$ and $\tilde{h} \ll R_{max}$. Then, we can derive that where where $\small{\widetilde{\Pi}_{1} \!=\! \mathbb{E}\!\!\left[\!\frac{\kappa_{u}^{2}}{\left(1 + N_{p}\rho_{p}\kappa_{u}\right)^{2}}\!

Figures (11)

  • Figure 1: The RSMA-assisted massive-MIMO xURLLC network architecture.
  • Figure 2: The illustration of the proposed JPRT optimization mechanism.
  • Figure 3: The convergence behavior of Algorithm 2. $P_{tot} = 4$ W, $N_{tot} = 10^{3}$ CUs, and $\mathcal{R}_{min} = 2.0$ bits/sec/Hz.
  • Figure 4: The optimality performance of Algorithm 2. $U = 5$, $N_{R} = 4$, and $\mathcal{R}_{min} = 2.0$ bits/sec/Hz.
  • Figure 5: Maximum ETR versus delay and reliability constraints. $U = 5$, $P_{tot} = 5$ W, and $\mathcal{R}_{min} = 2.0$ bits/sec/Hz.
  • ...and 6 more figures

Theorems & Definitions (16)

  • Lemma 1
  • proof
  • Theorem 1
  • proof
  • Lemma 2
  • proof
  • Corollary 1
  • proof
  • Lemma 3
  • proof
  • ...and 6 more