Gholamreza Alirezaei

Optimum Power Allocation in Sensor Networks for Passive Radar Applications

We investigate the power allocation problem in distributed sensor networks that are used for target object classification. In the classification process, the absence, the presence, or the type of a target object is observed by the sensor nodes independently. Since these local observations are noisy and thus unreliable, they are fused together as a single reliable observation at a fusion center. The fusion center uses the best linear unbiased estimator to accurately estimate the reflection coefficient of target objects. We utilize the average deviation between the estimated and the actual reflection coefficient as a metric for defining the objective function. First, we demonstrate that the corresponding optimization of the power allocation leads to a signomial program which is in general quite hard to solve. Nonetheless, by using the proposed system model, fusion rule and objective function, we are able to optimize the power allocation analytically and can hence present a closed-form solution. Since the power consumption of the entire network may be limited in various aspects, three different cases of power constraints are discussed and compared with each other. In addition, a sensitivity analysis of the optimal power allocation with respect to perfect and imperfect parameter knowledge is worked out.

Optimum Power Allocation With Sensitivity Analysis for Passive Radar Applications

In this paper, we investigate the power allocation problem in distributed sensor networks and give a sensitivity analysis for perfect and imperfect knowledge of system parameters. As it is common for sensors with weak power-supplies, constraints by sum and individual power-range limitations are imposed. The power allocation problem leads to a signomial program, and is analytically solved by a Lagrangian setup. Typical examples of such networks are passive radar systems with multiple nodes, whose aim is to detect and classify target signals. For each sensor node, an amplify-and-forward strategy for the received target signal is proposed. This per-node information is transmitted over a communication channel and combined at a fusion center. The fusion center carries out the final decision about the type of the target signal by a best linear unbiased estimator and a subsequent classification. In contrast to approaches in the literature, which combine discrete local decisions into a single global one, the approach in this paper offers many advantages, ranging from the simplicity of its implementation to the achievement of an optimal solution in closed-form. In addition, it allows for a sensitivity analysis of the whole sensor network under variations of different system parameters.

Optimum power allocation for sensor networks that perform object classification

This publication analyzes the power allocation problem for a distributed sensor network. We consider a network that may have power-limited sensor nodes and is used for target object classification. In the classification process, the absence, the presence, or the type of a target object is observed by the sensor nodes independently. Since the observations are noisy, and are thus unreliable, they are fused together as a reliable global decision in order to increase the overall classification probability. The global decision is performed at a remotely located fusion center, after combining the local observations. The combiner uses the best linear unbiased estimator in order to estimate the reflection coefficient of the present object accurately. By using the proposed system architecture, we are able to optimize the power allocation analytically in order to maximize the classification performance if the total power of the sensor network is limited. Two different cases of power constraints are discussed and compared with each other. The corresponding results are valid for additive white Gaussian channels as well as for frequency-flat slow-fading channels.

SABA: a testbed for a real-time MIMO system

The growing demand for high data rates for wireless communication systems leads to the development of new technologies to increase the channel capacity thus increasing the data rate. MIMO (multiple-input multiple-output) systems are best qualified for these applications. In this paper, we present a MIMO test environment for high data rate transmissions in frequency-selective environments. An overview of the testbed is given, including the analyzed algorithms, the digital signal processing with a new highly parallel processor to perform the algorithms in real time, as well as the analog front-ends. A brief overview of the influence of polarization on the channel capacity is given as well.

Lifetime and power consumption analysis of sensor networks

Power consumption and lifetime are essential features of sensor networks. On the one hand, the power consumption should be as low as possible to enable an energy-aware system. On the other hand, the lifetime should be as long as possible to ensure for a comprehensive coverage. Especially, for application of sensor networks in extreme environments, it is also necessary to achieve high reliability over the whole lifetime. However, these features are contrary and they must be optimized simultaneously to achieve an optimal performance. In this paper, we thus study the minimization of the overall power consumption for any given lifetime and any required signal quality. First, a theoretical and challenging approach is proposed, which shows the feasible boundaries for both power reduction and achievability of a certain lifetime. Then, a practical approach is shown, which is nearly optimal and fits sufficiently together with the theoretical approach. Finally, selected results are visualized to show the performance of the new methods and to discuss the power consumption of the entire sensor network.

Complexity-reduced optimal power allocation in passive distributed radar systems

In this paper, we provide an alternative derivation of the optimal power allocation for distributed passive radar systems in closed-form. Our approach provides new insights to the nature of the power allocation problem and extracts some optimality conditions which are in turn used to achieve a new algorithm with reduced complexity for a reliable sensor selection. Finally, we show the computational complexity and the run-time of the proposed algorithm against the previously available one by analytic and simulative comparisons.

Power optimization in sensor networks for passive radar applications

In the present work, we investigate the power allocation problem in distributed sensor networks that are used for passive radar applications. The signal emitted by a target is observed by the sensor nodes independently. Since these local observations are noisy and are thus unreliable, they are fused together as a single reliable observation at a remotely located fusion center in order to increase the overall system performance. The fusion center uses the best linear unbiased estimator in order to estimate the present target signal accurately. By using the proposed system architecture and fusion rule, we are able to optimize the power allocation analytically. Two different cases of power constraints are discussed and compared with each other. The main applications of the proposed results are issues concerning the sensor selection and energy efficiency in passive sensor networks.

Optimal energy efficient design for passive distributed radar systems

In this paper, we address the energy efficiency maximization problem for a distributed passive radar system, with application in signal classification. Two energy efficiency maximization strategies are studied. Firstly, the total power consumption of the network is minimized, while maintaining a required classification quality. We show that the resulting optimization problem has a similar solution structure to the famous water-filling algorithm and can be obtained analytically. Secondly, the energy efficiency of the network is viewed as the ratio of the observed useful information to the total energy consumption of the network for each estimation process. The optimal solution for the latter case is achieved by converting the original problem into an iterative convex feasibility check with a guaranteed convergence to optimality. Finally, the optimal behavior of the defined system in terms of energy efficiency is examined with respect to different system parameters and design approaches by performing extensive numerical simulations.

Geometrical sensor selection in large-scale high-density sensor networks

In this paper, we consider large-scale high-density sensor networks consisting of small battery-powered sensor nodes. As these sensors are heavily limited in terms of energy consumption and thus the lifetime of the entire network is restricted, it is reasonable to introduce a sensor power as well as a total network power constraint. Both power constraints can simultaneously hold by means of smart power allocation methods. For a large number of sensor nodes the complexity of the utilized selection algorithm can become intolerably high. In order to simplify the power allocation procedure as well as a consecutive selection of most reliable sensor nodes, we propose an analytic-geometric pre-selection of 3-dimensional subspaces, in which more reliable sensor nodes are located. Our investigation is based on the distance between uniformly distributed sensor nodes, the target object and the fusion center as well as a free space signal propagation model. We present analytical solutions for the selection procedure and derive simplified equations in order to directly determine the region of active sensor nodes in closed-form.

Optimum Power Allocation for Sensor Networks That Perform Object Classification

In this publication, the power allocation problem for a distributed sensor network is formulated as a signomial program, and analytically solved by a Lagrangian setup. Typical examples of such networks are active radar systems with multiple nodes whose aim is to detect and classify target objects. As it is common for sensors with weak power-supplies, constraints by sum and individual power limitations are imposed. For each sensor node, an amplify-and-forward strategy for the reflected and received echo is proposed. This per-node information is transmitted over a communication channel and combined at a fusion center. The fusion center carries out the final decision about the type of the target object by a best linear unbiased estimator and a subsequent distance classification. In contrast to approaches in the literature, which combine discrete local decisions into a single global one, the approach in the current paper offers many advantages, ranging from the simplicity of its implementation to the achievement of an optimal solution in closed-form and design of the sensor network.