A communication link aided by a Reconfigurable Intelligent Surface (RIS) is studied, in which the transmitter can control the state of the RIS via a finite-rate control link. Prior work mostly assumed a fixed RIS configuration irrespective of the transmitted information. In contrast, this work derives information-theoretic limits, and demonstrates that the capacity is achieved by a scheme that jointly encodes information in the transmitted signal as well as in the RIS configuration. In addition, a novel signaling strategy based on layered encoding is proposed that enables practical successive cancellation-type decoding at the receiver. Numerical experiments demonstrate that the standard max-SNR scheme that fixes the configuration of the RIS as to maximize the Signal-to-Noise Ratio (SNR) at the receiver is strictly suboptimal, and is outperformed by the proposed strategies at all practical SNR levels.
Multi-user (MU) diversity yields sum-rate gains by scheduling a user for transmission at times when its channel is near its peak. These gains are limited in environments with line-of-sight (LoS) channel components and/or spatial correlation. To remedy this, previous works have proposed opportunistic beamforming (OBF) using multiple antennas at the BS to transmit the same signal, modulated by time-varying gains, to the best user at each time slot. In this paper, we propose reconfigurable surface (RS)-assisted OBF to increase the range of channel fluctuations in a single-antenna broadcast channel (BC), where opportunistic scheduling (OS) strategy achieves the sum-rate capacity. The RS is abstracted as an array of passive reflecting elements that only induce random phase shifts onto the impinging electromagnetic waves. We develop the sum-rate scaling laws under Rayleigh, Rician and correlated Rayleigh fading and show that RS-assisted OBF with a single-antenna BS can outperform multi-antenna BS-assisted OBF using a moderate number of elements.
Intelligent reflecting surface (IRS) is a promising solution to enhance the wireless communication capacity both cost-effectively and energy-efficiently, by properly altering the signal propagation via tuning a large number of passive reflecting units. In this paper, we aim to characterize the fundamental capacity limit of IRS-aided point-to-point multiple-input multiple-output (MIMO) communication systems with multi-antenna transmitter and receiver in general, by jointly optimizing the IRS reflection coefficients and the MIMO transmit covariance matrix. We consider narrowband transmission under frequency-flat fading channels, and develop an efficient alternating optimization algorithm to find a locally optimal solution by iteratively optimizing the transmit covariance matrix or one of the reflection coefficients with the others being fixed. Numerical results show that our proposed algorithm achieves substantially increased capacity compared to traditional MIMO channels without the IRS, and also outperforms various benchmark schemes.
In this paper, we study the channel acquisition problem in a reconfigurable intelligent surface (RIS) assisted multiuser multiple-input multiple-output (MIMO) system, where an RIS with fully passive phase-shift elements is deployed to assist the MIMO communication. The state-of-the-art channel acquisition approach in such a system estimates the cascaded transmitter-to-RIS and RIS-to-receiver channels by adopting excessively long training sequences. To estimate the cascaded channels with an affordable training overhead, we formulate the channel estimation problem as a matrix-calibration based matrix factorization task. By exploiting the information on the slow-varying channel components and the hidden channel sparsity, we propose a novel message-passing based algorithm to factorize the cascaded channels.