RIS-Aided Integrated Sensing and Communication: Joint Beamforming and Reflection Design

This repository contains the MATLAB simulation code for the paper:

H. Luo, R. Liu, M. Li, and Q. Liu, "RIS-aided integrated sensing and communication: Joint beamforming and reflection design," IEEE Trans. Veh. Technol., vol. 72, no. 7, pp. 9626-9630, Jul. 2023. [IEEE Xplore]

Overview

We propose a joint transmit beamforming and RIS reflection design framework for RIS-aided integrated sensing and communication (ISAC) systems. The dual-functional base station simultaneously serves multiple communication users and detects multiple targets, with a reconfigurable intelligent surface (RIS) assisting both functionalities. The code implements:

• Joint beamforming and reflection optimization via alternating optimization with penalty-based reformulation
• Transmit beamforming design using successive convex approximation (SCA) solved by CVX
• RIS phase-shift optimization via Riemannian conjugate gradient on the oblique manifold (Manopt)
• Auxiliary variable update for the penalty-based ADMM framework
• Performance evaluation of radar SNR vs. transmit power and number of RIS elements, comparing the proposed scheme against baselines (w/o RIS, random RIS)

Requirements

• MATLAB R2020a or later
• CVX (version 2.2 or later) with a compatible SDP solver - http://cvxr.com/cvx/
  • If MOSEK is unavailable, CVX will use its default solver (SDPT3 or SeDuMi)
• Manopt (Manifold optimization toolbox) - https://www.manopt.org/
  • Required for the Riemannian conjugate gradient solver in manifold_solution.m

Repository Structure

RIS-ISAC-Beamforming/
├── README.md                          # This file
├── LICENSE                            # MIT license
├── change_P.m                         # Fig. 3(a): Radar SNR vs. transmit power
├── change_N.m                         # Fig. 3(b): Radar SNR vs. number of RIS elements
│
└── function/                          # Supporting functions
    ├── generate_channel.m             # Generate all channel matrices (BS-user, RIS-user, etc.)
    ├── ini_W.m                        # Initialize the transmit beamforming matrix
    ├── opt_W.m                        # Optimize transmit beamformer via CVX (SCA subproblem)
    ├── opt_phi.m                      # Optimize RIS phase shifts via manifold optimization
    ├── opt_a.m                        # Optimize auxiliary variables via CVX (ADMM subproblem)
    ├── manifold_solution.m            # Riemannian conjugate gradient solver on oblique manifold
    ├── cal_d.m                        # Compute distance via the law of cosines
    └── cal_angle.m                    # Compute angle via the law of sines

Quick Start

Step 1: Install dependencies

Ensure CVX and Manopt are installed and on the MATLAB path:

run('/path/to/cvx/cvx_setup.m')
addpath('/path/to/manopt')

Step 2: Run figure-generation scripts

Script	Paper Figure	Description
change_P.m	Fig. 3(a)	Radar SNR vs. transmit power (P = 20 to 40 W)
change_N.m	Fig. 3(b)	Radar SNR vs. number of RIS elements (N = 10 to 60)

run('change_P.m')   % Takes several hours with default 500 Monte Carlo iterations
run('change_N.m')   % Takes several hours with default 500 Monte Carlo iterations

Note: Both scripts involve large-scale Monte Carlo simulations with CVX optimization in each iteration. To obtain quick preliminary results, reduce ITER (e.g., to 3-5) and conv_times (e.g., to 5-10) at the top of each script.

System Parameters

The default parameters correspond to a narrowband RIS-aided ISAC system:

Parameter	Value	Description
Transmit antennas (M)	16	ULA at the BS
RIS elements (N)	36 (change_P) / 10-60 (change_N)	Passive reflecting elements
Communication users (K)	4	Randomly located
Targets (T)	3	Located at -30, 0, +30 degrees
Transmit power (P)	20-40 W (change_P) / 35 W (change_N)	Total power budget
SINR threshold	5 dB	Per-user communication QoS
BS-RIS distance	35 m
BS-target distance	30 m
RIS-user distance	3 m
Rician factor	3 dB	RIS-user channel
Reference path loss	-30 dB	At 1 m reference distance

Citation

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

@ARTICLE{10052711,
  author  = {Luo, Honghao and Liu, Rang and Li, Ming and Liu, Qian},
  journal = {IEEE Transactions on Vehicular Technology},
  title   = {RIS-Aided Integrated Sensing and Communication: Joint Beamforming and Reflection Design},
  year    = {2023},
  volume  = {72},
  number  = {7},
  pages   = {9626-9630},
  doi     = {10.1109/TVT.2023.3248657}
}

Contact

• Rang Liu - Dalian University of Technology - liurang520@gmail.com

More information can be found at: https://www.minglabdut.com/resource.html

License

This project is licensed under the MIT License - see the LICENSE file for details.