Adaptive Transmission for Reconfigurable Intelligent Surface-Assisted OFDM Wireless Communications

Reconfigurable intelligent surfaces (RISs) have recently emerged as an innovative technology for improving the coverage, throughput, and energy/spectrum efficiency of future wireless communications. In this paper, we propose a new transmission protocol for wideband RIS-assisted single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) communication systems, where each transmission frame is divided into multiple sub-frames to execute channel estimation simultaneously with passive beamforming. As the training symbols are discretely distributed over multiple sub-frames, the channel state information (CSI) associated with RIS cannot be estimated at once. As such, we propose a new channel estimation method to progressively estimate the associated CSI over consecutive sub-frames, based on which the passive beamforming at the RIS is fine-tuned to improve the achievable rate for data transmission. In particular, during the channel training, the RIS plays two roles of embedding training reflection states for progressive channel estimation and performing passive beamforming for data transmission on the data tones. Based on the estimated CSI in each sub-frame, we formulate an optimization problem to maximize the average achievable rate by designing the passive beamforming at the RIS, which needs to balance the received signal power over different sub-carriers and different receive antennas. As the formulated problem is non-convex and thus difficult to solve optimally, we propose two efficient algorithms to find high-quality solutions. Simulation results validate the effectiveness of the proposed channel estimation and beamforming optimization methods. It is shown that the proposed joint channel estimation and passive beamforming scheme is able to drastically improve the average achievable rate and reduce the delay for data transmission as compared to existing schemes.

I. Introduction

In the last decade, various wireless communication technologies such as millimeter wave (mmWave) communications and massive multiple-input multiple-output (MIMO) systems have been extensively investigated to meet the requirements for higher data rate, enhanced energy efficiency, low-latency, ultra-reliability, etc. of the fifth-generation (5G) wireless communication system [1]. However, none of them can support all 5G requirements and applications individually. Moreover, most of them require costly hardware and suffer from increasingly higher complexity and energy consumption, thus severely hindering their extensive practical deployment [2], [3]. For example, massive MIMO systems operating at mmWave frequency bands require costly and energy consuming radio frequency (RF) chains as well as sophisticated signal processing capability. Thanks to recent advances in reconfigurable surfaces [4], [5], reconfigurable intelligent surfaces (RISs) (a.k.a. intelligent reflecting surfaces) have emerged as an innovative technology for improving the coverage, throughput, and energy/spectrum efficiency of future wireless networks [6]–[16]. Specifically, RISs are planar surfaces consisting of a large number of low-cost unit cell elements, each of which is able to independently adjust the amplitude and/or phase shift of the reflected signal, thus reconfiguring the wireless propagation environment. Compared to existing techniques such as amplify-and-forward (AF) relays, RISs work in a full-duplex mode without incurring self-interference and thermal noise, and yet possess substantially reduced hardware cost and energy consumption due to the nearly passive components [17]–[19].