Determining whether an input utterance includes fake or bona fide segments without additional segment annotation is a frequently investigated topic in Partially Spoofed Speech Detection (PSSD). Nevertheless, most existing works on this topic usually fail to sufficiently consider inter-segment temporal dependence within an utterance, and further, these works usually overlook representative possibly-fake segments, which may be critical for detecting spoofed utterances. In response, we propose an approach of Fake segment Mining based Graph neural network (FMG) to address these issues. For the first issue, we employ a Graph Neural Network (GNN) based module for processing segment-level representations, through comprising Adjacent Temporal Dependence (ATD) and Across Temporal Correlation (ATC) branches for jointly modelling the GNNs’ adjacent segments’ dependence and different segments’ correlation. Then, regarding the second issue, we propose a Fake Segment Mining (FSM) module, which contains attentive pooling, fake segment prototype loss and entropy loss parts, in order to achieve utterance-level predictions and pinpoint representative fake segments. Afterwards, experimental evaluations on spoofed-speech datasets demonstrate that, the proposed approach outperforms compared models, showcasing its effectiveness for detecting partially spoofed utterances without segment annotation.

Time-frequency (T-F) domain methods for monaural speech enhancement have benefited from the success of deep learning. Recently, focus has been put on designing two-stream network models to predict amplitude mask and phase separately, or, coupling the amplitude and phase into Cartesian coordinates and constructing real and imaginary pairs. However, most methods suffer from the alignment modeling of amplitude and phase (real and imaginary pairs) in a two-stream network framework, which inevitably incurs performance restrictions. In this paper, we introduce a graph Fourier transform defined with the singular value decomposition (GFT-SVD), resulting in real-valued time-graph representation for neural speech enhancement. This real-valued representation-based GFT-SVD provides an ability to align the modeling of amplitude and phase, leading to avoiding recovering the target speech phase information. Our findings demonstrate the effects of real-valued time-graph representation based on GFT-SVD for neutral speech enhancement. The extensive speech enhancement experiments establish that the combination of GFT-SVD and DNN outperforms the combination of GFT with the eigenvector decomposition (GFT-EVD) and magnitude estimation UNet, and outperforms the short-time Fourier transform (STFT) and DNN, regarding objective intelligibility and perceptual quality. We release our source code at: https://github.com/Wangfighting0015/GFTproject.

In this study, we investigate symbiotic radio-assisted cell-free massive multiple-input multiple-output (SCF-mMIMO) systems in which multiple access points serve primary users and backscatter devices. We propose a semi-blind channel estimation scheme based on the expectation maximization (EM) algorithm to reduce pilot overhead and iteratively achieve performance close to that of maximum likelihood (ML) estimation. Unlike the traditional EM algorithm, we derive a modified EM algorithm by incorporating suitable priors for the channel coefficients to estimate the aggregate channel. Simulation results show that the proposed EM algorithm for the SCF-mMIMO system achieves good performance with fewer pilots, approaching that of the ML estimation. In addition, the modified EM algorithm using channel priors outperforms traditional EM algorithms. These results suggest that semi-blind channel estimation holds considerable promise for SCF-mMIMO systems.

ABSTRACT Reconfigurable intelligent surface (RIS) is an emerging technology that can enhance service coverage and spectral efficiency in sixth‐generation wireless networks. In contrast, relays are a traditional technique for achieving coverage extension. To explore potential benefits, we investigate three probable RIS‐relay cascaded scenarios over Nakagami‐ fading channels, where the full‐duplex relay is utilized for ensuring spectral efficiency. To evaluate the performance across different scenarios, we first derive their closed‐form expressions for the outage probability. Next, we determine the respective diversity order using asymptotic approximations in the high signal‐to‐noise‐ratio regime. Finally, we present the system throughput in delay‐limited transmission. Simulation results validate our analysis and demonstrate significant performance differences among various cascaded configurations, with the multi‐RIS‐assisted relay scenario achieving the best performance. Furthermore, the considered RIS‐decode‐and‐forward relay schemes outperform the corresponding RIS‐amplify‐and‐forward relay schemes in the low‐SNR regime.

The progressing development of Internet of Things (IoT) has accelerated the emergence of resource-intensive and latency-sensitive mobile applications, which throws out a great challenge to the battery-powered wireless devices (WDs) with low-computing capabilities. To solve this intractable issue, we investigate an intelligent reflecting surface (IRS)-assisted multiantenna wireless-powered mobile edge computing (WP-MEC) system, in which WDs first harvest wireless energy emitted by a hybrid access point (HAP), then offload their tasks to the edge server, and finally download the results. In consideration of the practical scenarios, the finite computing capability of edge server and the nonlinear end-to-end power conversion of energy harvesting (EH) circuits at WDs are considered. In addition, an IRS is deployed to improve the efficiency of wireless power transfer (WPT) and the rate of data transmission between HAP and WDs. Under this setup, both space division multiple access (SDMA) and time division multiple access (TDMA) protocols are exploited and evaluated for data transmission. For each protocol, we maximize the computational rate by jointly optimizing time allocation, beamforming designs of HAP and IRS, as well as offloading strategies of WDs. To solve the problem formulated under the SDMA protocol, we propose an efficient alternating optimization (AO) algorithm. For the problem under the TDMA protocol, an AO algorithm with low complexity is proposed. Numerical results demonstrate the high effectiveness of the proposed algorithms and the superiority of the SDMA protocol over the TDMA protocol.

In this article, we propose a movable antenna (MA)-enhanced scheme for wireless-powered mobile-edge computing (WP-MEC) system, where the hybrid access point (HAP) equipped with multiple MAs first emits wireless energy to charge wireless devices (WDs), and then receives the offloaded tasks from the WDs for edge computing. The MAs deployed at the HAP enhance the spatial Degrees of Freedom (DoFs) by flexibly adjusting the positions of MAs within an available region, thereby improving the efficiency of both downlink wireless energy transfer (WPT) and uplink task offloading. To balance the performance enhancement against the implementation intricacy, we further propose three types of MA positioning configurations, i.e., dynamic MA positioning, semidynamic MA positioning, and static MA positioning. In addition, the nonlinear power conversion of energy harvesting (EH) circuits at the WDs and the finite computing capability at the edge server are taken into account. Our objective is to maximize the sum computational rate (SCR) by jointly optimizing the time allocation, positions of MAs, energy beamforming matrix, receive combing vectors, and offloading strategies of WDs. To solve the nonconvex problems, efficient alternating optimization (AO) frameworks are proposed. Moreover, we propose a hybrid algorithm of particle swarm optimization with variable local search (PSO-VLS) to solve the subproblem of MA positioning. Numerical results validate the superiority of exploiting MAs over the fixed-position antennas (FPAs) for enhancing the SCR performance of WP-MEC systems.

In this paper, we propose an active simultaneously transmitting and reflecting reconfigurable intelligent surface (aSTAR-RIS) assisted wireless information and power transfer system. The aSTAR-RIS is employed to not only assist the downlink simultaneous wireless information and power transfer for the energy harvesting at the wireless powered devices (WPDs) and information receiving at the information receiving devices (IRDs), but also aid the uplink wireless information transmission from the WPDs to the hybrid access point. Our objective is to maximize the uplink sum-rate, considering the quality-of-service requirements of IRDs, by optimizing the power allocation, time scheduling, transmission and reflection beamforming at the aSTAR-RIS. To solve this non-convex problem, we propose an effective alternating optimization algorithm with semidefinite relaxation and fractional programming techniques. Numerical results demonstrate that the proposed aSTAR-RIS scheme can achieve up to 640.68% performance gains compared to the passive STAR-RIS scheme in our settings.

End-to-end time-domain speech separation with masking strategy has shown its performance advantage, where a 1-D convolutional layer is used as the speech encoder to encode a sliding window of waveform to a latent feature representation, i.e. an embedding vector. A large window leads to low resolution in the speech processing, on the other hand, a small window offers high resolution but at the expense of high computational cost. In this work, we propose a graph encoding technique to model the fine structural knowledge of speech samples in a window of reasonable size. Specifically, we build a graph representation for each latent representation, and encode the structural details with a graph convolutional network encoder. The encoded graph feature representation complements the original latent feature representation and benefits the separation and reconstruction of speech. Experiments on various models and datasets show that our proposed encoding technique significantly improves the speech quality over other time-domain speech encoders.

Entropy minimization has been widely used in unsupervised domain adaptation (UDA). However, existing works reveal that the use of entropy-minimization-only may lead to collapsed trivial solutions for UDA. In this article, we try to seek possible close-to-ideal UDA solutions by focusing on some intuitive properties of the ideal domain adaptation solution. In particular, we propose to introduce diversity maximization for further regulating entropy minimization. In order to achieve the possible minimum target risk for UDA, we show that diversity maximization should be elaborately balanced with entropy minimization, the degree of which can be finely controlled with the use of deep embedded validation in an unsupervised manner. The proposed minimal-entropy diversity maximization (MEDM) can be directly implemented by stochastic gradient descent without the use of adversarial learning. Empirical evidence demonstrates that MEDM outperforms the state-of-the-art methods on four popular domain adaptation datasets.

Ligand molecules naturally constitute a graph structure. Recently, many excellent deep graph learning (DGL) methods have been proposed and used to model ligand bioactivities, which is critical for the virtual screening of drug hits from compound databases in interest. However, pharmacists can find that these well-trained DGL models usually are hard to achieve satisfying performance in real scenarios for virtual screening of drug candidates. The main challenges involve that the datasets for training models were small-sized and biased, and the inner active cliff cases would worsen model performance. These challenges would cause predictors to overfit the training data and have poor generalization in real virtual screening scenarios. Thus, we proposed a novel algorithm named adversarial feature subspace enhancement (AFSE). AFSE dynamically generates abundant representations in new feature subspace via bi-directional adversarial learning, and then minimizes the maximum loss of molecular divergence and bioactivity to ensure local smoothness of model outputs and significantly enhance the generalization of DGL models in predicting ligand bioactivities. Benchmark tests were implemented on seven state-of-the-art open-source DGL models with the potential of modeling ligand bioactivities, and precisely evaluated by multiple criteria. The results indicate that, on almost all 33 GPCRs datasets and seven DGL models, AFSE greatly improved their enhancement factor (top-10%, 20% and 30%), which is the most important evaluation in virtual screening of hits from compound databases, while ensuring the superior performance on RMSE and $r^2$. The web server of AFSE is freely available at http://noveldelta.com/AFSE for academic purposes.

In the paper, we investigate a graph representation of speech signals and graph speech enhancement technology. Specifically, we first propose a new graph k-shift operator Ck to map speech signals into the graph domain and construct a novel graph Fourier basis by using its singular eigenvectors for speech graph signals (SGSs). On this basis, we propose an improved graph Wiener filtering method based on the minimum mean square error (MMSE) criterion to suppress the noise interference in noisy speech. Comparing with the traditional methods in DSP and the existed graph Wiener filtering methods by applying graph shift operators in GSP, our numerical simulation results show that the performance of the proposed method outperforms that of these methods in terms of both average SSNR and mean PESQ score. Moreover, the computational complexity of the proposed method is much lower than that of the existed graph Wiener filtering methods and a little higher than that of classical methods in DSP.

This paper proposes a hybrid-relaying scheme empowered by a self-sustainable intelligent reflecting surface (IRS) in a wireless powered communication network (WPCN), to simultaneously improve the performance of downlink energy transfer (ET) from a hybrid access point (HAP) to multiple users and uplink information transmission (IT) from users to the HAP. We propose time-switching (TS) and power-splitting (PS) schemes for the IRS, where the IRS can harvest energy from the HAP's signals by switching between energy harvesting and signal reflection in the TS scheme or adjusting its reflection amplitude in the PS scheme. For both the TS and PS schemes, we formulate the sum-rate maximization problems by jointly optimizing the IRS's phase shifts for both ET and IT and network resource allocation. To address each problem's non-convexity, we propose a two-step algorithm to obtain the near-optimal solution with high accuracy. To show the structure of resource allocation, we also investigate the optimal solutions for the schemes with random phase shifts. Through numerical results, we show that our proposed schemes can achieve significant system sum-rate gain compared to the baseline scheme without IRS.

In this paper, we investigate the application of graph signal processing (GSP) theory in speech enhancement. We first propose a set of shift operators to construct graph speech signals, and then analyze their spectrum in the graph Fourier domain. By leveraging the differences between the spectrum of graph speech and graph noise signals, we further propose the graph spectral subtraction (GSS) method to suppress the noise interference in noisy speech. Moreover, based on GSS, we propose the iterative graph spectral subtraction (IGSS) method to further improve the speech enhancement performance. Our experimental results show that the proposed operators are suitable for graph speech signals, and the proposed methods outperform the traditional basic spectral subtraction (BSS) method and iterative basic spectral subtraction (IBSS) method in terms of both signal-to-noise ratios (SNR) and mean Perceptual Evaluation of Speech Quality (PESQ).

In this paper, we investigate the physical layer security (PLS) of cooperative wireless-powered networks where a source transmits confidential information to a destination with the aid of wireless-powered intermediate nodes equipped with multiple antennas in the presence of a passive eavesdropper. We consider two generalized joint relay and jammer selection (GJRJS) frameworks based on the power splitting (PS) and time switching (TS) techniques, respectively. Specifically, the intermediate nodes which cannot decode the source signal successfully are selected to act as friendly jammers to transmit artificial noise, and the remaining nodes are exploited as relays to simultaneously forward the source signal through cooperative beamforming. We further propose two cooperative secure beamforming (CSB) schemes for the PS-based GJRJS (PS-GJRJS) and TS-based GJRJS (TS-GJRJS) frameworks, respectively. To be specific, we investigate the optimization of the beamforming vector of our selected relays for maximizing the secrecy rate of the source–destination transmission. A closed-form solution is derived under the assumption of available instantaneous channel state information (CSI) of the main link and statistical CSI of the wiretap link. In addition, we also illustrate that the pure relay selection (PRS) scheme is a special case of our GJRJS framework at high signal-to-noise ratios (SNRs). The numerical results show that the proposed CSB scheme achieves a higher secrecy rate than the traditional maximal ratio transmission (MRT) method for both the PS-GJRJS and TS-GJRJS frameworks. Additionally, the GJRJS framework outperforms the PRS as well as the joint best relay and jammer selection (JBRJS) methods in terms of secrecy rate.

In this paper, we propose a relay cooperation scheme for backscatter communication systems for performance enhancement, in which one user backscatters incident signals from a power beacon (PB) to a relay and a receiver simultaneously, and then the relay decodes the received signals and forwards the decoded signals to the receiver. We consider two cases that the relay is with/without an embedded energy source. In particular, if the relay does not have an energy source, an energy harvesting phase is required, during which the relay harvests energy from the PB while the user backscatters information to the receiver. We first formulate system throughput maximization problems for both cases by finding the optimal time allocation schemes, from which some useful insights are provided. Then, with a given amount of information required to be delivered, the transmission time minimization problems for both cases are also formulated, and the optimal solutions are derived in closed-form. Numerical results reveal the proposed scheme can significantly enhance the system throughput and transmission time.

A new joint best relay and jammer selection (JBRJS) scheme is conceived for enhancing the physical layer security (PLS) of cooperative networks relying on multiple energy harvesting (EH) aided intermediate nodes, which accumulate energy based on the power splitting (PS) protocol. Specifically, we select the best intermediate node as the relay, whilst exploiting all the remaining nodes as friendly jammers. Furthermore, we investigate the joint optimization of the PS ratio and the relay-aided beamforming for maximizing the system's secrecy rate and present a full channel state information (CSI) based joint PS and beamforming (fJPSB) scheme as the optimal solution by converting the optimization problem formulated into a singlevariable optimization problem. We also propose a partial-CSI based JPSB (pJPSB) method for the scenario where only the main link's CSI is available. Our numerical results show that the proposed JBRJS scheme beneficially enhances the PLS compared to the joint random relay and jammer selection (JRRJS) and to the pure best relay selection (PBRS) schemes. Moreover, the secrecy rate of the proposed fJPSB and pJPSB schemes is obviously higher than that of the fixed PS and beamforming (FPSB) method, and it further increases with the number of the relay's transmit antennas.

In this paper, we examine the physical layer security of a cooperative relay network where two source-destination pairs communicate through a decode-and-forward (DF) relay in the presence of multiple eavesdroppers. To safeguard the legitimate communications against eavesdropping, we propose a novel two-stage secure relay selection (TSSRS) with a non-orthogonal multiple access (NOMA) scheme to maximize the capacity of one source-destination pair, while guaranteeing the successful communication of the other source-destination pair. To explicitly reveal the benefits of our proposed scheme, we derive the exact and asymptotic expressions for its secrecy outage probability. As a benchmark, we also analyze the secrecy performance of the TSSRS strategy with an orthogonal multiple access (OMA) scheme. Both theoretical analysis and simulation results demonstrate that our proposed TSSRS-NOMA scheme significantly outperforms the TSSRS-OMA scheme when the transmit power at the source and relay is in the low and medium regimes. In addition, we show that the advantage of the TSSRS-NOMA scheme over the TSSRS-OMA scheme becomes obvious when the two source-destination pairs have profoundly different secrecy requirements.

Hashing-based image retrieval methods have become a cutting-edge topic in the information retrieval domain due to their high efficiency and low cost. In order to perform efficient hash learning by simultaneously preserving the semantic similarity and data structures in the feature space, this paper presents the semi-supervised metric learning-based anchor graph hashing method. Our proposed approach can be divided into three parts. First, we exploit a transformation matrix to construct the anchor-based similarity graph of the training set. Second, we propose the objective function based on the triplet relationship, in which the optimal transformation matrix can be learned by using the smoothness of labels and the margin hinge loss incurred by the triplet constraint. Moreover, the stochastic gradient descent (SGD) method leverages the gradient on each triplet to update the transformation matrix. Finally, a penalty factor is designed to accelerate the execution speed of SGD. Through comparison with the retrieval results of several state-of-the-art methods on several image benchmarks, the experiments validate the feasibility and advantages of our proposed methods.

In this paper, we propose a novel beamforming design to enhance physical layer security of a non-orthogonal multiple access (NOMA) system with the aid of artificial noise (AN). The proposed design uses two factors to balance the useful signal strength and interference at the strong and weak users, which is a generalized version of the existing beamforming designs in the context of physical layer security for NOMA. We determine the optimal power allocation among useful signals and AN together with the two optimal factors in order to maximize the secrecy sum rate (SSR). Our asymptotic analysis in the high signal-to-noise ratio regime provides an efficient and near-optimal solution to optimize the beamforming scalars and power allocation coefficients. Our analysis indicates that it is not optimal to form a beam toward either the strong user or the weak user in NOMA systems for security enhancement. In addition, the asymptotically optimal power allocation informs that, as the transmit power increases, more power should be allocated to the weak user or AN signals, while the power allocated to the strong user keeps constant. Our examination shows that the proposed novel beamforming design can significantly outperform two benchmark schemes.

In this paper, we consider a relay assisted backscatter communication (RaBackCom) system, where a user backscatters incident signals from a carrier emitter (CE) to a relay and a receiver simultaneously, and then the relay forwards the user's information to the receiver for throughput improvement. We consider two cases that the relay is with/without an embedded energy source. Specifically, if the relay does not have an energy source, it first harvests energy from the signals from the CE and then uses its harvested energy for information forwarding. For both cases, we formulate time allocation problems on the user's information backscattering, the user's information forwarding, or the relay's energy harvesting to maximize the system throughput, and then derive closed-form solutions. Simulation results demonstrate the advantages of the proposed relay cooperation scheme with the optimal time allocation in terms of system throughput.

In this study, the authors examine the performance of an uplink non‐orthogonal multiple access (NOMA) system with cooperative full‐duplex relaying (CFR‐NOMA), where the user closer to the base station (BS) is considered as a full‐duplex relay to aid the transmission from the other user to the BS. The closer user first decodes the signals transmitted from the far user and then forwards them using superposition coding to the BS on top of transmitting its own information signals to the BS. First, they analyse the performance of the CFR‐NOMA system in terms of the outage probability and average sum rate. Then, they analytically obtain the optimal power allocation coefficients that minimise the outage probability. They also study the optimal power allocation to maximise the average sum rate under specified constraints. Their examination demonstrates that the CFR‐NOMA system can significantly outperform the uplink conventional orthogonal multiple access and uplink NOMA with half‐duplex relaying system in terms of achieving a lower outage probability or a higher average sum rate.

In this paper, we consider a cognitive wireless powered communication network for Internet of Things applications, which consists of a primary communication pair and a secondary communication system. We propose a novel hybrid harvest-then-transmit (HTT) and backscatter communication (BackCom) mode for the information transmission of the secondary communication system. When the primary channel is busy, cognitive users (CUs) backscatter the incident signal from the primary transmitter to the information receiver in the ambient backscatter (AB) mode or harvest energy for the future information transmission. When the primary channel is idle, CUs backscatter the incident signal from the power beacon in the bistatic scatter (BS) mode or work in the HTT mode to transmit information. We further investigate the optimal time allocation between the AB mode and energy harvesting and that between the BS mode and the HTT mode for the sake of maximizing the throughput of the secondary communication system, and derive the numerical solutions. To be specific, we derive the closed-form optimal solution for a single CU case, and moreover, obtain the optimal combination of the working modes. Numerical results demonstrate the advantage of our proposed hybrid HTT and BackCom mode over the benchmark mode in terms of system throughput.

An i-vector is a compact representation that captures both the speaker and session variabilities rendered in a spoken utterance. Over the past years, it has prevailed over other techniques and is now the de facto representation for text-independent speaker recognition. Standard i-vector extraction requires intense computation at run-time. Reducing the computation will allow effective use of i-vector in more applications. Such intense computation arises from the posterior covariance matrix, when estimating the i-vector. There have been studies on how to simplify the computation of posterior covariance matrix with modest success. In this paper, we propose a novel approach to i-vector extraction without the need to evaluate the full posterior covariance thereby speeding up the run-time extraction process. This is achieved by generalizing the i-vector estimation in two ways. First, we introduce the use of occupancy reweighting in conjunction with whitening over the Baum-Welch statistics as part of the preprocessing step. Second, we introduce the so-called subspace-orthogonalizing prior (SOP) to replace the standard Gaussian prior in i-vector formulation. Experiments conducted on the extended-core task of NIST SRE'10 show that the proposed rapid SOP approach achieves considerable speed-up over the standard i-vector with comparable equal error rates.

Cooperation between primary and secondary networks offers significant benefits in data forwarding. But, cost implication in such cooperation is not well understood. In this paper, we explore cost incurred in both primary and secondary networks when they are allowed to relay each other's traffic in a cooperative manner. We model costs in both networks and formulate a multiobjective optimization problem. For this problem, we present a novel algorithm to construct an ε-approximation curve and prove its error bounds in both cost dimensions. Based on the ε-approximation curve, we develop three important applications. The first application is to show the minimum cost value for a single objective (by fixing the other objective as constant) or the relationship between the two objectives over the entire range of possible values. The second application is to address different cost parameters in the primary and secondary networks. We show how to obtain a new approximation curve by scaling the original ε-approximation curve with appropriate factors and quantify its error bounds. The third application is to use the ε-approximation curve to study a single objective optimization problem with a guaranteed error bound. The results in this paper offer some deep theoretical insights on potential costs incurred in both networks when they are allowed to relay each other's traffic cooperatively.

In this paper, we consider a hybrid satellite- terrestrial relay network (HSTRN) composed of a geostationary satellite, a terrestrial destination and multiple terrestrial relays in the presence of an eavesdropper, where the satellite transmits its data to the terrestrial destination via decode-and- forward (DF) relays while the secure transmission from relay to destination may leak to the eavesdropper. We explore the physical-layer security against eavesdropping attacks in HSTRN and propose the optimal relay selection scheme for improving the transmission security. Additionally, the classic round-robin scheduling is also considered to a benchmark scheme. Furthermore, the closed-form expressions of secrecy outage probability for both the round-robin scheduling and optimal relay selection schemes are derived. Finally, numerical results show that our proposed scheme generally outperforms the round-robin scheduling in terms of the secrecy outage probability.

We propose a novel joint user and full-duplex (FD) relay selection (JUFDRS) scheme to enhance physical layer security in a multi-user multi-relay network. In this scheme, the user and the FD decode-and-forward relay are selected such that the capacity of the end-to-end user-relay-destination channel is maximized to ensure the highest quality of cooperative transmission. To fully examine the benefits of the JUFDRS scheme, we derive a new closed-form expression for the secrecy outage probability. We show that the JUFDRS scheme significantly outperforms the joint user and half-duplex relay selection (JUHDRS) scheme when the self-interference at the FD relay can be reasonably suppressed. This result indicates that adopting the FD technique at relays can effectively enhance the physical layer secrecy performance in the multi-user multi-relay network.

In this paper, we examine the physical layer security for cooperative wireless networks with multiple intermediate nodes, where the decode-and-forward (DF) protocol is considered. We propose a new joint cooperative beamforming and jamming (JCBJ) scheme to protect wireless communication against eavesdropping, where the intermediate nodes which successfully decode the source signal are selected as the relays for simultaneously forwarding the source signal by employing a weight vector, and meanwhile, the others are used to act as the friendly jammers for broadcasting artificial noise to disturb the eavesdropper. We further investigate the optimal power allocation among the source, relays and friendly jammers for maximizing the secrecy rate of the proposed JCBJ scheme and derive a closed-form sub-optimal solution. Simulation results show that the proposed JCBJ scheme achieves obviously higher secrecy rate than the conventional generalized singular-value-decomposition (GSVD) based beamforming and pure jamming schemes. Additionally, the proposed optimal power allocation scheme performs much better than the equal power allocation scheme in terms of secrecy rate.

In this study, the authors propose a secure transmission scheme using polar codes and artificial noise (AN) for the wiretap system without assuming the channel quality advantage of main channel over wiretap channel. They first derive lower and upper bounds on the symmetric capacity of the polarised bit‐channels , which depend on the signal‐to‐noise ratio of each use of physical channel. According to the bounds, they prove existence of the bit‐channels that are beneficial to the signal reception of the main channels but hostile to the wiretap channel, and a method based on injecting the AN at the transmitter is introduced to achieve those bit‐channels. Through theoretical analysis, the security of the proposed AN method is proven. Thereafter, they elaborate two AN power allocation schemes denoted by AN‐PA‐I and AN‐PA‐II for each use of physical channel. The former proves the existence of the minimum AN power to achieve the secrecy rate that is equal to the capacity of the main channel, referred to as the maximum secrecy rate, and the latter introduces how to obtain it with an optimisation method. Numerical results show that both the two schemes can achieve the maximum secrecy rate.

In this paper, a transmit antenna selection (TAS)-based incremental hybrid decode-amplify-forward (IHDAF) scheme is proposed to enhance physical layer security in cooperative relay networks. Specifically, TAS is adopted at the source in order to reduce the feedback overhead. In the proposed TAS-based IHDAF scheme, the network transmits signals to the destination adaptive select direction transmission (DT) mode, AF mode, or DF mode depending on the capacity of the source-relay link and source-relay link. In order to fully examine the benefits of the proposed TAS-based IHDAF scheme, we first derive its secrecy outage probability (SOP) in a closed-form expression. We then conduct asymptotic analysis on the SOP, which reveals the secrecy performance floor of the proposed TAS-based IHDAF scheme when no channel state information is available at the source. Theoretical analysis and simulation results demonstrate that the proposed TAS-based IHDAF scheme outperforms the selective decode-and-forward, the incremental decode-and-forward, and the noncooperative DT schemes in terms of the SOP and effective secrecy throughout, especially when the relay is close to the destination. Furthermore, the proposed TAS-based IHDAF scheme offer a good tradeoff between complexity and performance compared with using all antennas at the source.

In this paper, we examine the physical layer security for cooperative wireless networks with multiple intermediate nodes, where the decode-and-forward protocol is considered. We propose a new joint relay and jammer selection (JRJS) scheme for protecting wireless communications against eavesdropping, where an intermediate node is selected as the relay for the sake of forwarding the source signal to the destination and meanwhile, the remaining intermediate nodes are employed to act as friendly jammers, which broadcast the artificial noise for disturbing the eavesdropper. We further investigate the power allocation among the source, relay and friendly jammers for maximizing the secrecy rate of proposed JRJS scheme, and derive a closed-form sub-optimal solution. Specifically, all the intermediate nodes, which successfully decode the source signal, are considered as relay candidates. For each candidate, we derive the sub-optimal closed-form power allocation solution and obtain the secrecy rate result of the corresponding JRJS scheme. Then, the candidate, which is capable of achieving the highest secrecy rate, is selected as the relay. Two assumptions about the channel state information (CSI), namely the full CSI (FCSI) and partial CSI (PCSI), are considered. Simulation results show that the proposed JRJS scheme outperforms the conventional pure relay selection, pure jamming, and generalized singular-value-decomposition-based beamforming schemes in terms of secrecy rate. Additionally, the proposed FCSI-based power allocation and PCSI-based power allocation schemes both achieve higher secrecy rates than the equal power allocation scheme.

We explore the physical-layer security for a multisource multi-relay wireless network in the presence of an eavesdropper, in which one of multiple sources is scheduled to send its message to a destination with the assistance of multiple decode-and-forward (DF) relays. One eavesdropper is considered at the receiver side, and assumed to be capable of overhearing the transmission from relays to destination. In this paper, the round-robin scheduling and the joint source-relay selection schemes are presented to improve the physical-layer security of the multi-source multi-relay system, and their closedform intercept probability expressions are derived over Rayleigh fadingchannels.Numericalresultsdemonstratethattheproposed joint source-relay selection scheme achieves a better performance than the traditional round-robin scheduling in terms of the intercept probability.

In this paper, we propose a new joint relay and jammer selection (JRJS) scheme to enhance the physical layer security for cooperative wireless networks with multiple intermediate nodes, where the decode-and-forward (DF) protocol is considered. In the proposed JRJS scheme, an intermediate node is selected as the relay for data transmission, while the others are used to act as friendly jammers for disrupting the eavesdropper by broadcasting artificial noise. We focus on the power allocation between the relay and jammers to maximize the secrecy rate of proposed JRJS scheme under a total transmit power constraint, and derive a closed-form optimal power allocation solution. For the purpose of comparision, the conventional pure relay selection and pure jammer selection schemes are considered as benchmark schemes. Simulation results show the advantage of the proposed JRJS scheme over conventional pure relay selection and pure jammer selection methods in terms of secrecy rate. Additionally, the derived optimal power allocation solution leads to a significantly higher secrecy rate than the equal power allocation scheme.

In the past, two main approaches for the purpose of authentication, including information-theoretic authentication codes and complexity-theoretic message authentication codes (MACs), were almost independently developed.In this paper, we propose a new cryptographic primitive, namely, artificial-noiseaided MACs (ANA-MACs), which can be considered as both computationally secure and information-theoretically secure.For ANA-MACs, we introduce artificial noise to interfere with the complexity-theoretic MACs and quantization is further employed to facilitate packet-based transmission.With a channel coding formulation of key recovery in the MACs, the generation of standard authentication tags can be seen as an encoding process for the ensemble of codes, where the shared key between Alice and Bob is considered as the input and the message is used to specify a code from the ensemble of codes.Then, we show that the introduction of artificial noise in ANA-MACs can be well employed to resist the key recovery attack even if the opponent has an unlimited computing power.Finally, a pragmatic approach for the analysis of ANA-MACs is provided, and we show how to balance the three performance metrics, including the completeness error, the false acceptance probability, and the conditional equivocation about the key.The analysis can be well applied to a class of ANA-MACs, where MACs with Rijndael cipher are employed.

In this paper, we propose a novel Artificial-Noise-Aided PHYsical layer Phase Challenge-Response Authentication Scheme (ANA-PHY-PCRAS) for practical orthogonal frequency division multiplexing (OFDM) transmission. In this new scheme, Tikhonov-distributed artificial noise is introduced to interfere with the phase-modulated key for resisting potential key-recovery attacks. Then, we address various practical issues for ANA-PHY-PCRAS with OFDM transmission, including correlation among subchannels, imperfect carrier, and timing recoveries. Among them, we show that the effect of sampling offset is significant and a search procedure in the frequency domain should be incorporated for verification. With practical OFDM transmission, the number of uncorrelated subchannels is often insufficient. Hence, we employ a time-separated approach for allocating enough subchannels, and a modified ANA-PHY-PCRAS is proposed to alleviate the discontinuity of channel phase at far-separated time slots. Finally, the key equivocation is derived for the worst case scenario. We conclude that the enhanced security of ANA-PHY-PCRAS comes from the uncertainties of both the wireless channel and introduced artificial noise, compared with the traditional challenge-response authentication scheme implemented at the upper layer.

We propose a low-rate turbo-Hadamard coding approach for narrow-band interference (NBI) suppression. The proposed approach can be viewed as the employment of a coded spread-spectrum signalling with time-varying spreading sequences and the NBI suppression is naturally achieved in iterative decoding. Compared with the traditional code-aided NBI suppression with direct-sequence spread-spectrum (DS-SS) signalling, the spreading sequences for the turbo-Hadamard coding approach are non-constant in time and determined from the columns of a Hadamard matrix subject to the low-rate coding rule. The low-rate turbo-Hadamard coding approach is sharply compared with the code-aided DS-SS approach with convolutional coding. With a spreading sequence of length 32, it is shown that the proposed turbo-Hadamard approach outperforms the code-aided DS-SS approach even when its transmission information rate is about 5 times higher than that of the coded-aided DS-SS approach.

Adaptive wireless video scheduling has been widely studied to improve network performance. However, the majority of existing scheduling algorithms assume that they are able to converge instantaneously to adapt to a dynamic network state, that is, the execution time of the scheduling can be ignored. Nevertheless, due to the limited computation capacity of wireless nodes, this assumption is very difficult, sometimes even impossible, to satisfy in practice. This motivates us to address in this paper the following challenging question: what is the effect of the execution time on the scheduling performance? To this end, we first characterize the scheduling as a stochastic optimization problem that enables us to open up a new degree of performance to exploit in a tractable manner. Next, we build a connection between the execution time and video quality, and rigorously prove that the execution time is disadvantageous to the stability region, but advantageous to the flow balance. Therefore, these results are helpful to shed insights on fundamental scheduling guidelines on designing an efficient video transmission system.

Traditional distributed wireless video scheduling is based on perfect control channels in which instantaneous control information from the neighbors is available. However, it is difficult, sometimes even impossible, to obtain this information in practice, especially for dynamic wireless networks. Thus, neither the distortion-minimum scheduling approaches aiming to meet the longterm video quality demands nor the solutions that focus on minimum delay can be applied directly. This motivates us to investigate the distributed wireless video scheduling with delayed control information (DCI). First, to exploit in a tractable framework, we translate this scheduling problem into a stochastic optimization rather than a convex optimization problem. Next, we consider two classes of DCI distributions: 1) the class with finite mean and variance and 2) a general class that does not employ any parametric representation. In each case, we study the relationship between the DCI and scheduling performance, and provide a general performance property bound for any distributed scheduling. Subsequently, a class of distributed scheduling scheme is proposed to achieve the performance bound by making use of the correlation among the time-scale control information. Finally, we provide simulation results to demonstrate the correctness of the theoretical analysis and the efficiency of the proposed scheme.

Graphical models have been widely applied in solving distributed inference problems in wireless sensor networks (WSNs). In this paper, we formulate the distributed multi-sensor tracking problem in a WSN as an inference problem on a factor graph. Using particle filtering methods, we propose a nonparametric variant of sum-product algorithm (SPA), called sequential particle-based SPA (SPSPA), for factor graphs to infer the multi-sensor target states over time. In the proposed algorithm, importance sampling methods are used to sample from message products, and the computational complexity of SPSPA is thus linear in the number of particles. We apply the SPSPA to a distributed multi-sensor tracking problem, and evaluate its performance in terms of the measurement noise and the number of particles.

Mel Frequency Cepstrum Coefficient(MFCC) has been proven extremely successful for text-independent speaker identification. We address the speaker identification problem by presenting a novel Sparse Representation-Subspace algorithm. We propose to develop an overcomplete dictionary of each speaker using the Mel filterbank log energies for all the training utterances. We therefore propose to represent learned dictionary as a linear combination of all the log energies, thereby generating a naturally sparse representation, which is the novel subspace of the speaker. Besides, DCT step of MFCC is a fixed matrix, learned dictionary for different speaker is more adaptive. In the identification process, the unknown vectors of Mel filterbank log energies coefficients are projected into each subspace to decide the matching speaker. Experiments have been conducted on the speech database in our anechoic chamber, and a comparison with MFCC based speaker identification algorithms yields a favorable performance index for the proposed algorithm. Different sparsity and dictionary size shows different results.

This paper presents an improved routing algorithm based on the social link awareness. In this algorithm, multiple social features of the nodes' behaviors are utilized to quantify the nodes pairs' social links. The social links of the nodes pairs are computed based on their encounter history. These social links can be used to construct the friendship communities of the nodes. The intra-community and inter-community forwarding mechanisms are implemented to raise the successful delivery ratio with low overhead and decrease the transmission delay. Simulation results show that the proposed algorithm shortens the routing delay and the overhead, and increases the successful delivery ratio, thereby improving the routing efficiency.

We propose a verification-based Interval-Passing (IP) algorithm for iteratively reconstruction of nonnegative sparse signals using parity check matrices of low-density parity check (LDPC) codes as measurement matrices. The proposed algorithm can be considered as an improved IP algorithm by further incorporation of the mechanism of verification algorithm. It is proved that the proposed algorithm performs always better than either the IP algorithm or the verification algorithm. Simulation results are also given to demonstrate the superior performance of the proposed algorithm.

Most existing Quality of Experience (QoE)-driven multimedia resource allocation methods assume that the QoE model of each user is known to the controller before the start of the multimedia playout. However, this assumption may be invalid in many practical scenarios. In this paper, we address the resource allocation problem with incomplete information where the realized mean opinion score (MOS) can only be observed over time, but the underlying QoE model and playout time are unknown. We consider two variants of this problem: 1) the form of the QoE model is known but the parameters are unknown; 2) both the form and the parameters of the QoE model are unknown. For both cases, we develop dynamic resource allocation schemes based on online test-optimization strategy. Simply speaking, one first spends appropriate time on testing the QoE model, then optimizes the sum of the MOS in the remaining playout time. The highlight of this paper lies in resolving the inherent tension between the test and optimization by jointly considering the uncertainties of QoE model and playout time. Furthermore, we derive tight bounds on the MOS loss incurred by the proposed schemes in comparison with the optimal scheme that knows the QoE model a priori and prove that the performance gap, as the playout time tends to infinity, asymptotically shrinks to zero.

In recent years there has been a growing interest in sparse representation of signals based on overcomplete dictionaries. Selecting few atoms that best match the signal structure, the signal is described by linear combination of these atoms. In this paper, we propose a novel overcomplete dictionary design algorithm for sparse representation of piecewise stationary signals. An effective and easy-to-use overcomplete dictionary is constructed in accordance with the parametric autocorrelation function model of piecewise stationary processes. Furthermore, a sparse decomposition algorithm in terms of nonlinear approximation is designed to obtain sparse representation of piecewise stationary signals, which has lower computational complexity and better practicability than the conventional sparse decomposition algorithms. The experimental results demonstrate that the proposed method avails for higher sparsiry of signal representation and better reconstruction performance than sparse representation of signals based on overcomplete DCT dictionary.

The authors investigate the performance of the probabilistic data association (PDA) detector in a cooperative system that consists of a source, a relay and a destination, which are all multiantenna nodes. Taking advantage of the soft information provided by the PDA detector at the relay, the authors present a relaying scheme in light of which the relay can just transmit signals to the destination under the condition of having high detection reliability. Characterised by the means on how to exploit the information provided by both the source and the relay at the destination, two detectors named as combing PDA (C-PDA) and iterative PDA (I-PDA) are developed, respectively. C-PDA adopts one PDA detector to retrieve the transmit signals by combining the received signals from the source and the relay. I-PDA employs two concatenated PDA detectors to perform detection with respect to the received signals from the source and the relay, respectively. Just as in turbo decoding, soft information is iteratively exchanged between these two concatenated PDA detectors to enhance the performance of I-PDA. Simulation results have shown that significant performance gains can be achieved by both C-PDA and I-PDA over the conventional zero-forcing detector.