Resource Allocation for Reconfigurable Intelligent Surface assisted Wireless Communications

Abstract

Reconfigurable intelligent surfaces (RIS) offer a promising solution to meet the growing demand for connectivity while ensuring stringent quality-of-service (QoS) requirements. An RIS consists of a 2D array of nearly passive reflecting elements, which can be configured to dynamically shape incident electromagnetic waves, thereby engineering the channel between a transmitter and a receiver. An RIS, which typically lacks active components (e.g., RF chains), provides a cost-effective, nearly passive solution for smart radio environments. However, its inability to perform advanced digital signal processing presents challenges, requiring channel state information (CSI) acquisition and RIS configuration optimization to be handled at the transmitter or receiver side, with control signals fed back to the RIS. Thus, efficient channel estimation and optimization of RIS reflection coefficients are essential for practical integration into existing communications systems. Subsequently, this research focuses on addressing these challenges by investigating resource allocation problems in RIS-assisted systems and developing efficient RIS configuration schemes for various practical scenarios.

First, we focus on RIS design in multi-user frequency-division-duplexing (FDD) and time-division-duplexing (TDD) systems, proposing a joint uplink-downlink RIS design where the same configuration supports both transmissions. In FDD, this is essential as uplink (UL) and downlink (DL) occur simultaneously, requiring a joint configuration. In TDD, while not strictly necessary, a joint design reduces feedback overhead, power consumption, and configuration periods associated with updating the RIS. To compute the trade-off between uplink and downlink rates achieved by a joint design, a weighted-sum problem is formulated and optimized using a developed block-coordinate descent (BCD) algorithm. The resulting uplink-downlink trade-off regions are investigated by numerical simulation to gain insights into different scenarios. For many considered scenarios, the proposed joint design is shown to bring significant improvements over the fixed-uplink (fixed-downlink) heuristic of using the RIS configuration optimized for uplink(downlink) to assist downlink (uplink) transmissions. Moreover, the proposed joint design substantially bridges the gap to the individual design upper bound of allowing different RIS configurations in uplink and downlink.

In the second part, we develop a learning-based framework that directly exploits noisy pilots to optimize a multi-user RIS system while accommodating different service priorities and fairness via user weights. Towards this goal, an adaptive beamforming configuration problem is formulated to generate the RIS system’s beamforming configurations that optimize the weighted sum-rate (WSR). Under mild regularity conditions, this problem is shown to attain a maximum. To learn approximate solutions, a novel hypernetwork-based beamforming (HNB) framework is proposed. Particularly, a beamforming network (BFN) exploits available information, including noisy pilots, to generate optimized beamforming configurations. Rather than learning one BFN, a hypernetwork is trained to dynamically generate BFN learning parameters from an input conditioning vector. When the conditioning vector is chosen as the user weights, the trained HNB can tune the BFN to the user weights without the need for retraining. Numerical experiments demonstrate that tuning allows the proposed HNB to perform close to a block-coordinate descent with perfect CSI benchmark and significantly outperform static learning where a BFN is directly trained to optimize beamforming configurations. Additionally, employing the HNB to also tune the BFN to location information considerably reduces the pilots needed to generate optimized beamforming configurations.

In the third part, to enable RIS implementation with diverse technologies, we develop RIS configuration schemes under relaxed hardware constraints. Particularly, we consider the case where an RIS is configured with discrete phase shifts and the amplitude response associated with an RIS element is non-linearly dependent on the phase shift introduced by the element. This RIS configuration problem is formulated as a constrained virtual-channel selection problem. Subsequently, it is demonstrated that the configuration problem, when restricted to subsets of the virtual channels where the maximum phase-variation is θmax < π, can be bounded between monotone upper bound (UB) and lower bound (LB) matroid-constrained problems. We illustrate discrete RIS configuration problems whose optimal solution satisfy this maximum phase-variation property. Moreover, under the maximum phase-variation property, performance guarantees are obtained using a low-complexity configuration scheme. Motivated by this, a practical low-complexity configuration framework is proposed to optimize general discrete RIS problems. Numerical experiments demonstrate the efficacy of the proposed RIS configuration scheme.