Omid Taghizadeh

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.

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.

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.

Transmit beamforming aided amplify-and-forward MIMO full-duplex relaying with limited dynamic range

In this paper we address the design of a relay-assisted communication system where two half-duplex (HD) users communicate with each other via the help of an amplify-and-forward (AF) and full-duplex (FD) relay node. We design joint user beamforming and relay transmit strategies to deal with the self-interference signal at the FD relay node. Our approach is to maximize the system sum rate via linear transmit strategies by exploiting multiple antennas at all the involved nodes. Convex optimization based sub-optimal and optimal solutions are developed. More specifically, an alternating optimization for self-interference aware FD relaying (AO) is devised when the transmission of multiple streams is considered. Moreover, a unified approach via gradient projections is proposed as a benchmark. It can be applied regardless of the number of antennas at each node but has a significantly higher computational complexity than the AO algorithm. Simulation results show that the proposed algorithms can achieve a good performance compared to the benchmark algorithm. Moreover, a significant FD gain is achieved in terms of the sum rate when the residual self-interference power is smaller than the noise. Graphical abstractDisplay Omitted HighlightsA joint analog and digital self-interference cancellation model is considered.Joint source and relay precoding is proposed for different setups.Optimal rank-one precoding is derived using convex optimization.Alternating optimization is proposed to achieve a multiple-stream transmission.Proposed precoding methods provide significant gain as the dynamic range decreases.

Power allocation for distributed passive radar systems with occasional node failure

In this paper, we address the optimal power allocation problem for a distributed passive radar system, where occasional node failures are taken into account. The goal of the network is to provide a reliable estimation from a target signal, by collecting and combining the individual observations from the network in a centralized node. In this regard, a minimum mean squared error (MMSE) problem is formulated for unbiased class of estimators, where a stochastical model regarding sensor failure is incorporated. As it is shown, the Karush Kuhn Tucker (KKT) conditions of optimality result in a solution algorithm with a water-filling (WF) structure, which provides an analytic optimal solution. In the end, numerical simulations illustrate the effect of the different network parameters on the resulting performance.

Cooperative strategies for distributed full-duplex relay networks with limited dynamic range

In this work we study cooperative communication schemes which can benefit from a full-duplex (FD) operation. In particular, we exploit natural characteristics of satellite communication channels which allow for a full-duplex and simultaneous operation of nodes. These characteristics result in significant gains when applied to the well-known cooperative communication schemes. We start our study by defining a system of amplify-and-forward (AF) relay network where relay nodes are empowered with FD capability. We incorporate the limits of FD nodes for suppressing the inherent loopback self-interference. Afterwards we derive optimal relay selection and optimal distributed beam-forming, given the global or local channel state information. We show that optimal solutions for the aforementioned problems can be achieved in polynomial time. At the end, we evaluate the effectiveness of the proposed algorithms and the defined system compared to the half-duplex counterpart.

Sum-Power Minimization under Rate Constraints in Full-Duplex MIMO Systems

Energy consumption in wireless communication systems is exponentially increasing due to growing wireless traffic. Combating adverse effects of excessive energy consumption calls for energy- aware system design, leading to a new research paradigm, green communications. To achieve energy awareness in a full-duplex (FD) bi-directional multiple antenna system, in this paper, we study a Quality-of-Service (QoS) problem, where the total transmit power is minimized subject to minimum rate constraints at each node, and propose two algorithms. We first apply a penalty-based method in order to obtain an efficient optimization strategy. Afterwards, in the second algorithm, we generalize the well-known relationship between Weighted-Sum-Rate (WSR) andWeightedMinimum-Mean- Squared-Error (WMMSE) problems, originally used to solve the sum-rate maximization problem, to tackle the sum-power minimization problem.

QoS Considerations for Full Duplex Multiuser MIMO Systems

We consider a full-duplex (FD) multiuser multiple-input multiple-output (MIMO) system where the base-station (BS) serves multiple uplink (UL) and downlink (DL) users simultaneously. We address the quality-of-service (QoS) problem in which the transmitted sum-power at the BS and UL users is minimized subject to minimum rate constraints at each user of the system. We first propose a centralized solution based on sequential convex programming (SCP), and then propose a distributed solution by using interference prices exchanged among the nodes to represent the cost of received interference. The proposed designs are evaluated via numerical simulations.