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Performance Analysis of Holographic MIMO Based Integrated Sensing and Communications

Boqun Zhao, Chongjun Ouyang, Xingqi Zhang, Yuanwei Liu

TL;DR

This work analyzes a holographic MIMO ISAC framework (HISAC) that incorporates spatial correlation in the communications channel and a spherical-wave model for sensing. It derives closed-form SR and CR expressions under both instantaneous and statistical CSI, for downlink and uplink, across sensing-centric and communications-centric beamforming designs, plus Pareto-optimal boundaries and time-sharing rate regions. The study shows HISAC achieves larger rate regions and higher high-SNR slopes than conventional MIMO-ISAC and FDSAC, with explicit DoF and diversity-order insights. The results demonstrate the practical benefits of HMIMO for joint sensing and communication tasks in next-generation networks, including downlink/uplink advantages and robust performance under CSI uncertainty.

Abstract

A holographic multiple-input multiple-output (MIMO)-based integrated sensing and communications (ISAC) framework is proposed for both downlink and uplink scenarios. The spatial correlation is incorporated into the communication channel modeling, while a spherical wave-based model is used to characterize the sensing link. By considering both instantaneous and statistical channel state information, closed-form expressions are derived for sensing rates (SRs), communication rates (CRs), and outage probabilities under various ISAC designs. This enables an investigation into the theoretical performance limits of the proposed holographic MIMO-based ISAC (HISAC) framework. Further insights are gained by examining the high signal-to-noise ratio (SNR) slopes and diversity orders. Specifically: i) for the downlink case, a sensing-centric (S-C) design and a communications-centric (C-C) design are investigated using different beamforming strategies, and a Pareto optimal design is proposed to characterize the attainable SR-CR region; ii) for the uplink case, the S-C design and the C-C design differ in the interference cancellation order between the communication and sensing signals, with the rate region obtained through a time-sharing strategy. Numerical results are provided to demonstrate that HISAC systems outperform both conventional MIMO-based ISAC systems and holographic MIMO-based frequency-division sensing and communications systems, underscoring the superior performance of the HISAC framework.

Performance Analysis of Holographic MIMO Based Integrated Sensing and Communications

TL;DR

This work analyzes a holographic MIMO ISAC framework (HISAC) that incorporates spatial correlation in the communications channel and a spherical-wave model for sensing. It derives closed-form SR and CR expressions under both instantaneous and statistical CSI, for downlink and uplink, across sensing-centric and communications-centric beamforming designs, plus Pareto-optimal boundaries and time-sharing rate regions. The study shows HISAC achieves larger rate regions and higher high-SNR slopes than conventional MIMO-ISAC and FDSAC, with explicit DoF and diversity-order insights. The results demonstrate the practical benefits of HMIMO for joint sensing and communication tasks in next-generation networks, including downlink/uplink advantages and robust performance under CSI uncertainty.

Abstract

A holographic multiple-input multiple-output (MIMO)-based integrated sensing and communications (ISAC) framework is proposed for both downlink and uplink scenarios. The spatial correlation is incorporated into the communication channel modeling, while a spherical wave-based model is used to characterize the sensing link. By considering both instantaneous and statistical channel state information, closed-form expressions are derived for sensing rates (SRs), communication rates (CRs), and outage probabilities under various ISAC designs. This enables an investigation into the theoretical performance limits of the proposed holographic MIMO-based ISAC (HISAC) framework. Further insights are gained by examining the high signal-to-noise ratio (SNR) slopes and diversity orders. Specifically: i) for the downlink case, a sensing-centric (S-C) design and a communications-centric (C-C) design are investigated using different beamforming strategies, and a Pareto optimal design is proposed to characterize the attainable SR-CR region; ii) for the uplink case, the S-C design and the C-C design differ in the interference cancellation order between the communication and sensing signals, with the rate region obtained through a time-sharing strategy. Numerical results are provided to demonstrate that HISAC systems outperform both conventional MIMO-based ISAC systems and holographic MIMO-based frequency-division sensing and communications systems, underscoring the superior performance of the HISAC framework.
Paper Structure (40 sections, 20 theorems, 110 equations, 9 figures, 2 tables, 1 algorithm)

This paper contains 40 sections, 20 theorems, 110 equations, 9 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Downlink Signal Model
  4. Sensing Model
  5. Communication Model
  6. Uplink Signal Model
  7. Downlink Performance Analysis
  8. Instantaneous Channel State Information
  9. Sensing-Centric Design
  10. Communications-Centric Design
  11. Pareto Optimal Design
  12. Statistical Channel State Information
  13. Sensing-Centric Design
  14. Communications-Centric Design
  15. Pareto Optimal Design
  16. ...and 25 more sections

Key Result

Lemma 1

Given $\mathbf{w}$, the SR can be expressed as follows:

Figures (9)

  • Figure 1: Illustration of HISAC.
  • Figure 2: Downlink communication performance.
  • Figure 3: Downlink SR.
  • Figure 4: Downlink rate regions.
  • Figure 5: Downlink rate regions with I-CSI for different antenna spacing.
  • ...and 4 more figures

Theorems & Definitions (41)

  • Lemma 1
  • Remark 1
  • Theorem 1
  • Remark 2
  • Remark 3
  • Theorem 2
  • Remark 4
  • Theorem 3
  • Remark 5
  • Theorem 4
  • ...and 31 more