MA-Aided Hierarchical Hybrid Beamforming for Multi-User Wideband Beam Squint Mitigation

In wideband near-field arrays, frequency-dependent array responses cause wavefronts at different frequencies to deviate from that at the center frequency, producing beam squint and degrading multi-user performance. True-time-delay (TTD) circuits can realign the frequency dependence but require large delay ranges and intricate calibration, limiting scalability. Another line of work explores one- and two-dimensional array geometries, including linear, circular, and concentric circular, that exhibit distinct broadband behaviors such as different beam-squint sensitivities and focusing characteristics. These observations motivate adapting the array layout to enable wideband-friendly focusing and enhance multi-user performance without TTD networks. We propose a movable antenna (MA) aided architecture based on hierarchical sub-connected hybrid beamforming (HSC-HBF) in which antennas are grouped into tiles and only the tile centers are repositioned, providing slow geometric degrees of freedom that emulate TTD-like broadband focusing while keeping hardware and optimization complexity low. We show that the steering vector is inherently frequency dependent and that reconfiguring tile locations improves broadband focusing. Simulations across wideband near-field scenarios demonstrate robust squint suppression and consistent gains over fixed-layout arrays, achieving up to 5\% higher sum rate, with the maximum improvement exceeding 140\%.

Diffusion Models-Aided Uplink Channel Estimation for RIS-Assisted Systems

This letter proposes a channel estimation method for reconfigurable intelligent surface (RIS)-assisted systems through a novel diffusion model (DM) framework. We reformulate the channel estimation problem as a denoising process, which aligns with the reverse process of the DM. To overcome the inherent randomness in the reverse process of conventional DM approaches, we adopt a deterministic sampling strategy with a step alignment mechanism that ensures the accuracy of channel estimation while adapting to different signal-to-noise ratio (SNR). Furthermore, to reduce the number of parameters of the U-Net, we meticulously design a lightweight network that achieves comparable performance, thereby enhancing the practicality of our proposed method. Extensive simulations demonstrate superior performance over a wide range of SNRs compared to baselines. For instance, the proposed method achieves performance improvements of up to 13.5 dB in normalized mean square error (NMSE) at SNR = 0 dB. Notably, the proposed lightweight network exhibits almost no performance loss compared to the original U-Net, while requiring only 6.59\% of its parameters.

Joint Antenna Position and Beamforming Optimization with Self-Interference Mitigation in MA-ISAC system

Beamforming design has been extensively investigated in integrated sensing and communication (ISAC) systems. The use of movable antennas has proven effective in enhancing the design of beamforming. Although some studies have explored joint optimization of transmit beamforming matrices and antenna positions in bistatic scenarios, there is a gap in the literature regarding monostatic full-duplex (FD) systems. To fill this gap, we propose an algorithm that jointly optimizes the beamforming and antenna positions at both the transmitter and the receiver in a monostatic FD system. In an FD system, suppressing self-interference is crucial. This interference can be significantly reduced by carefully designing transmit and receive beamforming matrices. To further enhance the suppression, we derive a formulation of self-interference characterized by antenna position vectors. This enables the strategic positioning of movable antennas to further mitigate interference. Our approach optimizes the weighted sum of communication capacity and mutual information by simultaneously optimizing beamforming and antenna positions for both tranceivers. Specifically, we propose a coarse-to-fine grained search algorithm (CFGS) to find optimal antenna positions. Numerical results demonstrate that our proposed algorithm provides significant improvements for the MA system compared to conventional fixed-position antenna systems.