Wireless Fading Channels Research Articles

Radix-Partition-Based Over-the-Air Aggregation and Low-Complexity State Estimation for IoT Systems Over Wireless Fading Channels

We consider remote state estimation for an internet-of-things (IoT) system, where a number of distributed IoT sensors monitor a dynamic plant and deliver the state measurements to a remote state estimator over an unreliable wireless network. We propose novel radix-partition-based over-the-air aggregation to coordinate the transmissions of the IoT sensors, which strongly enhances the spectral efficiency of radio resources and enables a smaller state estimation mean square error (MSE) at the remote state estimator, taking into consideration the quantization effect and radio resource allocation for the IoT sensors. We consider a fixed-filtering design at the remote state estimator, which significantly reduces the signal processing overhead at the remote state estimator compared to using the conventional Kalman-filtering-based state estimation approach. We show that the proposed low-complexity state estimation scheme enables state estimation stability via the linear matrix inequality (LMI) technique for on-off fading channels and via Lyapunov stability analysis for random fading channels. Based on this, we further propose efficient algorithms for the offline design of the filtering gain, connection topology and radix representation for IoT systems. Numerical results show that the proposed scheme has superior scalability performance and can achieve a smaller state estimation MSE compared to various state-of-the-art baselines.

Research on 5G Video Security Communication Technology Based on Massive MIMO

Background: The paper aimed at studying a large number of classified and sensitive information transmission security issues brought by 5G mobile communication system. Methods: In this paper, the principle of beamforming and its shortcomings are introduced by combining the large-scale MIMO technology and directional modulation technology, and the directional modulation technology based on antenna subset selection and the hybrid array is introduced to realize the safe transmission target. The results show that large-scale MIMO technology has become a key technology of 5G mobile communication, but the factors such as multipath fading in the actual wireless channel have not been fully considered, and the accurate estimation error of channel state information (CSI) will affect the ideal security level it can achieve. Results: The combined application of massive MIMO technology and directional modulation technology in the 5G system brings security to its communication. Conclusion : Directional modulation technology based on signal modulation has become an important research point of the 5G system because it does not need to consider the eavesdropper's computing power and can achieve the purpose of safe transmission by disturbing the signal constellation in the unexpected direction.

Learning Rate Optimization for Federated Learning Exploiting Over-the-Air Computation

Federated learning (FL) as a promising edge-learning framework can effectively address the latency and privacy issues by featuring distributed learning at the devices and model aggregation in the central server. In order to enable efficient wireless data aggregation, over-the-air computation (AirComp) has recently been proposed and attracted immediate attention. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To combat this effect, the concept of dynamic learning rate (DLR) is proposed in this work. We begin our discussion by considering multiple-input-single-output (MISO) scenario, since the underlying optimization problem is convex and has closed-form solution. We then extend our studies to more general multiple-input-multiple-output (MIMO) case and an iterative method is derived. Extensive simulation results demonstrate the effectiveness of the proposed scheme in reducing the aggregate distortion and guaranteeing the testing accuracy using the MNIST and CIFAR10 datasets. In addition, we present the asymptotic analysis and give a near-optimal receive beamforming design solution in closed form, which is verified by numerical simulations.

Delay Analysis of Wireless Federated Learning Based on Saddle Point Approximation and Large Deviation Theory

Federated learning (FL) is a collaborative machine learning paradigm, which enables deep learning model training over a large volume of decentralized data residing in mobile devices without accessing clients’ private data. Driven by the ever increasing demand for model training of mobile applications or devices, a vast majority of FL tasks are implemented over wireless fading channels. Due to the time-varying nature of wireless channels, however, random delay occurs in both the uplink and downlink transmissions of FL. How to analyze the overall time consumption of a wireless FL task, or more specifically, a FL’s delay distribution, becomes a challenging but important open problem, especially for delay-sensitive model training. In this paper, we present a unified framework to calculate the approximate delay distributions of FL over arbitrary fading channels. Specifically, saddle point approximation, extreme value theory (EVT), and large deviation theory (LDT) are jointly exploited to find the approximate delay distribution along with its tail distribution, which characterizes the quality-of-service of a wireless FL system. Simulation results will demonstrate that our approximation method achieves a small approximation error, which vanishes with the increase of training accuracy.

Coal mine personnel positioning algorithm based on improved adaptive unscented Kalman filter with wireless channel fading and unknown noise statistics

This paper is concerned with the problem of personnel localization in the complex coal mine environment with wireless channel fading and unknown noise statistics. Considering the random channel fading caused by signal fluctuation and transmission fault, an improved adaptive unscented Kalman filter (IAUKF) algorithm is proposed. The mean and error covariances of noise are estimated adaptively by adopting the improved Sage–Husa noise estimation method. In order to save energy and improve energy utilization, the multi-sensor clustering is performed to divide the spatial distribution of sensors into multiple clusters. The sensors in the same cluster can communicate with each other to maintain the consistency of estimation. The simulation results show that the IAUKF algorithm is better than extended Kalman filter (EKF), unscented Kalman filter (UKF), and improved unscented Kalman filter (IUKF) algorithms.

Efficient Gradient Updating Strategies with Adaptive Power Allocation for Federated Learning over Wireless Backhaul.

In this paper, efficient gradient updating strategies are developed for the federated learning when distributed clients are connected to the server via a wireless backhaul link. Specifically, a common convolutional neural network (CNN) module is shared for all the distributed clients and it is trained through the federated learning over wireless backhaul connected to the main server. However, during the training phase, local gradients need to be transferred from multiple clients to the server over wireless backhaul link and can be distorted due to wireless channel fading. To overcome it, an efficient gradient updating method is proposed, in which the gradients are combined such that the effective SNR is maximized at the server. In addition, when the backhaul links for all clients have small channel gain simultaneously, the server may have severely distorted gradient vectors. Accordingly, we also propose a binary gradient updating strategy based on thresholding in which the round associated with all channels having small channel gains is excluded from federated learning. Because each client has limited transmission power, it is effective to allocate more power on the channel slots carrying specific important information, rather than allocating power equally to all channel resources (equivalently, slots). Accordingly, we also propose an adaptive power allocation method, in which each client allocates its transmit power proportionally to the magnitude of the gradient information. This is because, when training a deep learning model, the gradient elements with large values imply the large change of weight to decrease the loss function.

A Review of Transmission Rate over Wireless Fading Channels: Classifications, Applications, and Challenges

A Review of Transmission Rate over Wireless Fading Channels: Classifications, Applications, and Challenges

On multi-carrier systems robustness to Doppler in fast varying flat fading wireless channel

In this paper, the performance of a generalized multi-carrier Frequency Division Multiplexing (FDM) system - including Orthogonal Frequency Division Multiplexing (OFDM) and Filter-Bank Multi-Carrier (FBMC) - is analyzed in terms of normalized interference power in a fast-varying flat-fading downlink channel. The analysis is done assuming a conventional correlator-based receiver using three families of pulse shapes: orthogonal time-limited pulse shapes, orthogonal frequency-limited pulse shapes, and non-orthogonal pulse shapes. The analysis is done for the Rayleigh random channel with different Doppler spreads, and interference distribution over sub-carriers and symbols is analyzed per pulse shape. Interference reduction by reducing the bandwidth efficiency through turning off some sub-carriers and reducing symbol density is then analyzed. After that, the special ability of non-orthogonal pulse shapes to have more flexible density reduction is indexed and studied. The theoretical study, validated by Monte Carlo simulations, showed that for low, high, and extreme Doppler spreads, the appropriate pulse shapes (resp. system density) to be used are respectively time or frequency limited orthogonal pulse shapes (resp. 100%), short-tail frequency-limited pulse shapes (resp. 50%), and time and frequency limited non-orthogonal pulse shapes (resp. 25%).

Average Contiguous Duration—A Novel Metric for Characterizing Wireless Fading Channels

We define the average contiguous duration (ACD) of the received fading signal as the average time duration during which the signal envelope contiguously remains within a bounded amplitude interval. Also, the multi-interval ACD or L-ACD function is defined as the set of ACD values for L non-overlapping amplitude-intervals whose union spans the received envelope's amplitude range. We derive closed-form expressions of the ACD for Rayleigh, Rice, and Nakagami- m fading signals, which are widely analyzed in the literature. The derived expressions hold practical significance for several signal processing applications such as non-uniform quantization of channel samples for secret key generation (SKG) in physical layer security (PLS) techniques. The well-known channel fading metric of average fade duration (AFD) is shown as a special case of the proposed ACD metric and the proposed theoretical analysis is validated by simulations.

RNN-Based Learning of Nonlinear Dynamic System Using Wireless IIoT Networks

We consider the recurrent neural network (RNN)-based remote state estimation for nonlinear dynamic systems with unknown state dynamics. The nonlinear dynamic plant is monitored by multiple distributed IIoT sensors over a random access wireless network with shared common spectrum. We focus on the remote state estimation algorithm design so as to achieve remote state estimation stability subject to noninvertible nonlinear sensor state observations, imperfect channel state information (CSI) at the remote estimator, and various wireless impairments, such as multisensor interference, wireless fading, and additive channel noise. Utilizing a state diffeomorphism, the original system is transformed into a canonical form with a linear rank deficient observation matrix. We propose a novel RNN remote state estimator based on the pole placement design associated with the transformed rank deficient state measurement matrices. We further propose a novel online training algorithm such that the RNN at the remote estimator can not only address the divergence issue over wireless networks but also effectively learn the unknown nonlinear plant dynamics despite rank deficiency and imperfect CSI. Using the Lyapunov drift analysis approach, we establish closed-form sufficient requirements on the communication resources needed to achieve almost sure stability of both state estimation and RNN online training in the high signal-to-noise ratio (SNR) regime. As a result, our proposed scheme is asymptomatic optimal for large SNR in the sense that both the plant state and the unknown plant nonlinearity can be perfectly recovered at the remote estimator. The proposed scheme is also compared with various baselines and we show that significant performance gains can be achieved.

Measurement and analysis of fading characteristics of V2V propagation channel in two tunnels

In order to fulfill the requirement of vehicle-to-vehicle communication system for wireless channel fading characteristics and corresponding models, vehicle to vehicle(V2V) wireless channel measurement activities are carried out at the frequency bands of 5.9 GHz and 5.2 GHz in two kinds of tunnel scenes. And small-scale fading characteristics are analyzed for outside the tunnel, inside the tunnel and their junction part. Based on the close-in(CI) log-distance model and ABG (α-β-γ) model, the distance dependent received power models are established. The received power inside and outside the tunnel in the two scenarios are evaluated and compared, and the path loss index is 1.83 and 1.9, respectively. The results show that the CI model with a reference distance of 1 m has higher fitting degree. In addition, the fading distribution of measured data amplitude is compared with five typical theoretical fading distributions, and it is found that its characteristic is closer to the Rice distribution with the minimum goodness of fit value, and the Rice K-factor in the tunnel is smaller than that outside the tunnel. Further, distance-based K-factor models for the tunnel and the junction part are presented. We find that the K-factor at the conjunction area are independent of the distance, while the K-factor inside the tunnel decreases with the increase of the distance.

Measurement-Based Investigations of the NB-IoT Downlink Performance in Fading Channels

The letter presents experimental results of investigations on the narrowband Internet of Things (NB-IoT) performance under two predefined multipath channel models, related to the reflections-free scenario of an unobstructed signal path. The NB-IoT downlink channel performance was systematically tested for resilience to the multipath propagation phenomenon (naturally occurring in real-life environments of IoT devices deployment), with the most robust modulation-coding scheme enforced, i.e., quadrature phase shift keying (QPSK) modulation without repetitions. Each of these three radio fading channels was artificially generated in electromagnetically isolated laboratory facilities. The NB-IoT equipment setup, the fading simulator for emulating the fading channels as well as the infrastructure that allowed obtaining desirable maximum coupling loss (MCL) values, were described along with the presentation of the measurement scenarios. Analysis of results led to the formulation of practical takeaways regarding the reduction in the maximum pathloss of a fading-free channel, in response to fadings incurred on the NB-IoT downlink signal by Extended Pedestrian-A (EPA) and Extended Typical Urban (ETU) channels.

Mitigation Techniques for Puea In Cognitive Radio Networks Using Hypothesis Testing

This paper detects primary user emulation attacks in cognitive radio networks using Neyman-Pearson composite hypothesis test and Wald’s sequential probability ratio test. Most approaches in the literature on Primary user emulation attacks assume the presence of underlying sensor networks for localization of the malicious nodes. There are no analytical studies available in the literature to study Primary user emulation attacks in the presence of multiple malicious users in fading wireless environments. An NPCHT and WSPRT based analysis to detect Primary user emulation attack in fading wireless channels in the presence of multiple randomly located malicious users. It shows there is a range of network radii in which PUEA is most successful. The results also show that for the same desired threshold on the probability of missing the primary, WSPRT can achieve a successful probability PUEA 50% less than that obtained by NPCHT.

Wireless Channel Modeling Based on Machine Learning in the High-speed Railway Scenario

In the high-speed railway (HSR) scenario, there is a problem of poor generalization ability caused by the overfitting phenomenon through wireless channel modeling. In this paper, a precise modeling method based on machine learning is proposed to address this issue under the wireless channel of HSR. According to the K-means clustering algorithm, the typical values of the K-factor in a multitude of scenarios are obtained through Rician K-factor clustering in the wireless channel fading model. Founded on the theory of model evaluation and selection in machine learning, the measured values of path loss are fitted by least squares regression using the cross-validation method. Then, we obtain the formulas of the path loss models and the expected generalization errors of the models in different scenarios. The mathematical relationship of the ergodic capacity depending on the Rician K-factor, the signal-noise ratio of the receiver, and the path loss generalization error are established after the ergodic capacity analysis of the HSR wireless channel. Finally, the lower boundary curves of the ergodic capacity are obtained by simulation experiments in different scenarios.

Wireless fading channels performance based on Taylor expansion and compressed sensing: A comparative approach

SummaryThis paper addresses the performance of wireless fading channels, namely, Rayleigh, Rician, and Nakagami‐m fading channels, taking both noiseless and additive noisy cases into account. The performance metrics investigated are channel capacity, channel model variance estimation, required average description length based on Shannon entropy, and relative entropy and symbol error probability assessment. Taylor expansion is considered as an approximation for the aforementioned metrics together with the compressed sensing (CS) compressibility rule. Technical comments are provided for supporting simulation results. Mathematical interpretations are provided to support the simulation results along with feasible applications in 5G wireless systems. Conclusions and future research directions finalize the paper.

Image Transmission Using SC-FDMA System Over mmWave Measured Channel at 29.5 GHz

Aims: This paper presents an investigation of image transmission using SC-FDMA system over wireless fading channels. Background: A comparison is performed between the performance of the wireless transmitted image over 3GPP LTE channel (vehicular and pederstain) and mmWaves measured channel at 29.5 GHz. Objective: One of the important benefits of this work is the real measurement obtained at 29.5 GHz under NLOS scenario using an original sounder based on the ensuring of phase coherence between the transmitter and the receiver. Methods: Cameraman image has been transmitted over the SC-FDMA with different subcarriers mapping schemes. PSNR and MSE are the metrics used to evaluate the performance of the image transmitted under Matlab simulation where their values of the received image are calculated for different SNR values. Results and Conclusion: : The simulation results show that the image transmission over mmWaves channel works and the best values of PSNR are attained beyond 12 dB and 14 dB for LSC-FDMA and ISC-FDMA respectively.

Multi‐antenna joint covert communication with a public communication link over wireless fading channel

This paper studies multi-antenna covert communication exploiting a public communication link over the wireless fading channel. In particular, a relatively practical covert communication scenario is considered for the first time, where a warden with radiometer may improve its detection accuracy by jointly detecting uplink covert pilot and downlink covert communication. To this end, a joint covert communication system, jointly considering uplink covert pilot, imperfect channel state information issue and downlink covert communication, is proposed and further studied. Specifically, the detection performance of warden and the covertness performance of system are comprehensively studied, respectively. Afterwards, an optimization problem for covert capacity is formulated to maximize overall performance. Then, with our analysis and simplification, an optimal power allocation algorithm is designed for covert capacity accordingly. Our examination shows a win-win situation has been achieved in the proposed system, that is, covert communication would rarely affect the normal communication of public link and occupy its communication resources. Interestingly enough, we analytically find an invariance property of downlink covert communication that covertness performance is not impacted by noise levels or warden's position. Based on this extraordinary finding, we conclude that the downlink covert communication system can perfectly combat with a powerful warden adopting power trend test method, while the joint covert communication system may resist that through a pilot deception scheme.

Max-Consensus Over Fading Wireless Channels

This article deals with the problem of achieving finite-time max-consensus in a multiagent system that communicates over a fading wireless channel. A key feature of the wireless channel is the superposition (or broadcast) property. In traditional wireless communication systems, the superposition property is usually undesired since it might cause interference that drastically degrades system performance. In contrast, in the multiagent system considered in this article, different agents aim at achieving max-consensus. Therefore, rather than combatting interference due to the superposition property, we design a communication system that exploits this property for a more efficient usage of wireless resources. By simultaneously accessing the wireless channel, each agent obtains a weighted average of the neighboring agents' information states, where weights (namely, channel coefficients) are unknown and fading. Given that each agent has access to this piece of information, we present a switching consensus protocol employing broadcast authorization for agents and show that max-consensus can be achieved under this protocol within a finite number of iterations.

Average Contiguous Duration (ACD)-Based Quantization for Secret Key Generation in Generalized Gamma Fading Channels

The wireless channel-based Secret Key Generation (SKG) algorithms aim at securing the wireless link against unauthorized eavesdropping by exploiting the channel’s randomness for generating matching secret keys at the legitimate nodes for message encryption/decryption. To counter differences in hardware and noise conditions at the legitimate nodes, which can lead to key mismatch, the SKG algorithms typically include the intermediate steps of sampling, quantization, information reconciliation, and privacy amplification. These steps collectively aim to improve the performance trade-offs between Key Generation Rate (KGR), Key Agreement Probability (KAP), and Secret Key Randomness (SKR) properties. This paper derives a closed-form expression for the Average Contiguous Duration (ACD) of Generalized Gamma (GG) fading wireless channels. The ACD is a recently introduced novel quantifier for characterizing the second-order statistics of fading channels, which includes Average Fade Duration (AFD) as its special case. The proposed GG fading ACD expression is shown to include, as its special cases, the ACD for commonly observed fading distributions such as Gamma, Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> , and Rayleigh. By exploiting the derived GG ACD expression, a multi-level quantization scheme for SKG is proposed that determines suitable quantization intervals for identical likelihood of an equal number of consecutive channel samples falling in each quantization interval. A comprehensive comparative analysis of the proposed ACD-based quantization for SKG is conducted in relation to conventional Uniform Quantization (UQ) and Cumulative Distribution Function (CDF)-based Non-Uniform Quantization (NUQ) schemes. The presented numerical results confirm the superior performance trade-off between KGR and KAP offered by the proposed ACD-based quantization in relation to that offered by UQ and CDF-based NUQ.

A novel offloading strategy for high speed mobile and data intensive intelligent sensor

In the paper, it proposes a novel offloading computing strategy for the mobile intelligent sensors in the high speed mobile scene. At present, the local computing resources are not able to afford the computing demand for the data intensive intelligent sensors. The current offloading computing mechanism are not capable of offloading computing in high speed scene. In general, it may cause extremely serious delay by the handover delay and the wireless channel fading. In the paper, the proposed novel strategy is named asynchronous parallel offloading computing strategy (APOCS). It makes use of one type of spatial computing diversity method to restrain the wireless channel fading. The simulation results show that it is able to reduce the offloading delay in the high speed moving scene effectively.