Channel State Information Research Articles

A statistical sensing method by utilizing Wi-Fi CSI subcarriers: Empirical study and performance enhancement

In modern Wi-Fi systems, channel state information (CSI) serves as a foundational support for various sensing applications. Currently, existing CSI-based techniques exhibit limitations in terms of environmental adaptability. As such, optimizing the utilization of subcarrier CSI stands as a critical avenue for enhancing sensing performance. Within the OFDM communication framework, this work derives sensing outcomes for both detection and estimation by harnessing the CSI from every individual measured subcarrier, subsequently consolidating these outcomes. When contrasted against results derived from CSI based on specific extraction protocols or those obtained through weighted summation, the methodology introduced in this study offers substantial improvements in CSI-based detection and estimation performance. This approach not only underscores the significance but also serves as a robust exemplar for the comprehensive application of CSI.

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%.

Manchester Return-to-Zero On–Off Keying Modulation for Free-Space Optical Communication

This paper proposes a Manchester return-to-zero on–off keying (M-RZ-OOK) modulation for free-space optical (FSO) communication. M-RZ-OOK modulation is achieved by introducing Manchester coding into the RZ-OOK format. M-RZ-OOK has the features of phase-flipped impulse series in the spectrum. Therefore, normal and inversed channel state information (CSI) can be extracted by applying a local oscillator (LO) with the frequencies of impulses, respectively. These extracted CSIs can be applied to realize adaptive threshold decision (ATD) and adaptive power transmission (APT) in the forward and backward links simultaneously. The proposed M-RZ-OOK modulation was verified in simulations using various turbulence channels. The simulation results demonstrated that ATD and APT were effectively accomplished in the forward and backward links with the estimated normal and inversed CSIs.

Performance Prediction of Power Beacon-Aided Wireless Sensor-Powered Non-Orthogonal Multiple-Access Internet-of-Things Networks under Imperfect Channel State Information

In this paper, we investigate a novel power beacon (PB)-aided wireless sensor-powered non-orthogonal multiple-access (NOMA) Internet-of-Things (IoT) network under imperfect channel state information (CSI). Furthermore, the exact expression outage probability (OP) of two IoT users is derived to analyze the performance of the considered network. To give further insight, the expression asymptotic OP and diversity order are also expressed when the transmit power at the PB goes to infinity. Furthermore, a deep neural network (DNN) framework is proposed to concurrently forecast IoT users’ OP in relation to real-time setups for IoT users. Additionally, when compared to the traditional analysis, our created DNN shows the shortest run-time prediction, and the outcomes predicted by the DNN model almost match those of the simulation. In addition, numerical results validate our analysis, simulation, and prediction through a Monte Carlo Simulation. Furthermore, the results show the impact of the main parameter on our proposed system. Finally, these findings show that NOMA performs better than the conventional orthogonal multiple-access (OMA) techniques.

Statistical spatial information based power optimization algorithm for multi-user massive MIMO systems

Power optimization has a significant role in multi-user massive multiple-input and multiple-output (mMIMO) wireless communication systems, whereas the acquisition of instantaneous channel state information (I-CSI) is a serious challenge in practical scenarios. The statistical spatial information (SPI), which contains angle-of-departures (AoDs) and corresponding large-scale fading factors, is slowly varying. The power optimization based on SPI to improve system performance needs to be explored for multi-user mMIMO systems. In this paper, two novel algorithms for SPI-based power optimization are proposed to maximize the ergodic sum-rate (ESR) under total power constraint for the system. Specifically, the minorization-maximization-based power optimization (MM-PO) algorithm and the closed-form power optimization (CF-PO) algorithm are proposed utilizing the minorization-maximization (MM) method and the fractional programming (FP) method, respectively. Moreover, the ESR is expressed based on the extracted SPI. The approximate expression of the ESR is derived. The complexity of the algorithms depends on the number of users, and the algorithms are scalable in the system. The proposed algorithms can effectively achieve the advantages of SPI while being robust to the uncertainty of I-CSI. Simulation results verify the ESR of our two proposed algorithms is 1.47 and 1.4 times higher than that of the equal power allocation algorithm at a high SNR when the number of antenna and user are equal to 64 and 20, respectively.

EC-MAC: Energy-Aware Cooperative MAC Protocol in Wireless Sensor Network

Nowadays, cooperative communication algorithms have been utilized in Wireless Sensor Network (WSN) to enhance the overall network performance. As is well known, a WSN needs to consider a number of important factors, such as the energy effectiveness and the longevity of the sensor nodes. The energy-aware cooperative medium access control (EC-MAC) protocol is a novel protocol proposed in this paper for usage in WSNs. EC-MAC protocol allows the source nodes to use the intermediate nodes as relays that can be used to transmit the source's data to the access point (AP). This paper demonstrates how the suggested relay selection method can be used by the EC-MAC protocol to choose the best relay node. After channel state information (CSI) has been calculated and acquired, the best relay should have the highest residual energy and the quickest transmission time. Then, by establishing suitable cooperative links, data transmission from a source node to an AP can be carried out. The effectiveness of the EC-MAC protocol in terms of system energy efficiency is examined in this study using the MATLAB simulation tool and compares the outcomes with other cooperative protocols like Modified Cooperative Access MAC Protocol (MCA-MAC) and Throughput and Energy aware Cooperative MAC Protocol (TEC-MAC) and the performance of WSNs employing the suggested EC-MAC protocol is examined in this research for both ideal and dynamic channel conditions. EC-MAC protocol achieved energy efficiency improvements of 20%, and 40% respectively, more than MCA-MAC and TEC-MAC protocols. The results indicated that EC-MAC protocol offers a higher level of energy efficiency for the WSN than other cooperative protocols currently in use.

Rate-Distortion-Perception Optimized Neural Speech Transmission System for High-Fidelity Semantic Communications.

We consider the problem of learned speech transmission. Existing methods have exploited joint source-channel coding (JSCC) to encode speech directly to transmitted symbols to improve the robustness over noisy channels. However, the fundamental limit of these methods is the failure of identification of content diversity across speech frames, leading to inefficient transmission. In this paper, we propose a novel neural speech transmission framework named NST. It can be optimized for superior rate-distortion-perception (RDP) performance toward the goal of high-fidelity semantic communication. Particularly, a learned entropy model assesses latent speech features to quantify the semantic content complexity, which facilitates the adaptive transmission rate allocation. NST enables a seamless integration of the source content with channel state information through variable-length joint source-channel coding, which maximizes the coding gain. Furthermore, we present a streaming variant of NST, which adopts causal coding based on sliding windows. Experimental results verify that NST outperforms existing speech transmission methods including separation-based and JSCC solutions in terms of RDP performance. Streaming NST achieves low-latency transmission with a slight quality degradation, which is tailored for real-time speech communication.

Critical Analysis of Data Leakage in WiFi CSI-Based Human Action Recognition Using CNNs.

WiFi Channel State Information (CSI)-based human action recognition using convolutional neural networks (CNNs) has emerged as a promising approach for non-intrusive activity monitoring. However, the integrity and reliability of the reported performance metrics are susceptible to data leakage, wherein information from the test set inadvertently influences the training process, leading to inflated accuracy rates. In this paper, we conduct a critical analysis of a notable IEEE Sensors Journal study on WiFi CSI-based human action recognition, uncovering instances of data leakage resulting from the absence of subject-based data partitioning. Empirical investigation corroborates the lack of exclusivity of individuals across dataset partitions, underscoring the importance of rigorous data management practices. Furthermore, we demonstrate that employing data partitioning with respect to humans results in significantly lower precision rates than the reported 99.9% precision, highlighting the exaggerated nature of the original findings. Such inflated results could potentially discourage other researchers and impede progress in the field by fostering a sense of complacency.

Beam pointing optimization for spatial diversity MIMO free space optical communications

In this paper, we propose a beam pointing algorithm to find optimal beam coordinates maximizing link performance in multiple-input multiple-output (MIMO) free space optical communications (FSOC) with spatial diversity. We first derive the received signal-to-noise ratio (SNR) with spatial diversity and analyze the convexity condition of SNR function with respect to transmit beam coordinates. Then, we obtain the beam coordinates maximizing the received SNR through an alternating optimization (AO) algorithm and Newton’s method. However, the proposed algorithm requires instantaneous channel state information (CSI), which requires a high signaling overhead in MIMO FSOC. Thus, instead of exploiting instantaneous CSI, we utilize statistical CSI and propose a robust beam pointing algorithm with respect to atmospheric turbulence and pointing error. Numerical results show that our proposed algorithm outperforms conventional schemes in a dynamic FSOC network. Furthermore, we confirm that the optimal coordinates which are robust to the channel conditions approach to the centroid of receiver lenses as the strength of turbulence and pointing error increases.

Optimal Decoding Order and Power Allocation for Sum Throughput Maximization in Downlink NOMA Systems.

In this paper, we consider a downlink non-orthogonal multiple access (NOMA) system over Nakagami-m channels. The single-antenna base station serves two single-antenna NOMA users based on statistical channel state information (CSI). We derive the closed-form expression of the exact outage probability under a given decoding order, and we also deduce the asymptotic outage probability and diversity order in a high-SNR regime. Then, we analyze all the possible power allocation ranges and theoretically prove the optimal power allocation range under the corresponding decoding order. The demarcation points of the optimal power allocation ranges are affected by target data rates and total power, without an effect from the CSI. In particular, the values of the demarcation points are proportional to the total power. Furthermore, we formulate a joint decoding order and power allocation optimization problem to maximize the sum throughput, which is solved by efficiently searching in our obtained optimal power allocation ranges. Finally, Monte Carlo simulations are conducted to confirm the accuracy of our derived exact outage probability. Numerical results show the accuracy of our deduced demarcation points of the optimal power allocation ranges. And the optimal decoding order is not constant at different total transmit power levels.

Developing Laboratory Experiments to teach Undergraduate Engineering Students Multi-User Massive MIMO Technology for 5G Cellular Networks

An effective method of facilitating student learning in a laboratory environment is “practice by doing”. The study of modules related to signal-processing for digital communications requires deep mathematical and theoretical foundations but the practice goal is not really emphasized in the undergraduate curriculum at the University of Mauritius. This causes the students to lose interest in the corresponding modules resulting in high rate of failures. In this work, we propose to develop laboratory experiments with a view to bridge the gap between theoretical and practical aspects in the field of Massive Multiple-Input Multiple-Output (MIMO) systems for 5G cellular networks. Various laboratory scenarios are set up that consider a Maximum Ratio Combining (MRC) receiver in the uplink with an uncorrelated Rayleigh fading channel. Moreover, from a signal processing perspective to enhance the student’s understanding, we analyze the efficiency and error performance of Massive MIMO systems with MPSK and MQAM modulation schemes as well as perfect and imperfect channel estimates. The leading industry software package MATLAB R2022 is used to develop all laboratory experiments and the codes are elaborated in this analysis. Data collected from the experiments are used to generate spectral efficiency and error performance curves which can be used for future research. The findings underscore the significance of accounting for both scenarios and illuminate promising avenues for future research in the realm of massive MIMO education and learning. The assimilation of MATLAB® flowcharts for each MRC receiver, MPSK, and MQAM with perfect and imperfect Channel State Information (CSI) adds further depth to the study, ensuring a comprehensive understanding of the intricacies of massive MIMO systems. Ultimately, this contribution helps in the nurturing of expertise such that future generations of wireless communication pioneers can be inspired.

WiFi-CSI Difference Paradigm

Passive tracking plays a fundamental role in numerous applications such as elderly care, security surveillance, and smart home. To utilize ubiquitous WiFi signals for passive tracking, the Doppler speed extracted from WiFi CSI (Channel State Information) is the key information. Despite the progress made, existing approaches still require a large number of samples to achieve accurate Doppler speed estimation. To enable WiFi sensing with minimum amount of interference on WiFi communication, accurate Doppler speed estimation with fewer CSI samples is crucial. To achieve this, we build a passive WiFi tracking system which employs a novel CSI difference paradigm instead of CSI for Doppler speed estimation. In this paper, we provide the first deep dive into the potential of CSI difference for fine-grained Doppler speed estimation. Theoretically, our new design allows us to estimate Doppler speed with just three samples. While conventional methods only adopt phase information for Doppler estimation, we creatively fuse both phase and amplitude information to improve Doppler estimation accuracy. Extensive experiments show that our solution outperforms the state-of-the-art approaches, achieving higher accuracy with fewer CSI samples. Based on this proposed WiFi-CSI difference paradigm, we build a prototype passive tracking system which can accurately track a person with a median error lower than 34 cm, achieving similar accuracy compared to the state-of-the-art systems, while significantly reducing the required number of samples to only 5%.

Adaptive-Duplex Relay Selection for Cooperative Non-Orthogonal Multiple Access Networks

To further improve the spectral efficiency of the cooperative non-orthogonalmultiple access (NOMA) network with half-duplex (HD) relaying and avoid the zero diversity gain due to the imperfect self interference estimation at high signa-to-noise ratio (SNR) value with full-duplex (FD) relaying, this paper developed a novel two-stage relay selection for a two-user multi-relay cooperative NOMA systems with adaptive-duplex (AD) relaying which can switch opportunistically between HD and FD relaying modes. An adaptive duplexbased on a fixed power allocation factor relay selection (AD-FPA-RS) scheme for cooperative NOMA networks is proposed in this paper, the relay can switch the work mode to the FD or HD state adaptively according to the current channel state information and the result of decode at the relay. Furthermore, the exact outage probability for the proposed AD-FPA-RS scheme is obtained in a closed-form expression. The simulation and numerical results show that the proposed AD-FPA-RS scheme not only outperforms the existing HD-FPARS and FD-FPA-RS schemes in terms of outage behavior significantly but also can obtain full diversity gain without the floor outage performance at high SNR region.

Predicted position-driven deep learning channel estimation for massive MIMO systems

Multiple-input multiple-output (MIMO) can significantly improve the energy efficiency and spectral efficiency of wireless communication systems, and obtaining accurate channel state information is a key prerequisite. However, traditional channel estimation techniques often have high computational complexity, poor channel estimation performance, and do not fully utilize the information in the communication scene. In this paper, a channel estimator (PrePD-CNN) with convolutional neural network (CNN) driven by predicted position information is proposed based on high-precision localization technology for 6G. The channel estimation performance is improved by rationally transforming the sensing information and accurately reconstructing the quasi-channel state as auxiliary information. Simulation results show that the BER performance and MSE performance of PrePD-CNN are significantly lower than that of traditional channel estimation algorithms. Therefore, the proposed scheme combines stronger robustness and good generalization, which not only reduces the complexity of the algorithm but also improves the performance.

Improving Throughput and Sum Secrecy Rate in Wireless Networks Against Illegal Intelligent Reflecting Surface (IIRS) Based Jamming Attacks

Illegal intelligent reflecting surfaces (I-IRSs), i.e., the illegal deployment and utilization of IRSs, impose serious harmful impacts on wireless networks. The existing I-IRS-based illegal jammer (IJ) requires channel state information (CSI) or extra power or both, and therefore, the I-IRS-based IJ seems to be difficult to implement in practical wireless networks. Due to continued sharing of resources, wireless networks often come under security attacks, most common of which are eavesdropping attacks. In the case of eavesdropping attacks, deliberately designed random eavesdropping data is added to the channel. These eavesdropping along with noise result in packet losses and low throughput, degrading the overall performance of the cognitive network. In this work, a security aware jamming and eavesdropping rejection mechanism is proposed which detects suspicious signals in the channel frequency response and employs discrete equalization to recover transmitted data. The proposed approach uses a chaos based FFH protocol for possible adversarial eavesdropping attacks. The error rate and average sum rate show that the proposed system outperforms the exiting systems in terms of the performance metrics. Keywords:- Illegal Intelligent Reflecting Surface (IIRS), Jamming Attack, Packet Intercept. Error Rate, Sum Rate

TomFi: Small Object Tracking Using Commodity WiFi

Rodent infestation has always been one of the most severe threats to humans, which to solve consumes massive manpower and resources. People usually use traps or poisons to treat rat infestation. Such passive countermeasures are inefficient. Damage often occurs when the rats are finally trapped or killed, not to mention the potential risk to injure humans or cause pollution. In this article, we propose a WiFi-based active small object tracking system named TomFi . The core components of TomFi include several deep learning techniques that bridge rat locations/motions and WiFi signal variations (represented by the channel state information). TomFi first employs a detection model to detect the appearance of the rat and then localize it based on a two-branch localization model. Through the design of the two-branch localization model, to our best knowledge, we are the first to solve the information loss and distortion problem. We conducted extensive experiments in both the laboratory environment and real-world kitchen scenarios. The results show that TomFi can achieve a detection success rate of 99%+ and a centimeter-level localization accuracy in real time.

Wave-CapNet: A Wavelet Neuron-based Wi-Fi Sensing Model for Human Identification

Gait is regarded as a unique feature for identifying people, and gait recognition is the basis of various customized services of the IoT. Unlike traditional techniques for identifying people, the Wi-Fi-based technique is unconstrained by illumination conditions and such that it eliminates the need for dense, specialized sensors and wearable devices. Although deep learning-based sensing models are conducive to the development of Wi-Fi-based identification, the latter technique relies on a large amount of data and requires a long training time, where this limits the scope of its use for identifying people. In this study, we propose a Wi-Fi sensing model called Wave-CapNet for human identification. We use data processing to eliminate errors in the raw data so that the model can extract the characteristics in channel state information (CSI). We also design a dedicated adaptive wavelet neural network to extract representative features from Wi-Fi signals with only a few epochs of training and a small number of parameters. Experiments show that it can identify human gait with an average accuracy of 99%. Moreover, it can achieve an average accuracy of 95% by using only 10% of the data and fewer than five epochs and outperforms state-of-the-art (SOTA) methods.

Outdoor-to-Indoor mmWave Relaying with Massive MIMO: Impact of Imperfect Channel Estimation

Assuming incomplete knowledge of the channel state information (CSI), we investigate two scenarios involving millimeter wave (mmWave) relaying to support outdoor-to-indoor communications. We proceed to derive the average signal-to-noise ratio (SNR) expressions for two relaying scenarios and quantify the asymptotic SNR. The performance of the two relaying scenarios is evaluated using the outage probability—for which we have derived closed-form equations—the end-to-end channel capacity, and the energy efficiency. The obtained results are compared with those derived assuming complete knowledge of the CSI. The effect of the imperfect CSI is therefore assessed in relation to the reference of perfect CSI. In these scenarios, an outside base station (BS) in an urban cellular network serves several indoor users. In the context of a two-hop full-duplex (FD) relaying scheme, we initially suggest a method in which the base station (BS) utilizes zero-forcing (ZF) precoding, and we take into account the overall channel response. Furthermore, we make the assumption that the base station (BS) engages in precoding only depending on the response of the channel in the first hop; in this second design, the relay precodes (using the response of the second-hop channel), amplifies, and sends the signals. Both techniques utilize massive multiple-input–multiple-output (mMIMO) arrays to permit transmission. We also present Monte Carlo simulation results to assess the accuracy of our analytical results. Finally, the two systems are compared in terms of channel estimation and precoding complexity, the number of antennas, as well as the number of users. Practical deployment recommendations are formulated at the end of this work.

Analysis of the outage performance of energy-harvesting cooperative-NOMA system with relay selection methods

Recent years have witnessed the remarkable progress in wireless communication systems due to the escalating demand for higher data rates, improved reliability, and increased energy efficiency. In this regard, Non-Orthogonal Multiple Access (NOMA) has emerged as a promising technology, enhancing spectral efficiency and accommodating multiple users concurrently within the same time and frequency resources. Simultaneously, the energy harvesting has surfaced as a sustainable solution, converting ambient environmental energy into usable electrical power for operating communication nodes. This paper proposes a cooperative NOMA transmission scheme integrating energy harvesting and utilizing Least Squares (LS) channel estimation for precise Channel State Information (CSI) acquisition. The objective is to establish an optimal communication path from source to destination. Relay selection methods: Optimal Relay Selection (ORS) and Max-Min Relay Selection (MMRS), are compared, focusing on their impact on the system performance. The analysis considers the influence of the number of relays and power allocation factor on the system, with a specific emphasis on the outage probability expressions. Comparative analysis between the cooperative-NOMA and the traditional cooperative relaying without NOMA reveals the superior performance of the cooperative-NOMA. Additionally, the ORS scheme outperforms MMRS in terms of the outage performance.

Non-contact multimodal indoor human monitoring systems: A survey

Indoor human monitoring systems are integral in various applications. They leverage a wide range of sensors, including cameras, radio devices, and inertial measurement units, to collect extensive data from users and the environment. These sensors contribute distinct data modalities, encompassing video feeds from cameras, received signal strength indicators and channel state information from WiFi devices, and three-axis acceleration data from accelerometers. In this context, we present a comprehensive survey of multimodal approaches applied to indoor human monitoring systems, with a specific focus on their relevance in elderly care. Our survey primarily highlights non-contact technologies, particularly cameras and radio devices, as key components in the development of indoor human monitoring systems. Throughout this article, we explore well-established techniques for extracting features from multimodal data sources. Our exploration extends to methodologies for fusing these features and harnessing multiple modalities to improve the accuracy and robustness of machine learning models. Furthermore, we conduct comparative analysis across different data modalities in diverse human monitoring tasks and undertake a comprehensive examination of existing multimodal datasets. This extensive survey not only highlights the significance of indoor human monitoring systems but also emphasizes their versatile applications. In particular, we emphasize their critical role in enhancing the quality of elderly care, offering valuable insights into the development of non-contact monitoring solutions tailored to the needs of aging populations.