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IEEE Transactions on Signal ProcessingInformation and Energy Cooperation in Cognitive Radio NetworksCooperation between the primary and secondary systems can improve the spectrum efficiency in cognitive radio networks. The key idea is that the secondary system helps to boost the primary system's performance by relaying, and, in return, the primary system provides more opportunities for the secondary system to access the spectrum. In contrast to most of existing works that only consider information cooperation, this paper studies joint information and energy cooperation between the two systems, i.e., the primary transmitter sends information for relaying and feeds the secondary system with energy as well. This is particularly useful when the secondary transmitter has good channel quality to the primary receiver but is energy constrained. We propose and study three schemes that enable this cooperation. First, we assume there exists an ideal backhaul between the two systems for information and energy transfer. We then consider two wireless information and energy transfer schemes from the primary transmitter to the secondary transmitter using power splitting and time splitting energy harvesting techniques, respectively. For each scheme, the optimal and zeroforcing solutions are derived. Simulation results demonstrate promising performance gain for both systems due to the additional energy cooperation. It is also revealed that the power splitting scheme can achieve larger rate region than the time splitting scheme when the efficiency of the energy transfer is sufficiently large.
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Secure Beamforming for MIMO TwoWay Communications With an Untrusted RelayThis paper studies the secure beamforming design in a multipleantenna threenode system where two source nodes exchange messages with the help of an untrusted relay node. The relay acts as both an essential signal forwarder and a potential eavesdropper. Both twophase and threephase twoway relay strategies are considered. Our goal is to jointly optimize the source and relay beamformers for maximizing the secrecy sum rate of the twoway communications. We first derive the optimal relay beamformer structures. Then, iterative algorithms are proposed to find source and relay beamformers jointly based on alternating optimization. Furthermore, we conduct asymptotic analysis on the maximum secrecy sumrate. Our analysis shows that when all transmit powers approach infinity, the twophase twoway relay scheme achieves the maximum secrecy sum rate if the source beamformers are designed such that the received signals at the relay align in the same direction. This reveals an important advantage of signal alignment technique in against eavesdropping. It is also shown that if the source powers approach zero, the threephase scheme performs the best while the twophase scheme is even worse than direct transmission. Simulation results have verified the efficiency of the proposed secure beamforming algorithms as well as the analytical findings.
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Optimal Power Allocation for Parameter Tracking in a Distributed AmplifyandForward Sensor NetworkWe consider the problem of optimal power allocation in a sensor network where the sensors observe a dynamic parameter in noise and coherently amplify and forward their observations to a fusion center (FC). The FC uses the observations in a Kalman filter to track the parameter, and we show how to find the optimal gain and phase of the sensor transmissions under both global and individual power constraints in order to minimize the mean squared error (MSE) of the parameter estimate. For the case of a global power constraint, a closedform solution can be obtained. A numerical optimization is required for individual power constraints, but the problem can be relaxed to a semidefinite programming problem (SDP), and we show that the optimal result can be constructed from the SDP solution. We also study the dual problem of minimizing global and individual power consumption under a constraint on the MSE. As before, a closedform solution can be found when minimizing total power, while the optimal solution is constructed from the output of an SDP when minimizing the maximum individual sensor power. For purposes of comparison, we derive an exact expression for the outage probability on the MSE for equalpower transmission, which can serve as an upper bound for the case of optimal power control. Finally, we present the results of several simulations to show that the use of optimal power control provides a significant reduction in either MSE or transmit power compared with a nonoptimized approach (i.e., equal power transmission).
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SparsityAware Sphere Decoding: Algorithms and Complexity AnalysisInteger leastsquares problems, concerned with solving a system of equations where the components of the unknown vector are integervalued, arise in a wide range of applications. In many scenarios the unknown vector is sparse, i.e., a large fraction of its entries are zero. Examples include applications in wireless communications, digital fingerprinting, and arraycomparative genomic hybridization systems. Sphere decoding, commonly used for solving integer leastsquares problems, can utilize the knowledge about sparsity of the unknown vector to perform computationally efficient search for the solution. In this paper, we formulate and analyze the sparsityaware sphere decoding algorithm that imposes $ell_0$norm constraint on the admissible solution. Analytical expressions for the expected complexity of the algorithm for alphabets typical of sparse channel estimation and source allocation applications are derived and validated through extensive simulations. The results demonstrate superior performance and speed of sparsityaware sphere decoder compared to the conventional sparsityunaware sphere decoding algorithm. Moreover, variance of the complexity of the sparsityaware sphere decoding algorithm for binary alphabets is derived. The search space of the proposed algorithm can be further reduced by imposing lower bounds on the value of the objective function. The algorithm is modified to allow for such a lower bounding technique and simulations illustrating efficacy of the method are presented. Performance of the algorithm is demonstrated in an application to sparse channel estimation, where it is shown that sparsityaware sphere decoder performs close to theoretical lower limits.
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IEEE Transactions on Signal Processing publication informationProvides a listing of current staff, committee members and society officers.
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Table of contentsPresents the table of contents for this issue of the periodical.
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Algorithms for Secrecy Guarantee With Null Space Beamforming in TwoWay Relay NetworksIn this paper, we consider a twoway relay network with two sources and multiple cooperative relays in the presence of an eavesdropper. To guarantee the secrecy, a null space beamforming scheme is applied, where the relay beamforming vector lies in the nullspace of the equivalent channel of relay link from two sources to the eavesdropper. Our goal is to obtain the optimal beamforming vector as well as two sources’ transmit power subject to various criteria. We propose three different approaches and solve them in an alternating iterative way, where subproblems for solving beamforming and sources’ power are formulated in each iteration, respectively. First, we minimize the total transmit power under secrecy rate constraint at two sources. For beamforming vector subproblem, two different methods, semidefinite programming (SDP) and sequential quadratic programming (SQP), are proposed, where we analyze and verify SQP has lower complexity than SDP. Second, we maximize the secrecy sum rate, subject to total transmit power constraint. The beamforming vector subproblem is equivalent to a generalized Rayleigh quotient problem with rank constraint. Third, the problem of minimum peruser secrecy rate maximization under the total power constraint is investigated for user fairness. An iterative procedure utilizing the SDP with bisection search method is proposed to solve beamforming subproblem. In each approach the subproblem with two sources’ power is formulated as a single variable problem and solved by Newton’s method with line search. Simulation results demonstrate the validity of proposed approaches and algorithms for both symmetric and asymmetric scenarios.
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Robust Estimation in NonLinear StateSpace Models With StateDependent NoiseIn this paper, we present a robust estimation algorithm for nonlinear statespace models driven by statedependent noise. The algorithm is robust to outliers in the data. We derive the algorithm step by step from first principles, from theory to implementation. The implementation is straightforward and consists mainly of two components: 1) a slightly modified version of the RauchTungStriebel recursions, and 2) a backtracking line search strategy. Since it preserves the underlying chain structure of the problem, its computational complexity grows linearly with the number of data. The algorithm is iterative and is guaranteed to converge, under mild assumptions, to a local optimum from any starting point. We validate our approach via experiments on synthetic data from a multivariate stochastic volatility model.
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IEEE Transactions on Signal Processing EdicsEditors' information classification scheme (EDICS).
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Special issue on signal processing for big dataDescribes the abovenamed upcoming special issue or section. May include topics to be covered or calls for papers.
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IEEE Signal Processing Society InformationProvides a listing of current committee members and society officers.
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FrequencyDomain GLR Detection of a SecondOrder Cyclostationary Signal Over Fading ChannelsCyclostationary processes exhibit a form of frequency diversity. Based on that, we show that a digital waveform with symbol period $T$ can be asymptotically represented as a rank1 frequencydomain vector process which exhibits uncorrelation at different frequencies inside the Nyquist spectral support of $1/T$. By resorting to the fast Fourier transform (FFT), this formulation obviates the need of estimating a cumbersome covariance matrix to characterize the likelihood function. We then derive the generalized likelihood ratio test (GLRT) for the detection of a cyclostationary signal in unknown white noise without the need of a assuming a synchronized receiver. This provides a sound theoretical basis for the exploitation of the cyclostationary feature and highlights an explicit link with classical square timing recovery schemes, which appear implicitly in the core of the GLRT. Moreover, to avoid the wellknown sensitivity of cyclostationarybased detection schemes to frequencyselective fading channels, a parametric channel model based on a lower bound on the coherence bandwidth is adopted and incorporated into the GLRT. By exploiting the rank1 structure of small spectral covariance matrices, the obtained detector outperforms the classical spectral correlation magnitude detector.
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Active Learning of Multiple Source Multiple Destination TopologiesWe consider the problem of inferring the topology of a network with $M$ sources and $N$ receivers (an $M$ by $N$ network), by sending probes between the sources and receivers. Prior work has shown that this problem can be decomposed into two parts: first, infer smaller subnetwork components (1by$N$ 's or 2by2's) and then merge them to identify the $M$ by $N$ topology. We focus on the second part, which had previously received less attention in the literature. We assume that a 1by $N$ topology is given and that all 2by2 components can be queried and learned using endtoend probes. The problem is which 2by2's to query and how to merge them with the given 1by $N$, so as to exactly identify the 2by$N$ topology, and optimize a number of performance metrics, including the number of queries (which directly translates into measurement bandwidth), time complexity, and memory usage. We provide a lower bound, $lceil {{ N}over { 2}} rceil $, on the number of 2by2's required by any active learning algorithm and propose two greedy algorithms. The first algorithm follows the framework of multiple hypothesis testing, in particular Generalized Binary Search (GBS). The second algorithm is called the Receiver Elimination Algorithm (REA) and f
llows a bottomup approach. It requires exactly $N1$ steps, which is much less than all $ { Nchoose 2}$ possible 2by2's. Simulation results demonstrate that both algorithms correctly identify the 2by$N$ topology and are nearoptimal, but REA is more efficient in practice.
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Multiple Source Localization in Wireless Sensor Networks Based on Time of Arrival MeasurementWe investigate the localization of multiple signal sources based on sensors performing timeofarrival (TOA) measurement in wireless sensor networks. Moving beyond the widely studied single source localization problem, concurrently active multiple sources substantially complicate the problem since anchored sensor nodes are unaware of associations between measured signals and source nodes. At the same time, as the total number of possible sourcemeasurement associations grows exponentially with the number of sensor nodes, it is inefficient to attempt conventional singlesource localization algorithm for each possible association in a bruteforce manner. In this work, we address this difficult problem from a joint optimization perspective. Specifically, we consider simultaneous estimation of sourcemeasurement associations and the source locations, in addition to finding the initial signal transmission time. This joint optimization problem includes both discrete and continuous variables. We propose an efficient threestep algorithm that progressively simplifies the original problem through convex relaxation and sensible approximations. Our proposed algorithm demonstrates results comparable to a genieaided method that utilizes known sourcemeasurement associations.
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Optimal Joint Base Station Assignment and Beamforming for Heterogeneous NetworksConsider a downlink MIMO heterogeneous wireless network with multiple cells, each containing many mobile users and a number of base stations with varying capabilities. A central task in the management of such a network is to assign each user to a base station and design a linear transmit strategy to ensure a satisfactory level of network performance. In this work, we propose a formulation for the joint base station assignment and linear transceiver design problem based on the maximization of a system wide utility. We first establish the NPhardness of the resulting optimization problem for a large family of $alpha$fairness utility functions. Then, we propose an efficient algorithm to approximately solve this problem for a variety of different utility functions. Our numerical experiments show that the proposed algorithm can achieve significantly higher levels of system throughput and user fairness than what is possible by optimizing the precoders alone.
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