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Downlink and Uplink NOMA-ISAC with Signal Alignment

Boqun Zhao, Chongjun Ouyang, Xingqi Zhang, Yuanwei Liu

TL;DR

Numerical results reveal that in both downlink and uplink cases, ISAC outperforms FDSAC in terms of sensing and communications performance and is capable of achieving a broader rate region, clearly showcasing its superiority.

Abstract

Integrated Sensing and Communications (ISAC) surpasses the conventional frequency-division sensing and communications (FDSAC) in terms of spectrum, energy, and hardware efficiency, with potential for greater enhancement through integration of non-orthogonal multiple access (NOMA). Leveraging these advantages, a multiple-input multiple-output NOMA-ISAC framework is proposed in this paper, in which the technique of signal alignment is adopted. The performance of the proposed framework for both downlink and uplink is analyzed. 1) The downlink ISAC is investigated under three different precoding designs: a sensing-centric (S-C) design, a communications-centric (C-C) design, and a Pareto optimal design. 2) For the uplink case, two scenarios are investigated: a S-C design and a C-C design, which vary based on the order of interference cancellation between the communication and sensing signals. In each of these scenarios, key performance metrics including sensing rate (SR), communication rate (CR), and outage probability are investigated. For a deeper understanding, the asymptotic performance of the system in the high signal-to-noise ratio (SNR) region is also explored, with a focus on the high-SNR slope and diversity order. Finally, the SR-CR rate regions achieved by ISAC and FDSAC are studied. Numerical results reveal that in both downlink and uplink cases, ISAC outperforms FDSAC in terms of sensing and communications performance and is capable of achieving a broader rate region, clearly showcasing its superiority.

Downlink and Uplink NOMA-ISAC with Signal Alignment

TL;DR

Numerical results reveal that in both downlink and uplink cases, ISAC outperforms FDSAC in terms of sensing and communications performance and is capable of achieving a broader rate region, clearly showcasing its superiority.

Abstract

Integrated Sensing and Communications (ISAC) surpasses the conventional frequency-division sensing and communications (FDSAC) in terms of spectrum, energy, and hardware efficiency, with potential for greater enhancement through integration of non-orthogonal multiple access (NOMA). Leveraging these advantages, a multiple-input multiple-output NOMA-ISAC framework is proposed in this paper, in which the technique of signal alignment is adopted. The performance of the proposed framework for both downlink and uplink is analyzed. 1) The downlink ISAC is investigated under three different precoding designs: a sensing-centric (S-C) design, a communications-centric (C-C) design, and a Pareto optimal design. 2) For the uplink case, two scenarios are investigated: a S-C design and a C-C design, which vary based on the order of interference cancellation between the communication and sensing signals. In each of these scenarios, key performance metrics including sensing rate (SR), communication rate (CR), and outage probability are investigated. For a deeper understanding, the asymptotic performance of the system in the high signal-to-noise ratio (SNR) region is also explored, with a focus on the high-SNR slope and diversity order. Finally, the SR-CR rate regions achieved by ISAC and FDSAC are studied. Numerical results reveal that in both downlink and uplink cases, ISAC outperforms FDSAC in terms of sensing and communications performance and is capable of achieving a broader rate region, clearly showcasing its superiority.
Paper Structure (38 sections, 23 theorems, 89 equations, 7 figures, 2 tables)

This paper contains 38 sections, 23 theorems, 89 equations, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. System Model
  3. Downlink ISAC
  4. Sensing Model
  5. Communication Model
  6. Uplink ISAC
  7. Downlink Performance
  8. Sensing-Centric Design
  9. Performance of Sensing
  10. Performance of Communications
  11. Communications-Centric Design
  12. Performance of Communications
  13. Performance of Sensing
  14. Pareto Optimal Design
  15. Performance of FDSAC
  16. ...and 23 more sections

Key Result

Lemma 1

For a given $\mathbf{P}$, ${\mathcal{R}}_{\rm{d},\rm{s}}$ can be calculated as

Figures (7)

  • Figure 1: Illustration of a downlink/uplink NOMA-ISAC system.
  • Figure 2: Downlink communications performance.
  • Figure 3: Downlink sensing rate.
  • Figure 4: Downlink rate region with $p=25$ dB.
  • Figure 5: Uplink communications performance.
  • ...and 2 more figures

Theorems & Definitions (45)

  • Lemma 1
  • Theorem 1
  • Remark 1
  • Lemma 2
  • Theorem 2
  • Corollary 1
  • Remark 2
  • Theorem 3
  • Corollary 2
  • Remark 3
  • ...and 35 more