Non-Cooperative Game Theoretic Power Allocation Strategy for Distributed Multiple-Radar Architecture in a Spectrum Sharing Environment

This paper investigates the problem of non-cooperative game theoretic power allocation (NGTPA) for distributed multiple-radar architectures in a spectrum sharing environment, where multiple radars coexist with a communication system in the same frequency band. The primary objective of the multiple-radar system is to minimize the power consumption of each radar by optimizing the transmission power allocation, which is constrained by a predefined signal-to-interference-plus-noise ratio requirement for target detection and a maximum interference tolerant limit for communication system. Since each radar is rational and selfish to maximize its own utility, we utilize the non-cooperative game theoretic technique to tackle the distributed power allocation problem. Taking into consideration the target detection performance and received interference power at the communication receiver, a novel utility function is defined and employed as the optimization criterion for the NGTPA strategy. Furthermore, the existence and uniqueness of the proposed game’s Nash equilibrium point are analytically proved. An iterative power allocation algorithm with low computational complexity and fast convergence is developed, where the optimal value of each radar’s transmission power is simultaneously updated at the same time step. Numerical simulations are provided to verify the analysis and evaluate the performance of the proposed strategy as a function of the system parameters. It is shown that the distributed algorithm is effective for power allocation and could protect the communication system with limited implementation overhead.

[1]  Luca Sanguinetti,et al.  A game-theoretic approach for energy-efficient detection in radar sensor networks , 2012, 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM).

[2]  Zoran Gajic,et al.  A nash game algorithm for SIR-based power control in 3G wireless CDMA networks , 2005, IEEE/ACM Transactions on Networking.

[3]  Visa Koivunen,et al.  Delay estimation method for coexisting radar and wireless communication systems , 2017, 2017 IEEE Radar Conference (RadarConf).

[4]  Shengli Zhou,et al.  Optimal power allocation for MIMO radars with heterogeneous propagation losses , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[5]  Luca Sanguinetti,et al.  Energy-Aware Competitive Power Allocation for Heterogeneous Networks Under QoS Constraints , 2014, IEEE Transactions on Wireless Communications.

[6]  Alexander M. Haimovich,et al.  Spatial Diversity in Radars—Models and Detection Performance , 2006, IEEE Transactions on Signal Processing.

[7]  Chenguang Shi,et al.  Transmitter Subset Selection in FM-Based Passive Radar Networks for Joint Target Parameter Estimation , 2016, IEEE Sensors Journal.

[8]  K. Doganay Online Optimization of Receiver Trajectories for Scan-Based Emitter Localization , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[9]  Athina P. Petropulu,et al.  Joint Transmit Designs for Coexistence of MIMO Wireless Communications and Sparse Sensing Radars in Clutter , 2017, IEEE Transactions on Aerospace and Electronic Systems.

[10]  H. Vincent Poor,et al.  Sensor Selection in Distributed Multiple-Radar Architectures for Localization: A Knapsack Problem Formulation , 2012, IEEE Transactions on Signal Processing.

[11]  Sangarapillai Lambotharan,et al.  Game theoretic power allocation for a multistatic radar network in the presence of estimation error , 2014, 2014 Sensor Signal Processing for Defence (SSPD).

[12]  Sangarapillai Lambotharan,et al.  Game theoretic distributed waveform design for multistatic radar networks , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[13]  Sandeep Gogineni,et al.  Game theoretic design for polarimetric MIMO radar target detection , 2012, Signal Process..

[14]  Alexander M. Haimovich,et al.  Noncoherent MIMO Radar for Location and Velocity Estimation: More Antennas Means Better Performance , 2010, IEEE Transactions on Signal Processing.

[15]  Petre Stoica,et al.  Unified Optimization Framework for Multi-Static Radar Code Design Using Information-Theoretic Criteria , 2013, IEEE Transactions on Signal Processing.

[16]  Caijun Zhong,et al.  Joint Spectrum and Power Allocation for D2D Communications Underlaying Cellular Networks , 2016, IEEE Transactions on Vehicular Technology.

[17]  Victor C. M. Leung,et al.  Energy Efficient User Association and Power Allocation in Millimeter-Wave-Based Ultra Dense Networks With Energy Harvesting Base Stations , 2017, IEEE Journal on Selected Areas in Communications.

[18]  S. Lambotharan,et al.  Power allocation game between a radar network and multiple jammers , 2016, 2016 IEEE Radar Conference (RadarConf).

[19]  Chenguang Shi,et al.  Power Minimization-Based Robust OFDM Radar Waveform Design for Radar and Communication Systems in Coexistence , 2018, IEEE Transactions on Signal Processing.

[20]  Bin Li,et al.  Adaptive power control algorithm in cognitive radio based on game theory , 2015, IET Commun..

[21]  Chenguang Shi,et al.  Modified Cramér‐Rao lower bounds for joint position and velocity estimation of a Rician target in OFDM‐based passive radar networks , 2017 .

[22]  Symeon Papavassiliou,et al.  Supermodular Game-Based Distributed Joint Uplink Power and Rate Allocation in Two-Tier Femtocell Networks , 2017, IEEE Transactions on Mobile Computing.

[23]  Yuanwei Jin,et al.  A joint design of transmit waveforms for radar and communications systems in coexistence , 2014, 2014 IEEE Radar Conference.

[24]  Linda M. Davis,et al.  Adaptive waveform selection for multistatic target tracking , 2015, IEEE Transactions on Aerospace and Electronic Systems.

[25]  Peter B. Luh,et al.  The MIMO Radar and Jammer Games , 2012, IEEE Transactions on Signal Processing.

[26]  H. Vincent Poor,et al.  Distributed target tracking in multiple widely separated radar architectures , 2012, 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM).

[27]  Victor C. M. Leung,et al.  Incomplete CSI Based Resource Optimization in SWIPT Enabled Heterogeneous Networks: A Non-Cooperative Game Theoretic Approach , 2018, IEEE Transactions on Wireless Communications.

[28]  Sangarapillai Lambotharan,et al.  Game theoretic power allocation technique for a MIMO radar network , 2014, 2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP).

[29]  Linda M. Davis,et al.  Joint transmitter waveform and receiver path optimization for target tracking by multistatic radar system , 2014, 2014 IEEE Workshop on Statistical Signal Processing (SSP).

[30]  Zheng Bao,et al.  Joint Beam Selection and Power Allocation for Multiple Target Tracking in Netted Colocated MIMO Radar System , 2016, IEEE Transactions on Signal Processing.

[31]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[32]  Sangarapillai Lambotharan,et al.  A Bayesian game theoretic framework for resource allocation in multistatic radar networks , 2017, 2017 IEEE Radar Conference (RadarConf).

[33]  Sangarapillai Lambotharan,et al.  Game-Theoretic Power Allocation and the Nash Equilibrium Analysis for a Multistatic MIMO Radar Network , 2017, IEEE Transactions on Signal Processing.

[34]  Anthony F. Martone,et al.  A game-theoretic approach for radar and LTE systems coexistence in the unlicensed band , 2016, 2016 USNC-URSI Radio Science Meeting.

[35]  Marco Lops,et al.  Joint Design of Overlaid Communication Systems and Pulsed Radars , 2017, IEEE Transactions on Signal Processing.