Joint Transceiver Beamforming and Reflecting Design for Active RIS-Aided ISAC Systems

This repository contains the MATLAB simulation code for the paper:

Q. Zhu, M. Li, R. Liu, and Q. Liu, "Joint transceiver beamforming and reflecting design for active RIS-aided ISAC systems," IEEE Trans. Veh. Technol., vol. 72, no. 7, pp. 9636-9640, Jul. 2023. [IEEE Xplore]

Overview

We propose a joint transceiver beamforming and reflecting design framework for active reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) systems. An alternating optimization (AO) algorithm is developed to maximize the radar output signal-to-noise ratio (SNR) while guaranteeing communication quality-of-service (QoS) requirements. The code implements:

• Joint AO-based optimization: alternating updates of the receive filter, transmit beamforming, and active RIS reflection coefficients
• Active vs. passive RIS comparison: performance evaluation under different amplification gain limits
• ISAC vs. radar-only trade-off: impact of communication QoS constraints on sensing performance
• Scalability analysis: performance vs. number of RIS elements and transmit antennas

Requirements

• MATLAB R2022b or later
• CVX (version 2.2 or later) with a compatible SDP solver — http://cvxr.com/cvx/
  • If the default solver is unavailable, change cvx_begin quiet solver settings in get_W.m and get_Phi.m

Repository Structure

Active-RIS-ISAC-detection/
├── README.md              # This file
├── LICENSE                # MIT License
│
├── main_vs_Pb.m           # Fig. 2: Radar SNR vs. transmit power
├── main_vs_Gamma.m        # Fig. 3: Radar SNR vs. SINR requirement
├── main_vs_M.m            # Fig. 4: Radar SNR vs. number of RIS elements
│
├── design_main.m          # Joint AO optimization (receive filter + beamforming + RIS)
├── channel.m              # Channel generation (LoS + Rayleigh fading)
├── get_u.m                # Receive filter optimization (generalized eigenvalue)
├── get_W.m                # Transmit beamforming optimization (CVX)
├── get_Phi.m              # Active RIS reflection optimization (CVX)
└── eigsort.m              # Eigenvalue sorting utility

Quick Start

Step 1: Install CVX

Download and install CVX from http://cvxr.com/cvx/. Run cvx_setup in MATLAB to configure it.

Step 2: Run figure-generation scripts

Each main script can be run independently:

Script	Paper Figure	Description
main_vs_Pb.m	Fig. 2	Radar SNR vs. BS transmit power budget
main_vs_Gamma.m	Fig. 3	Radar SNR vs. communication SINR requirement
main_vs_M.m	Fig. 4	Radar SNR vs. number of RIS elements

Note: Each script runs 1000 Monte Carlo iterations by default and may take several hours. To obtain quick preliminary results, reduce ITER (e.g., set ITER = 10).

System Parameters

The default parameters correspond to an active RIS-aided MU-MISO ISAC system:

Parameter	Value	Description
M	32	Number of active RIS elements
N	16 / 8	Number of BS transmit antennas
K	4	Number of communication users
varsigma	1	Target radar cross section (RCS)
sigma	-80 dBm	Noise power
Pb	30–50 dBm	BS transmit power budget
Pr	20 dBm	Active RIS power budget
Gamma	8–28 dB	Communication SINR requirement
a_max	8 / 4 / 1	Maximum RIS amplification gain
ITER	1000	Number of Monte Carlo iterations

Citation

If you use this code in your research, please cite:

@ARTICLE{10054402,
  author   = {Zhu, Qi and Li, Ming and Liu, Rang and Liu, Qian},
  journal  = {IEEE Transactions on Vehicular Technology},
  title    = {Joint Transceiver Beamforming and Reflecting Design for Active RIS-Aided ISAC Systems},
  year     = {2023},
  volume   = {72},
  number   = {7},
  pages    = {9636-9640},
  doi      = {10.1109/TVT.2023.3249752}
}

Contact

• Qi Zhu — Dalian University of Technology — qzhu@mail.dlut.edu.cn
• Rang Liu — Dalian University of Technology — liurang520@gmail.com

More resources: https://www.minglabdut.com/resource.html

License

This code is provided under the MIT License.