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Achievable Sum Rate Optimization on NOMA-aided Cell-Free Massive MIMO with Finite Blocklength Coding

Baolin Chong, Hancheng Lu, Yuang Chen, Langtian Qin, Fengqian Guo

TL;DR

This paper investigates the performance of the NOMA-aided CFmMIMO system with FBC in terms of achievable sum rate (ASR) and formulate an ASR maximization problem by jointly considering power allocation and user equipment (UE) clustering.

Abstract

Non-orthogonal multiple access (NOMA)-aided cell-free massive multiple-input multiple-output (CFmMIMO) has been considered as a promising technology to fulfill strict quality of service requirements for ultra-reliable low-latency communications (URLLC). However, finite blocklength coding (FBC) in URLLC makes it challenging to achieve the optimal performance in the NOMA-aided CFmMIMO system. In this paper, we investigate the performance of the NOMA-aided CFmMIMO system with FBC in terms of achievable sum rate (ASR). Firstly, we derive a lower bound (LB) on the ergodic data rate. Then, we formulate an ASR maximization problem by jointly considering power allocation and user equipment (UE) clustering. To tackle such an intractable problem, we decompose it into two sub-problems, i.e., the power allocation problem and the UE clustering problem. A successive convex approximation (SCA) algorithm is proposed to solve the power allocation problem by transforming it into a series of geometric programming problems. Meanwhile, two algorithms based on graph theory are proposed to solve the UE clustering problem by identifying negative loops. Finally, alternative optimization is performed to find the maximum ASR of the NOMA-aided CFmMIMO system with FBC. The simulation results demonstrate that the proposed algorithms significantly outperform the benchmark algorithms in terms of ASR under various scenarios.

Achievable Sum Rate Optimization on NOMA-aided Cell-Free Massive MIMO with Finite Blocklength Coding

TL;DR

This paper investigates the performance of the NOMA-aided CFmMIMO system with FBC in terms of achievable sum rate (ASR) and formulate an ASR maximization problem by jointly considering power allocation and user equipment (UE) clustering.

Abstract

Non-orthogonal multiple access (NOMA)-aided cell-free massive multiple-input multiple-output (CFmMIMO) has been considered as a promising technology to fulfill strict quality of service requirements for ultra-reliable low-latency communications (URLLC). However, finite blocklength coding (FBC) in URLLC makes it challenging to achieve the optimal performance in the NOMA-aided CFmMIMO system. In this paper, we investigate the performance of the NOMA-aided CFmMIMO system with FBC in terms of achievable sum rate (ASR). Firstly, we derive a lower bound (LB) on the ergodic data rate. Then, we formulate an ASR maximization problem by jointly considering power allocation and user equipment (UE) clustering. To tackle such an intractable problem, we decompose it into two sub-problems, i.e., the power allocation problem and the UE clustering problem. A successive convex approximation (SCA) algorithm is proposed to solve the power allocation problem by transforming it into a series of geometric programming problems. Meanwhile, two algorithms based on graph theory are proposed to solve the UE clustering problem by identifying negative loops. Finally, alternative optimization is performed to find the maximum ASR of the NOMA-aided CFmMIMO system with FBC. The simulation results demonstrate that the proposed algorithms significantly outperform the benchmark algorithms in terms of ASR under various scenarios.
Paper Structure (18 sections, 3 theorems, 44 equations, 7 figures, 3 algorithms)

This paper contains 18 sections, 3 theorems, 44 equations, 7 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work and Research Gap
  3. Contribution and Outline
  4. System Model and Problem Formation
  5. Uplink Pilot Training and Channel Estimation
  6. DL Data Transmission
  7. Achievable Data Rate for URLLC UE
  8. Problem Formation
  9. Problem Analysis and Solution
  10. DL power allocation based on SCA
  11. UE Clustering Matrix Design based on Graph Theory
  12. Globe ASR Maximization
  13. Numerical Results and Discussion
  14. Simulation Setup and Comparison Algorithms
  15. Convergence and Complexity Analysis
  16. ...and 3 more sections

Key Result

Theorem 1

The LB of the ergodic data rate for URLLC UE $n$ ,$\forall n \in \mathcal{N}$ under FBC in the NOMA-aided CFmMIMO system can be given byThe LB of the ergodic rate is affected by the accuracy of channel estimation. Too small a value of $\theta_{mn}$ directly leads to a large $\bar{\gamma}_{n}$, and t where $\bar{\gamma}_n = \min(\bar{\gamma}_n^n,\bar{\gamma}_{n_1}^n), \forall n_1 \le n \cap \pi_n =

Figures (7)

  • Figure 1: The NOMA-aided CFmMIMO system for URLLC.
  • Figure 2: Illustration of the weighted directed graph, shift league, and exchange league in NOMA-aided CFmMIMO system.
  • Figure 3: Convergence and complexity analysis of the proposed algorithm: a. Convergence of S-GSA vs. number of URLLC UEs; b. Convergence of S-EBFA vs. number of URLLC UEs; c. The performance of algorithm complexity vs. the number of URLLC UEs.
  • Figure 4: Tightness of derived data rate LB vs. the number of APs.
  • Figure 5: ASR vs. the number of URLLC UEs.
  • ...and 2 more figures

Theorems & Definitions (10)

  • Theorem 1
  • proof
  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Lemma 1
  • proof
  • Theorem 2
  • proof