Channel State Information Research Articles

Indoor Localization in Commercial 5G Environment with Single BS

Abstract. As commercial 5G systems rapidly expand, indoor positioning using 5G signals holds great potential for serving a large number of users. In this paper, an effective fingerprint solution is proposed for indoor positioning with 5G signal base station by exploring the multi-beam property. Multi-beam channel state information (CSI) and multi-beam reference signal received power (RSRP) are used as the observations for fingerprinting. To assess the effectiveness of the proposed scheme, field tests were conducted across various indoor environments. The results showed that the positioning accuracy is improved by more than 45% compared with the single beam by using the multi-beam characteristics of the 5G signal. Based on the multi-beam RSRP, the proposed scheme can achieve a positioning error of 67% below 1 meter. In the awareness of the two typical indoor deployments of 5G systems, i.e., the digital indoor distribution (DID) and the distributed antenna system (DAS), the paper also compared the 5G positioning performance in these two scenarios. The field tests showed that, the multi-beam in DID has more features than in DAS, which lead to a better positioning performance than that in DAS.

Coverage Probability Analysis of a Double IRS Assisted SISO Communication System

Intelligent reflecting surface (IRS) improves wireless communication efficiency and coverage probability. Existing studies focus on IRS deployment but overlook optimal fading channels. This research aims to identify and analyze the most effective channel fading estimation for the double IRS system model. The coverage probability using two-IRS is derived under different fading channels like Rayleigh and Rician to find the optimal solution in a Single-Input-Single-Output (SISO) system. Additionally, a closed-form expression for the coverage probability using statistical channel state information is derived while assuming no direct path between the transmitter and receiver. The analytical results are validated through simulations. It offers valuable insights into the modeling and analysis of double IRS-assisted wireless communication systems.

Device-free crowd counting using multi-link WiFi CSI for occupant-driven energy management of HVAC systems

ABSTRACT Accurate estimation of the number of occupants in a building’s thermal zone is a crucial input for the dynamic estimation of the cooling load of a heating, ventilation, and air conditioning equipment. This data, in conjunction with an intelligent control algorithm, can be judiciously employed for enhancing occupant comfort and achieving substantial energy savings through demand-driven operation. In this work, we propose a WiFi channel state information (CSI) based passive people counting model that utilizes a wavelet-based signal denoising scheme and an artificial neural network (ANN) model for automatic feature extraction. The application of the proposed model on a public dataset shows more than 99% accuracy in detecting up to eight people in three rooms of varying sizes. The proposed model was also validated on CSI samples collected from a laboratory environment with five occupants, using ESP32, a low cost and low power IoT device. The results show that the proposed model can perform people counting with an accuracy of up to 98%, using single- and multi-link measurements.

Maximum Doppler Shift Identification Using Decision Feedback Channel Estimation

This paper introduces a new method for estimating the maximum Doppler shift using decision feedback channel estimation (DFCE). In highly mobile environments, which are expected to be covered beyond 5G and 6G systems, the relative movement between the transmitter and receiver causes Doppler shifts. This leads to inter-carrier interference (ICI), significantly degrading communication quality. To mitigate this effect, systems that estimate the maximum Doppler shift and adaptively adjust communication parameters have been extensively studied. One of the most promising methods for maximum Doppler shift estimation involves inserting pilot signals at both the beginning and end of the packet. Although this method achieves high estimation accuracy, it introduces significant latency due to the insertion of the pilot signal at the packet’s end. To address this issue, this paper proposes a new method for rapid estimation using DFCE. The proposed approach compensates for faded signals using channel state information obtained from decision feedback. By treating the compensated signal as a reference, the Doppler shift can be accurately estimated without the need for pilot signals at the end of the packet. This method not only maintains high estimation accuracy but also significantly reduces the latency associated with conventional techniques, making it well-suited for the requirements of next-generation communication systems.

Channel estimation via compressed sampling matching pursuit for hybrid MIMO architectures in millimeter wave communication

ABSTRACT In recent times, there are considerable research efforts in utilising millimetre wave cellular system for meeting rising mobile traffic volume and escalating data rate demand. In this regard, several analytical models have been developed for obtaining Channel State Information (CSI) for the design of combiner and precoder architectures for millimetre wave communication. One prominent method that has caught the attention of most researchers is the Orthogonal Matching Pursuit (OMP) which performs poorly in noisy condition. As a result, this work introduces Compressed Sampling Matching Pursuit (CoSAMP) as alternative to OMP for obtaining CSI for the design of hybrid combiner. A comparative analysis of all the cases examined reveals that CoSAMP surpasses OMP and Least Square (LS) across SNR range of −30 dB to −10 dB, recording lowest Normalized Mean Square Error (NMSE), highest Spectral Efficiency (SE) and bit rate. For instance at −20 dB, NMSE of CoSAMP exhibits 95% and 86% gain over LS and OMP, respectively while SE and bit rate of CoSAMP are better than OMP and LS by 21% and 91%, respectively. It is therefore seen that CoSAMP is a better technique for estimating CSI and for combiner design in such condition.

Tensor Based Semi-Blind Channel Estimation for Reconfigurable Intelligent Surface-Aided Multiple-Input Multiple-Output Communication Systems

Reconfigurable intelligent surfaces (RISs) are a promising technology for sixth-generation (6G) wireless networks. However, a fully passive RIS cannot independently process signals. Wireless systems equipped with it often encounter the challenge of large channel matrix dimensions when acquiring channel state information using pilot-assisted algorithms, resulting in high pilot overhead. To address this issue, this article proposes a semi-blind joint channel and symbol estimation receiver without a pilot training stage for RIS-aided multiple-input multiple-output (MIMO) (including massive MIMO) communication systems. In a semi-blind system, a transmission symbol matrix and two channel matrices are coupled within the received signals at the base station (BS). We decouple them by building two parallel factor (PARAFAC) tensor models. Leveraging PARAFAC tensor decomposition, we transform the joint channel and symbol estimation problem into least square (LS) problems, which can be solved by Alternating Least Squares (ALSs). Our proposed scheme allows duplex communication. Compared to recently proposed pilot-based methods and semi-blind receivers, our results demonstrate the superior performance of our proposed algorithm in estimation accuracy and speed.

Analysis of Optimal Diversity Combining with Imperfect Channel State Information for Cooperative Jamming-Aided Internet of Things Security

Cooperative jamming (CJ) combined with single-input multiple-output (SIMO) technology can effectively improve the security and reliability of Internet of Things (IoT) systems. However, imperfect main channel state information (CSI) leads to incomplete CJ cancellation, which degrades the performance after combining. In this paper, by considering channel estimation errors and residual CJ at each receiving antenna, the residual CJ power after jamming suppression is analyzed, and the optimal combining coefficient for maximizing the signal-to-jamming-noise ratio (SJNR) is derived. The output SJNR of optimal diversity combining is given, and the achievable secrecy rate is analyzed on this basis. The theoretical and simulation results verify the combined effect of channel estimation errors and residual CJ on the combining performance. Moreover, it is worth noting that the combining performance is closely related to the spatial correlation of residual CJ.

BER Analysis of Underlay Cooperative Cognitive Radio-based NOMA System

Background: The integration of Cooperative Communications (CC), Cognitive Radio (CR) technology, and Non-Orthogonal Multiple Access (NOMA) techniques, termed Cooperative Cognitive Radio used NOMA (CCR-NOMA) systems, has emerged as a promising solution to address spectrum scarcity and connectivity challenges anticipated in sixth generation (6G) networks. Aim: This studyaims to investigate the Bit Error Rate (BER) performance of underlay CCRNOMA systems. Methods: We derive precise closed-form expressions for BER at distant users under perfect and imperfect Channel State Information (CSI) conditions. These mathematical formulations are validated through Monte Carlo simulations. Results: Our results indicate that the near user CU! exhibits superior performance compared to the far user CU!. Additionally, distant users utilizing the CCR-OMA protocol demonstrate better BER performance than those employing the CCR-NOMA protocol. Conclusion: The presence of imperfect CSI adversely affects BER performance. Moreover, the derived closed-form expressions for BER in the investigated system align well with Monte Carlo simulations. These findings provide valuable insights for optimizing the performance of CCR-NOMA systems in real-world scenarios.

Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM

This work was conducted mainly to provide a healthy and safe monitoring system for the elderly living in the home environment. In this paper, two different target fall detection schemes are proposed based on whether the target is visible or not. When the target is visible, a vision-based fall detection algorithm is proposed, where an image of the target captured by a camera is transmitted to the improved You Only Look Once version 5s (YOLOv5s) model for posture detection. In contrast, when the target is invisible, a WiFi-based fall detection algorithm is proposed, where channel state information (CSI) signals are used to estimate the target’s posture with an improved long short-term memory (LSTM) model. In the improved YOLOv5s model, adaptive picture scaling technology named Letterbox is used to maintain consistency in the aspect ratio of images in the dataset, and the weighted bidirectional feature pyramid (BiFPN) and the attention mechanisms of squeeze-and-excitation (SE) and coordinate attention (CA) modules are added to the Backbone network and Neck network, respectively. In the improved LSTM model, the Hampel filter is used to eliminate the noise from CSI signals and the convolutional neural network (CNN) model is combined with the LSTM to process the image made from CSI signals, and thus the object of the improved LSTM model at a point in time is the analysis of the amplitude of 90 CSI signals. The final monitoring result of the health status of the target is the result of combining the fall detection of the improved YOLOv5s and LSTM models with the physiological information of the target. Experimental results show the following: (1) the detection precision, recall rate, and average precision of the improved YOLOv5s model are increased by 7.2%, 9%, and 7.6%, respectively, compared with the original model, and there is almost no missed detection of the target; (2) the detection accuracy of the improved LSTM model is improved by 15.61%, 29.36%, and 52.39% compared with the original LSTM, CNN, and neural network (NN) models, respectively, while the convergence speed is improved by 90% compared with the original LSTM model; and (3) the proposed algorithm can meet the requirements of accurate, real-time, and stable applications of health monitoring.

Trajectory mapping through channel state information by triangulation method and fine-tuning

Trajectory mapping techniques have widespread applications in diverse fields, including robotics, localization, smart environments, gaming, and tracking systems. However, existing free devices encounter challenges in representing trajectories, thereby limiting the effectiveness of applications such as robotics, localization, and tracking systems. The imprecise mappings generated by these methods lead to suboptimal performance and unreliable results. The proposed approach leverages WiFi sensing through channel state information (CSI), triangulation techniques, and a fine-tuning mechanism to enhance trajectory precision within indoor environment trajectory mapping. The proposed solution employs a domain adapter fine-tuning technique to enable location-independent tracking via CSI, minimizing errors. The use of CSI MIMO signals for trajectory mapping offers enhanced spatial resolution, robust multipath handling, and improved accuracy in tracking movement by leveraging multiple antenna channels and exploiting the rich information embedded in signal reflections and scattering, while triangulation aids in accurately determining the location of objects or targets. Furthermore, incorporating a fine-tuning mechanism refines the generated trajectories. The findings demonstrate substantial enhancements in mapping precision, with an accuracy of 95.5% in tracking 13 paths within the new domain. These results underscore the effectiveness of the proposed approach in overcoming the limitations of existing methods and achieving highly accurate trajectory mapping.

Performance evaluation of pre-equalized UVLC links over outdated lognormal turbulence channels

Underwater visible light communication (UVLC) is important for various underwater applications, including diver-to-diver information sharing, oil field exploration, port security, underwater surveillance systems, and environmental monitoring. However, it should be remembered that UVLC links are strongly affected by underwater optical turbulence (UOT). This may necessitate frequent adjustments in transmit power based on current channel state information (CSI) to mitigate fading effects. In some applications, such as diver-to-diver links, the quasi-static variations in the channel coefficient between transmission frames—attributable to the semi-fixed positions of the transmitting and/or receiving nodes—lead to practical implementations of the transmit power selection that may rely on outdated CSI. In this paper, we investigate the degradation in error rate performance caused by the use of outdated channel information in setting transmit power. Especially, we derive a closed-form expression for the bit error rate (BER) for a pre-equalized UVLC link over outdated lognormal turbulence channels. We verify the derived expression using Monte Carlo simulations.

SCANet: A lightweight deep learning network for massive MIMO CSI feedback based on spatial and channel attention mechanism

Multi-user massive multiple-input multiple-output (MIMO) communication systems consume too much downlink bandwidth due to the huge channel state information (CSI) feedback, deep learning-based CSI feedback approaches fortunately can alleviate the feedback overhead while obtaining an accurate CSI. However, there is a trade-off between the high feedback performance and low computational complexity. In this paper, a low-complexity CSI feedback approach is proposed based on spatial and channel attention mechanism, namely the Spatial and Channel Attention Network (SCANet). Specifically, the spatial and channel attention mechanism makes the network’s attention mainly focus on the specific spatial regions and key feature channels. We devise a serial architecture in the encoder that composes of Spatial and Channel Attention Block (SCAB) and Encoder Transformer Block. Moreover, we design a hybrid architecture in the decoder that composes of the CNNs Block and Decoder Transformer Block. These designs enable the network to effectively extract both global and local CSI features. Computer simulations in both the indoor and outdoor scenarios show that under the same system configurations, the proposed low-complexity SCANet achieves almost the same performance as the state-of-the-art network while reducing the computational complexity by 85.76% fewer floating-point operations per second (FLOPS) on average.

Channel Estimation for Deep Space Communications Under the Effect of Solar Scintillation

AbstractDuring probe‐to‐Earth superior conjunction, deep space communication channels will suffer from solar scintillation, leading to amplitude attenuation of received signals. This study aims to obtain the channel state information (CSI) on deep space channels affected by solar scintillation. Classical least squares (LS) and minimum mean squared error (MMSE) methods are adopted to perform channel estimation and compensate for the channel fading. Simulation results indicate that under the effect of solar scintillation, performing channel estimation technology can significantly improve bit error rate (BER) performance compared to systems without CSI, and the MMSE algorithm outperforms the LS for both BER and normalized mean squared error (NMSE). In addition, we also find that pilot density, geometric parameters, and the outer scale of solar wind turbulence has great influence on the estimation performance.

Secrecy Performance Review for IoT-based Cognitive Farming System

Abstract: Smart farm management has complex challenges; therefore, there is a need to improve the overall adaptability, profitability, and environmental soundness of the farming systems. Agricultural production can be enhanced with the help of IoT-based cognitive tools. The rapid increase in IoT-based smart systems has raised critical concerns about efficiently utilizing the wireless spectrum. To drastically bring the improvement in the utilization of the wireless spectrum, cognitive radio (CR) has emerged as an effective solution. The inherent openness and broadcast nature of the wireless medium have given rise to many security issues in cooperative networks. Informationtheoretic security or physical layer security (PLS) is evolved as an efficacious wireless prototype for reliable transmissions to avert the eavesdropper from expropriating transmittal data on the wireless connections. Security is the key factor in these networks as they are exposed and active in behavior, thus, are susceptible to fraudulent activities. We investigated the performance of the CR system in terms of secrecy and also examined the system in terms of diversity receipt and selection of relay. The secrecy outcome of channel state information at the sender was also studied. The detrimental effects of fading in cooperative networks were also extensively discussed. Cooperative diversity was examined by deploying MRC and SC strategies at receptors. It has been deduced that CSI knowledge at the sender also has an eloquent concussion on the secrecy of the cognitive system.

A Deep Learning-based Approach for Channel Estimation in Multi-access Multi-antenna Systems

This paper studies estimating the channel state information at the end of receiver (CSIR) for multiple transmitters communicating with only one receiver so that the latter can decode the incoming signal more efficiently. The transmitters and the receiver are all equipped with multi-antennas and using orthogonal space-time block codes (OSTBC). An algorithm is developed based on deep learning for estimating multi-user multiple-input multiple-output (MU-MIMO) channels. The algorithm could estimate the CSIR using a single pilot block. The proposed convolutional neural network (CNN) architecture designed for this task begins with an input layer that accepts grayscale images, followed by six convolutional blocks for feature extraction and processing. The network concludes with a fully connected layer to output the estimated channel information. It is trained using a regression loss function to map input images to accurate channel information accurately. The performance of the proposed method is compared with classical methods like least square and subspace-based methods, including Capon and rank revealing QR (RRQR) methods. CNN achieved better performance in comparison with the reference. Computer simulations are included to validate the proposed method.

Joint Power Allocation and Hybrid Beamforming for Cell-Free mmWave Multiple-Input Multiple-Output with Statistical Channel State Information.

Cell-free millimeter wave (mmWave) multiple-input multiple-output (MIMO) can effectively overcome the shadow fading effect and provide macro gain to boost the throughput of communication networks. Nevertheless, the majority of the existing studies have overlooked the user-centric characteristics and practical fronthaul capacity limitations. To solve these practical problems, we introduce a resource allocation scheme using statistical channel state information (CSI) for uplink user-centric cell-free mmWave MIMO system. The hybrid beamforming (HBF) architecture is deployed at each access point (AP), while the central processing unit (CPU) only combines the received signals by the large-scale fading decoding (LSFD) method. We further frame the issue of maximizing sum-rate subject to the fronthaul capacity constraint and minimum rate constraint. Based on the alternating optimization (AO) and fractional programming method, we present an algorithm aimed at optimizing the users' transmit power for the power allocation (PA) subproblem. Then, an algorithm relying on the majorization-minimization (MM) method is given for the HBF subproblem, which jointly optimizes the HBF and the LSFD coefficients.

Key Synchronization Method Based on Negative Databases and Physical Channel State Characteristics of Wireless Sensor Network.

Due to their inherent openness, wireless sensor networks (WSNs) are vulnerable to eavesdropping attacks. Addressing the issue of secure Internet Key Exchange (IKE) in the absence of reliable third parties like CA/PKI (Certificate Authority/Public Key Infrastructure) in WSNs, a novel key synchronization method named NDPCS-KS is proposed in the paper. Firstly, through an initial negotiation process, both ends of the main channels generate the same initial key seeds using the Channel State Information (CSI). Subsequently, negotiation keys and a negative database (NDB) are synchronously generated at the two ends based on the initial key seeds. Then, in a second-negotiation process, the NDB is employed to filter the negotiation keys to obtain the keys for encryption. NDPCS-KS reduced the risk of information leakage, since the keys are not directly transmitted over the network, and the eavesdroppers cannot acquire the initial key seeds because of the physical isolation of their eavesdropping channels and the main channels. Furthermore, due to the NP-hard problem of reversing the NDB, even if an attacker obtains the NDB, deducing the initial key seeds is computationally infeasible. Therefore, it becomes exceedingly difficult for attackers to generate legitimate encryption keys without the NDB or initial key seeds. Moreover, a lightweight anti-replay and identity verification mechanism is designed to deal with replay attacks or forgery attacks. Experimental results show that NDPCS-KS has less time overhead and stronger randomness in key generation compared with other methods, and it can effectively counter replay, forgery, and tampering attacks.

Channel estimation for Massive MIMO systems aided by intelligent reflecting surface using semi-super resolution GAN

Intelligent Reflecting Surfaces (IRSs) coupled with Massive Multiple-Input-Multiple-Output (MIMO) millimeter wave (mmWave) systems hold immense promise for the next generation of wireless communications. However, harnessing their full potential requires accurate channel state information (CSI). Despite the benefits of IRSs, such as passive element integration and energy efficiency, precise channel estimation becomes a formidable challenge due to the absence of active elements. In this paper, we tackle these challenges by employing generative adversarial networks (GANs) to estimate the channel’s cascade matrix between the base station (BS) and mobile users. To leverage the high correlation among adjacent elements in the IRS, we propose turning off a majority of these elements during the estimation phase, effectively creating a low-resolution channel. We then introduce the semi-super resolution GAN (SSRGAN) model, capable of inferring channel values for the deactivated elements based on existing correlations. Our new SSRGAN-based channel estimation method transforms low-resolution channel data into high-resolution channel data. Through a comprehensive comparative analysis, our study showcases the superior performance of our SSRGAN channel estimation method compared to established benchmark schemes.

Wi-Crack: a smartphone keystroke recognition system based on multi-dimensional information

Abstract With the popularity of mobile payment, password protection has become more and more important. Channel state information (CSI) has recently been used to crack passwords in public WiFi environments. The feasibility of password cracking lies in the fact that different keystrokes lead to different finger movement directions and distances, resulting in unique interference to WiFi signal transmission. The unique interference can be recorded by CSI and used for keystroke inference. In this paper, we propose Wi-Crack, a keystroke recognition system for numerical keypad input on smartphones. Two computers equipped with commercial off-the-shelf WiFi NIC comprise the system, with one serving as the transmitter and the other as the receiver. Previous keystroke recognition systems only used the amplitude of CSI for keystroke recognition. Different from them, Wi-Crack combines the amplitude, phase, amplitude difference and phase difference of CSI for keystroke recognition. The use of multi-dimensional information has enabled Wi-Crack to improve keystroke recognition. Experimental results show that Wi-Crack improves the accuracy of keystroke segmentation to consistently above 90%. It also improves the keystroke recognition accuracy on DTW-KNN, SVM, 1D-CNN, and LSTM with over 90% in the best case.

Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks

The use of satellites to cover remote areas is a promising approach for increasing communication availability and reliability. The satellite resources, however, can be quite costly, and developing ways to optimize their usage is of great interest. Optimizing spectral efficiency while keeping the transmission error rate above a certain threshold represents one of the crucial aspects of resource optimization. This paper provides a novel strategy for adaptive coding and modulation (ACM) employment in land mobile satellite networks. The proposed solution incorporates machine learning techniques to predict channel state information and subsequently increase the overall spectral efficiency of the network. The Digital Video Broadcasting Satellite Second Generation (DVB-S2X) satellite protocol is considered as the use case, and by using the developed channel simulator, this paper performs an evaluation of the proposed machine learning solutions for channels with various characteristics, with a total of 90 different observed channels. The results show that a convolutional neural network with a modified loss function consistently achieves an improvement (over 100% in some scenarios) of spectral efficiency compared to the state-of-the-art ACM implementation while keeping the transmission error rate under 0.01 for single channel evaluation. When observing two channels, an improvement of more than 300% compared to the outdated information spectral efficiency was obtained in multiple scenarios, showing the effectiveness of the proposed approach and allowing optimization of the handover strategy in satellite networks that allow user-centric handover executions.