Deploying an amplify-and-forward (AF) relay and a reconfigurable intelligent surface (RIS) can significantly enhance the quality of received signals in wireless communication systems. However, most existing studies focus on beamforming design at the relay and phase-shift design at the RIS under fixed device locations, neglecting the substantial impact of geometric placement on communication performance. Moreover, in practical systems, the phase gain of RIS elements varies with the applied phase shift. This paper considers the physical characteristics of practical RIS phase shifters and jointly optimizes the deployment of the relay and RIS under geometric and power constraints to maximize the signal-to-interference-plus-noise ratio (SNR) at the receiver. The associated optimization problem is highly nonlinear and non-convex, making it difficult to obtain satisfactory suboptimal solutions using traditional convex approximation techniques. To address this challenge, we propose two numerical approaches, deep deterministic policy gradient (DDPG) and particle swarm optimization (PSO), to approximate the optimal solution. Simulation results verify that the proposed approaches can effectively achieve near-optimal performance, as benchmarked by exhaustive grid search. Furthermore, the optimal deployment places both the relay and the RIS closer to the transmitter, outperforming the conventional midpoint deployment strategy.

MIMO-OFDM is a key physical-layer architecture for current and next-generation wireless communication. This paper proposes a receive beamforming design scheme for MIMO-OFDM radars configured with partially-connected hybrid linear arrays, in an attempt to improve target parameter (i.e., angle, range, and velocity) estimation. Unlike existing joint estimation methods, our approach first adopts a subspace algorithm to acquire the angle information at the analog beamformer output. Then, maximum signal-to-interference ratio (MSINR) digital beamforming is conducted for target signal alignment to achieve (i) accurate range-velocity estimation thanks to suppressed interference, and (ii) automatic pairing of the identified parameter triple. Moreover, the received signal structure at the analog beamformer output is exploited for obtaining side information of the target range, which can be used to accelerate the search of range and velocity. Simulation results are used to illustrate the effectiveness of the proposed scheme.

Deep reinforcement learning (DRL) has recently found comprehensive application in communication systems to address complex resource allocation problems. However, conventional DRL algorithms suffer from slow convergence, severely limiting their utility in dynamic communication environments. In this paper, we present a novel approach, namely hybrid model-based and data-driven DRL (HM-DRL) optimization, to tackle these challenges. In the HM-DRL, the original problem is split into inner and outer optimization tasks. The inner optimization is accomplished using low-complexity model-based methods, while the outer optimization employs DRL techniques. The HM-DRL scheme reduces the number of optimized parameters in outer optimization, thereby improving convergence. As a case study, we apply HM-DRL to optimize reflector configurations in a reconfigurable intelligent surface (RIS)-assisted orthogonal frequency division multiplexing (OFDM) system. By jointly optimizing the activation of antennas and associated reflectors at the RIS, our HM-DRL approach significantly accelerates learning speed, facilitating the discovery of near-optimal solutions.

Reconfigurable Intelligent Surface (RIS) devices inherently lack A/D and D/A converters and power amplifiers, resulting in limited signal processing capability and effective radiation range. The design of RIS reflectors typically requires channel state information (CSI). Conventional RIS designs for far-field transmission simplify the process by considering only the direction of radiation. However, in near-field RIS-assisted mmWave transmissions, the near-field effect necessitates consideration of both the direction of propagation and the distance between the transmitter and receiver in the near-field model. Consequently, complete CSI is essential for accurately configuring reflectors for near-field propagation. However, acquiring complete CSI is impractical, especially in mmWave RIS-assisted massive MIMO systems. In this paper, we propose a method for jointly optimizing beamforming and RIS reflectors in RIS-assisted multiple-input single-output (MISO) systems using Deep Deterministic Policy Gradient (DDPG) reinforcement learning (RL) with and without CSI. When near-field CSI is unavailable, the proposed design directly configures the reflectors by leveraging the received signal power corresponding to pilot signals. Alternatively, with near-field CSI, the proposed joint design can explore a more feasible solution set through the DDPG framework, achieving superior performance. Simulation results provide valuable insights into beamforming and RIS design in near-field channels, demonstrating that the proposed methods outperform existing approaches. Furthermore, the proposed beamforming and RIS design without CSI eliminates the need for explicit channel estimation, significantly enhancing its practicality for real-world system applications.

The estimation of channel state information (CSI) is a well-known challenge in reconfigurable intelligent surface (RIS)-assisted communication systems due to the limited signal processing capabilities of the RIS node. Conventional methods often resort to employing a substantial number of pilot sequences to obtain the necessary degrees of freedom, leading to a significant reduction in the data transmission rate. In this study, we present deep learning-based channel estimator designed for scenarios with limited pilots in an RIS-assisted single-input-multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) system. Specifically, we introduce an untrained deep neural network (DNN) based on the deep image prior (DIP) network. This DIP-based DNN is employed to denoise the estimated channel using orthogonal matching pursuit (OMP), ultimately achieving a more accurate channel estimation. Numerical results from our experiments demonstrate that the proposed untrained DIP-based DNN estimator offers significant improvements in accuracy when compared to both conventional OMP-based approaches and training-based DNN estimators.

Angle-of-arrival (AoA) estimation via hybrid uniform linear arrays is subject to inherent ambiguity incurred by mixing many subarray measurements into just few RF chains. With the aid of non-uniform subarray placement, this paper proposes a low-complexity beam-space MUSIC algorithm capable of achieving ambiguity-free multiple AoA estimation. An ambiguity-free condition, specified by inter-subarray spacings, is derived, leading to various array configurations guaranteeing unique AoA recovery. Simulation results show that our proposed approach compares favorably with an existing temporal-domain based MUSIC method at reduced computational complexity.

This paper proposes a new scheme for distributed spectrum sensing in the blind scenario, wherein the channel gains, the signal power, and the noise power are unknown. By utilizing energy detection and homogeneity test concepts, the cognitive radios (CRs) make binary local decisions based on whether a CR has the maximum sample energy within a sampling window among all CRs. With independent channels, the use of this maximum energy indicator generates a desirable discrepancy among the CR decisions when the frequency band is in use. Closed-form results on the distribution of the CR decisions are obtained for Gaussian noises and signal. Asymptotic analytical expressions are derived on the detection performance of the proposed scheme. Simulation results show the advantage of the proposed scheme and validate the analysis.

This paper proposes a new scheme for blind distributed spectrum sensing (DSS), where multiple distributed cognitive radios (CRs) and a fusion center (FC) collaboratively detect the availability of a frequency band of interest under communications constraints without knowledge on the channels, signal power, or noise power. By following the energy detection and homogeneity test concepts, in the proposed scheme, the CRs make local binary decisions based on relative comparison of the total energy in a sampling window, particularly, whether a CR has the maximum total energy. With independent channels, the use of the maximum function generates desirable discrepancy among the CR decisions when the frequency band is in use. The introduction of the window size enables the balancing between communications costs and the performance. Closed-form results on the distribution of the CR decisions are obtained for Gaussian noises and signals. Further, asymptotic analytical expressions are derived on the detection performance of the proposed scheme for both the generalized likelihood ratio test (GLRT) and$\chi ^{2}$-test at the FC. Simulation results are shown which validate the theoretical results and show the advantage of the proposed scheme to others.

This paper studies the problem of developing an efficient signal sampling framework for distributed blind spectrum sensing, together with the corresponding local cognitive radio (CR) decision and fusion rules. A novel collaborative sampling and binary output (CSBO) generating scheme is proposed to overcome the disadvantage of traditional energy detectors for blind spectrum sensing, in which the signal-to-noise ratio (SNR) and level of noise power at the CRs are unknown and thus the decision threshold of local energy detectors cannot be specified. In designing the fusion rule in conjunction with the proposed CSBO scheme, both fixed-sample-size tests and sequential tests are considered. It is theoretically shown that the proposed CSBO scheme outperforms traditional blind cooperative energy detectors with uniform sampling schemes in terms of its ability to increase the amount of information acquired under a given number of samples. Simulation results confirm the theoretical analyses, and demonstrate that, compared with the fixed-sample-size test, the sequential test requires fewer observations on average to achieve comparable detection performance. The reduction in the expected sample size needed in the sequential CSBO test when compared to the fixed-sample-size CSBO can reach 50% when primary signal is present.

M -ary source coding scheme on sensor detection is able to improve the detection performance on the final decision made at the fusion center (FC). In this paper, a new$M$-ary source coding scheme using analog transmission is proposed for distributed binary detection. In the scheme, the source coding is through the quantization process, but the output of the quantizer is transmitted directly without digitalizing and coding process. In the FC, the linear combiner detection rule is adopted to make the final decision. The problem of huge channel bandwidth demand can be avoided by using the considered Mary source coding scheme. The goal of the proposed scheme is to minimize the decision errors at the FC via optimizing the region allocation. The error performances using maximum-a-posteriori (MAP) and equal-gain-combining (EGC) fusion rules are analyzed. The proposed$M$-ary source coding scheme is illustrated with numerical examples highlighting its significant improvement in error performance and enhanced information available at the FC when the transmission is via either additive white Gaussian noise (AWGN) or Rayleigh faded channel.

In this paper, we study the problem of blind spectrum sensing by exploring signal sampling at each cognitive radio (CR) in a distributed cognitive radio network. Specifically, a new cooperative sampling scheme is proposed to deal with the challenge of unknown signal-to-noise ratio and level of noise power at each CR, which is a more realistic assumption in distributed spectrum sensing. We prove that the observations acquired by the proposed collaborative sampling are informative from an information theoretic perspective. Simulation results also demonstrate better receiver operating characteristics (ROC) of the proposed sampling scheme than the traditional uniform sampling scheme.

This work proposes efficient methods for the detection of malicious crowdsourcing workers using only privacy-aware group queries. In the proposed system, the crowdsourcing platform first issues a series of standard tasks to the workers, and allows users (i.e., data owners) to access aggregate responses from the workers through group queries that can be described by sparse encoding vectors. The identities of workers associated with individual responses are not explicitly revealed. By exploiting the sparse nature of the encoding vectors, we first propose an approximate maximum a posteriori probability (Approx. MAP) detector to perform the detection. Then, to further reduce computational complexity, we devise a generalized likelihood ratio test (GLRT) where probable malicious workers are first identified before a simple hypothesis test is performed. The identification of malicious workers is performed by a low-complexity probability-based rule that exploits a certain sparse structure inherent in the crowd data as well as the associated statistical assumptions. Computer simulations show that the proposed methods outperform the conventional energy detector.

This paper proposes a compressive sensing (CS) based active user identification scheme over frequency-selective fading channels. Unlike the conventional cyclic prefix (CP) based preamble transmission, our approach does not utilize CP in order to conserve signaling overhead, in turn reducing the system-wide processing latency. Our approach first estimates the multi-user channel impulse response vectors by solving a mixed ℓ2/ℓ1-norm optimization problem; then, the active users are identified via a sorting of the norms of the estimated channel vectors. By exploiting the Toeplitz channel matrix structure resulting from CP-free preamble transmission, analytic performance guarantee in term of the block restricted isometry property of the preamble matrix is given. Computer simulations are used to illustrate the performance of the proposed method.

To strike a balance between energy efficiency and data quality control, this paper proposes a sensor censoring scheme for distributed sparse signal recovery via compressive-sensing-based wireless sensor networks. In the proposed approach, each sensor node employs a sparse sensing vector with known support for data compression, meanwhile enabling making local inference about the unknown support of the sparse signal vector of interest. This naturally leads to a ternary censoring protocol, whereby each sensor: 1) directly transmits the real-valued compressed data, if the sensing vector support is detected to be overlapped with the signal support; 2) sends a one-bit hard decision if empty support overlap is inferred; and 3) keeps silent if the measurement is judged to be uninformative. Our design then aims at minimizing the error probability that empty support overlap is decided but otherwise is true, subject to the constraints on a tolerable false-alarm probability that non-empty support overlap is decided but otherwise is true, and a target censoring rate. We derive a closed-form formula of the optimal censoring rule; a low complexity implementation using bi-section search is also developed. In addition, the average communication cost is analyzed. To aid global signal reconstruction under the proposed censoring framework, we propose a modified ℓ1-minimization based algorithm, which exploits certain sparse nature of the hard decision vector received at the fusion center. Analytic performance guarantees, characterized in terms of the restricted isometry property, are also derived. Computer simulations are used to illustrate the performance of the proposed scheme.

Collaborative detection for continuously-varying event-region scenarios using wireless sensor networks (WSNs) has attracted much attention recently. However, most existing works adopt either a centralized approach, in which a powerful control center is used to reconstruct the entire random field, or a semi-centralized approach, in which extensive data exchange takes place among the sensors by means of real-value data communications. By contrast, the present study proposes a distributed dynamic event-region detection scheme for WSNs. The proposed scheme is based on a two-phase cooperative space-time decision fusion protocol. In Phase I, each sensor makes an initial decision in accordance with its Gaussian corrupted observation and previous decision. In Phase II, the nodes update their decisions in accordance with the decision information received from their neighboring nodes. The performance of the proposed scheme is compared with that of a semi-centralized detection scheme by means of computer simulations.

Various methods have been proposed for monitoring continuously varying event-region scenarios using wireless sensor networks (WSNs). However, these methods use either a centralized approach, in which a powerful control center is used to reconstruct the entire random field, or a semi-centralized approach, in which extensive real-value data exchange takes place among the sensors. By contrast, this paper proposes a two-phase distributed dynamic event-region detection scheme for WSNs characterized by a space-time Markov random field with a particular Gibbs distribution. In Phase I of the proposed scheme, each sensor makes an initial decision in accordance with its current Gaussian corrupted observation and previous decision. In Phase II, the nodes update their decisions based on the decision information received from their neighbors. Notably, the proposed scheme has a low-communication-rate characteristic, and is thus ideally suited to WSN applications. The performance of the proposed scheme is compared with that of a semicentralized detection scheme by means of computer simulations.

Abstract Cognitive radio (CR) is a newly developed technology for increasing spectral efficiency in wireless communication systems. In the CR networks, there exist two traditional spectrum‐sharing technologies called spectrum overlay and spectrum underlay. A new hybrid overlay/underlay paradigm has also been discussed in the literature. In this work, we create a unified spectrum sensing and throughput analysis model, which is suitable for overlay, underlay, and hybrid overlay/underlay paradigms in the CR networks. In the proposed model, the energy detection scheme is employed for the spectrum sensing in the network in which the co‐channel interference is present among primary users and secondary users (SUs). The SUs' throughput in the proposed CR system model is then analyzed. The simulations are also carried out for demonstrating the performance of overlay, underlay, and hybrid overlay/underlay paradigms. Copyright © 2013 John Wiley & Sons, Ltd.

This paper investigates the performance of a dual-hop amplify-and-forward relaying system with co-channel interference at both the relay and destination nodes. The source node is equipped with multiple antennas and adopts the orthogonal space-time block code to increase the system performance. Both the outage probability and the average bit error rate under the presence of multiple Rayleigh fading interferers are obtained in closed form for the variable and fixed-gain relaying schemes. Moreover, the effect of imperfect channel estimation on the variable-gain relaying scheme is also discussed. The effect of co-channel interference at both the relay and destination nodes are discussed. Simulation experiments are also conducted to verify the theoretical derivations.

Distributed detection with dependent observations is always a challenging problem. In this paper, we consider a special dependent case where sensors share some common information. Specifically, we investigate a tandem network with sensor 1 sending a one-bit decision to sensor 2 where the final decision is made. Along with their common observation X2, sensors 1 and 2 possess their conditionally independent measurements X1and X3, respectively. After obtaining the relationship between the optimal sensor 1 rule and sensor 2 fusion rule, we derive the necessary condition for the optimal sensor decision rules for both sensors. We compare the optimal rules with two suboptimal rules for distributed detection of a constant DC signal in Gaussian noise with various of signal-to-noise ratios.

This work examines the large-scale deployment of energy-harvesting sensors for the purpose of sensing and reconstruction of a spatially correlated Gaussian random field. The sensors are assumed to be deployed randomly according to a spatially non-homogeneous Poisson point process and our goal is to determine the optimal spatially-dependent sensor density to minimize the field reconstruction error. During each observation period, each sensor takes a local sample of the random field and transmits a scaled version of its observation to the sink node. The sink node then performs reconstruction of the random field based on the received information. The transmit power of each sensor is converted from ambient energy and thus, depends highly on the energy arrival at each location. For the purpose of field reconstruction, the sensors should, on the one hand, be deployed uniformly in space to gather more informative samples, but should, on the other hand, be placed at locations with large energy arrival or large channel gain to the sink node to increase the reliability of data transmission. The optimal sensor intensity and the optimal energy-aware transmission policy, which maximizes the effectiveness of energy usage at the sensors, are determined by minimizing an upper bound of the average mean-square reconstruction error. The efficacy of the proposed scheme is demonstrated through numerical simulations.

This study presents a new relay strategy and associated diversity combining technique for improving the communication performance in semi-blind cooperative networks. In constructing the network model, it is assumed that each relay can obtain the perfect channel state information (CSI) from the source to itself, and the destination can acquire the perfect CSI from all the relays to itself, but does not require the CSI from the source to the participating relays. In performing the considered semi-blind cooperative communications, the relays forward their reliability to the destination using a quantized reliability-relaying (QRR) scheme. Specifically, the relays partition their reliability into three levels in accordance with the log-likelihood ratio (LLR) value of the received signal, and forward a regenerative symbol to the destination if the quantized reliability falls within the "send +1" or "send -1" region; and remain silent otherwise. It is shown theoretically that the QRR scheme achieves a higher deflection coefficient than the regular decode-and-forward (DF) scheme, in which all the participating relays forward their regenerative messages to the destination irrespective of their reliability. Moreover, the simulation results show that given the same semi-blind model, the QRR scheme achieves a lower bit error rate (BER) than existing relay selection DF schemes for all considered values of the average input signal-to-noise ratio (SNR).

This paper discusess the reply to "Corrections on Cooperative Communications Using Reliability-Forwarding Relays".

Motivated by the fact that system parameter mismatch occurs in real-world sensing environments, this paper proposes power allocation schemes for robust distributed bestlinear-unbiased estimation (BLUE) that take account of the uncertainty in the local sensing noise levels. Assuming that (i) the sensing noise variance follows a statistical distribution widely used in the literature and (ii) the link channel gains between sensor nodes and the fusion center (FC) are i.i.d. Rayleigh fading, we propose to use the average reciprocal mean square error (ARMSE), averaged with respect to the distributions of sensing noise variance and fading channels, as the distortion measure. A fundamental inequality characterizing the relation between ARMSE and the average mean square error (AMSE) is established to justify the proposed design metric. While the exact formula for ARMSE is difficult to find, we derive an associated closed-form lower bound which involves the incomplete gamma function. To further ease analysis, we further derive a key inequality that specifies the range of the ARMSE lower bound. Particularly, it is shown that the boundary points of this inequality are characterized by a common function, which involves the Gaussian-tail Q(·) and is thus more analytically appealing. By conducting optimization on the basis of such a function, we obtain closed-form robust solutions for two power allocation problems: (i) optimizing distortion metric under a total power constraint, and (ii) minimizing total power under a target distortion requirement. In case that instantaneous channel state information (CSI) is available to the FC, the proposed approach can be easily modified to derive analytic robust power allocation factors best matched to the CSI realizations. Computer simulations evidence the effectiveness of the proposed schemes.

ABSTRACT A joint data detection and channel estimation scheme is proposed for cooperative communications in cluster‐based networks. In the proposed approach, each relay detects the source information using its joint detection and estimation rules and then compresses this information and forwards it to the destination node. The source information received from all the relays in the network is combined at the destination node in accordance with a fusion decision rule in order to determine the corresponding symbol vector. The joint detection and estimation rule at the relays and the fusion rule at the destination node are both processed using an expectation maximization algorithm. In order to reliably avoid poor local maxima, we utilized one pilot symbol per signal block to obtain a better initial starting point for the expectation maximization algorithm. The simulation results show that the feedback mechanism accelerates the convergence of the local channel estimates toward the Cramer–Rao lower bound. Copyright © 2011 John Wiley & Sons, Ltd.

In wireless packet-forwarding networks, selfish nodes always want to maximize their utilities and do not like to help others forward data streams, thus causing severe network performance degradation. To solve the packet-forwarding problem, a novel coalitional game approach based on the selective decode-and-forward (SDF) relaying scheme is proposed. In the game-theoretic analysis, we study the properties and stability of the coalitions and prove that the cohesive behavior can be obtained by the aspect of outage probability. Simulation results show that the proposed SDF coalitional game can enforce cooperation and is beneficial in forming the cooperative groups.

This paper examines the impact of sensor censoring on the decision fusion performance in networks with an unknown number of sensors. In performing the decision fusion process, the fusion center applies the Chair-Varshney test; suitably modified to take account of the unknown network size. A closed-form analytical expression is derived for the error probability of the modified fusion rule. It is shown that reducing the censoring probability, i.e., allowing a greater number of sensors to transmit their decisions, does not necessarily improve the decision fusion performance. Rather, there exists a certain censoring probability threshold below which increasing the number of transmitting sensors simply incurs a greater intra-network communication overhead but without improving the global decision performance. Our findings establish that the design of energy-efficient local detection rules should commence with the censoring rate threshold. Hence, it is desirable that the value of this censoring probability threshold be known in advance. Accordingly, the present study proposes an efficient method for identifying the censoring probability threshold value and determining the corresponding local censoring rule.

This study presents several schemes for adaptively allocating the number of ultra wideband (UWB) pulses used by each node within a wireless sensor network (WSN) in such a way as to enhance the detection performance while simultaneously satisfying a constraint imposed on the maximum number of pulses consumed. The study focuses on WSNs in which the data communications take place over multipath fading channels. Significantly, the proposed schemes take account of both the local detection performance of the individual nodes and the quality of the communication links between the nodes and the fusion center. Solutions are obtained for both high signal-to-noise ratio communication channels and low signal-to-noise ratio communication channels. A modulation scheme is proposed in which each node is assigned a random number of UWB pulses in accordance with the non-integer number of pulses obtained from the theoretical solutions. It is shown that the detection performance obtained when using the random UWB pulse modulation scheme is virtually identical to that achieved when using the theoretical solutions.

Spectrum sensing in next-generation wireless cognitive systems, such as overlay femtocell networks, is typically subject to timing misalignment between the primary transmitter and the secondary receiver. In this paper, we investigate the performance of the energy detector (ED) when the arrival time of the primary signal is modeled as a uniform random variable over the observation interval. The exact formula for the detection probability is derived and corroborated via numerical simulation. To further improve the detection performance, we propose a robust ED based on the Bayesian principle. Computer simulation confirms the effectiveness of the Bayesian based solution when compared with the conventional ED.

This paper proposes a collaborative change detection scheme for cognitive radio networks, where secondary users can collaboratively and efficiently detect the departure and arrival of the primary user's signal. Specifically, we consider a system where each of the secondary users in the network can make a local detection in each time frame and forward its decision to the fusion center where a CUSUM-based change detector is used to efficiently and reliably detect the presence of the primary user. The change detector is developed to maximize the throughput of the secondary network subject to constraints on the interference imposed on the primary user. In accordance with the considered system model and the adopted detection scheme, this paper also analyzes the average throughput of the secondary network and the average interference time to primary users. The simulation results show that the proposed spectrum sensing scheme outperforms the fixed-sample-size detector in terms of the average throughput of the secondary network.

This paper considers the joint symbol detection and channel estimation problem for cooperative communications in cluster-based networks. The proposed joint detection and estimation scheme is derived by using the expectation maximization (EM) algorithm. In addition, the cooperative communication between the relays and the destination is based on the concept of compress-and-forward (CF) scheme. Most importantly, the distribution detection theory is incorporated in developing the joint detection and estimation rules. Specifically, each relay makes its decision based on its joint detection and estimation rule and then forwards it to the destination node. The destination node makes a decision by fusing the decisions sent from the relays using its joint detection and estimation rule. In our considered structure, the feedback channels are also allowed from the destination node to all the relays to further improves the performance of cooperative communications. The simulation results show that the performance of cooperative communication can improve significantly by using the fusion technique and the feedback channels.

This work proposes a distributed sampling design for the signal detection application in the cluster-based wireless sensor networks (WSNs). Considering the energy saving requirement in the cluster-based WSNs, a linear weighting data fusion scheme for data reduction at the cluster head is also developed in this paper. Both the distributed sampling and the data reduction schemes are designed based on Ali-Silvey distance measures. The objective functions are derived in a closed form and two numerical examples are presented to illustrate our distributed sampling design and data reduction scheme. Numerical results show that our sampling design outperforms the uniform sampling and is insensitive to the sampling jitter. The results also show that the performance loss caused by the data reduction is quiet small. Therefore, the proposed schemes are very suitable for the detection applications in battery-powered WSNs.

This work addresses the problem of robust distributed estimation in the presence of sensor faults when the fusion center sequentially receives quantized messages from local sensors. The mean square error (MSE) of distributed estimation schemes increases dramatically if the information received from the faulty sensors within the network is not excluded from the estimation process. Accordingly, an efficient collaborative sensor-fault detection (CSFD) scheme is proposed in which the results of a homogeneity test are used to identify the faulty nodes within the network such that their quantized messages can be filtered out when estimating the parameter of interest. Utilizing an asymptotic analytical technique, a lower bound is derived for the MSE of the proposed distributed estimation scheme. A good agreement is observed between the simulated MSE results and the lower bound values, and thus it is inferred that the lower bound provides a convenient and reliable means of predicting the performance of the proposed estimation scheme in real-world sensor networks. In addition, a low-complexity CSFD (LC-CSFD) scheme is proposed to identify faulty sensors in WSNs with a very large number of nodes. The simulation results confirm that the accuracy of the estimates obtained from the CSFD and LCCSFD schemes is significantly better than that obtained from a conventional estimation scheme when applied in sensor networks characterized by an unknown number of sensor faults of various types.

This work addresses fault-tolerant distributed decision fusion in the presence of sensor faults when local sensors sequentially send their local decisions to a fusion center. This work also proposes a collaborative sensor fault detection (CSFD) scheme for eliminating unreliable local decisions when performing distributed decision fusion. In particular, the concept of pseudo-sensor faults is presented. Due to the difficulty in determining pseudo-sensor faults in real time based on the minimization of the error probability, an upper bound is established on the fusion error probability, where distributions of local decisions are not necessarily identical. Given a pre-designed fusion rule under the assumption of identical local decision rules in fault-free environments, this bound can then characterize the fusion error probability when local decisions are no longer identical due to sensor faults. Hence, a criterion is proposed based on this error bound to determine a set of pseudo-faulty nodes at each time. Once the fusion center identifies the pseudo-faulty nodes, all corresponding local decisions are removed from the computation of likelihood ratios adopted to make the final decision. To enhance the efficiency of the proposed search criterion, another, less complex criterion is developed by integrating the Kullback-Leibler distance. Simulation results indicate that this less complex criterion provides even better fault-tolerance capability in some situations when sensor faults significantly deviate from normally operating sensors. Performance evaluation results also indicate that the fault-tolerance capability of the proposed approach employing a CSFD scheme is superior to conventional decision fusion.

This work addresses the design of a distributed fault-tolerant decision fusion in the presence of sensor faults when the local sensors sequentially send their decisions to a fusion center. A collaborative sensor fault detection (CSFD) scheme is proposed here to eliminate unreliable local decisions when performing distributed decision fusion. Based on the pre-designed fusion rule, assuming identical local decision rules and fault-free environments, an upper bound is established on the fusion error probability. According to this error bound, a criterion is proposed to search the faulty nodes. Once the fusion center identifies the faulty nodes, all corresponding local decisions are removed from the computation of the likelihood ratios that are adopted to make the final decision. Performance evaluation results indicate a significant improvement in fault-tolerance capability from the proposed approach over the conventional decision fusion without utilizing CSFD.

This paper presents a novel frequency offset estimator for inter-carrier-interference (ICI) self-cancellation coded orthogonal frequency division multiplexing (OFDM) systems, which map data symbols onto adjacent or symmetric subcarriers with weighting coefficients of +1 and -1. There are two major drawbacks for traditional ICI self-cancellation schemes. First, the common phase error (CPE) caused by the carrier frequency offset (CFO) can not be removed. Second, the performance is substantially degraded when CFO is large. To remedy these drawbacks, pilot sub-carriers are inserted and the maximum likelihood estimator (MLE) proposed by Moose (1994) is adopted to eliminate CFO. If residual CFO is small enough, traditional ICI self-cancellation scheme can operate efficiently. In this paper, a second order estimator (SOE) is proposed to exploit the second order statistical properties of the demodulated pilot signals at the output of ICI self-cancellation demodulation. The SOE is combined with MLE to estimate CFO and the combined estimator is termed second order with maximum likelihood estimator (SO-MLE). The optimal weighting coefficient is determined by conducting simulation experiments. It is demonstrated that the proposed SO-MLE has the advantage of tolerating larger CFO and CPE than traditional MLE, and the ICI can thus be significantly diminished.

In this paper, the problem of event-region detection in wireless sensor networks (WSNs) are explored. Sensor fusion techniques are developed to tackle this problem. However, in contrast to the existing sensor fusion rules in the literature, sensor nodes considered here could observe different phenomena or events, and each node makes its decision with respect to its own hypothesis. In particular, a space-memory fusion rule that considers both space-memory information and local detection performance is derived for event-region detection. In addition, the proposed fusion rule performs in a sequential manner. The need of the fault-tolerance capability for possible sensor faults is also integrated with the design of the space-memory fusion rule. Specifically, the a priori sensor-fault model is used in this paper to address the issue of fault-tolerance capability. Simulation results demonstrate the superiority of the proposed fusion rule in the event-region detection.

This work considers the problem of collaborative detection in sensor networks with an unknown number of operating nodes. In wireless sensor networks, both the energy resource and the bandwidth of communication channel are limited. This work employs the sensor censoring scheme to achieve energy-efficiency and low communication rate on the design of distributed decision fusion when the number of operating sensors is unknown to the fusion center. Very surprisingly, in this work, we showed that the energy conservation does not necessary result in the degradation of fusion performance in both theoretical analysis and numerical simulations. Indeed, in many cases, utilizing more energy or bandwidth actually degrades the fusion performance, and the design of energy-efficient local detection rule should start from a nonzero censoring rate, which gives the optimal fusion performance

Distributed classification fusion using error-correcting codes (DCFECC) has recently been proposed for wireless sensor networks operating in a harsh environment. It has been shown to have a considerably better capability against unexpected sensor faults than the optimal likelihood fusion. In this paper, we analyze the performance of a DCFECC code with minimum Hamming distance fusion. No assumption on identical distribution for local observations, as well as common marginal distribution for the additive noises of the wireless links, is made. In addition, sensors are allowed to employ their own local classification rules. Upper bounds on the probability of error that are valid for any finite number of sensors are derived based on large deviations technique. A necessary and sufficient condition under which the minimum Hamming distance fusion error vanishes as the number of sensors tends to infinity is also established. With the necessary and sufficient condition and the upper error bounds, the relation between the fault-tolerance capability of a DCFECC code and its pair-wise Hamming distances is characterized, and can be used together with any code search criterion in finding the code with the desired fault-tolerance capability. Based on the above results, we further propose a code search criterion of much less complexity than the minimum Hamming distance fusion error criterion adopted earlier by the authors. This makes the code construction with acceptable fault-tolerance capability for a network with over a hundred of sensors practical. Simulation results show that the code determined based on the new criterion of much less complexity performs almost identically to the best code that minimizes the minimum Hamming distance fusion error. Also simulated and discussed are the performance trends of the codes searched based on the new simpler criterion with respect to the network size and the number of hypotheses.

In this work, we analyze the performance of a wireless sensor network with distributed classification codes, where independence across sensors, including local observations, local classifications and sensor-fusion link noises, is assumed. In terms of large deviations technique, we establish the necessary and sufficient condition under which the minimum Hamming distance fusion error vanishes as the number of sensors tends to infinity. With the necessary and sufficient condition and the upper performance bounds, the relation between the fault-tolerance capability of a distributed classification code and its pair-wise Hamming distances is characterized

In this paper, we consider the distributed classification problem in wireless sensor networks. Local decisions made by local sensors, possibly in the presence of faults, are transmitted to a fusion center through fading channels. Classification performance could be degraded due to the errors caused by both sensor faults and fading channels. Integrating channel decoding into the distributed fault-tolerant classification fusion algorithm, we obtain a new fusion rule that combines both soft-decision decoding and local decision rules without introducing any redundancy. The soft decoding scheme is utilized to combat channel fading, while the distributed classification fusion structure using error correcting codes provides good sensor fault-tolerance capability. Asymptotic performance of the proposed approach is also investigated. Performance evaluation of the proposed approach with both sensor faults and fading channel impairments is carried out. These results show that the proposed approach outperforms the system employing the MAP fusion rule designed without regard to sensor faults and the multiclass equal gain combining fusion rule

Distributed (M-ary) detection and fault-tolerance have been considered as two fundamental functions in the context of large-scale sensor networks. Distributed multiclass classification fusion using error correcting codes (DCFECC) has been proposed to provide good fault-tolerance capability in wireless sensor networks. Minimum Hamming distance fusion is an essential part of the DCFECC approach. In this paper, we study the asymptotic performance of minimum Hamming distance fusion for both fault-free and faulty situations when the number of sensors tends to infinity. We conclude that the error probability vanishes asymptotically as long as the minimum Hamming distance d/sub min/ of the DCFECC code approaches infinity, and the probabilities of correct local classification for all hypotheses are greater than one half. In case d/sub min//2, normalized by the number of sensors, can be made larger than the largest local classification error, an explicit expression for the error exponent of the DCFECC system in terms of the Kullback-Leibler divergence can be established. A converse where the DCFECC decoding error is bounded away from zero is also addressed.

Fault-tolerance and data fusion have been considered as two fundamental functions in wireless sensor networks. In this paper, we propose a novel approach for distributed multiclass classification using a fault-tolerant fusion rule for wireless sensor networks. Binary decisions from local sensors, possibly in the presence of faults, are forwarded to the fusion center that determines the final classification result. Classification fusion in our approach is implemented via error correcting codes to incorporate fault-tolerance capability. This new approach not only provides an improved fault-tolerance capability but also reduces computation time and memory requirements at the fusion center. Code matrix design is essential for the design of such systems. Two efficient code matrix design algorithms are proposed in this paper. The relative merits of both algorithms are also studied. We also develop sufficient conditions for asymptotic detection of the correct hypothesis by the proposed approach. Performance evaluation of the proposed approach in the presence of faults is provided. These results show significant improvement in fault-tolerance capability as compared with conventional parallel fusion networks.

In this paper, we consider the distributed classification problem in wireless sensor networks. Local decisions made by local sensors, possibly in the presence of faults, are transmitted to the fusion center through fading channels. We integrate channel coding with the distributed fault-tolerant classification fusion approach, i.e., the DCFECC approach. We obtain a new fusion rule that combines both soft-decision decoding and local decision rules without introducing any additional redundancy. The soft decoding scheme is utilized to combat channel fading, while the DCFECC fusion structure provides excellent fault-tolerance capability.