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Geometric mean rate maximization in RIS-aided mmWave ISAC systems relying on a non-diagonal phase shift matrix

Geometric mean rate maximization in RIS-aided mmWave ISAC systems relying on a non-diagonal phase shift matrix
Geometric mean rate maximization in RIS-aided mmWave ISAC systems relying on a non-diagonal phase shift matrix
The joint optimization of the hybrid transmit precoders (HTPCs) and reflective elements of a millimeter wave (mmWave) integrated sensing and communication (ISAC) system is considered. The system also incorporates a reconfigurable intelligent surface (RIS) relying on a non-diagonal RIS (NDRIS) phase shift matrix. Specifically, we consider a hybrid architecture at the ISAC base station (BS) that serves multiple downlink communication users (CUs) via the reflected links from the RIS, while concurrently detecting multiple radar targets (RTs). We formulate an optimization problem that aims for maximizing the geometric mean (GM) rate of the CUs, subject to the sensing requirement for each RT. Additional specifications related to the limited transmit power and unit modulus (UM) constraints for both the HTPCs and the reflective elements of the NDRIS phase shift matrix make the problem challenging. To solve this problem, we first transform the intractable GM rate expression to a tractable weighted sum rate objective and next split the transformed problem into sub-problems. Consequently, we propose an iterative alternating optimization approach that leverages the majorization-minimization (MM) framework and block coordinate descent (BCD) method to solve each sub-problem. Furthermore, to tackle the UM constraints in the sub-problem of the HTPC design, we propose a penalty-based Riemannian manifold optimization (PRMO) algorithm, which optimizes the HTPCs on the Riemannian manifold. Similarly, the phases of the reflective elements of the NDRIS are optimized by employing the Riemannian manifold, and the locations of the non-zero entries of the NDRIS phase shift matrix are obtained by the maximal ratio combining (MRC) criterion. Finally, we present our simulation results, which show that deploying an NDRIS achieves additional gains for the CUs over a conventional RIS, further enhancing both the communication efficiency and sensing reliability. Furthermore, we compare the results to the pertinent benchmarks, which validate the effectiveness of our proposed algorithms.
Reconfigurable intelligent surface, geometric mean rate, integrated sensing and communication, millimeter wave
2644-125X
4756-4771
Singh, Jitendra
a98cf279-387d-412e-b5f8-8f1d623f3607
Jagannatham, Aditya K.
ae9274e6-c98c-4e15-a5be-f4eb0fc179ff
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Singh, Jitendra
a98cf279-387d-412e-b5f8-8f1d623f3607
Jagannatham, Aditya K.
ae9274e6-c98c-4e15-a5be-f4eb0fc179ff
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Singh, Jitendra, Jagannatham, Aditya K. and Hanzo, Lajos (2025) Geometric mean rate maximization in RIS-aided mmWave ISAC systems relying on a non-diagonal phase shift matrix. IEEE Open Journal of the Communications Society, 6, 4756-4771. (doi:10.1109/OJCOMS.2025.3573196).

Record type: Article

Abstract

The joint optimization of the hybrid transmit precoders (HTPCs) and reflective elements of a millimeter wave (mmWave) integrated sensing and communication (ISAC) system is considered. The system also incorporates a reconfigurable intelligent surface (RIS) relying on a non-diagonal RIS (NDRIS) phase shift matrix. Specifically, we consider a hybrid architecture at the ISAC base station (BS) that serves multiple downlink communication users (CUs) via the reflected links from the RIS, while concurrently detecting multiple radar targets (RTs). We formulate an optimization problem that aims for maximizing the geometric mean (GM) rate of the CUs, subject to the sensing requirement for each RT. Additional specifications related to the limited transmit power and unit modulus (UM) constraints for both the HTPCs and the reflective elements of the NDRIS phase shift matrix make the problem challenging. To solve this problem, we first transform the intractable GM rate expression to a tractable weighted sum rate objective and next split the transformed problem into sub-problems. Consequently, we propose an iterative alternating optimization approach that leverages the majorization-minimization (MM) framework and block coordinate descent (BCD) method to solve each sub-problem. Furthermore, to tackle the UM constraints in the sub-problem of the HTPC design, we propose a penalty-based Riemannian manifold optimization (PRMO) algorithm, which optimizes the HTPCs on the Riemannian manifold. Similarly, the phases of the reflective elements of the NDRIS are optimized by employing the Riemannian manifold, and the locations of the non-zero entries of the NDRIS phase shift matrix are obtained by the maximal ratio combining (MRC) criterion. Finally, we present our simulation results, which show that deploying an NDRIS achieves additional gains for the CUs over a conventional RIS, further enhancing both the communication efficiency and sensing reliability. Furthermore, we compare the results to the pertinent benchmarks, which validate the effectiveness of our proposed algorithms.

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More information

Accepted/In Press date: 20 May 2025
Published date: 23 May 2025
Keywords: Reconfigurable intelligent surface, geometric mean rate, integrated sensing and communication, millimeter wave

Identifiers

Local EPrints ID: 504253
URI: http://eprints.soton.ac.uk/id/eprint/504253
ISSN: 2644-125X
PURE UUID: 092334d8-888b-4a2e-b13f-2d535e3737a0
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 02 Sep 2025 16:50
Last modified: 04 Sep 2025 01:58

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Contributors

Author: Jitendra Singh
Author: Aditya K. Jagannatham
Author: Lajos Hanzo ORCID iD

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