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http://dx.doi.org/10.4218/etrij.2019-0351

PSO-optimized Pareto and Nash equilibrium gaming-based power allocation technique for multistatic radar network  

Harikala, Thoka (Department of Electronics and Communication, S.V. University)
Narayana, Ravinutala Satya (Department of Electronics and Communication, S.V. University)
Publication Information
ETRI Journal / v.43, no.1, 2021 , pp. 17-30 More about this Journal
Abstract
At present, multiple input multiple output radars offer accurate target detection and better target parameter estimation with higher resolution in high-speed wireless communication systems. This study focuses primarily on power allocation to improve the performance of radars owing to the sparsity of targets in the spatial velocity domain. First, the radars are clustered using the kernel fuzzy C-means algorithm. Next, cooperative and noncooperative clusters are extracted based on the distance measured using the kernel fuzzy C-means algorithm. The power is allocated to cooperative clusters using the Pareto optimality particle swarm optimization algorithm. In addition, the Nash equilibrium particle swarm optimization algorithm is used for allocating power in the noncooperative clusters. The process of allocating power to cooperative and noncooperative clusters reduces the overall transmission power of the radars. In the experimental section, the proposed method obtained the power consumption of 0.014 to 0.0119 at K = 2, M = 3 and K = 2, M = 3, which is better compared to the existing methodologies-generalized Nash game and cooperative and noncooperative game theory.
Keywords
Kernel fuzzy C-means; multiple input multiple output; Nash equilibrium; particle swarm optimization; power allocation;
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1 G. Bacci et al., A game-theoretic approach for energy-efficient detection in radar sensor networks, in Proc. IEEE Sensor Array Multichannel Signal Process. Workshop (Hoboken, NJ, USA), June 2012, pp. 157-160.
2 F. Zhao et al., Game-theoretic beamforming and power allocation in MIMO cognitive radio systems with transmitter antenna correlation, Mobile Inf. Syst. 2015 (2015), 1-7.
3 H. Chen, S. Ta, and B. Sun, Cooperative game approach to power allocation for target tracking in distributed MIMO radar sensor networks, IEEE Sens. J. 15 (2015), no. 10, 5423-5432.   DOI
4 A. Ajorloo, A. Amini, and M. H. Bastani, A compressive sensing-based colocated MIMO radar power allocation and waveform design, IEEE Sens. J. 18 (2018), no. 22, 9420-9429.   DOI
5 N. Garcia et al., Resource allocation in MIMO radar with multiple targets for non-coherent localization, IEEE Trans. Signal Process. 62 (2014), no. 10, 2656-2666.   DOI
6 J. Yan et al., Joint beam selection and power allocation for multiple target tracking in netted colocated MIMO radar system, IEEE Trans. Signal Process. 64 (2016), no. 24, 6417-6427.   DOI
7 Y. Yu et al., Power allocation and waveform design for the compressive sensing based MIMO radar, IEEE Trans. Aerosp. Electron. Syst. 50 (2014), no. 2, 898-909.   DOI
8 L. Wang et al., Jamming power allocation strategy for MIMO radar based on MMSE and mutual information, IET Radar Sonar Nav. 11 (2017), no. 7, 1081-1089.   DOI
9 B. Ma et al., A joint scheme of antenna selection and power allocation for localization in MIMO radar sensor networks, IEEE Commun. Lett. 18 (2014), no. 12, 2225-2228.   DOI
10 X. Song et al., A joint resource allocation method for multiple targets tracking in distributed MIMO radar systems, EURASIP J. Adv. Signal Process. 65 (2018).
11 L. Xing et al., MIMO radar and target Stackelberg game in the presence of clutter, IEEE Sens. J. 15 (2015), no. 12, 6912-6920.   DOI
12 A. Panoui, S. Lambotharan, and J. A. Chambers, Game theoretic distributed waveform design for multistatic radar networks, IEEE Trans. Aerosp. Electron. Syst. 52 (2016), no. 4, 1855-1865.   DOI
13 C. Shi et al., Low probability of intercept-based optimal power allocation scheme for an integrated multistatic radar and communication system, IEEE Syst. J. 14 (2019), no. 1, 983-994.   DOI
14 C. Shi et al., Joint subcarrier assignment and power allocation strategy for integrated radar and communications system based on power minimization, IEEE Sens. J. 19 (2019), no. 23, 11167-11179.   DOI
15 C. Shi et al., Nash bargaining game-theoretic framework for power control in distributed multiple-radar architecture underlying wireless communication system, Entropy 20 (2018), 267.   DOI
16 C. Shi et al., Power control scheme for spectral coexisting multistatic radar and massive MIMO communication systems under uncertainties: A robust Stackelberg game model, Digit. Signal Process. 94 (2019), 146-155.   DOI
17 C. Shi et al., Non-cooperative game theoretic power allocation strategy for distributed multiple-radar architecture in a spectrum sharing environment, IEEE Access 6 (2018), 17787-17800.   DOI
18 D. Mishra and G. C. Alexandropoulos, Jointly optimal spatial channel assignment and power allocation for MIMO SWIPT systems, IEEE Wirel. Commun. Lett. 7 (2018), no. 2, 214-217.   DOI
19 S. K. Injeti, A Pareto optimal approach for allocation of distributed generators in radial distribution systems using improved differential search algorithm, J. Electr. Syst. Inf. Technol. 5 (2017), no. 3, 908-927.   DOI
20 D. Wu, Y. Cai, and M. Guizani, Auction-based relay power allocation: Pareto optimality, fairness, and convergence, IEEE Trans. Commun. 62 (2014), no. 7, 2249-2259.   DOI
21 D. Anastasios et al., Game-theoretic power allocation and the Nash equilibrium analysis for a multistatic MIMO radar network, IEEE Trans. Signal Process. 65 (2017), no. 24, 6397-6408.   DOI
22 A. Deligiannis, S. Lambotharan, and J. A. Chambers, Game theoretic analysis for MIMO radars with multiple targets, IEEE Trans. Aerosp. Electron. Syst. 52 (2016), no. 6, 2760-2774.   DOI
23 J. Yan et al., Robust chance constrained power allocation scheme for multiple target localization in colocated MIMO radar system, IEEE Trans. Signal Process. 66 (2018), no. 15, 3946-3957.   DOI
24 X. Wang et al., An approach to the modulation recognition of MIMO radar signals, EURASIP J. Wirel. Commun. Netw. 2013 (2013), no. 66.
25 H. Gao et al., Antenna allocation in MIMO radar with widely separated antennas for multi-target detection, Sensors 14 (2014), no. 11, 20165-20187.   DOI
26 S. Gogineni and A. Nehorai, Game theoretic design for polarimetric MIMO radar target detection, Signal Process. 92 (2012), no. 5, 1281-1289.   DOI
27 X. Song et al., The MIMO radar and jammer games, IEEE Trans. Signal Process. 60 (2012), no. 2, 687-699.   DOI
28 M. Piezzo et al., Non-cooperative code design in radar networks: a game-theoretic approach, EURASIP J. Adv. Signal Process. 63 (2013), no. 1.
29 T. Harikala and R. V. S. Satyanarayana, Power efficient technique for MIMO radar using co-operative and non-co-operative game theory in wireless applications, Int. J. Recent Technol. Eng. 7 (2019), 273-278.