Two-timescale Beamforming Optimization for Intelligent Reflecting Surface Aided Multiuser Communication with QoS Constraints

Abstract

Intelligent reflecting surface (IRS) is an emerging technology that is able to reconfigure the wireless channel via tunable passive signal reflection and thereby enhance the spectral and energy efficiency of wireless networks cost-effectively. In this paper, we study an IRS-aided multiuser multiple-input single-output (MISO) wireless system and adopt the two-timescale (TTS) transmission to reduce the signal processing complexity and channel training overhead as compared to the existing schemes based on the instantaneous channel state information (I-CSI), and at the same time, exploit the multiuser channel diversity in transmission scheduling. Specifically, the long-term passive beamforming is designed based on the statistical CSI (S-CSI) of all links, while the short-term active beamforming is designed to cater to the I-CSI of all users' reconfigured channels with optimized IRS phase shifts. We aim to minimize the average transmit power at the access point (AP), subject to the users' individual quality of service (QoS) constraints. The formulated stochastic optimization problem is non-convex and difficult to solve since the long-term and short-term design variables are complicatedly coupled in the QoS constraints. To tackle this problem, we propose an efficient algorithm, called the primal-dual decomposition based TTS joint active and passive beamforming (PDD-TJAPB), where the original problem is decomposed into a long-term problem and a family of short-term problems, and the deep unfolding technique is employed to extract gradient information from the short-term problems to construct a convex surrogate problem for the long-term problem. The proposed algorithm is proved to converge to a stationary solution of the original problem almost surely. Simulation results are presented which demonstrate the advantages and effectiveness of the proposed algorithm as compared to benchmark schemes.

arXiv comment16 pages, 10 figures, accepted by IEEE Transactions on Wireless communications

References in corpus (11)

• Beyond Massive-MIMO: The Potential of Data-Transmission with Large Intelligent Surfaces
• Intelligent Reflecting Surface-Enhanced OFDM: Channel Estimation and Reflection Optimization
• A Framework of Robust Transmission Design for IRS-aided MISO Communications with Imperfect Cascaded Channels
• Intelligent Reflecting Surface Assisted Multi-User OFDMA: Channel Estimation and Training Design
• Secure Wireless Communication via Intelligent Reflecting Surface
• Intelligent Reflecting Surface Enhanced Wireless Network: Two-timescale Beamforming Optimization
• Intelligent Reflecting Surface Aided MIMO Broadcasting for Simultaneous Wireless Information and Power Transfer
• Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures
• Stochastic Successive Convex Approximation for Non-Convex Constrained Stochastic Optimization
• A Parallel Stochastic Approximation Method for Nonconvex Multi-Agent Optimization Problems
• Two-Stage Subspace Constrained Precoding in Massive MIMO Cellular Systems

Cited by in corpus (9)

• An Overview of Signal Processing Techniques for RIS/IRS-aided Wireless Systems
• Configuring Intelligent Reflecting Surface with Performance Guarantees: Blind Beamforming
• Configuring Intelligent Reflecting Surface with Performance Guarantees: Optimal Beamforming
• Channel Customization for Joint Tx-RISs-Rx Design in Hybrid mmWave Systems
• Coordinating Multiple Intelligent Reflecting Surfaces without Channel Information
• Joint Uplink-Downlink Resource Allocation for Multi-User IRS-Assisted Systems
• Two-Timescale Design for Reconfigurable Intelligent Surface-Aided Massive MIMO Systems with Imperfect CSI
• Intelligent Reflecting Surfaces and Next Generation Wireless Systems
• Channel Tracking and Prediction for IRS-aided Wireless Communications