This paper investigates the moving target localization problem in distributed multiple input multiple output (MIMO) systems in the presence of clock and frequency offset using a calibration target, and proposes two semidefinite programming (SDP) solutions based on time delay (TD) and Doppler shift (DS) measurements. For the first solution, the differences of TDs and DSs are constructed to eliminate the offsets via the pair-wise substraction. After that, a series of pseudo-linear equations are established to formulate a weighted least squares (WLS) problem only concerning target location parameters. Due to the non-convexity, semidefinite relaxation (SDR) is applied to transform the WLS problem into a convex SDP problem. Owing to the unavailability of the offset estimations in the first solution, for the second solution, closed-form estimations of the offsets are first derived and then substituted into the TD and DS measurement models of the moving target. Subsequently, another WLS problem with synchronization errors is proposed after pseudo-linearization, which is also relaxed into the corresponding SDP problem. Besides, theoretical analysis is conducted to demonstrate that the performance of the second solution is comparable with that of the first solution in terms of the target location parameter estimation accuracy. Simulation results validate the theoretical analysis and demonstrate that both solutions reach the Cramer-Rao lower bound (CRLB) performance under the mild noise condition.

As the aging population grows and more elderly individuals live independently, the demand for reliable, unobtrusive home health monitoring becomes increasingly important. Existing in-home health monitoring systems often face limitations such as privacy concerns, dependence on unreliable wearable devices, degraded accuracy in complex environments, and lack of continuous monitoring capability. To address these challenges, we propose a long-term home health monitoring system that primarily relies on audio sensing, supplemented by other noninvasive modalities. Our approach is able to accurately detect and recognize overlapping acoustic events with fine-grained temporal resolution, surpassing conventional audio-based methods for activity recognition. The system incorporates a transformer-based time-frequency fusion module and a category dynamic threshold strategy to improve detection performance under semi supervised conditions. Experiments on real-world dataset demonstrate that our method outperforms existing baselines, achieving PSDS$_{1}$, PSDS$_{2}$, and EB-F1 scores of 0.581, 0.930 and 55.1%, with improvements of 0.054, 0.019, and 2.3%, respectively. In addition, a 30 day field deployment involving 10 elderly participants confirms the robustness and practicality of the system for real-world applications. By allowing continuous passive monitoring of daily activities and abnormal acoustic events, our system has significant potentials for early detection of health risks, behavioral anomalies, and long-term wellness tracking in aging in place scenarios.

Severe Doppler shift and the obstruction of the Line-of-Sight (LoS) path significantly decrease the communication performance. This article considers a downlink channel estimation problem for reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) multiple-input-multiple-output nonorthogonal multiple access (MIMO-NOMA) system in Internet of Vehicles (IoV) with high-mobility scenarios. By introducing the concept of aggregated slot and half slot, a 5G subframe partitioning scheme without changing the standard 5G frame structure is first proposed to facilitate the formulation of the received signal. Then, the received signal is modeled as a quadrilinear tensor, meeting with a canonical polyadic decomposition (CPD) form, which separates Angle of Arrival (AoA), Angle of Departure (AoD), time delay, and Doppler shift into four corresponding factor matrices and avoids parameters coupling. Subsequently, by leveraging the Vandermonde structure of the factor matrix and the low-rank property of the mmWave channel, we design a four-dimension (4-D) structured tensor decomposition-based method to decompose the tensor into four factor matrices in a closed-form solution, which can avoid initialization and iteration. Accordingly, the channel parameters can be extracted by a simple correlation-based estimator. After obtaining the channel parameters, we construct a least squares problem to obtain the channel path gain, which can avoid solving the scaling matrix. Finally, numerical experiments are conducted to confirm the effectiveness of the proposed algorithm, in which the Cramér-Rao bound (CRB) results for channel parameters are derived as the benchmark.

This article addresses the localization problem of wireless sensor networks (WSNs) in complex non line-of-sight (NLOS) environments where multiple regions are separated by the blockages. Reconfigurable intelligent surface (RIS) and ultrawideband (UWB) technology are exploited to form a novel multiple anchors and RIS-aided localization (MARL) method, in which anchors and RISs are deployed in pairs in each separated region. The method consists of two stages: 1) a coarse stage and 2) a fine stage. In the coarse stage, each RIS is dynamically adjusted in accordance with predesigned conjugate orthogonal configuration sequence to extract the direct or reflected path components effectively from all anchors during each UWB pulse period. A coarse position of the target is then determined based on the time-delay estimation of the extracted path components, while NLOS bias is assumed to exist in each path. In this stage, the region where the target exists is also identified. In the fine stage, a reflection response vector estimation process is initiated by adjusting the RIS located in the identified region, which is used to formulate a minimum mean square error (MMSE) problem. The solution of the MMSE problem is calibrated with the path coefficient generated from the previously acquired coarse estimate. Then, a reflection vector that contains the true position of the target is obtained. Finally, an accurate estimate is achieved by using gradient descent search algorithm. Simulation results show that the proposed MARL method is a cost-effective scheme for target localization in NLOS environments of WSNs.

In this article, we address the time-of-arrival (TOA)-based source localization problem in mixed light-of-sight (LOS)/non-LOS (NLOS) environments, where localization accuracy is degraded by both measurement noise and NLOS error. A novel exponential optimization problem is formulated based on new minimum joint error entropy criteria and statistical characteristics of measurement noise. After that, a two-step relaxation method is proposed. In the first step, the original problem is relaxed into a nonexponential problem which maintains the consistency of the solution. The second step is to transform the nonexponential problem into a convex problem. Furthermore, we extend the method to asynchronous networks where the source and anchors are not time synchronized. Numerical results show that the proposed method can provide significant robust performance in synchronous or asynchronous networks, whether in sparse or dense NLOS scenarios.

This article develops a robust source localization method using time delay and Doppler shift measurements, where the sensor motion effect accompanied by sensor location errors cannot be ignored. We begin by transforming the time delay and Doppler shift measurement models into a series of nonlinear equations that take sensor location errors into account, and then construct a constrained weighted least squares (CWLS) problem based on these equations. Because of the nonconvex nature of the problem, we relax it into a semidefinite programming (SDP) problem via convex relaxation and further propose a scheme to eliminate the influence of the additional estimation bias caused by the approximation applied in the transformation of measurement models. To perform the bias reduction, we derive the theoretical expression of the solution bias and then subtract it to obtain a bias-reduced solution. We also derive the closed-form expression of the hybrid Cramer–Rao lower bound (HCRLB) as the performance benchmark. Theoretical analysis and simulation results demonstrate that the mean-square error (MSE) performance of the proposed method can achieve the HCRLB accuracy and the bias can be significantly reduced with bias reduction.

High-speed train (HST) generates strong and repeatable vibrations that could be used for subsurface imaging and monitoring. Compared with other ambient noise, HST vibrations are generated by a moving source and have striking characteristics as a deterministic source. However, little attention has been paid to the effects of the characteristics of HST sources on the seismic wave retrieval using seismic interferometry. The aim of this study is to investigate what types of seismic waves are retrievable by applying seismic interferometry to HST-induced vibrations. By analyzing the cross correlation of the HST-induced vibrations between two receivers, we find that cross terms are introduced during the cross correlation. These cross terms are nonnegligible for reflection-wave retrieval but can be negligible for the retrieval of direct waves, scattered waves, and refraction waves. This finding has been validated by field data tests. We further demonstrate that the retrieved surface waves can be used to estimate near-surface velocities and that the retrieved scattered surface waves can be used to locate near-surface heterogeneous bodies.

Applying the orthogonal matching pursuit (OMP) to estimate the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) channels is attractive because of its high estimation accuracy and low computational cost. However, most existing OMP‐based algorithms suffer the limited estimation accuracy in impulsive noise (IN) cases. Through the studies can be found, only part of channels’ estimation is affected due to the random IN which appears transient and intermittent in time and frequency. Based on this observation, joint time‐frequency OMP (JTF‐OMP) method is proposed, where the estimation of the affected channels benefits adaptively from that of adjacent channels in time or frequency. It is well known that preliminary Doppler scale estimation is key to the subsequent OMP algorithm, which is difficult to deal with due to the IN. To solve this problem, an adaptive Doppler scale estimation (ADSE) method is proposed. It involves generating two shorter identical cyclic prefixes (CPs) for each OFDM symbol, placed before two adjacent OFDM symbols. The repetition pattern can adaptively defend the IN which appears randomly and shortly in time. Simulation results show that the proposed algorithms integrating JTF‐OMP with ADSE can achieve much higher estimation accuracy and better system reliability than the OMP in the IN environment.

Environmental sound classification (ESC) is a challenging problem due to the complexity of sounds. To date, a variety of signal processing and machine learning techniques have been applied to ESC task, including matrix factorization, dictionary learning, wavelet filterbanks and deep neural networks. It is observed that features extracted from deeper networks tend to achieve higher performance than those extracted from shallow networks. However, in ESC task, only the deep convolutional neural networks (CNNs) which contain several layers are used and the residual networks are ignored, which lead to degradation in the performance. Meanwhile, a possible explanation for the limited exploration of CNNs and the difficulty to improve on simpler models is the relative scarcity of labeled data for ESC. In this paper, a residual network called EnvResNet for the ESC task is proposed. In addition, we propose to use audio data augmentation to overcome the problem of data scarcity. The experiments will be performed on the ESC-50 database. Combined with data augmentation, the proposed model outperforms baseline implementations relying on mel-frequency cepstral coefficients and achieves results comparable to other state-of-the-art approaches in terms of classification accuracy.

Since underwater acoustic channels are shared by multiple heterogeneous entities and can suffer from severe interference, underwater acoustic communication networks (UACNs) are faced with the challenge of mitigating interference and improving communication quality by implementing distributed resource allocation approaches. In this article, we introduce the concept of reinforced learning in intelligent control to the UACNs by treating the nodes as intelligent agents and the node networks as multiagent networks. By partitioning the state space and the action space, we formulate a reward function and a search strategy and propose a distributed resource allocation algorithm based on cooperative Q -Learning. In addition, we verify the convergence of the proposed algorithm. Finally, simulation results in two different underwater application scenarios show that the proposed algorithm outperforms the existing algorithms in improving the network transmission capacity, and can reduce the overhead of resource allocation by using cooperative Q -Learning.

Cognitive radio (CR) is a promising technology to address the challenge of spectrum scarcity due to the massive number of objects in the Internet of Things (IoT). Equipping IoT objects with CR capability can also alleviate interference situations and achieve seamless connectivity in IoT. This paper deals with CR spectrum sensing and proposes a new eigenvalue-based detector by exploiting the summation of multiple large eigenvalues of the covariance matrix of received signals. By analyzing the distribution of the sum of the dependent large eigenvalues, we derive an approximate but explicit expression for the theoretical performance of the proposed detector. The theoretical analysis of the proposed detector is validated and its superior performance is demonstrated with real world signals. It is shown that the proposed detector outperforms the existing eigenvalue-based detectors and is more robust against noise uncertainty.

Energy efficiency (EE) maximization problem for Cognitive Underwater Acoustic Network is investigated in this study. Available works on EE usually assume that spectrum sensing is accurate or that channel state information (CSI) is perfect, which is often impractical. Thus, an adaptive resource allocation scheme is proposed to maximize the EE, subject to the transmission power constraint of secondary user (SU) and the interference power constraint of primary user (PU). By taking the spectrum sensing errors into account, we add power interference from PU to SU in the objective function. Besides, interference tolerance factor is introduced to control the interference from SU to PU. Assuming CSI uncertainties of the involved channels are bounded, they are separately modeled as stochastic-case or worst-case according to their nature. Since the established optimization problem is nonconvex, it is converted into a convex one and then solved by the techniques of fractional programming and dual decomposition. Simulation results validate that the EE can be improved by classifying the CSI uncertainties and solving the expectation of the CSI correlation function. Furthermore, the interference from SU to PU can be controlled well by the adjustment of the interference tolerance factor.

The following topics are dealt with: cellular radio; probability; wireless channels; radiofrequency interference; mobile radio; MIMO communication; error statistics; telecommunication traffic; Long Term Evolution; quality of service.

This work presents a blind detector for sensing orthogonal frequency division multiplexing (OFDM) signals under multiple fading channels. The detector exploits the fact that the AWGN at secondary receivers has a flat power spectral density, while the received OFDM signal under multipath fading channels does not. Closed form expression of the decision threshold is provided. It is shown that the decision threshold is independent of the number of samples. In addition, the proposed detector is robust against frequency offsets. Numerical results demonstrate the superior performance of the proposed detector.

Accurate channel state information is the foundation of the receiver design for power line communication (PLC) system. However, the presence of impulsive noise makes the problem more complex. As traditional impulsive noise mitigation methods are based on the assumption that the channel state information is known in advance, which limits their applications. In this paper, by exploiting the parametric sparsity, we propose a joint channel and impulsive noise estimation scheme based on compressed sensing theory for narrowband PLC systems. Observing the strong coherence of measurements matrix, we adopt the sparse Bayesian learning (SBL) to solve the compressed sensing problem. Simulation results verify our proposed algorithm has smaller mean square error and bit error rate.

In this paper, we investigate the cooperative localization problem in non-line-of-sight (NLOS) environments using time-of-arrival measurements. We propose a novel robust localization method that is robust against the effect of NLOS errors on the localization performance. The proposed method only requires knowledge of the upper bound on the magnitude of the NLOS errors rather than their statistics, which are more difficult to obtain in practice. Our method is shown to perform better than the existing non-robust method especially in severe NLOS environments.

In this paper we propose a novel multiple relay selection scheme based on the log-likelihood ratio (LLR). In this scheme, the joint design of precoding matrices at both the source and relay nodes in a multiple relay system based on perfect channel information is considered, which is based on the maximum signal-to-noise ratio. Using eigen-decomposition of matrices and monotonicity of functions, the joint design problem is transformed into two kinds of independent sub-problems and a fast algorithm is proposed. Compared with the traditional scheme based on signal-to-noise ratio (SNR), the proposed scheme is better in the symbol error rate (SER) performance. Simulation results show that the scheme based on LLR is superior to SNR in the SER performance. Meanwhile the multi-relay system with the proposed scheme can improve the SER performance effectively compared with only space-time coding or only precoding system.

To exploit the potential of content caching and device-to-device (D2D) communication, we propose a user-centric joint device caching and channel assignment (DCA) policy to facilitate content exchanges between user equipments (UEs). The objective is to minimize the average content delivery delay by effectively leveraging D2D communications using as few channels as possible, subject to the UEs' cache capacities and availability of D2D links. This joint design problem is formulated as a nonlinear combinatorial optimization problem which is NP-hard. We first analyze the optimal DCA policy in two special cases. Then, a low-complexity heuristic algorithm is proposed for general cases which alternatively performs greedy device caching and graphcoloring based channel allocating. Simulation results show that the proposed DCA policy can reduce the average content delivery delay by more than half, in contrast to baseline schemes with locally popular caching.

This study investigates the application of artificial fish swarm algorithm (AFSA) in the power allocation for multiple‐input and multiple‐output orthogonal frequency‐division multiplexing (MIMO‐OFDM) relay underwater acoustic (UWA) communication systems. First, by using the singular value decomposition technique, the two‐hop transmission links are converted into the virtual direct links in an single‐input and single‐output OFDM (SISO‐OFDM) system. Then, a power allocation optimisation problem, together with the assignment of subcarriers and relay nodes, are formulated for the virtual SISO‐OFDM system. Finally, the problem‐solving algorithms are proposed in two parts. Computer simulation results show that the proposed AFSA scheme improves in both power consumptions and diversity gains compared with two existing schemes for UWA communication systems.

Cooperative spectrum sensing has been studied to combat the hidden terminal problem by exploiting the spatial diversity in cognitive radio (CR) networks. This paper concerns blind cooperative spectrum sensing with soft fusion, where thea prioriknowledge of channels and primary signals is unavailable, and soft information is transmitted from each secondary user (SU) to a fusion center for detection. We first introduce the Quade test to design a blind detector. Then, a new detector with both lower computational complexity and lower overhead is derived, where only the estimated power and the variance of the instantaneous power at each SU are required at the fusion center. The analytical expressions for the detection performance, in terms of false-alarm probability and detection probability, are derived for the proposed detector. Simulation results are provided to validate the theoretical analyses and demonstrate the superior performance of proposed detector compared to the state-of-the-art detectors. It is also shown that, with the increase of the number of hidden terminals in the CR, the proposed detector can maintain high detection performance while the conventional detectors exhibit rapid performance degradation.

Low-Voltage powerline communications (LV PLC) is a promising technique for the last-mile broadband access networks and in-home multimedia applications. In this paper we focus on the broadband cooperative relaying systems over LV PLC channel using Fountain Codes. The closed-form expressions of capacity for Fountain Codes and Forward (FCF) cooperative relay system is derived. Numerical simulations reveal the performances in FCF mode in terms of the system capacity, which is complying with Electromagnetic Compatibility (EMC) regulations in the United States, European Union and Germany respectively. It's shown that FCF cooperative relaying LV PLC systems can obtain significant capacity improvement compared with direct transmission (DT), AF and DF cooperative relaying transmission. In addition, the effect of the relay locations to the FCF cooperative relaying system capacity is analyzed in this paper.

In this letter, the two-way time-of-arrival (TW-TOA)-based localization problem with clock imperfections in an asynchronous network is addressed. In the TW-TOA measurement model, the unknown turn-around times and clock skews may significantly degrade the localization performance. Under the assumption that the ranges of the turn-around times and the upper bound of the clock skews are known, we propose a robust least squares (RLS) formulation by taking the turn-around times and the clock skews as nuisance parameters. The RLS problem is approximately solved by employing the second-order cone relaxation technique. Simulation results illustrate the superior performance of the proposed method over the existing methods.

In this letter, the time-difference-of-arrival-based localization problem under mixed line-of-sight (LOS)/non-LOS (NLOS) conditions is addressed. Under the assumption that the path status information is known, we formulate a robust weighted-least-squares method to solve this problem. To fully utilize the more accurate LOS measurements, we impose a weight to the term with respect to the NLOS measurements, and the choice of the weight is derived and explicitly given. We then employ the second-order cone relaxation technique to relax the problem as a tractable second-order cone program. Simulation results show that the prior path status information significantly improves the localization performance.

In this study, the authors propose a polar coding (PC) scheme for the power line communication (PLC) system to cope with the impulsive noise and thereby promote the transmission performance. This new error‐correcting coding scheme is essentially inspired on a novel conception of channel polarisation. To be specific, via recursively channel combing and splitting, a group of channels with ideal transmission conditions, that is having a capacity of 1, will be constructed to carry the useful information, while the other band sub‐channels bear useless information. The decoding performance of PC under realistic impulsive noises is investigated under the condition that the impulsive noise is modelled by a well‐known Middleton Class‐A model. To mitigate the error propagation caused by sudden strong impulsive noises and further enhance the decoding performance, a matrix interleave operator is integrated. Simulations validate the suggested PC scheme in PLC systems. Compared with another commonly used low‐density parity‐check (LDPC) coding scheme, the suggested PC scheme, which has the low complexity, can significantly improve the bit error rate (BER) performance of PLC transmissions with impulsive interference. The PC scheme, as demonstrated by simulation results, can be of great importance to practical PLC systems.

In this paper, we address the time-of-arrival (TOA) based localization problem in an adverse environment, where line-of-sight (LOS) signal propagation between the source and the sensor is not readily available, in which case we have to resort to non-line-of-sight (NLOS) signals. Two convex relaxation methods, i.e., the semidefinite relaxation (SDR) and the second-order cone relaxation (SOCR) methods, are proposed to mitigate the effect of NLOS errors on the localization performance. We consider two separate cases in which the information of the NLOS status is totally unknown and perfectly known, respectively. The proposed methods can be applied without knowing the distribution of NLOS errors. Moreover, we propose a NLOS error mitigation method that is robust to detection errors, which are generated in the process of detecting NLOS paths. Simulation results show that the proposed convex relaxation methods outperform some existing state-of-the-art methods.

An algorithm for approximating crosstalk channel matrix inversion is proposed in this paper. The algorithm decomposes the crosstalk channel matrix into a tridiagonal matrix plus a residual matrix composed of the remaining elements. By using diagonal dominance property, the inverse of the crosstalk channel matrix is approximated. Based on these results we propose a novel precoding method with lower computational complexity for canceling the crosstalk in downstream VDSL. Computer simulation results based on measured channel data are provided to verify the efficiency of the proposed algorithm.

In order to improve the network management ability, an important issue is to design a network survivability management system as an independent function. This paper proposes new structure to preplan and download different response procedures for every network element at different layers, according to overall information of the network. Cooperation among different restoration techniques in different layers is performed by a hybrid escalation mechanism, which is based on the sequential activation of different restoration techniques. General rules and timing issues are also discussed in detail. The design is based on multi-agent framework and describes the cooperation methods for multi-agent system considering the characteristics of network management function

A time domain equalizer is a finite impulse response filter that shortens the channel impulse response to mitigate inter-symbol interference (ISI). Al-Dhahir and Cioffi proposed a design criterion for single input single output TEQ based on designing an MMSE decision feedback equalizer. They also extended this method to MIMO channels. We propose a simple implementation of time domain equalizer for MIMO channels, also based on the MMSE criterion. The solution is simplified compared to the above solution by eliminating the cross channel linear equalizers. This results in a set of M independent equalizers, each designed to meet a multi-objective channel shortening MMSE criterion. The new method is simple and provides good results for the MIMO problem. Finally, we demonstrate the efficiency of the proposed approach compared to Al-Dhahir's on measured channels, where the NEXT channels are simultaneously shortened with the direct channels. These are the first published results demonstrating MIMO-TEQ design on real life measured DSL channels.

We assess the capacity potential of very short very-high data-rate digital subscriber line loops using full-binder channel measurements collected by France Telecom R&D. Key statistics are provided for both uncoordinated and vectored systems employing coordinated transmitters and coordinated receivers. The vectoring benefit is evaluated under the assumption of transmit precompensation for the elimination of self-far-end crosstalk, and echo cancellation of self-near-end crosstalk. The results provide useful bounds for developers and providers alike.

A novel theorem on the wavelet transform and Hilbert transform (HT) is proposed and applied to extract the instantaneous parameters of energy-limited, real signals. Numerical simulations shows advantages of the presented method in both precision and antinoise performance.