Game-Theoretic Power Allocation and the Nash Equilibrium Analysis for a Multistatic MIMO Radar Network

Abstract

We investigate a game-theoretic power allocation scheme and perform a Nash equilibrium analysis for a multistatic multiple-input multiple-output radar network. We consider a network of radars, organized into multiple clusters, whose primary objective is to minimize their transmission power, while satisfying a certain detection criterion. Since there is no communication between the distributed clusters, we incorporate convex optimization methods and noncooperative game-theoretic techniques based on the estimate of the signal-to-interference-plus-noise ratio (SINR) to tackle the power adaptation problem. Therefore, each cluster egotistically determines its optimal power allocation in a distributed scheme. Furthermore, we prove that the best response function of each cluster regarding this generalized Nash game belongs to the framework of standard functions. The standard function property together with the proof of the existence of the solution for the game guarantees the uniqueness of the Nash equilibrium. The mathematical analysis based on Karush-Kuhn-Tucker conditions reveals some interesting results in terms of the number of active radars and the number of radars that over satisfy the desired SINRs. Finally, the simulation results confirm the convergence of the algorithm to the unique solution and demonstrate the distributed nature of the system.

Adaptive Energy Efficiency Based Power Allocation for MIMO Radar Network

“…The 1, 2 3 C C and C mentioned in the Table 2 specifies the clusters of the network. Table 2 conclude that the power consumption of the EEPA-GLRT methodology is less than the GNG [18] method. Because, the EEPA-GLRT methodology provides an effective power allocation based on the energy efficiency.…”

“…This section used to analyze the effectiveness of the EEPA-GLRT methodology with one existing method called GNG [18]. This GNG is used to perform the communication among the clusters and performance of this GNG is analyzed based on the game theoretic analysis.…”

“…Also, this EEPA-GLRT methodology allocates the power to the members in the clusters as well as across the clusters. Moreover, the convergence of optimal power allocation of EEPA-GLRT methodology took 5 iterations, it is less than the GNG [18] i.e., 6 iterations.…”

“…Deligiannis, A., Panoui, A., Lambotharan, S. and Chambers, J.A [18] presented the power allocation in the multistatic and distributed radar network. In that network, the MIMO radars are divided into multiple clusters.…”

Radar Waveform Strategy Based on Game Theory

“…In later work conducted by Panoui and Deligiannis, the power allocation schemes of Multistatic MIMO radar network based on different game models were investigated. Generalized Nash equilibrium, Stackelberg Nash equilibrium and Bayesian Nash equilibrium power strategies were solved to achieve high signal-to-noise ratio (SNR) gain and save power resources [13][14][15]. Considering the joint design of amplitudes and frequency-hopping codes for frequencyhopping waveforms, Han proposed a game theory framework to improve MIMO radar performance [16].…”