Zhen Gao

Driven by the ultra-high throughput requirements of 6G, wireless communications are migrating to centimeter wave (cmWave) bands to overcome the limitations of current spectral resources. Massive multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) systems aim to achieve high spectral efficiency in cmWave regimes but are often constrained by the heavy overhead of downlink channel state information (CSI) feedback. This paper proposes a deep learning scheme based on the multi-axis multi-layer perceptron for image processing (MAXIM) architecture for joint semantic CSI feedback and hybrid beamforming in multi-user cmWave MIMO-OFDM systems, which maximizes the downlink sum rate by end-to-end optimization. Specifically, distributed encoders at multiple user equipments (UEs) perform limited CSI feedback, while the decoder at the base station (BS) jointly designs the hybrid beamforming matrices without explicit CSI reconstruction. The uplink transmission is implemented via deep joint source–channel coding (DJSCC) to enhance CSI compression efficiency and noise robustness. Furthermore, considering the high correlation between vertical and horizontal polarization channels in dual-polarized massive MIMO systems, a cross-polarization interaction module is introduced at the UEs to exploit polarization correlations for joint CSI compression. Simulation results demonstrate that the proposed method improves the downlink sum rate under various signal-to-noise ratio (SNR) conditions with a limited number of feedback symbols, validating its robustness and superiority in multi-user dual-polarized cmWave MIMO-OFDM systems.

In this paper, we study a sampling-based detection strategy for massive multiple-input multiple-output (MIMO) systems, driven by a modified discrete diffusion model formulated as an analytical, non-learning sampling process. Built upon this framework, the proposed discrete diffusion-based sampling (DDS) algorithm improves decoding performance by leveraging residual-dependent sampling, compared to the independent randomized successive interference cancellation (SIC). Specifically, the modified diffusion model incorporates a shortcut perturbation toward the SIC solution, a forward diffusion step to enhance diversity, and step-wise alignment with the perturbed received signal. Within this framework, the DDS algorithm further adopts one-dimensional discrete Gaussian distribution, involving a reformulated discrete Gaussian noise and an explicitly characterized sampling range, but retains computational complexity amenable to practical deployment. Moreover, we theoretically demonstrate an improved expected decoding radius over randomized SIC. Finally, simulation results based on massive MIMO detection are presented to confirm performance gain of the proposed DDS algorithm.

The soft-output successive cancellation list (SOSCL) decoder provides a methodology for estimating the aposteriori probability log-likelihood ratios by only leveraging the conventional SCL decoder of polar codes. However, the sequential decoding nature of SCL introduces high decoding latency to SOSCL. In this paper, we incorporate node-based fast decoding into the SO-SCL framework. After addressing the challenge of soft output extraction in special node decoding, we proposed the soft-output fast SCL (SO-FSCL) decoding algorithm, along with its log-domain implementation and hardware-friendly version. The proposed SO-FSCL decoder can be regarded as an addon extension to FSCL decoder, enabling us to autonomously choose whether to output only hard decisions like FSCL or to provide additional soft outputs. Latency and complexity analyses demonstrate that SO-FSCL can significantly reduce, for example, decoding time steps by 81.8% (with unlimited resources), the number of additions by 41.3%, and the number of comparisons by 46.4%. Meanwhile, simulation results indicate that SO-FSCL delivers almost the same soft-output performance as SO-SCL, outperforming other soft-output polar decoders, especially in scenarios involving iterative decoding.

Movable antenna (MA) has emerged as a promising technology for future wireless systems. Compared with traditional fixed-position antennas, MA improves system performance by antenna movement to optimize channel conditions. For multiuser wideband MA systems, this paper proposes deep learning-based framework integrating channel estimation (CE), antenna position optimization, and beamforming, with a clear workflow and enhanced efficiency. Specifically, to obtain accurate channel state information (CSI), we design a two-stage CE mechanism: first reconstructing the channel matrix from limited measurements via compressive sensing, then introducing a Swin-Transformer-based denoising network to refine CE accuracy for subsequent optimization. Building on this, we address the joint optimization challenge by proposing a Transformer-based network that intelligently maps CSI sequences of candidate positions to optimal MA positions while combining a model-driven weighted minimum mean square error (WMMSE) beamforming approach to achieve better performance. Simulation results demonstrate that the proposed methods achieve superior performance compared with existing counterparts under various conditions. The codes about this work are available at https://github.com/ZiweiWan/Code-4-DL-MA-CE-BF.

To address the challenges of high-dimensional channel estimation and underutilized spatial correlations among users in holographic MIMO (HMIMO) systems, this paper proposes a joint graph-cut algorithm for multi-user channel estimation in the wavenumber domain. The size of the conventional angular domain channel matrix increases with the number of antennas in densely-spaced HMIMO. Therefore, user channels are projected into the wavenumber domain via a Fourier harmonic transform, revealing their inherent clustered sparsity and exploiting common scatterer clusters among users. Subsequently, a joint graph-cut channel estimation (JGC-CE) algorithm based on multi-user common supports is designed. In each iteration, the algorithm first partitions user clusters to extract shared supports. Then for each user, it performs users' individual graph update and channel estimation to reconstruct the channel matrix. Simulation results demonstrate that the proposed method outperforms independent estimation schemes for individual users in accuracy while reducing pilot length.

Massive multiple-input multiple-output (MIMO) systems offer high spectral efficiency but generate high-dimensional downlink channel state information (CSI), posing challenges for real-time channel acquisition and precoding, particularly in centimeter-wave (cmWave) vehicle-to-infrastructure (V2I) communications where rapid channel variations due to high mobility further complicate CSI acquisition. To address this, we propose an uplink sounding reference signal (SRS)-aided joint design of downlink CSI reference signal (CSI-RS), CSI feedback, and base-station (BS) precoding with end-to-end (E2E) deep learning. Firstly, we design a multi-axis multi-layer perceptron (MAXIM)-based multi-domain CSI-RS network, which takes the uplink sounding reference signals (SRS) as input and outputs a frequency-, beam-, and port-domain projection matrices. Secondly, user equipment (UE) then compresses/quantizes the received CSI-RS and feeds a compact representation to the BS. Thirdly, at the BS, two complementary branches produce candidate precoders: one is named feedback-only precoding network driven by quantized CSI feedback, and the other is named SRS-only precoding network driven by uplink SRS. These candidate precoders are subsequently combined by a precoding fusion network to yield the final transmit precoder. Finally, all these modules are trained with a spectral-efficiency-oriented loss in an E2E deep learning manner. Simulation results in high-mobility vehicular scenarios demonstrate that the proposed approach effectively harnesses both SRS-derived and CSI-feedback information, achieving markedly better performance than conventional baselines, especially under severe channel aging conditions.