Recently, the decentralized baseband processing (DBP) paradigm and relevant uplink detection methods have been proposed to enable extremely large-scale massive multiple-input multiple-output technology. Under the DBP architecture, base station antennas are divided into several independent clusters, each connected to a local computing fabric. However, current detection methods tailored to DBP only consider ideal white Gaussian noise scenarios, while in practice, the noise is often colored due to interference from neighboring cells. Moreover, in the DBP architecture, linear minimum mean-square error (LMMSE) detection methods require the knowledge of noise covariance matrix which must be estimated using distributedly stored noise samples. This presents a significant challenge for decentralized LMMSE-based equalizer design. To address this issue, this paper proposes decentralized LMMSE equalization methods under colored noise scenarios for both star and daisy chain DBP architectures. Specifically, we first propose two decentralized equalizers for the star DBP architecture based on dimensionality reduction techniques. Then, we derive an optimal decentralized equalizer using the block coordinate descent method for the daisy chain DBP architecture with a bandwidth reduction enhancement scheme based on decentralized low-rank decomposition. Finally, simulation results demonstrate that our proposed methods can achieve excellent detection performance while requiring much less communication bandwidth.

To cope with the bottlenecks of the high computational complexity and excessive inter-connection communication in the conventional centralized baseband processing architecture, the decentralized baseband processing (DBP) architecture has been proposed, where the antennas are partitioned into multiple clusters, each connected to a local baseband unit (BBU). In this paper, we are interested in the distributed channel estimation (DCE) method under such DBP architecture, which is rarely studied in the literature. Our goal is to devise a DCE algorithm that can perform as well as the centralized scheme but with a small inter-connection communication cost. Specifically, based on the low-complexity diagonal minimum mean square error channel estimator, we propose an aggregate-then-estimate based DCE algorithm. In contrast to the existing DCE algorithm which requires iterative information exchanges among BBUs, our algorithm only requires one round-trip communication between the nodes. Experiment results are presented to demonstrate the efficacy of the proposed DCE algorithm.

This paper discusses the equivalence of the performance between the optimal distributed and centralized fusion based on best linear unbiased estimation (BLUE). A necessary and sufficient condition for the optimal distributed BLUE fusion to have identical performance as their centralized counterparts is obtained by setting the difference of two optimal estimates based on the two fusion rules be zero. Furthermore, under some very mild conditions on estimatee and observation error, we provide two theorems when the observations are linear in the estimatee. Specifically, the optimal distributed BLUE fusion is identical to the centralized BLUE fusion if observation errors are uncorrelated across sensors or the observation matrix of each sensor is full row rank. Numerical examples corroborate our analysis.

This paper studies the problem of distributed filtering for state estimation of a dynamic system by using observations from sensors in a network, and proposes a greedy gossip-based distributed filtering (GG-DF) algorithm. The sensor nodes can make estimation and work collaboratively. The information transmission across the network abides by the asynchronous gossip strategy that only two neighboring nodes are selected to communicate and exchange information with each other in each communication round. First, we propose a cost function of the estimation error of the entire network. Then, we derive our algorithm by making a greedy selection to minimize the cost. Finally, we provide performance and convergence analysis of the proposed algorithm, along with simulation results compared with existing methods.

We consider the problem of choosing the best subset of sensors that results in a prescribed error probability Pein Bayesian setting. Since minimizing the error probability is often difficult to evaluate and manipulate, conventional methods adopt Bhattacharyya distance instead of it. In fact, Chernoff distance is the best achievable exponent in the Bayesian error probability and it is more accurate than Bhattacharyya distance. In this paper, we design Chernoff Cyclical Iteration Algorithm (CCIA) to choose the best subset of sensors, provided the conditional distributions of sensor measurements. By leveraging on the convexity of Chernoff distance, we are able to derive the closed form of optimal parameters for the conditionally independent Gaussian measurements, exponential measurements and gamma measurements. Specifically, the optimal parameter is searched by using gold-cut algorithm. Furthermore, we prove that Chernoff distance is equivalent to Bhattacharyya distance for Gaussian measurements with uncommon means and common covariances structure under both hypotheses. In numerical examples, we supplement the comparisons between our proposed CCIA and the approach based on Bhattacharyya distance for exponential measurements and gamma measurements, which show that the detection performance based on Chernoff distance is superior to Bhattacharyya distance in the prescribed threshold.

In this paper, the problem of robust minimax testing of binary composite hypothesis is considered, while the actual probability densities are located in neighborhoods characterized by the Itakura-Saito divergence. And then the existence of a saddle value condition is proved under Sion's minimax theorem. Moreover, we derive the least favorable distributions and the robust decision rule involved four parameters, thus an infinite-dimensional optimization problem could be transformed into a nonlinear system of four equations with four unknown parameters, which can be solved numerically. Finally, experimental results evaluate and illustrate the theoretical derivations.

This paper studies joint source transmit beamforming and relay amplification matrix design to achieve rate maximization for full-duplex (FD) MIMO amplify-and-forward (AF) relay systems with consideration of relay processing delay (RPD). The problem is difficult to solve due mainly to the self-interference constraint induced by the RPD. In this paper, we first propose a penalty-based algorithmic framework, called P-BSUM, for a class of constrained optimization problems with difficult equality constraints in addition to some convex constraints. We then apply the P-BSUM algorithm to the rate maximization problem and obtain a simple iterative algorithm. Finally, numerical results illustrate the efficiency of the proposed algorithm.

This paper deals with distributed estimation fusion under unknown cross-covariance between errors of local estimates. We propose a formulation to restrict the set of possible cross-covariance matrices. The constraint in the formulation, named allowance of cross-covariance, provides a flexible way to utilize some prior information on cross-correlation in fusion methods. Then based on the allowance, an optimal robust fusion method is proposed in the minimax sense via semi-definite programming, and suboptimal fusion methods are also discussed to reduce the computational load. We analyze the properties of the proposed fusion methods and describe the relationships between our proposed fusion and some existing fusion methods. Numerical examples are given to illustrate their performance.

Consider the joint power and admission control (JPAC) problem for a multiuser single-input single-output (SISO) interference channel. Most existing works on JPAC assume the perfect instantaneous channel state information (CSI). In this paper, we consider the JPAC problem with the imperfect CSI, i.e., we assume that only the channel distribution information (CDI) is available. We formulate the JPAC problem into a chance (probabilistic)-constrained program, where each link's SINR outage probability is enforced to be less than or equal to a specified tolerance. To circumvent the computational difficulty of the chance SINR constraints, we propose to use the sample (scenario) approximation scheme to convert them into finitely many simple linear constraints. Furthermore, we reformulate the sample approximation of the chance SINR-constrained JPAC problem as a composite group sparse minimization problem and then approximate it by a second-order cone program (SOCP). The solution of the SOCP approximation can be used to check the simultaneous supportability of all links in the network and to guide an iterative link removal procedure (the deflation approach). We exploit the special structure of the SOCP approximation and custom-design an efficient algorithm for solving it. Finally, we illustrate the effectiveness and efficiency of the proposed sample approximation-based deflation approaches by simulations.

This paper considers a basic MIMO information-energy broadcast system, where a multi-antenna transmitter transmits information and energy simultaneously to a multi-antenna information receiver and a dual-functional multi-antenna energy receiver which is also capable of decoding information. Due to the open nature of wireless medium and the dual purpose of information and energy transmission, secure information transmission while ensuring efficient energy harvesting is a critical issue for such a broadcast system. Providing that physical layer security techniques are adopted for secure transmission, we study beamforming design to maximize the achievable secrecy rate subject to a total power constraint and an energy harvesting constraint. First, based on semidefinite relaxation, we propose global optimal solutions to the secrecy rate maximization (SRM) problem in the single-stream case and a specific full-stream case. Then, we propose inexact block coordinate descent (IBCD) algorithm to tackle the SRM problem of general case with arbitrary number of streams. We prove that the IBCD algorithm can monotonically converge to a Karush-Kuhn-Tucker (KKT) solution to the SRM problem. Furthermore, we extend the IBCD algorithm to the joint beamforming and artificial noise design problem. Finally, simulations are performed to validate the effectiveness of the proposed beamforming algorithms.

In this paper, we consider the robust secure beamformer design for multiple-input-single-output wiretap channels. Assuming that the eavesdroppers' channels are only partially available at the transmitter, we seek to maximize the secrecy rate under the transmit power and the secrecy rate outage probability constraint. The outage probability constraint requires that the secrecy rate exceed certain thresholds with high probability. Therefore, including such constraint in the design naturally ensures the desired robustness. Unfortunately, the presence of the probabilistic constraints makes the problem nonconvex and, hence, difficult to solve. In this paper, we investigate the outage probability constrained secrecy rate maximization problem using a novel two-step approach. Under a wide range of uncertainty models, our developed algorithms can obtain high-quality solutions, sometimes even exact global solutions, for the robust secure beamformer design problem. Simulation results are presented to verify the effectiveness and robustness of the proposed algorithms.

MIMO wiretap channel consists of a multiple antenna transmitter, a legitimate multiple antenna receiver and a multiple antenna eavesdropper. Signaling strategy optimization for MIMO wiretap channel, including secrecy capacity maximization problem and power minimization problem with a secrecy rate constraint, are studied. We identify when the two problems are globally solvable. Further, iterative algorithms based on concave-convex procedure are proposed for the two problems, which can monotonically converge to a Karush-Kuhn-Tucker (KKT) point of the two problems. Numerical examples validate the effectiveness of the proposed algorithms.

Although the globally optimal distributed Kalman filtering fusion has been proposed and studied for more than twenty years, the invertibility of estimation error and measurement noise covariances has been always a restrictive assumption to derive a globally optimal distributed Kalman filtering fusion equivalent to the centralized Kalman filtering fusion. This letter proposes an optimal distributed Kalman filtering fusion algorithm for general dynamic systems without invertibility of estimation error and measurement noise covariances. The new algorithm uses the convex combination fusion, whose fusion weights are recursively given. Computer experiments show that the performance of this fusion algorithm is very likely to be equivalent to that of the centralized Kalman filtering fusion. In practice, the new fusion algorithm can be applied to any distributed Kalman filtering fusion, such as the equality constrained distributed Kalman filtering fusion.

We consider the robust beamforming problem under imperfect channel state information (CSI) subject to SINR constraints in a downlink multiuser MISO system. One popular approach to solve this nonconvex optimization problem is via semidefinite relaxation (SDR). In this paper, we prove that the SDR method is tight when the channel uncertainty bound is small or when the base station is equipped with two antennas.

In this paper, a multisensor linear dynamic system with model uncertainty and bounded noises is considered. Based on previously developed set-valued estimation methods in terms of convex optimization, we propose several efficient algorithms of centralized sensor fusion, distributed sensor fusion, and multi-algorithm fusion to minimize the Euclidian estimation error of the state vector. Obviously, an ellipsoid/box with a larger “size” cannot be in general guaranteed to contain another ellipsoid/box with a smaller “size” since centers and shapes of the two ellipsoids/boxes may be different from each other. This fact and the complementary advantages of multiple sensors and multiple algorithms motivate us to construct multiple estimation ellipsoids/boxes squashed along each entry of the state vector as much as possible respectively by using the technique of multiple differently weighted objectives. Then intersection fusion of these estimation ellipsoids/boxes yields a final Euclidian-error-minimized state estimate. Numerical examples show that the new method with multi-algorithm at both the sensors and the fusion center can significantly reduce the Euclidian estimation error of the state.

In this paper, the robust estimation fusion problem in multisensor systems with norm-bounded uncertainties concerning the error covariance matrix between local estimates is addressed. A robust fusion method by minimizing the worst-case fused mean-squared error (MSE) for all feasible error covariance matrices of local estimates is proposed. The minimax robust fusion weighting matrices can be explicitly formulated as a function of solution of a semidefinite programming (SDP). Some numerical examples demonstrate that when the error covariance matrix suffers disturbance, the proposed fusion method is more robust than the nominal fusion method which ignores the uncertainties, and can improve the performance when the disturbance is considerably large.

In this paper, the communication direction problem of a two-sensor tandem binary decision system is considered. Rigorous analysis shows that the performance of communication from the sensor with higher noise power to the sensor with lower noise power is not always better than the performance of the reverse communication direction when the signal and sensor noises are both Gaussian. This result can be extended to a more general two-sensor tandem binary decision system without the assumption of a specific data distribution. This seems somewhat counterintuitive but has significance for optimization design of sensor communication direction. Computer experiments support our analytic results and illustrate interesting information which requires need further study.

Under the assumption of independent observation noises across sensors, Bar-Shalom and Campo proposed a distributed fusion formula for two-sensor systems, whose main calculation is the inverse of submatrices of the error covariance of two local estimates instead of the inverse of the error covariance itself. However, the corresponding simple estimation fusion formula is absent in a general distributed multisensor system. In this paper, an efficient iterative algorithm for distributed multisensor estimation fusion without any restrictive assumption on the noise covariance (i.e., the assumption of independent observation noises across sensors and the two-sensor system, and the direct computation of the Moore-Penrose generalized inverse of the joint error covariance of local estimates are not necessary) is presented. At each iteration, only the inverse or generalized inverse of a matrix having the same dimension as the error covariance of a single-sensor estimate is required. In fact, the proposed algorithm is a generalization of Bar-Shalom and Campo's fusion formula and reduces the computational complexity significantly since the number of iterative steps is less than the number of sensors. An example of a three-sensor system shows how to implement the specific iterative steps and reduce the computational complexities