arXiv subjects

Liujia Yao

Publications and source records attributed to Liujia Yao.

3 recordsLinked to original sources

arXiv2026

Codebook Design for Limited Feedback in Near-Field XL-MIMO Systems

In this paper, we study efficient codebook design for limited feedback in extremely large-scale multiple-input-multiple-output (XL-MIMO) frequency division duplexing (FDD) systems. It is worth noting that existing codebook designs for XL-MIMO, such as polar-domain codebook, have not well taken into account user (location) distribution in practice, thereby incurring excessive feedback overhead. To address this issue, we propose in this paper a novel and efficient feedback codebook tailored to user distribution. To this end, we first consider a typical scenario where users are uniformly distributed within a specific polar-region, based on which a sum-rate maximization problem is formulated to jointly optimize angle-range samples and bit allocation among angle/range feedback. This problem is challenging to solve due to the lack of a closed-form expression for the received power in terms of angle and range samples. By leveraging a Voronoi partitioning approach, we show that uniform angle sampling is optimal for received power maximization. For more challenging range sampling design, we obtain a tight lower-bound on the received power and show that geometric sampling, where the ratio between adjacent samples is constant, can maximize the lower bound and thus serves as a high-quality suboptimal solution. We then extend the proposed framework to accommodate more general non-uniform user distribution via an alternating sampling method. Furthermore, theoretical analysis reveals that as the array size increases, the optimal allocation of feedback bits increasingly favors range samples at the expense of angle samples. Finally, numerical results validate the superior rate performance and robustness of the proposed codebook design under various system setups, achieving significant gains over benchmark schemes, including the widely used polar-domain codebook.

cs.IT↗

arXiv2025

Rotatable IRS-Assisted 6DMA Communications: A Two-timescale Design

Intelligent reflecting surface (IRS) and movable antenna (MA) are promising technologies to enhance wireless communication by reconfiguring channels at the environment and transceiver sides. However, their performance is constrained by practical limitations. To address this, we propose a multi-functional antenna/surface system that leverages their complementary advantages. A rotatable IRS (R-IRS) is deployed to enhance downlink communications from a six-dimensional MA (6DMA)-equipped base station (BS) to multiple single-antenna users. To reduce the complexity of real-time channel estimation and beamforming, we formulate an optimization problem to maximize the average sum-rate using a two-timescale (TTS) transmission protocol. Specifically, the BS antenna configuration (including position and rotation) and IRS rotation and reflection are optimized based on statistical channel state information (S-CSI), while BS transmit beamforming is designed using instantaneous CSI (I-CSI) in the short timescale. We first consider a single-user case and show that the 6DMA at the BS should form a sparse array for multi-beam transmission towards both the IRS and the user, allowing efficient coordination of direct and reflected channels, while the IRS rotation achieves effective multi-path alignment. For the general multi-user case, the optimization problem is non-convex and challenging to solve. To tackle this, we propose an efficient algorithm combining weighted minimum mean-square error (WMMSE) and stochastic successive convex approximation (SSCA) techniques. A low-complexity algorithm is also proposed to reduce computational complexity. Numerical results validate the proposed system, showing significant performance gains by jointly exploiting the spatial degrees of freedom of the 6DMA-BS and R-IRS under the TTS protocol.

cs.IT↗

arXiv2025

Position Optimization for Two-layer Movable Antenna Systems

Movable antenna (MA) is a promising technology for improving the performance of wireless communication systems by providing new degrees-of-freedom (DoFs) in antenna position optimization. However, existing works on MA systems have mostly considered element-wise single-layer MA (SL-MA) arrays, where all the MAs move within the given movable region, hence inevitably incurring high control complexity and hardware cost in practice. To address this issue, we propose in this letter a new two-layer MA array (TL-MA), where the positions of MAs are jointly determined by the large-scale movement of multiple subarrays and the small-scale fine-tuning of per-subarray MAs. In particular, an optimization problem is formulated to maximize the sum-rate of the TL-MA-aided communication system by jointly optimizing the subarray-positions, per-subarray (relative) MA positions, and receive beamforming. To solve this non-convex problem, we propose an alternating optimization (AO)-based particle swarm optimization (PSO) algorithm, which alternately optimizes the positions of subarrays and per-subarray MAs, given the optimal receive beamforming. Numerical results verify that the proposed TL-MA significantly reduces the sum-displacement of MA motors (i.e., the total moving distances of all motors) of element-wise SL-MA, while achieving comparable rate performance.

eess.SP↗