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Joint Transceiver Optimization for MmWave/THz MU-MIMO ISAC Systems

Peilan Wang, Jun Fang, Xianlong Zeng, Zhi Chen, Hongbin Li

TL;DR

This work tackles the nonconvex problem of jointly designing transceivers for mmWave/THz MU-MIMO ISAC by maximizing a weighted sum of downlink rates and radar SCNR. It introduces a low-dimensional subspace property that enables a computationally efficient BCD-based algorithm and an analytic BD-inspired solution, with a hybrid precoding design to approximate digital performance. A key contribution is the identification of the RF-chain requirement $N_t^{\mathrm{RF}} \ge r$ (or $\tilde r$ under clutter nulling) to match fully digital performance, aided by closed-form updates for BCD blocks and a simple sub-optimal BD-like design with interpretable power allocation. Simulation results show that with an appropriate trade-off parameter $\eta$, the proposed methods achieve a good balance between communication and sensing with only mild penalties relative to single-task optimization, highlighting practical impact for ISAC in future 6G systems.

Abstract

In this paper, we consider the problem of joint transceiver design for millimeter wave (mmWave)/Terahertz (THz) multi-user MIMO integrated sensing and communication (ISAC) systems. Such a problem is formulated into a nonconvex optimization problem, with the objective of maximizing a weighted sum of communication users' rates and the passive radar's signal-to-clutter-and-noise-ratio (SCNR). By exploring a low-dimensional subspace property of the optimal precoder, a low-complexity block-coordinate-descent (BCD)-based algorithm is proposed. Our analysis reveals that the hybrid analog/digital beamforming structure can attain the same performance as that of a fully digital precoder, provided that the number of radio frequency (RF) chains is no less than the number of resolvable signal paths. Also, through expressing the precoder as a sum of a communication-precoder and a sensing-precoder, we develop an analytical solution to the joint transceiver design problem by generalizing the idea of block-diagonalization (BD) to the ISAC system. Simulation results show that with a proper tradeoff parameter, the proposed methods can achieve a decent compromise between communication and sensing, where the performance of each communication/sensing task experiences only a mild performance loss as compared with the performance attained by optimizing exclusively for a single task.

Joint Transceiver Optimization for MmWave/THz MU-MIMO ISAC Systems

TL;DR

This work tackles the nonconvex problem of jointly designing transceivers for mmWave/THz MU-MIMO ISAC by maximizing a weighted sum of downlink rates and radar SCNR. It introduces a low-dimensional subspace property that enables a computationally efficient BCD-based algorithm and an analytic BD-inspired solution, with a hybrid precoding design to approximate digital performance. A key contribution is the identification of the RF-chain requirement (or under clutter nulling) to match fully digital performance, aided by closed-form updates for BCD blocks and a simple sub-optimal BD-like design with interpretable power allocation. Simulation results show that with an appropriate trade-off parameter , the proposed methods achieve a good balance between communication and sensing with only mild penalties relative to single-task optimization, highlighting practical impact for ISAC in future 6G systems.

Abstract

In this paper, we consider the problem of joint transceiver design for millimeter wave (mmWave)/Terahertz (THz) multi-user MIMO integrated sensing and communication (ISAC) systems. Such a problem is formulated into a nonconvex optimization problem, with the objective of maximizing a weighted sum of communication users' rates and the passive radar's signal-to-clutter-and-noise-ratio (SCNR). By exploring a low-dimensional subspace property of the optimal precoder, a low-complexity block-coordinate-descent (BCD)-based algorithm is proposed. Our analysis reveals that the hybrid analog/digital beamforming structure can attain the same performance as that of a fully digital precoder, provided that the number of radio frequency (RF) chains is no less than the number of resolvable signal paths. Also, through expressing the precoder as a sum of a communication-precoder and a sensing-precoder, we develop an analytical solution to the joint transceiver design problem by generalizing the idea of block-diagonalization (BD) to the ISAC system. Simulation results show that with a proper tradeoff parameter, the proposed methods can achieve a decent compromise between communication and sensing, where the performance of each communication/sensing task experiences only a mild performance loss as compared with the performance attained by optimizing exclusively for a single task.
Paper Structure (27 sections, 5 theorems, 73 equations, 6 figures, 1 algorithm)

This paper contains 27 sections, 5 theorems, 73 equations, 6 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. MU-MIMO Communication Performance
  4. MIMO Sensing Performance
  5. Problem Formulation
  6. Proposed BCD-Based Method
  7. Problem Reformulation
  8. The Proposed BCD-Based Algorithm
  9. Update $\boldsymbol{w}$
  10. Update $\boldsymbol{W}_k$
  11. Update $\boldsymbol{\Lambda}_k$
  12. Update $\boldsymbol{X}_k$
  13. Update $\boldsymbol{u}_k$
  14. Hybrid Precoder/Combiner Design
  15. Initialization
  16. ...and 12 more sections

Key Result

Proposition 1

Denote as the matrix consisting of the steering vectors from the BS to all UEs, the steering vectors from the BS to the target and the steering vectors from the BS to all clutter patches, where we have $r\triangleq\sum_{k=1}^K L_k + I +1$. Then, any non-trivial Karush-Kuhn-Tucker (KKT) point of eq-opt-simp where $\boldsymbol{X}_k \in \mathbb C^{r \times N_{s}}$.

Figures (6)

  • Figure 1: Illustration of mmWave/THz MU-MIMO ISAC systems
  • Figure 2: Convergence behaviour of the proposed BCD-based method.
  • Figure 3: Weighted sum of WSR and SCNR, WSR, and SCNR v.s. the transmit power $P_t$.
  • Figure 4: Weighted sum of WSR and SCNR v.s. $N_t^{\rm RF}$.
  • Figure 5: SCNR-WSR performance region by varying $\eta$ in $[0,1]$.
  • ...and 1 more figures

Theorems & Definitions (10)

  • Proposition 1
  • proof
  • Proposition 2
  • proof
  • Proposition 3
  • proof
  • Proposition 4
  • proof
  • Proposition 5
  • proof