Two-timescale Beamforming Optimization for Intelligent Reflecting Surface Enhanced Wireless Network

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/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 (i.e., IRS phase shifts) is designed based on the statistical CSI (S-CSI) of all links, while the short-term active beamforming (i.e., transmit precoding vectors at the access point (AP)) 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 AP, subject to the users' individual quality of service (QoS) constraints on the achievable long-term average rate. The formulated stochastic optimization problem is nonconvex 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 passive beamforming problem and a family of shortterm active beamforming 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. We show that both the long-term and short-term problems can be efficiently solved and 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.

Towards intelligent reflecting surface empowered 6G terahertz communications: A survey

“…Recent researches on the capacity optimization of IRS-aided communication system mainly focus on jointly optimizing the active beamforming at the transmitter and the passive beamforming at the IRS [118,[125][126][127][128][129][130]. Specifically, a locally optimal solution for the achievable rate maximization problem can be obtained by iteratively optimizing one matrix variable (i.e.…”

“…However, the reflecting elements with continuous phase shifts are practically difficult to implement due to the complex hardware design, and definitely result in much higher system cost. For ease of practical applications, the IRS elements with discrete phase shifts are considered [129,130]. When the phase shifts are discrete, the capacity optimization problem of IRS-aided THz system become a non-convex problem due to the constraints of discrete variables.…”

“…Specifically, the ability of the IRS fulfills the concepts of beamforming and beam tracking. With respect to the beam control, although the beamforming technique for the IRS assisted communication system is extensively studied in [118,[125][126][127][128][129][130], the beam tracking technique, which requires the dynamic CSI acquisition between the IRS and the mobile station (MS), is still a major challenge for IRS assisted THz communications due to the passive reflecting elements at IRS side. As shown in Figure 9, a typical IRS-aided beam tracking scenario is presented, where a BS serves a MS via the IRS.…”

On the Trade-Off between Energy Efficiency and Spectral Efficiency in RIS-Aided Multi-User MISO Downlink

“…where a refers to the weight of EE, 0 < a < 1, and (5b) refers to the constraint on the transmit power at the BS side. In this paper, F is chosen as the continuous phase set [4,[27][28][29], which means that θ n 2 = 1. Therefore, F can be expressed as…”

Power Optimization of IRS-Assisted D2D System Based on Imperfect Channel

“…These characteristics make IRS technology more promising from an energy efficiency perspective. Fortunately, by subtly tweaking reflective components, IRS helps create a good wireless propagation environment to greatly optimize spectral efficiency and energy efficiency [10][11][12]. The authors of [13,14] adopted IRS to assist communication, which greatly improved the reachable rate of the communication system.…”