Deep learning optimizes resources in intelligent wireless networks
Robust Resource Allocation for STAR-RIS-Assisted ISAC Networks Using Deep Learning
Performance
Summary
Sending signals for both communication and sensing can be tricky, especially when the environment changes and the system has limited information. The authors address this by using a kind of artificial intelligence called deep learning to smartly adjust how signals are sent and reflected by special surfaces that control wireless waves. Their method works well even when the data about channels isn’t perfect and runs faster than older methods. This approach helps improve wireless network performance while keeping the computer work manageable.
What this means in practice
- •For wireless network engineers: Improve resource allocation algorithms to enhance communication and sensing in networks using intelligent surfaces under practical channel knowledge limits.
- •For telecommunications equipment developers: Develop faster, robust hardware controllers for STAR-RIS devices to support adaptive beamforming and waveform control in dynamic environments.$Commercial implications: Enables commercial deployment of intelligent surface products with integrated sensing capabilities offering improved real-time adaptability.
Authors
Yuwen Cao, Jiajun Wu, Howard H. Yang, Tomoaki Ohtsuki, Tony Q. S. Quek
Abstract
In this paper, we focus on investigating the resource allocation in an integrated sensing and communication (ISAC) system assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Due to the coupled non-convex nature of the optimization problem, solving the above joint resource optimization problem while guaranteeing superior sensing performance is challenging, especially in dynamic environment. To conquer such challenge, prior iterative optimization algorithms have been presented. However, practical implementations of these prior algorithms face notable computational challenges, e.g., the reliance on accurate global channel information and the assumption of ideal phase shifts. In this context, we propose a model-driven deep learning framework for jointly optimizing the transmit beamforming and waveform in ISAC networks with low-overhead. In addition, we adopt the Cramer-Rao lower bound (CRLB) as the sensing performance metric, and the multi-user interference (MUI) energy as the communication performance metric for performance evaluation. Besides, our framework jointly optimizes the transmit beamforming, waveform and the STAR-RIS reflection/transmission coefficient matrices by minimizing the weighted sum of MUI energy and CRLB. Our study also considers the impact of imperfect channel information, making the research scenario more aligned with real-world applications. Simulation results validate that the proposed framework not only achieves higher sum-rate performance comparable to traditional iterative algorithms but also significantly reduces computational complexity in terms of algorithm execution time, as well as exhibits inherent robustness.