In intelligent reflecting surface (IRS)-assisted multiple-input multiple-output (MIMO) systems, a strong line-of-sight (LoS) link is required to compensate for the severe cascaded path loss. However, such a link renders the effective channel highly rank-deficient and fundamentally limits spatial multiplexing. To overcome this limitation, this paper leverages the large aperture of sparse arrays to harness near-field spherical wavefronts, and establishes a deterministic deployment criterion that strategically positions the IRS in the near-field of a base station (BS). This placement exploits the spherical wavefronts of the BS–IRS link to engineer decorrelated channels, thereby fundamentally overcoming the rank-deficiency issue in far-field cascaded channels. Based on a physical channel model for the sparse BS array and the IRS, we characterize the rank properties and inter-user correlation of the cascaded BS–IRS–user channel. We further derive a closed-form favorable propagation metric that reveals how the sparse array geometry and the IRS position can be tuned to reduce inter-user channel correlation. The resulting geometry-driven deployment rule provides a simple guideline for creating a favorable propagation environment with enhanced effective degrees of freedom. The favorable channel statistics induced by our deployment criterion enable a low-complexity maximum-ratio transmission (MRT) precoding scheme. This serves as the foundation for an efficient algorithm that jointly optimizes the IRS phase shifts and power allocation based solely on long-term statistical channel state information (CSI). Simulation results validate the effectiveness of our deployment criterion and demonstrate that our optimization framework achieves significant performance gains over benchmark schemes.

Reconfigurable distributed antenna and reflecting surface (RDARS) is a new architecture for the sixth-generation (6G) millimeter wave (mmWave) communications. In RDARS-aided mmWave systems, the active and passive beamforming design and working mode configuration for reconfigurable elements are crucial for system performance. In this paper, we aim to maximize the weighted sum rate (WSR) in the RDARS-aided mmWave system. To take advantage of RDARS, we first design a reconfigurable codebook (RCB) in which the number and dimension of the codeword can be flexibly adjusted. Then, a low overhead beam training scheme based on hierarchical search is proposed. Accordingly, the active and passive beamforming for data transmission is designed to achieve the maximum WSR for both space-division multiple access (SDMA) and time-division multiple access (TDMA) schemes. For the TDMA scheme, the optimal number of RDARS transmit elements and the allocated power budget for WSR maximization are derived in closed form. Besides, the superiority of the RDARS is verified and the conditions under which RDARS outperforms RIS and DAS are given. For the SDMA scheme, we characterize the relationship between the number of RDARS connected elements and the user distribution, followed by the derivation of the optimal placement positions of the RDARS transmit elements. High-quality beamforming design solutions are derived to minimize the inter-user interference (IUI) at the base station and RDARS side respectively, which nearly leads to the maximal WSR. Finally, simulation results confirm our theoretical findings and the superiority of the proposed schemes.

Distributed antenna system (DAS) has been extensively applied in millimeter-wave (mmWave) communications due to its geographically dispersed and cooperating antennas. Incorporating reconfigurable intelligent surfaces (RISs) into DAS is a promising and effective approach to reducing hardware costs and energy consumption while simultaneously maintaining the benefits of distributed gains. Recently, an innovative architecture named reconfigurable distributed antenna and reflecting surface (RDARS) has attracted significant attention as each element can be flexibly switched between the connection and the reflection modes. This dynamic mode switching offers substantial gains by providing an additional degree of freedom (DoF) in system design. In this paper, we investigate the weighted sum rate (WSR) optimization problem in the RDARS-aided downlink mmWave multi-user system and propose a penalty item-based weighted minimum mean square error (PWM) algorithm to jointly optimize the passive phase coefficients, the mode switching, and the active beamforming for the base station and the RDARS elements in the connection mode. Numerical results demonstrate the superiority of the RDARS structure in improving WSR and verify the effectiveness of the proposed PWM algorithm.

Different types of intelligent reflecting surfaces (IRS) are exploited for assisting wireless communications. The joint use of passive IRS (PIRS) and active IRS (AIRS) emerges as a promising solution owing to their complementary advantages. They can be integrated into a single hybrid active-passive IRS (HIRS) or deployed in a distributed manner, which poses challenges in determining the IRS element allocation and placement for rate maximization. In this paper, we investigate the capacity of an IRS-aided wireless communication system with both active and passive elements. Specifically, we consider three deployment schemes: 1) base station (BS)$\rightarrow $HIRS$\rightarrow $user (BHU); 2) BS$\rightarrow $AIRS$\rightarrow $PIRS$\rightarrow $user (BAPU); 3) BS$\rightarrow $PIRS$\rightarrow $AIRS$\rightarrow $user (BPAU). Under the line-of-sight channel model, we formulate a rate maximization problem via a joint optimization of the IRS element allocation and placement. We first derive the optimized number of active and passive elements for BHU, BAPU, and BPAU schemes, respectively. Then, low-complexity HIRS/AIRS placement strategies are provided. To obtain more insights, we characterize the system capacity scaling orders for the three schemes with respect to the large total number of IRS elements, amplification power budget, and BS transmit power. Finally, simulation results are presented to validate our theoretical findings and show the performance difference among the BHU, BAPU, and BPAU schemes with the proposed joint design under various system setups.

Intelligent reflecting surface (IRS) has garnered growing interest and attention due to its potential for facilitating and supporting wireless communications and sensing. This paper studies a semi-passive IRS-enabled sensing system, where an IRS consists of both passive reflecting elements and active sensors. Our goal is to minimize the Cramér-Rao bound (CRB) for parameter estimation under both point and extended target cases. Towards this goal, we begin by deriving the CRB for the direction-of-arrival (DoA) estimation in closed-form and then theoretically analyze the IRS reflecting elements and sensors allocation design based on the CRB under the point target case with a single-antenna base station (BS). To efficiently solve the corresponding optimization problem for the case with a multi-antenna BS, we propose an efficient algorithm by jointly optimizing the IRS phase shifts and the BS beamformers. Under the extended target case, the CRB for the target response matrix (TRM) estimation is minimized via the optimization of the BS transmit beamformers. Moreover, we explore the influence of various system parameters on the CRB and compare these effects to those observed under the point target case. Simulation results show the effectiveness of the semi-passive IRS and our proposed beamforming design for improving the performance of the sensing system.