Wenhan Dai

Power optimization for network localization

Reliable and accurate localization of mobile objects is essential for many applications in wireless networks. In range-based localization, the position of the object can be inferred using the distance measurements from wireless signals exchanged with active objects or reflected by passive ones. Power allocation for ranging signals is important since it affects not only network lifetime and throughput but also localization accuracy. In this paper, we establish a unifying optimization framework for power allocation in both active and passive localization networks. In particular, we first determine the functional properties of the localization accuracy metric, which enable us to transform the power allocation problems into second-order cone programs (SOCPs). We then propose the robust counterparts of the problems in the presence of parameter uncertainty and develop asymptotically optimal and efficient near-optimal SOCP-based algorithms. Our simulation results validate the efficiency and robustness of the proposed algorithms .

A Mathematical Model for Wideband Ranging

Wideband ranging is essential for numerous emerging applications that rely on accurate location awareness. The quality of range information, which depends on network intrinsic properties and signal processing techniques, affects the localization accuracy. A popular class of ranging techniques is based on energy detection owing to its low-complexity implementation. This paper establishes a tractable model for the range information as a function of wireless environment, signal features, and energy detection techniques. Such a model serves as a cornerstone for the design and analysis of wideband ranging systems. Based on the proposed model, we develop practical soft-decision and hard-decision algorithms. A case study for ranging and localization systems operating in a wireless environment is presented. Sample-level simulations validate our theoretical results.

Distributed Power Allocation for Cooperative Wireless Network Localization

Device-to-device (D2D) communication in cellular networks is a promising concept that permits cooperation among mobile devices not only to increase data throughput but also to enhance localization services. In those networks, the allocation of transmitting power plays a critical role in determining network lifetime and localization accuracy. Meanwhile, it is a challenging task for implementation in cooperative D2D networks, since each device has only imperfect estimates of local network parameters in distributed settings. In this paper, we establish an optimization framework for robust power allocation in cooperative wireless network localization, and develop distributed power allocation strategies. In particular, we decompose the power allocation problem into infrastructure and cooperation phases, show the sparsity property of the optimal power allocation, and develop efficient power allocation strategies. Simulation results show that these strategies can achieve significant performance improvement in localization accuracy compared to the uniform strategies.

Energy-Efficient Network Navigation Algorithms

Network navigation is an emerging paradigm that enables high-accuracy location awareness in GPS-challenged environments. Two important operations of network navigation, location inference and power control, interrelate with each other, thus motivating the design of joint inference and control algorithms. In this paper, we develop efficient network navigation algorithms with optimized energy allocation. In particular, we first determine the confidence region for lzocation inference based on Fisher information analysis, and then design robust energy allocation strategies that minimize the position errors of the agents within the confidence region. Based on these strategies, both centralized and distributed energy-efficient network navigation algorithms are developed. Simulation results show that the proposed algorithms significantly reduce the position errors compared to the algorithms with uniform or non-robust power control.

Power Management for Cooperative Localization: A Game Theoretical Approach

Network cooperation among agents can significantly increase their position accuracy at the cost of power consumption. Current power management techniques aim at minimizing the total position estimation errors over all the agents subject to the power budgets. There are two main drawbacks for these approaches. First, the performance of a single agent may be sacrificed for the benefit of the whole network, and second, full power budget may be used for only marginal performance improvement on the position accuracy. This paper proposes a new type of power management strategies where each agent individually minimizes its square position error bound penalized by its power cost. The strategies are obtained as solutions to two power management games that are formulated under the knowledge of local information and global information, respectively. We show that agents are more likely to cooperate when global information is available or the channel quality is good. Analytical and numerical results show that the proposed strategies significantly reduce the energy consumption with only marginal performance loss in position accuracy.

Optimal power allocation for active and passive localization

Power resource allocation is crucial for localization since it affects not only the conventionally recognized lifetime, throughput, and covertness, but also localization efficiency of the network. In this paper, we present an optimization framework for range-based localization to improve the power efficiency and localization accuracy. Our framework unifies the analysis for active and passive localization through the examples of wireless network localization (WNL) and multiple radar localization (MRL). In particular, we determine the functional properties of localization accuracy metric, and based on those properties we formulate the power allocation problem as conic programs. Moreover, we propose robust counterparts that retain the conic structures for power allocation in the presence of parameter uncertainty. Our simulation results validate the efficiency and robustness of the proposed methods.

On the minimum number of active anchors for optimal localization

High-accuracy localization is crucial for numerous location-based applications. In wireless networks, the position information of a node (agent) can be obtained from range measurements with respect to nodes with known positions (anchors). The transmission power allocation among anchors not only affects network lifetime and throughput, but also determines the localization accuracy. In this paper, we formulate the power optimization problem that provides the best location accuracy under a total power constraint. For a given set of anchors, the minimum number of active anchors required for optimal localization in 2-D network is proven to be either two or three, depending on the network parameters. We then derive the closed-form expression for the optimal power allocation in the case of small networks and extend the results to general cases. We also develop a near-optimal strategy that requires less computational complexity, incurring negligible average performance loss. Our results provide a theoretical basis for designing anchor selection and power allocation algorithms for localization.

A Computational Geometry Framework for Efficient Network Localization

Network localization is an emerging paradigm for providing high-accuracy positional information in GPS-challenged environments. To enable efficient network localization, we propose node prioritization strategies for allocating transmission resources among network nodes. This paper develops a computational geometry framework for determining the optimal node prioritization strategy. The framework consists of transforming each node prioritization strategy into a point in a Euclidian space and exploiting geometric properties of these points. Under this framework, we prove the sparsity property of the optimal node prioritization vector (NPV) and reduce the search space of the optimal NPV. Our approach yields exact optimal solutions rather than

Resource Management Games for Distributed Network Localization

\epsilon

Power management game for cooperative localization in asynchronous networks

-approximate solutions for efficient network localization. Numerical results show that the proposed approach can significantly reduce the computational complexity of prioritization strategies and improve the accuracy of network localization.