Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks

Abstract

Reconfigurable intelligent surfaces (RIS) is a promising solution to build a programmable wireless environment via steering the incident signal in fully customizable ways with reconfigurable passive elements. In this paper, we consider a RISaided multiuser multiple-input single-output (MISO) downlink communication system. Our objective is to maximize the weighted sum-rate (WSR) of all users by joint designing the beamforming at the access point (AP) and the phase vector of the RIS elements, while both the perfect channel state information (CSI) setup and the imperfect CSI setup are investigated. For perfect CSI setup, a low-complexity algorithm is proposed to obtain the stationary solution for the joint design problem by utilizing the fractional programming technique. Then, we resort to the stochastic successive convex approximation technique and extend the proposed algorithm to the scenario wherein the CSI is imperfect. The validity of the proposed methods is confirmed by numerical results. In particular, the proposed algorithm performs quite well when the channel uncertainty is smaller than 10%.Index Terms-Reconfigurable intelligent surfaces (RIS), passive radio, multiple-input-multiple-output (MIMO), fractional programming, stochastic successive convex approximation.

Intelligent Reflecting Surface-Assisted Multi-User MISO Communication: Channel Estimation and Beamforming Design

“…Since bothr tr 1,k and h d,k are jointly Gaussian, the MMSE estimator is linear. Given the observed training signal,r tr 1,k in (18), the MMSE estimate of h d,k is given aŝ This completes the proof of Lemma 1.…”

“…where v tr 1 is the first S × 1 column of V tr . Based on the observation vector in (18), the BS can compute the estimate of h d,k and the result is stated in the following lemma.…”

“…By adaptively and intelligently adjusting the phase shifts of all the IRS elements, referred to as passive beamforming or reflect beamforming [13], [14], desired communication objectives can be realized. In the last year, several joint designs for precoding at the BS and phase shifts matrix at the IRS have been proposed to achieve different communication goals, for example: maximize the system's energy efficiency subject to the individual signal-to-interference-plus-noise ratio (SINR) constraints at the users in [15], maximize the minimum user rate subject to a transmit power constraint in the asymptotic regime in [16], minimize the transmit power at the BS subject to users' individual SINR constraints in [14] and maximize the sum-rate subject to a transmit power constraint in [17], [18]. Moreover the use of IRS to maximize the minimum secrecy rate for physical layer security has been studied in [19] and to assist in simultaneous wireless information and power transfer has been studied in [20].…”

“…They do not utilize the estimates to develop joint precoding and reflect beamforming designs for different downlink communication scenarios of interest, where the downlink rate loss caused by channel training is an important issue especially in IRS-assisted systems. The most notable work that proposes beamforming design under imperfect CSI is [18], that deals with the sum-rate maximization problem under a transmit power constraint by modeling the true channel coefficients as realizations from the sample space that is dominated by the knowledge of the imperfect CSI. However, the authors do not exploit any practical channel estimation protocol but rather assume a distribution for the channel estimation noise in the development of their algorithms.…”

Reconfigurable Intelligent Surfaces for Smart Cities: Research Challenges and Opportunities

“…For channel-level precoders, P does not depend on the particular symbols to be transmitted, whereas for symbol-level precoders P is determined in function of s. The possible performance advantages from adopting an SLP scheme comes at the price of the computational complexity. Both types of precoders require CSI information for designing P. Some channel-level precoding techniques have already been proposed [20], [21] for MU multiple-input single-output (MISO) RIS-assisted downlink systems. These works use efficient algorithms to maximize the sum-rate of all users by jointly optimizing the BS precoder…”