Channel State Information Data Research Articles

A proposal for fall-risk detection of hospitalized patients from wireless channel state information using Internet of Things devices

Falls of hospitalized patients are a breach of safety and contribute to increasing hospitalization time and worsening recovery conditions, besides impacting other costs. This work explores changes in Channel State Information (CSI), available in local area wireless networks, to detect the risk of falling for hospitalized patients. In practical scenarios, it is impossible to know all fall-risk situations; therefore, one should employ one-class-based detectors since only the risk-free status can be assessed with confidence. Wireless propagation environment changes may indicate the risk of falls and are signaled using the distance from the collected CSI data to the representation learned for the risk-free class. Considering the changes caused by inpatients and nurses’ movement in the infirmary, the learning of the fall-risk-free situation must rely on a limited amount of data and be repeated several times quickly and on the fly. This prevents data-hungry algorithms from being used to learn about risk-free situations. Consequently, we design systems using k-Nearest Neighbors (kNN), Kernel Principal Component Analysis, and Autoencoder. Assuming that the negative class corresponds to falling risk-free, the decision threshold is settled according to a prescribed False Positive Ratio (FPR), and the Matthews Correlation coefficient is used to set the decision threshold. The kNN system accomplishes the highest accuracy value of 87.95%. Thus the performance of the one-class kNN-based fall-risk detector is assessed in two environments that resemble those encountered by hospitalized patients for different FPR values, achieving recall values from 67.47% to 99.40% with an accuracy greater than 79.10%.

WIFI based human activity recognition using multi-head adaptive attention mechanism

Human Activity Recognition (HAR) utilizing Channel State Information (CSI) extracted from WiFi signals has garnered substantial interest across various domains and applications. This field’s potential paths and applications extend beyond CSI-based HAR and include smart homes, assisted living, security, gaming, surveillance, and context-aware computing. The ability of deep learning algorithms to effectively process and interpret CSI data opens up new possibilities for accurate and robust human activity recognition in real-world scenarios. However, traditional Recurrent Neural Networks (RNN) models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), rely solely on their internal memory cells to maintain information over time. Important details might be diluted or lost within the memory cells in complex CSI sequences. To address this limitation, we propose a lightweight approach that incorporates a multi-head adaptive attention weight mechanism MHAAM into the HAR framework. The multi-head attention mechanism allows the model to attend to different informative patterns within the CSI data simultaneously, capturing fine-grained temporal dependencies and improving the model’s ability to recognize complex activities. The implemented models effectively filter out noise and irrelevant information by assigning higher weights to informative CSI features, further enhancing activity classification accuracy. Experimental evaluations and comparative analyses of HAR for seven activities demonstrate that attention-based RNN models with multi-head attention consistently outperform traditional RNN models. The multi-head attention mechanism achieves improved generalization and testing for seven common human activities and environments, leading to a higher complex human activity classification accuracy of up to 98.5%.

Environment-independent textile fiber identification using Wi-Fi channel state information

Textile fiber identification is a technique that can help identify the type of target textile fiber. Existing methods usually rely on expensive detection instruments, specialized researchers, and complex processing techniques. The large number of textile fibers makes it difficult for researchers to use a stable and fast method for identification. This paper introduces a textile fiber identification method based on Wi-Fi signals, and at the same time, in the actual measurement, the signal characteristics of Wi-Fi are usually interfered with by the hardware noise and multipath propagation of channel state information (CSI) measurement equipment. To eliminate the inherent noise of CSI, we designed a denoising method based on the CSI data acquisition of textile fiber samples in independent environments. Then, the features of Wi-Fi signal wavelet packet decomposition could be extracted more stably, and the principal component analysis (PCA) method was used to reduce the data dimension. Finally, the convolutional neural network (CNN) was used to classify the data features. We conducted extensive experiments to verify the effectiveness of the proposed method. The results show that the proposed method can identify all 14 kinds of common textile fibers used in the experiment, and the average accuracy is 93.25%.

LiWi-HAR: Lightweight WiFi-Based Human Activity Recognition Using Distributed AIoT

Human activity recognition (HAR) based on WiFi channel state information (CSI) has received a lot of attentions recently due to its non-intrusive nature. Most CSI-based HAR systems use a WiFi router and a computing terminal for centralized processing, which makes it difficult to achieve real-time wide-range recognition. Recently, lightweight artificial intelligence internet of things (AIoT) devices are widely deployed. The equipped WiFi chips within such devices can collect and process CSI data in a distributed way. Thus, the AIoT devices extend the detection range of collecting CSI and enrich the applications. However, the memories of the AIoT devices are constrained and lack of appropriate lightweight CSI processing strategies. To address these challenges, we propose the LiWi-HAR system which employs a comprehensive lightweight CSI processing strategy in WiFi based AIoT devices. The proposed lightweight CSI processing strategy extracts the main related features while compressing the data size. Then, a double hidden layer BP neural network based on particle swarm optimization (PSO-BPNN) algorithm is developed for HAR. In this case, the computing memory occupation of the device is effectively reduced, and the real-time high accurate recognition is achieved. Extensive experimental results present that the efficiency of our system significantly outperforms other centralized deep learning based systems and the recognition accuracy achieves 91.7%.

Wi-Gitation: Replica Wi-Fi CSI Dataset for Physical Agitation Activity Recognition

Agitation is a commonly found behavioral condition in persons with advanced dementia. It requires continuous monitoring to gain insights into agitation levels to assist caregivers in delivering adequate care. The available monitoring techniques use cameras and wearables which are distressful and intrusive and are thus often rejected by older adults. To enable continuous monitoring in older adult care, unobtrusive Wi-Fi channel state information (CSI) can be leveraged to monitor physical activities related to agitation. However, to the best of our knowledge, there are no realistic CSI datasets available for facilitating the classification of physical activities demonstrated during agitation scenarios such as disturbed walking, repetitive sitting–getting up, tapping on a surface, hand wringing, rubbing on a surface, flipping objects, and kicking. Therefore, in this paper, we present a public dataset named Wi-Gitation. For Wi-Gitation, the Wi-Fi CSI data were collected with twenty-three healthy participants depicting the aforementioned agitation-related physical activities at two different locations in a one-bedroom apartment with multiple receivers placed at different distances (0.5–8 m) from the participants. The validation results on the Wi-Gitation dataset indicate higher accuracies (F1-Scores ≥0.95) when employing mixed-data analysis, where the training and testing data share the same distribution. Conversely, in scenarios where the training and testing data differ in distribution (i.e., leave-one-out), the accuracies experienced a notable decline (F1-Scores ≤0.21). This dataset can be used for fundamental research on CSI signals and in the evaluation of advanced algorithms developed for tackling domain invariance in CSI-based human activity recognition.

ResMon: Domain-Adaptive Wireless Respiration State Monitoring via Few-Shot Bayesian Deep Learning

Under the outbreak of the COVID-19 pandemic, respiration state monitoring plays an important role in assisting respiratory disease diagnosis and treatment. Thanks to the non-intrusive nature and low deployment cost, Wi-Fi based wireless respiration state monitoring methods have gained increasing popularity. By analyzing the variation of channel state information (CSI) of Wi-Fi signals, the respiration states of a target person under the wireless coverage, such as cough, sneeze, and yawn, can be accurately detected. A major problem of the current wireless respiration state monitoring methods is being overly domain-dependent. That is, a sensing algorithm fine-tuned to a specific device placement and background setting (i.e., a domain) can result in drastic drop in detection accuracy when applied to a dissimilar new domain. To enhance the robustness of wireless sensing and reduce the sensing cost across different domains, we propose in this paper a domain-adaptive Respiration state Monitoring system (ResMon) that achieves highly accurate cross-domain detection performance while requiring very limited labeled samples in the new domain. In a nutshell, the proposed ResMon consists of a source domain meta-training stage and a target domain meta-testing stage. In the meta-training stage, we leverage the rich source domain labeled dataset to train an embedding model as a feature extractor of high-dimensional CSI data measurements. In particular, we apply the statistical Bayesian deep learning technique to improve the generalization performance of the embedding model in cross-domain applications. In the meta-testing stage, we combine the embedding model with a few-shot learning technique to train a domain-specific classifier using very limited labeled samples in the target domain. Experiment results show that the proposed ResMon can achieve on average 87.26% cross-domain detection accuracy in a 4-class respiration state classification task using only 5 labeled samples per class, which significantly outperforms the considered benchmark methods.

Mini-Batch Alignment: A Deep-Learning Model for Domain Factor-Independent Feature Extraction for Wi-Fi–CSI Data

Unobtrusive sensing (device-free sensing) aims to embed sensing into our daily lives. This is achievable by re-purposing communication technologies already used in our environments. Wireless Fidelity (Wi-Fi) sensing, using Channel State Information (CSI) measurement data, seems to be a perfect fit for this purpose since Wi-Fi networks are already omnipresent. However, a big challenge in this regard is CSI data being sensitive to ‘domain factors’ such as the position and orientation of a subject performing an activity or gesture. Due to these factors, CSI signal disturbances vary, causing domain shifts. Shifts lead to the lack of inference generalization, i.e., the model does not always perform well on unseen data during testing. We present a domain factor-independent feature-extraction pipeline called ‘mini-batch alignment’. Mini-batch alignment steers a feature-extraction model’s training process such that it is unable to separate intermediate feature-probability density functions of input data batches seen previously from the current input data batch. By means of this steering technique, we hypothesize that mini-batch alignment (i) absolves the need for providing a domain label, (ii) reduces pipeline re-building and re-training likelihood when encountering latent domain factors, and (iii) absolves the need for extra model storage and training time. We test this hypothesis via a vast number of performance-evaluation experiments. The experiments involve both one- and two-domain-factor leave-out cross-validation, two open-source gesture-recognition datasets called SignFi and Widar3, two pre-processed input types called Doppler Frequency Spectrum (DFS) and Gramian Angular Difference Field (GADF), and several existing domain-shift mitigation techniques. We show that mini-batch alignment performs on a par with other domain-shift mitigation techniques in both position and orientation one-domain leave-out cross-validation using the Widar3 dataset and DFS as input type. When considering a memory-complexity-reduced version of the GADF as input type, mini-batch alignment shows hints of recuperating performance regarding a standard baseline model to the extent that no additional performance due to weight steering is lost in both one-domain-factor leave-out and two-orientation-domain-factor leave-out cross-validation scenarios. However, this is not enough evidence that the mini-batch alignment hypothesis is valid. We identified pitfalls leading up to the hypothesis invalidation: (i) lack of good-quality benchmark datasets, (ii) invalid probability distribution assumptions, and (iii) non-linear distribution scaling issues.

Classifying Participant Standing and Sitting Postures Using Channel State Information

Recently, channel state information (CSI) has been identified as beneficial in a wide range of applications, ranging from human activity recognition (HAR) to patient monitoring. However, these focused studies have resulted in data that are limited in scope. In this paper, we investigate the use of CSI data obtained from an ESP32 microcontroller to identify participants from sitting and standing postures in a many-to-one classification. The test is carried out in a controlled isolated environment to establish whether a pre-trained model can distinguish between participants. A total of 15 participants were recruited and asked to sit and stand between the transmitter (Tx) and the receiver (Rx), while their CSI data were recorded. Various pre-processing algorithms and techniques have been incorporated and tested on different classification algorithms, which have gone through parameter selection to enable a consistent testing template. Performance metrics such as the confusion matrix, accuracy, and elapsed time were captured. After extensive evaluation and testing of different classification models, it has been established that the hybrid LSTM-1DCNN model has an average accuracy of 84.29% and 74.13% for sitting and standing postures, respectively, in our dataset. The models were compared with the BedroomPi dataset and it was found that LSTM-1DCNN was the best model in terms of performance. It is also the most efficient model with respect to the time elapsed to sit and stand.

A Higher-Order Singular Value Decomposition-Based Target Localization Algorithm for WiFi Array Systems

Traditional Angle of Arrival (AoA)-based WiFi array indoor localization algorithms do not fuse Channel State Information (CSI) inter-packet data for estimation, which makes WiFi arrays less effective for localization in complex indoor environments. Most algorithms are overburdened leading to inefficient localization. To address these issues, in this article, an indoor positioning algorithm based on Higher-Order Singular Value Decomposition (HOSVD) is proposed. First, the CSI data are reconstructed as a new measurement matrix by borrowing subcarriers, and a third-order tensor is constructed. Next, tensor compression techniques are used to reduce computational complexity and the signal subspace is obtained by HOSVD. Then, the AoA is obtained by the Reduced Dimension Multiple Signal Classification (RD-MUSIC) method. Finally, the coordinates of the target can be obtained by triangulating the AoAs of the three Access Points (APs). According to the simulation experiments, the AoA can be estimated accurately at a low SNR and with low snapshots. In practical experiments, we can successfully estimate the AoA in complex indoor environments with shorter timelines using HOSVD without modifications to commercial hardware and produce a lower AoA error and localization error rates compared to other algorithms. The effectiveness of our proposed algorithm is proven by simulations and practical experiments.

WiFi-based human activity recognition through wall using deep learning

Wireless sensing is a promising method that integrates wireless mechanisms with strong sensing capabilities. The current focus of using WiFi Channel State Information (CSI) for human activity recognition (HAR) is the line-of-sight (LoS) path, which is mainly affected by human activities and is very sensitive to environmental changes. However, the signal on non-line-of-sight (nLoS) paths, particularly those passing through walls, is unpredictable due to the weak reflected signals destroyed by the wall. This work proposes a method to achieve high-accuracy wireless sensing based on CSI behavior recognition with low-cost resources by showing through-wall and wider-angle predictions using WiFi signals. The technique utilizes MIMO to exploit multipath propagation and increase the capability of signal transmission and receiving antennas. The signals captured by the multi-antenna are delivered into parallel channels with different spatial signatures. An RPi 4 B is attached to an ALFA AWUS 1900 adapter utilizing Nexmon firmware monitors and extracts CSI data with flexible C-based firmware for Broadcom/Cypress WiFi chips. Preprocessing techniques based on CSI are applied to improve the feature extraction from the amplitude data in an indoor environment. Furthermore, a deep learning algorithm based on RNN with an LSTM algorithm is used to classify the activity instances indoors, achieving up to 97.5% accuracy in classifying seven activities. The experiment shows CSI can achieve accurate wireless sensing in nLoS scenarios with extended antennas and a deep learning approach.

High-Resolution Indoor Sensing Using Channel State Information of WiFi Networks

Indoor sensing is becoming increasingly important over time as it can be effectively utilized in many applications from digital health care systems to indoor safety and security systems. In particular, implementing sensing operations using existing infrastructures improves our experience and well-being, and exhibits unique advantages. The physical layer channel state information for wireless fidelity (WiFi) communications carries rich information about scatters in the propagation environment; hence, we exploited this information to enable detailed recognition of human behaviours in this study. Comprehensive calibration and filtering techniques were developed to alleviate the redundant responses embedded in the channel state information (CSI) data due to static objects and accidental events. Accurate information on breathing rate, heartbeat and angle of arrival of the incoming signal at the receiver side was inferred from the available CSI data. The method and procedure developed can be extended for sensing or imaging the environment utilizing wireless communication networks.

A Comparison of Machine Learning Algorithms for Wi-Fi Sensing Using CSI Data

In today’s digital era, our lives are deeply intertwined with advancements in digital electronics and Radio Frequency (RF) communications. From cell phones to laptops, and from Wireless Fidelity (Wi-Fi) to Radio Frequency IDentification (RFID) technology, we rely on a range of electronic devices for everyday tasks. As technology continues to evolve, it presents innovative ways to harness existing resources more efficiently. One remarkable example of this adaptability is the utilization of Wi-Fi networks for Wi-Fi sensing. With Wi-Fi sensing, we can repurpose existing networking devices not only for connectivity but also for essential functions like motion detection for security systems, human motion tracking, fall detection, personal identification, and gesture recognition using Machine Learning (ML) techniques. Integrating Wi-Fi signals into sensing applications expands their potential across various domains. At the Gamgee, we are actively researching the utilization of Wi-Fi signals for Wi-Fi sensing, aiming to provide our clients with more valuable services alongside connectivity and control. This paper presents an orchestration of baseline experiments, analyzing a variety of machine learning algorithms to identify the most suitable one for Wi-Fi-based motion detection. We use a publicly available Wi-Fi dataset based on Channel State Information (CSI) for benchmarking and conduct a comprehensive comparison of different machine learning techniques in the classification domain. We evaluate nine distinct ML techniques, encompassing both shallow learning (SL) and deep learning (DL) methods, to determine the most effective approach for motion detection using Wi-Fi router CSI data. Our assessment involves six performance metrics to gauge the effectiveness of each machine learning technique.

Human Activity Recognition Using Self-Powered Sensors Based on Multilayer Bidirectional Long Short-Term Memory Networks

Sensor-based Human Activity Recognition (HAR) requires the acquisition of Channel State Information(CSI) data with time series based on sensors to predict human behavior. Many existing approaches are based on wearable sensors and cameras, which increase the burden and privacy issues for patients. Self-powered sensors are capable of non-contact collection of time series data generated by human activity while ensuring their own stable operation. In this paper, we propose a deep learning-based framework for contactless real-time activity detection of humans using self-powered sensors, which is called Multilayer Bi-directional Long Short-Term Memory (MBLSTM). The collected WIFI CSI data are fed into our proposed network model, which is then used to learn representative features of both sides from the original continuous CSI measurements. And the attention model is used to assign different weights to the learned features, and finally activity recognition is performed. Experimental results show that our proposed method achieves an accuracy of more than 96% for the recognition of six activities in multiple rounds of testing, outperforming other benchmark methods used for comparison.

Massive MIMO Channel Measurement Data Set for Localization and Communication

Channel state information (CSI) needs to be estimated for reliable and efficient communication, however, user location information is hidden inside and can be further exploited. This article presents a detailed description of a Massive Multi-Input Multi-Output (MaMIMO) testbed and provides a set of experimental location-labeled CSI data. We first focus on the design of the hardware and software of a MaMIMO testbed for gathering multiple CSI data sets. We then show this data can be used for learning-based localization and enhanced communication research. The presented data set is made fully available to the research community. We illustrate that a CSIbased joint communication and sensing processing pipeline can be evaluated and designed based on the collected data set. Specifically, the localization output obtained by a convolutional neural network (CNN) trained on the data sets is used to schedule users to improve the spectral efficiency (SE) of the communication system. Finally, we pose promising directions for further exploiting this data set and creating more data sets.

Attention-Based Hybrid Deep Learning Network for Human Activity Recognition Using WiFi Channel State Information

The recognition of human movements is a crucial aspect of AI-related research fields. Although methods using vision and sensors provide more valuable data, they come at the expense of inconvenience to users and social limitations including privacy issues. WiFi-based sensing methods are increasingly being used to collect data on human activity due to their ubiquity, versatility, and high performance. Channel state information (CSI), a characteristic of WiFi signals, can be employed to identify various human activities. Traditional machine learning approaches depend on manually designed features, so recent studies propose leveraging deep learning capabilities to automatically extract features from raw CSI data. This research introduces a versatile framework for recognizing human activities by utilizing CSI data and evaluates its effectiveness on different deep learning networks. A hybrid deep learning network called CNN-GRU-AttNet is proposed to automatically extract informative spatial-temporal features from raw CSI data and efficiently classify activities. The effectiveness of a hybrid model is assessed by comparing it with five conventional deep learning models (CNN, LSTM, BiLSTM, GRU, and BiGRU) on two widely recognized benchmark datasets (CSI-HAR and StanWiFi). The experimental results demonstrate that the CNN-GRU-AttNet model surpasses previous state-of-the-art techniques, leading to an average accuracy improvement of up to 4.62%. Therefore, the proposed hybrid model is suitable for identifying human actions using CSI data.

Data-Aided CSI Estimation Using Affine-Precoded Superimposed Pilots in Orthogonal Time Frequency Space Modulated MIMO Systems

An orthogonal affine-precoded superimposed pilot (AP-SIP)-based architecture is developed for the cyclic prefix (CP)-aided single input single output (SISO) and multiple input multiple output (MIMO) orthogonal time frequency space (OTFS) systems relying on arbitrary transmitter-receiver (Tx-Rx) pulse shaping. The data and pilot symbol matrices are affine-precoded and superimposed in the delay Doppler (DD)-domain followed by the development of an end-to-end DD-domain relationship for the input-output symbols. At the receiver, the decoupled pilot and data symbol are extracted by employing orthogonal precoder matrices, which eliminates the mutual interference. Furthermore, a novel pilot-aided Bayesian learning (PA-BL) technique is conceived for the channel state information (CSI) estimation of SISO OTFS systems based on the expectation-maximization (EM) technique. Subsequently, a data-aided Bayesian learning (DA-BL)-based joint CSI estimation and data detection technique is proposed, which beneficially harnesses the estimated data symbols for improved CSI estimation. In this scenario our sophisticated data detection rule also integrates the CSI uncertainty of channel estimation into our the linear minimum mean square error (LMMSE) detectors. The AP-SIP framework is also extended to MIMO OTFS systems, wherein the DD-domain input matrix is affine-precoded for each transmit antenna (TA). Then an EM algorithm-based PA-BL scheme is derived for simultaneous row-group sparse CSI estimation for this system, followed also by our data-aided DA-BL scheme that performs joint CSI estimation and data detection. Moreover, the Bayesian Cramer-Rao bounds (BCRBs) are also derived for both SISO as well as MIMO OTFS systems. Finally, simulation results are presented for characterizing the performance of the proposed CSI estimation techniques in a range of typical settings along with their bit error rate (BER) performance in comparison to an ideal system having perfect CSI.

Enhancing CSI-Based Human Activity Recognition by Edge Detection Techniques

Human Activity Recognition (HAR) has been a popular area of research in the Internet of Things (IoT) and Human–Computer Interaction (HCI) over the past decade. The objective of this field is to detect human activities through numeric or visual representations, and its applications include smart homes and buildings, action prediction, crowd counting, patient rehabilitation, and elderly monitoring. Traditionally, HAR has been performed through vision-based, sensor-based, or radar-based approaches. However, vision-based and sensor-based methods can be intrusive and raise privacy concerns, while radar-based methods require special hardware, making them more expensive. WiFi-based HAR is a cost-effective alternative, where WiFi access points serve as transmitters and users’ smartphones serve as receivers. The HAR in this method is mainly performed using two wireless-channel metrics: Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). CSI provides more stable and comprehensive information about the channel compared to RSSI. In this research, we used a convolutional neural network (CNN) as a classifier and applied edge-detection techniques as a preprocessing phase to improve the quality of activity detection. We used CSI data converted into RGB images and tested our methodology on three available CSI datasets. The results showed that the proposed method achieved better accuracy and faster training times than the simple RGB-represented data. In order to justify the effectiveness of our approach, we repeated the experiment by applying raw CSI data to long short-term memory (LSTM) and Bidirectional LSTM classifiers.

Deep-Reinforcement-Learning-Based Wireless IoT Device Identification Using Channel State Information

Internet of Things (IoT) technology has permeated into all aspects of today’s society and is playing an increasingly important role. Identity authentication is crucial for IoT devices to access the network, because the open wireless transmission environment of the IoT may suffer from various forms of network attacks. The asymmetry in the comprehensive capabilities of gateways and terminals in the IoT poses significant challenges to reliability and security. Traditional encryption-based identity authentication methods are difficult to apply to IoT terminals with limited capabilities due to high algorithm complexity and low computational efficiency. This paper explores physical layer identity identification based on channel state information (CSI) and proposes an intelligent identification method based on deep reinforcement learning (DRL). Specifically, by analyzing and extracting the features of the real received CSI information and a setting low-complexity state, as well as action and reward parameters for the deep neural network of deep reinforcement learning oriented to the scenario, we obtained an authentication method that can efficiently identify identities. The validation of the proposed method using collected CSI data demonstrates that it has good convergence properties. Compared with several existing machine-learning-based identity recognition methods, the proposed method has higher recognition accuracy.

Wireless Channel Prediction of GRU Based on Experience Replay and Snake Optimizer.

Aiming at the problem of poor prediction accuracy of Channel State Information (CSI) caused by fast time-varying channels in wireless communication systems, this paper proposes a gated recurrent network based on experience replay and Snake Optimizer for real-time prediction in real-world non-stationary channels. Firstly, a two-channel prediction model is constructed by gated recurrent unit, which adapts to the real and imaginary parts of CSI. Secondly, we use the Snake Optimizer to find the optimal learning rate and the number of hidden layer elements to build the model. Finally, we utilize the experience pool to store recent historical CSI data for fast learning and complete learning. The simulation results show that, compared with LSTM, BiLSTM, and BiGRU, the gated recurrent network based on experience replay and Snake Optimizer has better performance in the optimization ability and convergence speed. The prediction accuracy of the model is also significantly improved under the dynamic non-stationary environment.

LoS sensing-based superimposed CSI feedback for UAV-assisted mmWave systems

In Unmanned Aerial Vehicle (UAV)-assisted millimeter Wave (mmWave) systems, Channel State Information (CSI) feedback is critical for the selection of modulation schemes, resource management, beamforming, etc. However, traditional CSI feedback methods lead to significant feedback overhead and energy consumption of the UAV transmitter, therefore shortening the system operation time. To tackle these issues, inspired by superimposed feedback and Integrated Sensing and Communications (ISAC), a Line of Sight (LoS) sensing-based superimposed CSI feedback scheme is proposed. Specifically, on the UAV transmitter side, the Ground-to-UAV (G2U) CSI is superimposed on the UAV-to-Ground (U2G) data to feed back to the ground Base Station (gBS). At the gBS, the dedicated LoS Sensing Network (LoS-SenNet) is designed to sense the U2G CSI in LoS and NLoS scenarios. With the sensed result of LoS-SenNet, the determined G2U CSI from the initial feature extraction will work as the priori information to guide the subsequent operation. Specifically, for the G2U CSI in NLoS, a CSI Recovery Network (CSI-RecNet) and superimposed interference cancellation are developed to recover the G2U CSI and U2G data. As for the LoS scenario, a dedicated LoS Aid Network (LoS-AidNet) is embedded before the CSI-RecNet and the block of superimposed interference cancellation to highlight the feature of the G2U CSI. Compared with other methods of superimposed CSI feedback, simulation results demonstrate that the proposed feedback scheme effectively improves the recovery accuracy of the G2U CSI and U2G data. Besides, against parameter variations, the proposed feedback scheme presents its robustness.