We consider downlink channel estimation in massive multiple input multiple output (MIMO) systems using a Bayesian compressive sensing (BCS) approach. BCS exploits the sparse structure of the channel in the angular domain in order to reduce the pilot overhead. Due to limited local scattering, the massive MIMO channel has a block-sparse representation in the angular domain. Thus, we use a conditionally independent and identically distributed spike-and-slab prior to model the sparse vector coefficients representing the channel and a Markov prior to model its support. An expectation propagation (EP) algorithm is developed to approximate the intractable joint posterior distribution on the sparse vector and its support with a distribution from an exponential family. The unknown model parameters which are required by EP, are estimated using the expectation maximization (EM) algorithm. The proposed combination of EM and EP algorithms is reminiscent of variational EM and is referred to as EM-EP. The approximated distribution is then used for estimating the massive MIMO channel. Simulation results show that our proposed EM-EP algorithm outperforms several recently-proposed algorithms in channel estimation.

A semi-blind algorithm based on expectation propagation (EP) is proposed for multi-cell massive MIMO systems with spatially and temporally correlated channels. The performance of the algorithm in channel estimation and data detection is obtained from simulations and compared to other approaches. The results show that with only K pilots, where K is the number of users in the cell, the channel estimation performance of the proposed method approaches a lower bound as the number of BS antennas or the number of data symbols per frame increase. As expected, for time-varying channels, the proposed algorithm clearly outperforms those designed for block-fading channel models.

We propose a hop-by-hop relay selection strategy for multi-hop decode-and-forward (DF) underlay cognitive relay networks (CRNs). In this strategy, taking both maximum transmit power and maximum interference constraints into account, one relay in each decoding set is selected to transmit to the next hop. Relay selection at each stage only depends on the channel state information (CSI) of the following hop. We analyze the performance of the proposed strategy in terms of end-to-end outage probability. Numerical results are presented from analysis which closely match those obtained from simulation. The results are compared to those from other hop-by-hop strategies, showing improved outage probability. Moreover, the numerical results show that the outage probability of the proposed method is very close to that of exhaustive strategy which provides a lower bound for outage probability of all relay selection methods.

Localizing users in an orthogonal frequency-division multiple access (OFDMA) system without the knowledge of their subcarrier assignments poses a unique challenge. This problem arises in non-cooperative situations where the localizing algorithm does not have access to the OFDMA multiple access layer. In this paper we propose a clustering-based algorithm for joint spatial and spectral localization of OFDMA users with noncoherent arrays. The proposed method consists of three step. First, each array estimates the direction-of-arrival (DOA) of a potential source in every subcarrier. Next, the potential source's position is estimated via multiangulation of these per-subcarrier DOAs. Finally a clustering step identifies the source occupying each subcarrier and combines subcarrier-wise position estimates to form the aggregate estimate. Simulation results are included to illustrate the performance of the proposed algorithm.

Online hypothesis testing and non-parametric model estimation is studied for a heterogeneous network of sensors collecting correlated observations. It is assumed that the statistical model for sensor data is not available and nonparametric estimation is used to estimate the model. Copula densities are used to model the correlation in sensor data. The batch-mode expectation maximization (EM) algorithm is first developed for Gaussian copulas and then extended to an online EM-based algorithm which performs the hypothesis detection and model estimation on a sample-by-sample basis. Results are presented for three real-world datasets and compared with those from widely-used supervised and unsupervised methods. It is shown that the proposed method achieves significant improvements in hypothesis testing compared to other unsupervised and even some supervised learning methods.

We investigate the problem of localization of sources transmitting OFDM communication signals. A non-cooperative setting is considered whereby the set of subcarriers used by each source is unknown. We propose an iterative localization algorithm which jointly estimates the location of the sources in both spatial and spectral domains. Furthermore, the subarrays collecting the source signals are not required to be synchronized. This eliminates the need for accurate clock synchronization across the geographically dispersed subarrays. The proposed method is based on the expectation-maximization (EM) algorithm, with a nested likelihood ratio test within each EM iteration in order to detect the subcarrier occupancy of each source. The efficacy of the algorithm is illustrated via simulation results.

We present an online algorithm for hypothesis testing from correlated observations obtained from a network of heterogeneous sensors and in the presence of model uncertainty. The correlated observations are modeled using copula theory. The batch-mode expectation maximization (EM) algorithm is first developed and then extended to an online algorithm for model parameter estimation and hypothesis testing. Using real-world as well as simulation data, we compare the detection accuracy of our method with other supervised and unsupervised methods and also with a model which ignores the correlation in the data.

Massive MIMO systems exploit the favorable propagation condition of the radio channel, whereby the vector-valued channels between the base station (BS) and the terminals become mutually orthogonal. This property is used in a recently-proposed channel estimation method for multi-cell massive MIMO systems based on the eigenvalue decomposition (EVD) of the correlation matrix of the received vectors. In this paper, we present a blind channel estimation and symbol detection scheme for multi-cell massive MIMO systems based on expectation propagation (EP). The proposed algorithm is initialized with the channel estimation result from the EVD-based method. It is shown that in our EP formulation, channel estimation and symbol detection are “decoupled” in that EP iterations for channel estimation can be performed without the knowledge of the specific transmitted symbols. Therefore, channel estimation can be performed first followed by symbol detection. In particular, a liner symbol detection scheme such as zero-forcing (ZF) or minimum mean-squared error (MMSE) algorithm may be employed. Simulation results show that after a few iterations, the EP-based algorithm significantly improves the performance of the EVD-based method in both channel estimation and symbol error rate. Comparisons are also made with the results from a recently proposed blind detection scheme and it is shown that the proposed algorithm has better performance.

Multicarrier communication signals possess distinct characteristics different from narrowband signals often targeted by localization algorithms. Each source occupies a unique set of subcarriers and is practically absent from other subcarriers. Moreover, subcarrier allocation for a source may change over time. In noncooperative geolocation applications, knowledge of the set of subcarriers occupied by a source may not be available. This paper proposes iterative localization algorithms which jointly estimate the locations of the sources in both the spatial and spectral domains. It is assumed that the transmitted signals are intercepted by spatially dispersed subarrays which are asynchronous. This eliminates the need for accurate clock synchronization across the geographically distributed subarrays. The proposed method is based on the expectation-maximization (EM) algorithm, and the subcarrier occupancy detection is achieved via the likelihood ratio test within each the EM iteration. The performance of the algorithm is evaluated through simulation for a multi-source localization problem. It is shown that for signal-to-noise ratios of interest, the proposed method achieves a performance close to the algorithm, which is aware of all the subcarrier allocations.

A channel estimation method was recently proposed for multi-cell massive MIMO systems based on the eigenvalue decomposition of the correlation matrix of the received vectors (EVD-based). This algorithm, however, is sensitive to the size of the antenna array as well as the number of samples used in the evaluation of the correlation matrix. In this paper we present a noncoherent channel estimation and symbol detection scheme for multi-cell massive MIMO systems based on expectation propagation (EP). The proposed algorithm is initialized with the channel estimation result from the EVD-based method. Simulation results show that after a few iterations, the EP-based algorithm significantly outperforms the EVD-based method in both channel estimation and symbol error rate. Moreover, the EP-based algorithm is not sensitive to antenna array size or the inaccuracies of sample correlation matrix.

In this work, the inter-dependency of TCM signals is studied. Using this inter-dependency, correlation-based detectors are proposed for spectrum sensing of TCM signals in white Gaussian noise. In particular, a constant false alarm rate (CFAR) detector is presented and its performance is evaluated using simulations. We also describe an application of our detector for the classification of uncoded modulation systems vs. their TCM counterparts and present numerical results on their performance.

An algorithm based on expectation propagation (EP) is proposed for noncoherent symbol detection in large-scale SIMO systems. It is verified through simulation that in terms of symbol error rate (SER), the proposed detector outperforms the pilotbased coherent MMSE detector for blocks as small as two symbols. This makes the proposed detector suitable for fast fading channels with very short coherence times. In addition, the SER performance of this detector converges to that of the optimum ML receiver when the size of the blocks increases. Finally it is shown that for Rician fading channels, knowledge of the fading parameters is not required for achieving the SER gains.

This paper considers the problem of parameter estimation and hypothesis testing based on observations from a network of heterogeneous sensors. The data is assumed to be correlated among the samples collected over time as well as among the data collected by different sensors. Moreover, it is assumed that the model parameters for the sensor data is not available. Correlation in the data is modeled using copula theory and a Markov chain, with unknown model parameters. Our proposed method uses the expectation maximization (EM) algorithm to estimate the unknown parameters of the model and to detect the state of nature. Numerical results are presented from simulations showing significant improvements for both parameter estimation and hypothesis testing compared to methods that ignore the correlation in sensors measurements.

A hop‐by‐hop relay selection (RS) strategy for multi‐hop decode‐and‐forward cooperative relay networks referred to as Max‐DS is proposed. In this method, RS in each stage is only based on the channel state information (CSI) to relays in the next stage. In each stage, relays that successfully receive and decode the message from the previous stage form the candidate set for relaying, and among them, the relay with the largest number of ‘good’ channels to the next stage is selected for retransmission. It is shown that the proposed method can be implemented in a distributed manner without the need for a central controller. The authors analyse the performance of the proposed method in terms of end‐to‐end outage probability in the case of imperfect CSI of which the case of perfect CSI is a special case. Numerical results are presented from analysis which closely match those obtained from simulation. The results are compared with those from several other RS strategies, and show significant improvements.

We propose and analyze a hop-by-hop relay selection strategy for multi-hop decode-and-forward (DF) cooperative relay networks. In this method, relay selection in each stage is based on the channel state information (CSI) to the relays in the following stage. More specifically, in each stage, relays that successfully receive and decode the message from the previous hop form the candidate set for relaying, and the best relay in this set is selected for retransmission to the next hop. As such, a central controller (CC) for the entire relay network is not required and this strategy can be implemented in a distributed manner. We analyze the performance of this method in terms of end-to-end outage probability and ergodic and effective ergodic capacities. Numerical results from analysis are shown to closely match those obtained from simulation. We also compare our results to those from existing relay selection strategies in the cases of perfect and imperfect CSI and demonstrate the advantages of the proposed method.

Machine-to-machine (M2M) communication, also referred to as Internet of Things (IoT), is a global network of devices such as sensors, actuators, and smart appliances which collect information, and can be controlled and managed in real time over the Internet. Due to their universal coverage, cellular networks and the Internet together offer the most promising foundation for the implementation of M2M communication. With the worldwide deployment of the fourth generation (4G) of cellular networks, the long-term evolution (LTE) and LTE-advanced standards have defined several quality-of-service classes to accommodate the M2M traffic. However, cellular networks are mainly optimized for human-to-human (H2H) communication. The characteristics of M2M traffic are different from the human-generated traffic and consequently create sever problems in both radio access and the core networks (CNs). This survey on M2M communication in LTE/LTE-A explores the issues, solutions, and the remaining challenges to enable and improve M2M communication over cellular networks. We first present an overview of the LTE networks and discuss the issues related to M2M applications on LTE. We investigate the traffic issues of M2M communications and the challenges they impose on both access channel and traffic channel of a radio access network and the congestion problems they create in the CN. We present a comprehensive review of the solutions for these problems which have been proposed in the literature in recent years and discuss the advantages and disadvantages of each method. The remaining challenges are also discussed in detail.

A novel symmetric cryptosystem based on turbo codes with nonsystematic constituent codes is proposed. The proposed system introduces an interleaver for each constituent code and secures the interleavers with a secret key. It is verified by simulation results that without affecting the performance of the intended receiver, complete security for the unintended receiver can be achieved. More specifically, while the bit-error rate (BER) of the intended receiver remains the same as that of the insecure turbo code, for the unintended receiver BER is close to 0.5. The implementation issues for key-dependent interleaver design based on linear feedback shift registers are also discussed.

Big Data received from sources such as social media, in-stream monitoring systems, networks, and markets is often mined for discovering patterns, detecting anomalies, and making decisions or predictions. In distributed learning and real-time processing of Big Data, ensemble-based systems in which a fusion center (FC) is used to combine the local decisions of several classifiers, have shown to be superior to single expert systems. However, optimal design of the FC requires knowledge of the accuracy of the individual classifiers which, in many cases, is not available. Moreover, in many applications supervised training of the FC is not feasible since the true labels of the data set are not available. In this paper, we propose an unsupervised joint estimation-detection scheme to estimate the accuracies of the local classifiers as functions of data context and to fuse the local decisions of the classifiers. Numerical results show the dramatic improvement of the proposed method as compared with the state of the art approaches.

We introduce a new tendency measure for a probability mass function (pmf) referred to as “tenor,” and defined in terms of the phase of the first non-zero frequency of the discrete Fourier transform of the pmf. This statistic is in the vicinity of the region of highest probability of the pmf. Unlike mean, tenor is robust against outliers, and unlike mode and median, tenor can be evaluated using only arithmetic operations of addition and multiplication, without the need for comparison operations. We propose a distributed algorithm for computation of tenor in a graph and prove that for large networks represented by Erdos-Renyi graphs, [1] and by Watts-Strogatz graphs (small-world graphs), [2] the distributed algorithm converges. Numerical examples including the distributed computation of the majority vote are presented to demonstrate the operation of the algorithm.

In this paper we consider the problem of cooperative spectrum sensing in cognitive radio networks (CRN) in the presence of misbehaving nodes. We propose a novel approach based on the iterative expectation maximization (EM) algorithm to detect the presence of the primary users, to classify the cognitive radios, and to compute their detection and false alarm probabilities. In contrast to previous work we assume that the FC has no prior information about the radios in the network except that the honest radios are in majority. As shown in the paper this is required for any algorithm to uniquely identify the CRs. Another distinguishing feature is that our approach can classify the radios into more than just two classes of honest and malicious CRs. This applies in cases where the honest CRs have different detection and false alarm probabilities, which may arise when they employ different spectrum sensing techniques or encounter dissimilar channel and noise conditions. Another case is when the CRN includes more than one type of misbehaving CRs. Our numerical results show significant improvements over the widely popular reputation-based classifier (RBC). In particular, with only a few decisions from the CRs, the proposed algorithm can quickly and efficiently classify the CRs whereas the RBC method fails even for networks with a large number of CRs. In all of our numerical results the EM algorithm converged in five or fewer iterations resulting in fast convergence of the proposed method. This makes the proposed method a good candidate for implementation in CRNs. The numerical results are also compared with the Cramer-Rao lower bound and show a close match. Simulation results are also presented to demonstrate the efficacy of the proposed algorithm in the presence of correlated observations among the radios.

In distributed sensing, the statistical model of the data collected by the sensor elements is often unavailable. In addition, these statistics may vary among the sensors and over time, for instance due to: 1) hardware variations; 2) the sensors' geographical locations; 3) different noise statistics; 4) diverse channel conditions between the sensor elements and the fusion center (FC); and 5) the presence of misbehaving sensors sending false data to the FC. In this paper, we consider the problem of centralized binary hypothesis testing in a wireless sensor network consisting of multiple classes of sensors, where the sensors are classified according to the probability density function (PDF) of their received data (at the FC) under each hypothesis. The sensor nodes transmit their observed data to the FC, which must classify the nodes and detect the state of nature. To optimally fuse the data, the FC must also estimate the PDFs of the sensors' observations. We develop a method based on the expectation maximization (EM) algorithm to estimate the PDFs for each sensor class, to classify the sensors, and to detect the underlying hypotheses. The estimation of PDFs is nonparametric in that no prior model is assumed. Simulation results using fewer than three iterations of the EM algorithm demonstrate the efficacy of the proposed method.

We consider the problem of secure detection in wireless sensor networks operating over insecure links. It is assumed that an eavesdropping fusion center (EFC) attempts to intercept the transmissions of the sensors and to detect the state of nature. The sensor nodes quantize their observations using a multilevel quantizer. Before transmission to the ally fusion center (AFC), the senor nodes encrypt their data using a probabilistic encryption scheme, which randomly maps the sensor's data to another quantizer output level using a stochastic cipher matrix (key). The communication between the sensors and each fusion center is assumed to be over a parallel access channel with identical and independent branches, and with each branch being a discrete memoryless channel. We employ J-divergence as the performance criterion for both the AFC and EFC. The optimal solution for the cipher matrices is obtained in order to maximize J-divergence for AFC, whereas ensuring that it is zero for the EFC. With the proposed method, as long as the EFC is not aware of the specific cipher matrix employed by each sensor, its detection performance will be very poor. The cost of this method is a small degradation in the detection performance of the AFC. The proposed scheme has no communication overhead and minimal processing requirements making it suitable for sensors with limited resources. Numerical results showing the detection performance of the AFC and EFC verify the efficacy of the proposed method.

We present a novel algorithm for localization of Wireless Sensor Networks (WSNs) called Distributed Randomized Gradient Descent (DRGD) and prove that in the case of noise-free distance measurements, the algorithm converges and provides the true location of the nodes. For noisy distance measurements, the convergence properties of DRGD are discussed and an error bound on the location estimation error is obtained. In contrast to several recently proposed methods, DRGD does not require that the blind nodes be contained in the convex hull of the anchor nodes, and it can accurately localize the network with only a few anchors. Performance of DRGD is evaluated through extensive simulations and compared with three other algorithms, namely, the relaxation-based Second-Order Cone Programming (SOCP), the Simulated Annealing (SA), and the Semi-Definite Programing (SDP). Similar to DRGD, SOCP and SA are distributed algorithms, whereas SDP is centralized. The results show that DRGD successfully localizes the nodes in all the cases, whereas in many cases SOCP and SA fail. Finally, we present a modification of DRGD for mobile WSNs and demonstrate the efficacy of DRGD for localization of mobile networks with several simulation results.

The growing demand for wireless services has led to the introduction of new paradigms in spectrum sharing such as the unlicensed ISMand U-NII bands and dynamic or opportunistic spectrum access through cognitive radios. Coexistence of users in these technologies leads to increases in co-channel interference (CCI) which needs to be appropriately mitigated. CCI is often modeled as a white Gaussian noise process and assumed to simply reduce the signal-to-noise (plus interference) ratio. In this paper we consider the effect of CCI by a careful examination of the samples at the output of the matched filter receiver. We show that the timing offset between the interference and the desired signals may result in the correlation of errors in adjacent symbols. We evaluate the bit error rate (BER) resulting from CCI as well as the distribution of the total number of errors in a packet.

Spectrum sensing is a key function of cognitive radios and is used to determine whether a primary user is present in the channel or not. Many approaches have been proposed when both primary user and secondary user employ a single antenna. Recently several techniques have also been proposed assuming that the the secondary user employs multiple antennas. In this paper, we formulate and solve the generalized likelihood ratio test (GLRT) for spectrum sensing when both primary user transmitter and the secondary user receiver are equipped with multiple antennas. We do not assume any prior information about the channel statistics or the primary user's signal structure. Two cases are considered when the secondary user is aware of the energy of the noise and when it is not. The final test statistics derived from GLRT are based on the eigenvalues of the sample covariance matrix. Through analysis we exhibit the role of the eigenvalues in characterizing the signal+noise and noise subspaces in the received data. Simulation results are presented in terms of the receiver operating characteristics and detection probabilities for several cases of interest.

We propose an iterative algorithm based on expectation maximization (EM) to jointly estimate the system parameters and decode the data in a MIMO system using unitary space-time block codes. It is assumed that the receiver is unaware of the channel coefficients and their distribution, the average energy of the received signal, the prior probability of each signal in the constellation, and the noise power. The algorithm works for arbitrary modulation schemes and any channel model including Rayleigh and Rician fading. The complexity of the proposed receiver is computed and it is shown to be significantly lower than previously published methods, as it does not require any matrix inversion or trellis search. The performance of the proposed receiver is evaluated by simulations in terms of symbol error rate (SER) vs. signal-to-noise ratio (SNR) and it is shown that with only a few iterations of the algorithm it achieves a performance close to that of the receiver which knows all the parameters.

Wireless sensor networks are prone to node misbehavior arising from tampering by an adversary (Byzantine attack), or due to other factors such as node failure resulting from hardware or software degradation. In this paper, we consider the problem of decentralized detection in wireless sensor networks in the presence of one or more classes of misbehaving nodes. Binary hypothesis testing is considered where the honest nodes transmit their binary decisions to the fusion center (FC), while the misbehaving nodes transmit fictitious messages. The goal of the FC is to identify the misbehaving nodes and to detect the state of nature. We identify each class of nodes with an operating point (false alarm and detection probabilities) on the receiver operating characteristic (ROC) curve. Maximum likelihood estimation of the nodes' operating points is then formulated and solved using the expectation maximization (EM) algorithm with the nodes' identities as latent variables. The solution from the EM algorithm is then used to classify the nodes and to solve the decentralized hypothesis testing problem. Numerical results compared with those from the reputation-based schemes show a significant improvement in both classification of the nodes and hypothesis testing results. We also discuss an inherent ambiguity in the node classification problem which can be resolved if the honest nodes are in majority.

The problem of binary hypothesis testing is considered in a bandwidth-constrained low-power wireless sensor network operating over insecure links. Observations of the sensors are quantized and encrypted before transmission. The encryption method we propose maps the output of the quantizer to one of the possible quantizer output levels randomly according to a probability matrix. This operation is similar to that of a discrete memoryless channel. The intended (ally) fusion center (AFC) is aware of the encryption keys (probabilities) while the unauthorized (third party) fusion center (TPFC) is not. A constrained optimization problem is formulated from the point of view of AFC in order to design its decision rule along with the encryption probabilities. The objective function to be minimized is the error probability of AFC and the constraint is a lower bound on the error probability of TPFC. A good suboptimal solution to this problem is found. Numerical results are presented to show that it is possible to degrade the error probability of TPFC significantly and still achieve very low probability of error for AFC. As the number of levels in the quantizer increases the performance loss of the secure system compared to insecure system is reduced. Compared to the existing data encryption methods, the proposed method is highly scalable since it does not increase the packet overhead or transmit power of the sensors and has very low computational complexity. A scheme is described to randomize the keys so as to defeat any key space exploration attack.

Synthetic aperture radar (SAR) systems create massive amounts of data which require huge resources for transmission or storage. The limited capacity of the downlink channel demands efficient onboard compression of SAR data. However, SAR raw data exhibit very little correlation which can be exploited in a compression algorithm. Range focusing is shown to increase the data correlation by exposing some of the distinctive features of the scene under surveillance. In this paper, we first present analysis of spotlight-mode SAR to show the source of the increased correlation in the range-focused data. Next, we propose two algorithms-transform-domain block predictive quantization (TD-BPQ) and transform-domain block predictive trellis-coded quantization (TD-BPTCQ)-for the compression of the range-focused data. Experimental results indicate that, at the rate of 1 bit/sample, and for similar or lower computational complexity, TD-BPQ and TD-BPTCQ outperform the best method proposed in the literature by 1.5 and 2.3 dB in signal-to-quantization-noise ratio, respectively. Similar improvements are observed for the rate of 2 bits/sample.

The problem of binary hypothesis testing is considered in a bandwidth-constrained densely populated low-power wireless sensor network operating over insecure links. Observations of the sensors are quantized and encrypted before transmission. The encryption method maps the output of the quantizer to one of the possible quantizer output levels randomly according to a probability matrix. The intended (ally) fusion center (AFC) is aware of the encryption keys (probabilities) while the unauthorized (third party) fusion center (TPFC) is not. A constrained optimization problem is formulated from the point of view of AFC in order to design its decision rule along with the encryption probabilities. The objective function to be minimized is the error probability of AFC and the constraint is a lower bound on the error probability of TPFC. In the binary case the optimal solution is found and in the nonbinary case a good suboptimal solution is analytically obtained. Numerical results are presented to show that it is possible to degrade the error probability of TPFC significantly and still achieve very low probability of error for AFC. The proposed method which may be considered a PHY-layer security scheme is highly scalable since it does not increase the packet overhead or transmit power of the sensors and has very low computational complexity. A scheme is described to randomize the keys so as to defeat any key space exploration attack.

We consider the problem of spectrum sensing in cognitive radios when the secondary user (SU) radio is equipped with multiple antennas. Using an estimate of the cross-correlation among the signals received at different antenna elements, we propose a blind detection method which assumes no prior knowledge of the primary user's (PU) signaling scheme, the noise power, or the channel path coefficients. Detection and false alarm probabilities of the proposed algorithms are evaluated using an asymptotic analysis and the results are compared to simulation results. It is shown that the proposed method outperforms several recently-proposed blind sensing techniques for cognitive radios using multiple antennas.

The problem of binary hypothesis testing in a wireless sensor network is considered where observation of the sensors are quantized using identical binary quantizers and encrypted before transmission using a simple probabilistic cipher. The authorized or ally fusion center (AFC) is aware of the encryption process and the encryption parameters, whereas the unauthorized or third party fusion centers (TPFC) are unaware of the encryption parameters. The optimal threshold is evaluated for fixed values of the encryption parameters and numerical results are presented on the error probabilities of the two fusion centers. It is shown that by appropriate selection of the encryption parameters it is possible to degrade the performance of the TPFC significantly compared to that of AFC.

Synthetic aperture radar (SAR) systems collect large volumes of data that must be transmitted to a ground station for storage and processing. However, given the limited bandwidth of the downlink channel it is imperative that SAR data be compressed before transmission. While it is commonly believed that raw SAR data is uncorrelated, it is shown in that the inverse Fourier transform of spotlight-mode SAR exhibits non-negligible correlation that can be exploited in a predictive quantization scheme. In this paper, we propose two predictive quantization algorithms-transform-domain block predictive quantization (TD-BPQ), and transform-domain block predictive vector quantization (TD-BPVQ)-to encode dechirp-on-receive spotlight-mode SAR raw data. Experimental results indicate that, on average, TD-BPQ and TD-BPVQ outperform the well known block adaptive quantization (BAQ) by 5 and 6 dB, respectively.

Raw data collected by synthetic aperture radar (SAR) is commonly assumed to be uncorrelated and with a zero-mean Gaussian distribution. In this paper, we show-both analytically and numerically-that the range-wise inverse Fourier transform of the dechirp-on-receive circular SAR data exhibits significant correlation in the azimuth direction. Moreover, we show that a block adaptive autoregressive model well represents the transformed SAR data.

Spectrum sensing is used to identify the (temporarily) unused (licensed) frequency bands and as such plays a key role in dynamic spectrum access. Spectrum sensing is currently being investigated by a number of researchers. In this paper we compare the performance of three classes of algorithms-energy detectors, autocorrelation detectors, and the cyclic autocorrelation detector. The focus of the study is on the trade-offs of the three approaches under fixed false alarm and detection probabilities.

Cognitive radio is an enabling technology for opportunistic spectrum access. Spectrum sensing is a key feature of a cognitive radio whereby a secondary user can identify and utilize the spectrum that remains unused by the licensed (primary) users. Among the recently proposed algorithms the covariance-based method of [1] is a constant false alarm rate (CFAR) detector with a fairly low computational complexity. The low computational complexity reduces the detection time and improves the radio agility. In this paper, we present a framework to analyze the performance of this covariance-based method. We also propose a new spectrum sensing technique based on the sample autocorrelation of the received signal. The performance of this algorithm is also evaluated through analysis and simulation. The results obtained from simulation and analysis are very close and verify the accuracy of the approximation assumptions in our analysis. Furthermore, our results show that our proposed algorithm outperforms the algorithm in [1].

This paper proposes a method for direction of arrival (DOA) estimation which can be applied in case of both non-coherent and coherent sources. In comparison to the well-known subspace algorithms such as MUSIC, the proposed method has several advantages. First, in contrast to MUSIC, no forward/backward spatial smoothing for the covariance matrix is needed in the case of coherent sources. Second, the proposed method is more suitable for realtime implementation since it only requires one or a few snapshots in order to provide an accurate DOA estimation, whereas MUSIC requires a large number of snapshots. Third, the proposed method exploits the eigenvalue decomposition (EVD) of a real-valued covariance matrix thereby reducing the computational cost by at least a factor of four. Simulation results show that the proposed method can estimate the DO As of the incident sources with high accuracy even when the sources are coherent.

The optimal subcarrier, bit and power allocation problem for multiuser OFDM systems has been investigated. To achieve a numerically efficient solution the problem is divided into two separate optimization problems: one for subcarrier allocation and one for bit and power allocation. A novel and computationally efficient algorithm is presented for the bit and power allocation problem which results in a near-optimal solution. An efficient suboptimal solution is also presented for the subcarrier allocation problem. Numerical results are presented to show that the proposed methods achieve performance close to that of the optimal solution with considerably less computational complexity than those previously reported in the literature

A new call admission control algorithm is presented and analyzed for CDMA networks supporting multimedia services. Our algorithm uses a criterion based on the average SIR as well as the effective bandwidth of the connections. Handoff requests receive higher priority through resource reservation. The proposed algorithm is easy to implement as it only needs to examine the number of current connections in the cell in order to admit or reject a new connection request. The performance of the algorithm is evaluated in terms of blocking probabilities of new and handoff calls, outage probabilities and system throughput. Our results show that our algorithm achieves performance similar to those published in the literature with considerably less implementation complexity

In this paper, we investigate the performance of a multi-cell CDMA network by determining the maximum throughput that the network can achieve for a given grade-of-service requirement, quality-of-service requirement, network topology and call arrival rate profile. Our analysis is restricted to the reverse link and accounts for mobility of users between cells. A constrained nonlinear optimization problem is formulated that maximizes the network throughput subject to upper bounds on the blocking probabilities and a lower bound on the bit energy to interference ratio. The goal is to optimize the usage of network resources, provide consistent grade-of-service for all the cells in the network, and maintain a pre-specified quality-of-service. The solution to the optimization problem yields the maximum network throughput as well as the maximum number of calls that should be admitted in each cell for a given topology and call arrival rate profile. Our optimization algorithm yields significantly higher throughput compared with traditional call admission schemes.

Multiband keying (MBK) has been proposed as a means of modulating information bits into ultra wideband signals. In this paper, we are concerned with multiband keying modulation employing MPSK signals in each subband. The optimal receiver for the MBK symbols over an AWGN channel and the corresponding maximum likelihood (ML) decision rule have been presented and its performance is evaluated through analysis and simulation. An efficient algorithm, based on a depth-first tree search, is also introduced in order to simplify the maximum likelihood detection. It is shown by simulation that while the proposed algorithm significantly reduced the computational complexity, the performance in terms of symbol error rate is very close to that of the optimal ML receiver.

Modeling and estimation are investigated for orthogonal frequency division multiplexing (OFDM) channels in the frequency domain. The use of the discrete Fourier transform (DFT) enables the periodic extension of the channel impulse response (CIR) and channel frequency response (CFR), and allows the development of periodic random processes, and the related parametric dynamic systems, leading to interesting analytical results on modeling and estimation for OFDM systems. It is shown that low order parametric models are adequate for modeling OFDM channels in the frequency domain, and there exists an analytic relation between the power spectral density (PSD) of the CFR, and the power profile of the CIR. Based on the results of parametric modeling, an efficient design and implementation algorithm is developed for MMSE channel estimation. Simulation results show that the proposed simple channel estimation method is robust to channel statistics mismatch and is able to track time-varying dispersive fading channels.

In this paper we investigate traffic engineering for fault restoration techniques in multiprotocol label switching networks carrying QoS and best effort traffic. We consider off-line computation of the working and backup paths for the QoS traffic as well as the paths for the best effort traffic. Two cases of 1+1 and 1:1 path protection are considered. Our algorithms allow for control on the size of the label space for each node in the network. In addition, explicit routes can be accommodated supporting both node and link affinity.

We present a new multiple access protocol based on DS-CDMA and slotted Aloha random access protocol with reservation for integrated voice-data transmission for packet personal communication networks (PCN). Voice terminals are assigned a code which they relinquish during their silence periods to be used by the data terminals. The data terminals contend for the codes using a reservation slotted Aloha protocol. We analyze the performance of the protocol incorporating the multiple access effects as well as the self interference associated with DS-CDMA systems.

Designing a call admission control (CAC) algorithm that guarantees call blocking probabilities for arbitrary traffic distribution in CDMA networks is difficult. Previous approaches have assumed a uniform traffic distribution or excluded mobility to simplify the design complexity. We define a set of feasible call configurations that results in a CAC algorithm that captures the effect of having an arbitrary traffic distribution and whose complexity scales linearly with the number of cells. To study the effect of mobility and to differentiate between the effects of blocking new calls and blocking handoff calls, we define a net revenue function. The net revenue is the sum of the revenue generated by accepting a new call and the cost of a forced termination due to a handoff failure. The net revenue depends implicitly on the CAC algorithm. We calculate the implied costs which are the derivatives of the implicitly defined net revenue function and capture the effect of increases in the number of calls admitted in one cell on the revenue of the entire network. Given a network topology with established traffic levels, the implied costs are used in the calculation of a CAC algorithm that enhances revenue and equalizes call blocking probabilities. Moreover, our algorithm provides guaranteed grade-of-service for all the cells in the network for an arbitrary traffic distribution.

Extending ATM services to the wireless environment is intended to provide quality of service guarantees to multimedia applications. However, provisioning of QoS over the wireless link is made difficult by the fact that the burstiness of the channel and the re-transmission mechanism of the data link layer protocol result in a randomly varying transmission rate for the ATM connection. In this paper the randomly varying connection rate is modeled by a generalized Gilbert/Elliot (1959) channel model. A queueing analysis is performed for this system and the cell loss rate from the transmitter's buffer is evaluated in terms of the connection's allocated bandwidth, the buffer size and the parameters of the FEC code used in the ARQ system. Numerical results are presented for the cell loss rate as a function of the system parameters. These can be used for bandwidth allocation, buffer dimensioning and optimal code rate selection in order to guarantee the cell loss performance of the connection.

Traditional design rules, wherein cells are dimensioned in order to get an equal amount of demand in each cell are not directly applicable to CDMA networks where large cells can cause a lot of interference to adjacent small cells. In order to enable iterative cell placement we use a computationally efficient iterative process to calculate the inter-cell and intra-cell interferences as a function of pilot-signal power and base station location. These techniques enable us to improve the placement of cells in a CDMA network so as to enhance network capacity. We show examples of how networks using this design technique will provide higher capacity than ones designed using conventional techniques.

We evaluate the effects of mobility and reservations on new call blocking and handoff blocking in multirate wireless networks. The model evaluated uses fixed channel assignment (FCA) with priority for handoffs over new call arrivals by reserving a number of channels for handoff calls in all the cells. The performance measures used are new call blocking and handoff drop probabilities. The methodology used is that of implied costs which we calculate from the the network net revenue which considers the revenue generated by accepting a new call into the network as well as the cost of a handoff drop in any cell. Simulation and numerical results are presented showing the accuracy of the model. We present numerical results showing the effect of reservations on the call blocking probability. The implied cost analysis shows that mobility has a significant knock-on effect on the traffic elsewhere in the network and we capture this effect through the net revenue which is sensitive to the level of mobility. We calculate the sum revenue for a given network by maximizing the net revenue using implied costs in a gradient descent algorithm. This analysis indicates that in the case of multiple classes of traffic the call carrying capacity of the network is sensitive to the choice of reservation parameters.

A new MAC protocol which supports peer-to-peer direct communication is introduced for a packet switched wireless network. Terminals that are located within range of each other and are sufficiently isolated from the base station can communicate with their peers directly without the use of the base station as a relay. Slotted Aloha is used as the access protocol. Throughput and delay of the protocol are evaluated. Numerical results are presented which show that significant improvements in throughput/delay performance can be obtained over a system using slotted Aloha without peer-to-peer communication.

Shadow prices are calculated for least loaded routing (LLR) and aggregated least busy alternative (ALBA) routing in circuit-switched networks for the blocking probability obtained from fixed point algorithms. Numerical results are presented for the calculation of these shadow prices in small networks. As an application of these shadow prices, we also formulate a constrained optimization problem to calculate the sum capacity of LLR and ALBA for a given network. Comparison of the sum capacities indicate that the optimization using shadow prices results in a significant improvement. This provides evidence that matching capacity distribution to traffic is important even when adaptive routing schemes such as LLR and ALBA are used in the network. We also calculate upper bounds on the sum capacity which serve to indicate how well the optimized LLR and ALBA perform. The numerical results also confirm that with a small number of states the capacity of ALBA approaches that of LLR.

We introduce a novel multicast switching paradigm, the deflecting multicast switch (DMS), which accomplishes the replication and routing functions of a multicast switch simultaneously. The architecture is shown to have low connection complexity and can be implemented in a modular fashion. A self-routing and self-replication algorithm with minimal control overhead is described which allows for distributed control of these functions. A key requirement of ATM, namely the maintenance of cell sequence in a session, is easily ensured. Analytical results are presented for the calculation of the cell loss probability, the number of stages to guarantee an upper bound on the cell loss probability and the buffer delays. For the case of uniform traffic patterns, numerical results are presented which exhibit the number of stages required for a fixed cell loss probability as well as the buffer delays.

Introduces a new measure of conditional connectivity for large regular graphs by requiring each vertex to have at least g good neighbors in the graph. Based on this requirement, the vertex connectivity for the n-dimensional cube is obtained, and the minimal sets of faulty nodes that disconnect the cube are characterized.>

We propose new decoders for decoding convolutional codes over finite-state channels. These decoders are sequential and utilize the information about the channel state sequence contained in the channel output sequence. The performance of these decoders is evaluated by simulation and compared to the performance of memoryless decoders with and without interleaving. Our results show that the performance of these decoders is good whenever the channel statistics are such that the joint estimate of the channel state sequence and the channel input sequence is good, as, for example, when the channel is bursty. In these cases using even a partial search decoder such as the Fano decoder over the appropriate trellis is nearly optimal. However, when the information between the output sequence and the sequence of channel states and inputs diminishes, the memoryless decoder with interleaving outperforms even the optimal decoder which knows the channel state.

Fixed (nonadaptive) and forward adaptive differential pulse code modulation of regenerative composite sources is investigated. In the fixed code, an approximate formula is given for the optimal value of the prediction coefficient. This is then used as an initial guess to optimize the code (predictor and quantizer) through a numerical method. In the forward adaptive scheme, the state of the switch in the composite source is estimated using a MAP sequence estimation algorithm, and the code is then matched to the mode process corresponding to the estimated switch state. The performance of the two systems is evaluated with quantizers of 4, 8, and 16 levels. The results show that the forward adaptive scheme significantly outperforms optimized fixed DPCM in the sense of mean-squared error. Stochastic stability of the code is also established for the fixed DPCM scheme as well as for an adaptive scheme which receives the switch state as side information.>

The effects of redundancy and masking on the reliability of synchronizer circuits in the presence of metastability are considered. It is shown that in the jitter model developed by L. Kleeman (1990), in which circuit noise effects are considered, redundancy improves the probability of metastable failure of synchronizers, contrary to Kleeman's claim. A stochastic model that relates the noise model to the absorbing barrier problem for such noise effects is presented. It is demonstrated analytically that under considerably general conditions on the (masking) (Bcombinational circuit, clock delay, voter delay, and aperture alignment and width, the probability of metastable failure of the redundant synchronizer tends to zero with L, the number of component synchronizers. If the component synchronizers are identical, this probability of metastable failure decreases monotonically with L. Furthermore, the best combinational circuits to use in the general redundant synchronizer are the L-input AND and OR functions. Conditions are derived under which the majority voter function may or may not be effective in a general redundant synchronizer.>

The approach of Morales-Moreno and Pasupathy (1988) is extended for the design of trellis codes for 4-ary continuous phase frequency shift keying (CPFSK) with modulation index h=1/2. The criterion for comparison of codes is the maximum free Euclidean distance for a given rate and the number of states of the signal space code. For the same number of states of the signal space code, these codes improve the free Euclidean distance by up to 1.25 dB over previously published results. Finally, the implementation of the combined coding and modulation system is discussed.>

A method for computing the mean squared error distortion of differential pulse code modulation (DPCM) applied to Gaussian autoregressive sources is developed. This extends previous work wherein the code predictor was matched to the source. A two-dimensional version of the projection method for the computation of the stationary distribution of the joint source-state process is developed from which distortion can be readily evaluated. An iterative algorithm is used to optimize the quantizer for a given source and predictor. The results show that the matched predictor is very nearly optimal but not exactly so, and that DPCM is fairly robust to mismatch of the prediction coefficient to the correlation coefficient of a first-order autoregressive source.>

Continuity and singularity properties of the stationary state distribution of differential pulse code modulation (DPCM) are explored. Two-level DPCM (i.e. delta modulation) operating on a first-order autoregressive source is considered, and it is shown that, when the magnitude of the DPCM prediction coefficient is between zero and one-half, the stationary state distribution is singularly continuous; i.e. it is not discrete but concentrates on an uncountable set with a Lebesgue measure of zero. Consequently, it cannot be represented with a probability density function. For prediction coefficients with magnitude greater than or equal to one-half, the distribution is pure, i.e. either absolutely continuous and representable with a density function, or singular. This problem is compared to the well-known and still substantially unsolved problem of symmetric Bernoulli convolutions.>

The key step in the analysis of differential pulse code modulation (DPCM) is to find the steady-state probability distribution of a random process in terms of which the code distortion can be evaluated. For the case of an autoregressive source and a matched DPCM code, a well-known approximation technique has been used for the evaluation of the steady-state distribution of the prediction error process. However, the validity of this approximation method has not been justified before. A framework in which this approximation technique can be viewed as the projection method for the solution of integral equations is established. Sufficient conditions under which the approximation method can be rigorously justified are obtained.>