Bolstered by the growing interest in building wireless sensor and ad hoc networks with applications ranging across different engineering disciplines, distributed consensus algorithms have recently seen a new revival since their inception in the early 1980s. Of particular interest is the recently developed broadcast-based consensus algorithm, which is one special type of randomized consensus algorithms and is amenable to practical implementation in wireless networks. This paper focuses on the performance analysis of this broadcast-based consensus algorithm in the presence of non-zero-mean stochastic perturbations. It is demonstrated that as the algorithm proceeds, the deviation of the node states from their average will converge, in expectation, to a fixed value, which is determined by the Laplacian matrix of the network, the mixing parameter, and the mean of the stochastic perturbations. Asymptotic upper and lower bounds on the total mean-square deviation are derived, which describe the range of distances over which the node states deviate from consensus. These bounds can facilitate evaluation of the applicability of this algorithm in practice. Results are also provided on the algorithm's ε-converging time, i.e., the earliest time at which the deviation is ε close to its steady value, and on the mean and mean-square behaviors of the displacement of node states from their initial states at large iteration number. As a special case study, performance of the broadcast-based consensus algorithm under zero-mean stochastic disturbances is analyzed, and results regarding its convergence, mean-square deviation, and mean-square displacement are given. The theoretical results presented in this study hold true regardless of the statistics of the stochastic disturbances, and are valid for arbitrary network topology as long as the topology is connected.

To address the challenges inherent to a problem of practical interest - of Neyman-Pearson detection of fluctuating radar signals using wireless sensor networks, we propose in this paper a distributed and energy-efficient framework. Such framework is scalable with respect to the network size, and is able to greatly reduce the dependence on the central fusion center. It assumes a clustering infrastructure, and addresses signal processing and communications related issues arising from different layers. This framework includes a distributed scheduling protocol and a distributed routing protocol, which enable sensor nodes to make their own decisions about information transmissions, without requiring the knowledge of the network global information. In this framework, energy efficiency manifests itself at different network layers in a distributed fashion, and a balance between the detection performance and the energy efficiency is also attained.

This letter addresses the sensor placement problem for monitoring spatial phenomena by employing an estimation/prediction metric, i.e., noisy observations from a limited number of sensors are used to estimate the phenomena over the whole region. To solve the formulated problem, we propose a random search-based simulation optimization algorithm to iteratively select the sensor locations out of a possibly countably infinite subset of candidates. We further consider the sensor placement problem given a constraint on the energy consumption, and we propose a scheme which superimposes the Lagrange multiplier method for nonlinear programming upon our proposed discrete simulation optimization algorithm. We present numerical examples to demonstrate the fast convergence as well as the effectiveness of this simulation based algorithm.

In this paper, we consider energy-efficient routing for detection in wireless sensor networks (WSNs). Energy-efficient routing for WSNs has been intensely studied recently, but routing for signal detection in WSNs has not attracted much attention. Moreover, we are not aware of any previous work on routing for detection in WSNs that specifically considers the Neyman-Pearson criterion which is the most accepted metric for radar, sonar and related signal detection problems. By deploying a simple but illustrative model for target detection in WSNs, we formulate a problem of energy-efficient routing for signal detection under the Neyman-Pearson criterion. We propose a routing metric which aims for a route with the maximum mean detection-probability-to-energy ratio, and describe methods for solving the resulting optimization problem. This proposed metric, striving for a balance between the consumed energy and detection performance, is effective for finding the optimal routing that values such an energy-detection tradeoff.

Energy-efficient routing for wireless sensor networks (WSNs) has been a topic of great interest for the last few years, but thus far routing for signal detection in WSNs has not attracted much attention. In particular, little research has focused on the Neyman-Pearson criterion which is the most well accepted metric for radar, sonar and related signal detection problems. In this paper, we formulate the problem of energy-efficient routing for signal detection under the Neyman-Pearson criterion, apparently for the first time. We hereby propose two different routing metrics that aim at a tradeoff between the detection performance and the energy consumption. In particular, the first one leads to a combinatorial problem of identifying a path which achieves the largest possible mean detection-probability-to-energy ratio, while the second one reduces to finding a route which minimizes the consumed energy while maintaining a predeter-mined detection probability. We further provide efficient algorithms for solving these problems based on state-of-the-art research from operations research. We also present simulation results which indicate the distinctive energy-performance balance achieved by each proposed routing metric.

Frequency-domain equalization (FDE) is an effective technique to mitigate the distortion induced by long-spanning inter-symbol interference, but the receiver requires to estimate the channel information. In this paper, we present a training-based frequency-domain channel frequency response (CFR) estimation scheme, which is hardware efficient when integrating with FDE for space-time (ST) coded multiple-input multiple-output (MIMO) transmissions over frequency-selective fading channels. In this proposed scheme, training sequences are ST block encoded at the transmitter. Similar to when performing FDE, the training sequences are processed after the FFT operation to obtain the CFR. A thorough analysis of the CFR estimation mean square error (MSE) is provided based on both the least squares (LS) and minimum MSE (MMSE) criteria, with the transmit power constraint and the optimal choices of training signals being taken into account. Finally, we demonstrate that when the number of transmit antennas is equal to 2, this CFR estimation could be implemented recursively by means of certain adaptive algorithms. Simulation results are presented to demonstrate the effectiveness of our proposed scheme in joint with FDE in multipath fading channels.

In this paper, we study various frequency-domain equalizers (FDE) for single-carrier space-time block coded transmissions over frequency-selective fading channels. The linear and decision feedback equalizers based on zero forcing (ZF) and minimum mean-square error (MMSE) criteria are considered. These methods offer different tradeoff in terms of performance and the a priori knowledge of the data and receiver noise statistics, which is discussed. Besides, we also consider the adaptive FDE based on some low-complexity block-wise recursive algorithms, which obviates the need for distinct channel estimation and realizes joint channel estimation and equalization. Performance of these FDE schemes are investigated by applying them to the EDGE system in the multipath fading environment. Simulation results demonstrate their effectiveness and also provide implications for selection of these FDE schemes in practice

In this paper, we present low-complexity adaptive frequency-domain equalization (FDK) for space-time block coding (STBC) direct-sequence code-division multiple access (DS-CDMA) downlink system. The proposed scheme is powerful and computationally efficient, as the sophisticated matrix decomposition operations are not required in the processing and some block recursive processing, such as block least mean square (BLMS) algorithm and modified block recursive least squares (BRLS) algorithm can be easily incorporated for equalizer adaption. Simulation results demonstrate the effectiveness of such adaptive FDE with regard to different Doppler shifts.