Xl-mimo CSI Feedback Achieves Reduced Overhead with Vector Quantization and Channel Adaptation

Efficient channel state information (CSI) feedback represents a significant challenge for the development of 6G extremely large-scale multiple-input multiple-output (XL-MIMO) systems, as mitigating channel interference demands effective communication despite massive antenna arrays. Yuhang Ma, Nan Ma, and Jianqiao Chen, from the ZGC Institute of Ubiquitous-X Innovation and Applications, alongside Wenkai Liu from the State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, address this issue with a novel vector quantization (VQ)-aided scheme. Their research introduces VQ-DJSCC-F, a framework designed to reduce feedback overhead while maintaining precision and robustness in high-dimensional channel compression, particularly in near-field scenarios. By leveraging sparsity and incorporating advanced neural network architectures with entropy loss regularization, the team demonstrates improved CSI reconstruction accuracy and adaptability to varying channel conditions. This work offers a promising pathway towards practical implementation of XL-MIMO technology by substantially reducing the burden on feedback links.

To address limitations in channel robustness when compressing high-dimensional channel features, the researchers propose VQ-DJSCC-F, a vector quantized (VQ)-aided scheme for CSI feedback in XL-MIMO systems, designed to account for the near-field effect. The approach initially leverages the sparsity of near-field channels within the polar-delay domain, extracting energy-concentrated features to achieve dimensionality reduction. Subsequently, the researchers simultaneously designed Transformer and convolutional neural network (CNN) architectures as backbones to hierarchically extract CSI features. These features are then projected into a discrete space via VQ modules, facilitating efficient and robust transmission. The integration of entropy loss regularization with an exponential moving average (EMA) update strategy maximises quantization precision, effectively addressing the problem of ‘codebook collapse’.

Near-Field Sparsity Enables Accurate XL-MIMO CSI

Scientists have achieved a breakthrough in channel state information (CSI) feedback for extremely large-scale multiple-input multiple-output (XL-MIMO) systems, crucial for future 6G networks. The team developed a vector quantization (VQ)-aided scheme, termed VQ-DJSCC-F, designed to mitigate channel interference and reduce feedback overhead. Experiments revealed that by exploiting the sparsity of near-field channels in the polar-delay domain, the team successfully reduced dimensionality through energy-concentrated feature extraction. A hierarchical architecture, combining Transformer and CNN backbones, was implemented to extract CSI features, subsequently projecting them into a discrete latent space via VQ modules.

Measurements confirm that the integration of entropy loss regularization with an exponential moving average (EMA) update strategy maximises quantization precision. Further tests prove the effectiveness of a newly developed attention mechanism-driven channel adaptation module, which mitigates the impact of wireless channel fading on the transmission of index sequences. The research demonstrates that the proposed scheme achieves significantly improved CSI reconstruction accuracy while simultaneously lowering feedback overheads. The system establishes an end-to-end digital feedback link, consisting of near-field CSI pre-processing, VQ-based feature compression, and CSI reconstruction stages. By leveraging near-field sparsity, the team significantly reduced the dimensionality of the original CSI, paving the way for more efficient and reliable wireless communication in future 6G networks.

VQ-DJSCC-F Optimises 6G Channel Feedback

This research presents a novel digital channel state information (CSI) feedback framework, named VQ-DJSCC-F, designed to address the challenges of XL-MIMO systems in future 6G networks. The authors demonstrate effective dimensionality reduction by exploiting sparsity in near-field channels, coupled with a hierarchical feature extraction process utilising both Transformer and CNN architectures. This allows for efficient compression of channel information into a discrete latent space, reducing feedback overhead. The core contribution lies in a joint entropy and exponential moving average (EMA) codebook optimisation strategy, alongside an attention-driven channel adaptation mechanism, which enhances both the precision of quantization and the robustness of transmission. Simulation results confirm that the proposed scheme outperforms existing benchmark methods, achieving improved reconstruction accuracy and stability across a range of channel conditions. The demonstrated improvements in CSI feedback efficiency represent a significant step towards realising the potential of XL-MIMO technology, enabling more reliable and higher-capacity wireless communication systems.