Nash Bargaining Game-Theoretic Framework for Power Control in Distributed Multiple-Radar Architecture Underlying Wireless Communication System

Abstract

This paper presents a novel Nash bargaining solution (NBS)-based cooperative game-theoretic framework for power control in a distributed multiple-radar architecture underlying a wireless communication system. Our primary objective is to minimize the total power consumption of the distributed multiple-radar system (DMRS) with the protection of wireless communication user's transmission, while guaranteeing each radar's target detection requirement. A unified cooperative game-theoretic framework is proposed for the optimization problem, where interference power constraints (IPCs) are imposed to protect the communication user's transmission, and a minimum signal-to-interference-plus-noise ratio (SINR) requirement is employed to provide reliable target detection for each radar. The existence, uniqueness and fairness of the NBS to this cooperative game are proven. An iterative Nash bargaining power control algorithm with low computational complexity and fast convergence is developed and is shown to converge to a Pareto-optimal equilibrium for the cooperative game model. Numerical simulations and analyses are further presented to highlight the advantages and testify to the efficiency of our proposed cooperative game algorithm. It is demonstrated that the distributed algorithm is effective for power control and could protect the communication system with limited implementation overhead.

Dwell Time Allocation Algorithm for Multiple Target Tracking in LPI Radar Network Based on Cooperative Game

“…Suppose that there are radars in the radar network and each radar performs the detection independently. For the radar, the received signal is expressed as follows [ 23 ]: where the first term represents the component due to the radar own transmission, the second terms represents component due to the other radars’ transmission and the last term represents the noise. Note that subscripts , , denotes the indices of the radar, target, and time instant, respectively.…”

Stackelberg Game-Theoretic Low Probability of Intercept Performance Optimization for Multistatic Radar System

“…In the considered multistatic system, each radar performs target detection autonomously and sends its local decision to the fusion center, which takes a global decision once the data coming from all the radars is collected. It is also assumed that each radar can determine the presence of a target by employing a binary hypothesis testing on the received signal based on the generalized likelihood ratio test (GLRT) [18,20,26]. Thus, the M time-domain samples of the received signals for the ith radar, with H 0 corresponding to the target absence hypothesis and H 1 corresponding to the target presence hypothesis, can be expressed by:…”

“…Game theory provides a natural and efficient tool in modeling the interactions among independent players [12][13][14]. A lot of work has been developed for radar systems and made significant progress [15][16][17][18][19][20]. In [16], the authors model the interaction between a smart target and a smart MIMO radar as a two-person zero-sum game.…”

“…et al in [19] propose a novel non-cooperative game-theoretic power allocation strategy for multistatic radar in a spectrum sharing environment. As an extension, a cooperative game-theoretic framework is proposed for power control in multistatic radar underlaying a communication system [20]. However, the above game-theoretic resource management protocols ignore the presence of hostile intercept receiver.…”

“…Mathematically, the LPI optimization strategy can be formulated as a problem of minimizing the radiated power of each radar for a specified target detection performance. In earlier literature, although both non-cooperative game [19] and cooperative game [20] have been utilized to control the transmit power of multistatic radar system, as aforementioned, the hostile intercept receiver is not considered in such a scenario. Therefore, we take the hierarchical interactions between intercept receiver and multiple radars into consideration and formulate the LPI performance optimization between them as a single-leader multiple-follower Stackelberg game.…”