Channel State Information Matrix Research Articles

Hybrid CNN-transformer network for efficient CSI feedback

In recent years, many deep learning-based methods have been utilized for the feedback of Channel State Information (CSI) in massive MIMO systems. The Transformer-based networks leverage global self-attention mechanisms that can effectively capture remote correlations between antennas, while Convolutional Neural Networks (CNNs) excel in acquiring local information. To balance the advantages of both, this paper proposes an Efficient Feature Aggregation Network called EFANet, which hybrid CNNs and Transformer. Specifically, we propose a Refined Window Multi-head Self-Attention (RW-MSA) through hybrid Convolutional Embedding Unit (CEU) and Window Multi-head Self-Attention (W-MSA) to reduce information loss between windows and achieve efficient feature aggregation. Additionally, we develop a Local Enhanced Feedforward Network (LEFN) to further integrate local information in the CSI matrix and model detailed features of different regions. Finally, the Compensation Unit (CU) is designed to further compensate for global-local features in the CSI matrix. Through the above design, the global and local features are fully interactive to reduce information loss. Numerous experiments have shown that the proposed method achieves better CSI reconstruction performance while reducing computational complexity.

Attention-based deep learning approach for CSI feedback under 5G TDL channel

In 5G communication systems, accurate channel state information (CSI) is indispensable for signal detection and regulation at the base station side. However, frequent CSI feedback from users leads to excessive system overhead. To tackle this challenge, this paper puts forward a novel deep learning framework - HCNet based on attention mechanism and autoencoder, aiming to efficiently compress and reconstruct the high-dimensional time-varying CSI matrices in an end-to-end approach. The proposed framework incorporates a self-attention module in the encoder to explicitly capture global dependencies within the CSI matrix. Meanwhile, the decoder adopts gated recurrent unit networks to fully exploit inter-feature correlations and redundancy. To evaluate the performance, simulations are conducted using datasets conforming to current 5G time-varying TDL channel models. Results demonstrate superior performance over existing deep learning based feedback networks. Specifically, the proposed framework can reduce the normalized mean square error of CSI reconstruction by 4 dB under various compression ratios which confirms the effectiveness of the attention-enhanced autoencoder structure for compressive CSI sensing and feedback in practical dynamic communication systems.

IALNet: An Integration Attention Lightweight Neural Network for Massive MIMO CSI Feedback

Recent deep learning-based channel state information (CSI) feedback methods have made great progress. However, numerous existing methods improve CSI compression and reconstruction accuracy at the cost of computational complexity by designing more complex deep learning modules. In this letter, we propose a novel lightweight neural network IALNet for CSI feedback problem. In the proposed IALNet, we design an integration attention module (IAM) for improving the performance of the network. Specifically, By embedding the correlation information of the vertical and horizontal directions of CSI matrix into the channel attention, the IAM enabled IALNet to capture distribution characteristics of CSI matrix while focusing on the important regions of interest in the vertical and horizontal directions and enhancing the feature representation. Extensive experiment results demonstrate that our IALNet outperforms previous SOTA networks in both outdoor and indoor scenarios, providing an efficient, low cost and flexible CSI feedback solution.

Viewing Channel as Sequence Rather Than Image: A 2-D Seq2Seq Approach for Efficient MIMO-OFDM CSI Feedback

In this paper, we aim to design an effective learning-based channel state information (CSI) feedback scheme for the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems from a physics-inspired perspective. We first argue that the CSI matrix of a MIMO-OFDM system is physically closer to a two-dimensional (2-D) sequence rather than an image due to its apparent unsmoothness, non-scalability, and translational variance within both the spatial and frequency domains. On this basis, we introduce a 2-D long short-term memory (LSTM) neural network to represent the CSI and propose a 2-D sequence-to-sequence (Seq2Seq) model for CSI compression and reconstruction. Specifically, one two-layer 2-D LSTM is used for CSI feature extraction, and the other is used for CSI representation and reconstruction. The proposed scheme can not only fully utilize the unique 2-D characteristics of CSI but also preserve the index information and unsmooth features of the CSI matrix compared with current convolutional neural network (CNN) based schemes. We show that the computational complexity of the proposed scheme is linear in the number of transmit antennas and subcarriers. Its key performances, like reconstruction accuracy, convergence speed, generalization ability after short-term training, and robustness to lossy feedback, are comprehensively compared with existing popular convolutional networks. Experimental results show that our scheme can bring up to nearly 7 dB gain in reconstruction accuracy under the same overhead and reduce feedback overhead by up to 75% under the same accuracy compared with the conventional CNN-based approaches.

Self-Information Domain-Based Neural CSI Compression With Feature Coupling

Deep learning (DL)-based channel state information (CSI) feedback methods compressed the CSI matrix by exploiting its delay and angle features straightforwardly, while the measure in terms of information contained in the CSI matrix has rarely been considered. Based on this observation, we introduce self-information as an informative CSI representation from the perspective of information theory, which reflects the amount of information of the original CSI matrix in an explicit way. Then, a novel DL-based network is proposed for temporal CSI compression in the self-information domain, namely SD-CsiNet. The proposed SD-CsiNet projects the raw CSI onto a self-information matrix in the newly-defined self-information domain, extracts both temporal and spatial features of the self-information matrix, and then couples these two features for effective compression. Experimental results verify the effectiveness of the proposed SD-CsiNet by exploiting the self-information of CSI. Particularly for compression ratios <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1/8$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1/16$</tex-math></inline-formula> , the SD-CsiNet respectively achieves 7.17 dB and 3.68 dB performance gains compared to state-of-the-art methods.

Deep CSI Compression for Massive MIMO: A Self-Information Model-Driven Neural Network

In order to fully exploit the advantages of massive multiple-input multiple-output (mMIMO), it is critical for the transmitter to accurately acquire the channel state information (CSI). Deep learning (DL)-based methods have been proposed for CSI compression and feedback to the transmitter. Although most existing DL-based methods consider the CSI matrix as an image, structural features of the CSI image are rarely exploited in neural network design. As such, we propose a model of self-information that dynamically measures the amount of information contained in each patch of a CSI image from the perspective of structural features. Then, by applying the self-information model, we propose a model-and-data-driven network for CSI compression and feedback, namely IdasNet. The IdasNet includes the design of a module of self-information deletion and selection (IDAS), an encoder of informative feature compression (IFC), and a decoder of informative feature recovery (IFR). In particular, the model-driven module of IDAS pre-compresses the CSI image by removing informative redundancy in terms of the self-information. The encoder of IFC then conducts feature compression to the pre-compressed CSI image and generates a feature codeword which contains two components, i.e., codeword values and position indices of the codeword values. Subsequently, the IFR decoder decouples the codeword values as well as position indices to recover the CSI image. Experimental results verify that the proposed IdasNet noticeably outperforms existing DL-based networks under various compression ratios while it has the number of network parameters reduced by orders-of-magnitude compared with various existing methods.

On the Design of Beamforming Feedback for Wi-Fi Sensing

In recent years, channel state information (CSI) has been widely used for Wi-Fi sensing. Most of the existing Wi-Fi sensing researches are conducted based on the CSI matrix. It is noted that since the IEEE 802.11ac standard, beamforming weights feedback will be the only feedback type supported in the mainstream Wi-Fi standards. This letter aims to investigate Wi-Fi sensing based on beamforming weights feedback, where two feedback schemes namely Scheme 1 and Scheme 2 are analyzed and discussed. In Scheme 1, the transmit beamforming matrix (i.e., beamforming weight) is obtained by directly performing eigenvalue decomposition (EVD) on the channel autocorrelation matrix. As for Scheme 2, the transmit beamforming matrix is obtained based on performing EVD on the conjugate transpose of the channel autocorrelation matrix. The analysis shows that Scheme 1 retains the angle of departure (AOD), together with Doppler differences and time delay differences between different paths, while Scheme 2 can retain the entire AOD, Doppler, and time delay information. The effects of imperfect systems on sensing performance in practice are also discussed in this letter.

Wi-Exercise: An Indoor Human Movement Detection Method Based on Bidirectional LSTM Attention

Human motion recognition has become a hot topic in the field of human-computer interaction. Due to the traditional method of detecting human movement in indoor environments, there are still problems such as high hardware costs, influenced by the environment and the need for experimenters to be equipped with relevant equipment. With the large-scale deployment of wireless devices and the establishment of wireless network infrastructure, indoor personnel movement recognition systems based on wireless signals are gradually proposed. In this paper, we propose a device-independent motion-detection method Wi-exercise based on temporal recurrent neural networks. The acquired channel state information (CSI) is preprocessed by wavelet transform, and the output CSI matrix is filtered by the principal component analysis (PCA) to extract the feature values. The wavelet function is processed to further remove the noise also to make the action data more distinctive, and then PCA is used to track the entire CSI time series to improve and maintain its dimensional energy. The preprocessed data are subjected to feature extraction to obtain CSI of human motion and establish feature sequences, and then the action model is trained using the bidirectional long short-term memory attention (ABLSTM) mechanism to finally realize human motion recognition. Finally, the robustness of Wi-exercise is tested experimentally, and its performance is compared with that of existing recognition methods. The experimental results show that the average recognition rate of the method is 94.43% in the complex indoor environment, and the method is better than the traditional classifier method for action recognition, with lower and higher recognition accuracy.

Bi-Directional Beamforming Feedback-Based Firmware-Agnostic WiFi Sensing: An Empirical Study

In the field of WiFi sensing, as an alternative sensing source of the channel state information (CSI) matrix, the use of a beamforming feedback matrix (BFM) that is a right singular matrix of the CSI matrix has attracted significant interest owing to its wide availability regarding the underlying WiFi systems. In the IEEE 802.11ac/ax standard, the station (STA) transmits a BFM to an access point (AP), which uses the BFM for precoded multiple-input and multiple-output communications. In addition, in the same way, the AP transmits a BFM to the STA, and the STA uses the received BFM. Regarding BFM-based sensing, extensive real-world experiments were conducted as part of this study, and two key insights were reported: Firstly, this report identified a potential issue related to accuracy in existing uni-directional BFM-based sensing frameworks that leverage only BFMs transmitted for the AP or STA. Such uni-directionality introduces accuracy concerns when there is a sensing capability gap between the uni-directional BFMs for the AP and STA. Thus, this report experimentally evaluates the sensing ability disparity between the uni-directional BFMs, and shows that the BFMs transmitted for an AP achieve higher sensing accuracy compared to the BFMs transmitted from the STA when the sensing target values are estimated depending on the angle of departure of the AP. Secondly, to complement the sensing gap, this paper proposes a bi-directional sensing framework, which simultaneously leverages the BFMs transmitted from the AP and STA. The experimental evaluations reveal that bi-directional sensing achieves higher accuracy than uni-directional sensing in terms of the human localization task.

Randomized Low-Rank Approximation Based Massive MIMO CSI Compression

Massive multiple-input multiple-output (MIMO) is regarded as one enabling technique to improve channel capacity and energy/spectrum efficiency of 5G communications. To attain such potential benefits, accurate channel information is critical to the transmitter, which yet remains a challenging task for frequency division duplexing (FDD) systems, i.e., the channel state information (CSI) feedback tends to be resource-demanding especially for massive MIMO communications. In this work, we propose a novel CSI feedback method with low complexity and high precision, which is inspired by randomized matrix approximation. Our approach exploits the inherent low-rank characteristic of a large channel matrix, and approximates it by small sub-matrices which are then reported to transmitter to recover a CSI matrix. Theoretical bounds of the recovered CSI in both error-free and error cases are derived. Simulation results demonstrate our method could recover CSI accurately via an extremely low complexity and yet achieve a largely reduced compression ratio (or feedback overhead), compared to other schemes. It thus has the great potential in the emerging massive MIMO FDD communications.

MSE minimized joint transmission in coordinated multipoint systems with sparse feedback and constrained backhaul requirements

In a joint transmission coordinated multipoint (JT-CoMP) system, a shared spectrum is utilized by all neighbor cells. In the downlink, a group of base stations (BSs) coordinately transmit the users’ data to avoid serious interference at the users in the boundary of the cells, thus substantially improving area fairness. However, this comes at the cost of high feedback and backhaul load; In a frequency division duplex system, all users at the cell boundaries have to collect and send feedback of the downlink channel state information (CSI). In centralized JT-CoMP, although with capabilities for perfect coordination, a central coordination node have to send the computed precoding weights and corresponding data to all cells which can overwhelm the backhaul resources. In this paper, we design a JT-CoMP scheme, by which the sum of the mean square error (MSE) at the boundary users is minimized, while feedback and backhaul loads are constrained and the load is balanced between BSs. Our design is based on the singular value decomposition of CSI matrix and optimization of a binary link selection matrix to provide sparse feedback—constrained backhaul link. For comparison, we adopt the previously presented schemes for feedback and backhaul reduction in the physical layer. Extensive numerical evaluations show that the proposed scheme can reduce the MSE with at least 25%, compared to the adopted and existing schemes.

Binary Neural Network Aided CSI Feedback in Massive MIMO System

In massive multiple-input multiple-output (MIMO) system, channel state information (CSI) is essential for the base station (BS) to achieve high performance gain. The CSI matrix needs to be estimated and fed back from user equipment (UE) in frequency division duplexing (FDD) mode. Recently, deep learning is widely used in CSI compression to reduce the feedback overhead. However, applying neural network brings extra memory and computation cost, which is non-negligible especially for the resource limited UE. In this letter, a novel binarization aided feedback network named BCsiNet is introduced to lighten the encoder at UE. The proposed BCsiNet offers over 30× memory saving and around 2× inference acceleration for the encoder compared with CsiNet. Moreover, experiments show that the feedback performance of BCsiNet can be comparable with original CsiNet even with the encoder binarized.

Angular-Domain Massive MIMO Detection: Algorithm, Implementation, and Design Tradeoffs

In massive multiple-input multiple-output (MIMO) systems, the large size of channel state information (CSI) matrix significantly increases the computational complexity of uplink detection and size of required memory to store the channel data. To address these challenges, we propose to perform detection in the angular domain, where the channel information can be presented in a more condensed way. The underlying idea is to exploit the sparsity of massive MIMO channel in the angular domain to reduce the size of CSI matrix by selecting dominant beams. Then, an angular-domain linear detector followed by a non-linear post-processing scheme is proposed to perform detection using the reduced-size CSI. Evaluated using measured massive MIMO channels, our method results in 35%-73% reduction in complexity and required memory compared to traditional detectors while it achieves better performance. Moreover, this paper provides a framework, which trades between performance, complexity, and size of required memory. As a proof of concept, we implement the angular-domain detector in a 28 nm FD-SOI CMOS for a massive MIMO with 128 antennas communicating with up to 16 users. Synthesis result shows that our design attains a throughput of 2240 Mbps with an area of 829 k gates.

Machine Learning for Practical Localization System Using Multiview CSI

Various machine learning techniques on indoor localization using radio signals are being rapidly developed to achieve a sub-meter accuracy under noisy and complex environments. A fingerprint database using channel state information (CSI) extracted from a radio packet based on an orthogonal frequency diversity multiplexing (OFDM) channel can provide enough information to localize a transmitter device with a neural network (NN) based machine learning technique. In this article, we concern about the more practical use of the localization system using machine learning. We introduce a novel design of a signal preprocessing method for NN fingerprinting. To deal with the real building environment with corridors where certain signals cannot arrive at the receiver, our preprocessing with nonnegative matrix factorization (NMF) recovers multiview CSI of the original signal and complete the sparse CSI matrix, which enables robust and practical localization. The recovered CSI is then applied to variational inference-based machine learning that finds informative corridor views among multiview CSI. Our proposed system significantly outperforms other existing machine learning-based systems and shows a localization accuracy of 89 cm, while it still maintains the reliable accuracy even with 30% sparse network. It is the first time to consider how to design a practical localization system in an empirical building environment.

WiDriver: Driver Activity Recognition System Based on WiFi CSI

Driver is the most active factor in people–vehicle–road system, so the driver activity monitoring has become increasingly important to support the driver assistant system application. The possibility of using device-free sensing technology for driver activity recognition in a simulated driving environment is investigated in this paper. We present WiDriver, among the first efforts to employ channel state information (CSI) amplitude variation data to intelligently estimate driving actions with commodity WiFi devices. The WiDriver proposes the scheme of screening sensitive input data from original CSI matrix of WiFi signals based on BP neural network algorithm; and the continuous driving activities classification algorithm by introducing the posture sequence, driving context finite automate model. Our experimental driving study in carriage with 5 subjects shows that the sensitive input selection scheme can achieve high accuracy of 96.8% in posture recognition and the continuous action classification algorithm can reach 90.76% maneuver operation detection rate.

PHY‐Aided Secure Communication via Weighted Fractional Fourier Transform

A weighted fractional Fourier transform (WFRFT) based on channel state information (CSI), aiming to safeguard the physical (PHY) layer security of wireless communication system, is proposed. With the proposed scheme, WFRFT is first applied to satellite communications such that the transmitted signal is distorted and can only be neutralized by inverse‐WFRFT with the same parameter. Moreover, by exploiting the physical properties of wireless channels, the CSI matrix is transformed as a secret key. In addition, by adding phase rotation (PR) factors to each branch of the WFRFT system, a new unitary matrix with the encryption properties is constructed, and hence, the satellite communications secrecy is reliably guaranteed due to the variation in signal characteristics. Finally, the efficacy of the security enhancement is evaluated in terms of the average bit error rate (BER) and the secrecy capacity. Simulation results show that the proposed encryption method can make the detection and demodulation more difficult for the eavesdropper.

Performance modelling of open loop and closed loop multiuser multiple‐input multiple‐output‐orthogonal frequency division multiplexing systems through channel analysis

Multiple-input multiple-output (MIMO) technology can be combined with orthogonal frequency division multiplexing to achieve diversity gain and improvement in system spectrum efficiency. The relative capacity of different system configurations are obtained using various channel characteristics. In this study, the probability density function (PDF) and the cumulative distribution function expressions of the condition number [i.e. the maximum to minimum singular value ratio (MMSVR)] of the channel state information matrix are derived. This ratio is directly related to noise enhancement in open loop (OL) multiuser MIMO systems. The probability density function (PDF) of MMSVR is directly used to compute the channel capacity if specific channels fail at high throughput. Capacity expressions in terms of MMSVR are derived for OL and closed loop (CL) multiuser MIMO systems. The capacity of both OL and CL systems are plotted for different system specifications, different fading channels and compared with each other under both semi and doubly correlated channels. A novel algorithm is developed to compute the channel capacity and PDF of MMSVR of a generic NT × NR MIMO system.