For frequency division duplexing massive multiple-input multiple-output systems, downlink channel state information (CSI) is required to be compressed and fed back to the base station (BS) to support beamforming. Recently, deep learning (DL) has demonstrated overwhelming performance in CSI feed-back, wherein multimodal information is explored to further improve the performance. With the emergence of integrated sensing and communications, radar-equipped BSs exhibit the capability to sense the wireless environment and assist communication design. In this paper, we propose a sensing-aided DL-based CSI feedback method, in which the angle information of scatters in the communication channel is sensed by the BS and utilized to reduce feedback overhead. A novel two-stage feedback scheme with lightweight network structures is carefully designed to improve feedback performance. Experiments demonstrate that compared to previous methods without utilization of sensing information, our sensing-aided methods significantly enhance performance in low-bit scenarios with reduced computational complexity. The open-source codes are available at https://github.com/zhang-xd18/safb.

Automatic modulation recognition (AMR) has received widespread attention as a crucial aspect of non-cooperative communication. Despite this, large carrier frequency offsets (CFOs) and sample rate offsets (SROs) caused by inaccurate parameter estimation at the receiver are harmful to the recognition accuracy, which is still to be addressed. In this letter, we focus on intelligent modulation recognition tasks under such offsets. A novel transformer-based method named TransGroupNet is designed that can extract deep features of signals from the instantaneous amplitude, phase, and frequency (APF) domain. In addition, lightweight group convolution layers are added ahead of the transformer blocks for better feature preprocessing. Simulations demonstrate that the proposed TransGroupNet achieves better recognition accuracy under large offsets compared with the previous state-of-the-art methods, even though these methods adopt correction modules (CM) to address such offsets.

Recent work has shown that Binarized Neural Networks (BNNs) are able to greatly reduce computational costs and memory footprints, facilitating model deployment on resource-constrained devices. However, in comparison to their full-precision counterparts, BNNs suffer from severe accuracy degradation. Research aiming to reduce this accuracy gap has thus far largely focused on specific network architectures with few or no 1 × 1 convolutional layers, for which standard binarization methods do not work well. Because 1 × 1 convolutions are common in the design of modern architectures (e.g. GoogleNet, ResNet, DenseNet), it is crucial to develop a method to binarize them effectively for BNNs to be more widely adopted. In this work, we propose an “Elastic-Link” (EL) module to enrich information flow within a BNN by adaptively adding real-valued input features to the subsequent convolutional output features. The proposed EL module is easily implemented and can be used in conjunction with other methods for BNNs. We demonstrate that adding EL to BNNs produces a significant improvement on the challenging large-scale ImageNet dataset. For example, we raise the top-1 accuracy of binarized ResNet26 from 57.9% to 64.0%. EL also aids con-vergence in the training of binarized MobileNet, for which a top-1 accuracy of 56.4% is achieved. Finally, with the integration of ReActNet, it yields a new state-of-the-art result of 71.9% top-1 accuracy.

Massive multiple-input multiple-output (MIMO) is one of the key techniques to achieve better spectrum and energy efficiency in 5G system. The channel state information (CSI) needs to be fed back from the user equipment to the base station in frequency division duplexing (FDD) mode. However, the overhead of the direct feedback is unacceptable due to the large antenna array in massive MIMO system. Recently, deep learning is widely adopted to the compressed CSI feedback task and proved to be effective. In this paper, a novel network named aggregated channel reconstruction network (ACRNet) is designed to boost the feedback performance with network aggregation and parametric rectified linear unit (PReLU) activation. The practical deployment of the feedback network in the communication system is also considered. Specifically, the elastic feedback scheme is proposed to flexibly adapt the network to meet different resource limitations. Besides, the network binarization technique is combined with the feature quantization for lightweight and practical deployment. Experiments show that the proposed ACRNet outperforms loads of previous state-of-the-art networks, providing a neat feedback solution with high performance, low cost and impressive flexibility.

Superpixel is generated by automatically clustering pixels in an image into hundreds of compact partitions, which is widely used to perceive the object contours for its excel-lent contour adherence. Although some works use the Convolution Neural Network (CNN) to generate high-quality superpixel, we challenge the design principles of these net-works, specifically for their dependence on manual labels and excess computation resources, which limits their flexibility compared with the traditional unsupervised segmentation methods. We target at redefining the CNN-based superpixel segmentation as a lifelong clustering task and pro-pose an unsupervised CNN-based method called LNS-Net. The LNS-Net can learn superpixel in a non-iterative and lifelong manner without any manual labels. Specifically, a lightweight feature embedder is proposed for LNS-Net to efficiently generate the cluster-friendly features. With those features, seed nodes can be automatically assigned to cluster pixels in a non-iterative way. Additionally, our LNS-Net can adapt the sequentially lifelong learning by rescaling the gradient of weight based on both channel and spatial context to avoid overfitting. Experiments show that the proposed LNS-Net achieves significantly better performance on three benchmarks with nearly ten times lower complexity compared with other state-of-the-art methods.

In massive multiple-input multiple-output (MIMO) system, user equipment (UE) needs to send downlink channel state information (CSI) back to base station (BS). However, the feedback becomes expensive with the growing complexity of CSI in massive MIMO system. Recently, deep learning (DL) approaches are used to improve the reconstruction efficiency of CSI feedback. In this paper, a novel feedback network named CRNet is proposed to achieve better performance via extracting CSI features on multiple resolutions. An advanced training scheme that further boosts the network performance is also introduced. Simulation results show that the proposed CRNet outperforms the state-of-the-art CsiNet under the same computational complexity without any extra information. The open source codes are available at https://github.com/Kylin9511/CRNet.

Hybrid precoding is introduced to millimeter wave (mmWave) multiple-input multiple-output (MIMO) System in order to reduce the number of radio frequency (RF) chains. In traditional hybrid precoding scheme, the maximum number of independent data streams is restricted by the number of RF-chains, which limits the achievable spectral efficiency (SE) rate. To further improve the SE rate, the generalized spatial modulation (GenSM) structure is considered. However, current gradient ascent iteration (GAI) algorithm of GenSM aided hybrid precoding is too complicated when the antenna array is large. In this paper we propose a novel low complexity algorithm for the analog and digital precoder design under GenSM hybrid precoding structure. Our approach gives additional unitary constraint to the digital precoder, formulating its design as a masked orthonormal procrustes problem. With this simplification, we accomplish the whole hybrid precoder design via turbo optimization (TO). Finally, numerical simulations are demonstrated to compare the achievable SE performance between our proposed low complexity scheme and some traditional hybrid precoding algorithms including the GAI algorithm.