Power control scheme for spectral coexisting multistatic radar and massive MIMO communication systems under uncertainties: A robust Stackelberg game model

Shi, Chenguang; Qiu, Wei; Wang, Fei; Salous, Sana; Zhou, Jianjiang

doi:10.1016/j.dsp.2019.05.007

Cited by 21 publications

(7 citation statements)

References 28 publications

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“…Shi and others [24] developed the robust Stackelberg game–based power control (RSG‐PC) for multistatic radar and massive MIMO communication systems. For each radar, the radiated power in the worst case is reduced by RSG‐PC.…”

Section: Literature Surveymentioning

confidence: 99%

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

Harikala

Narayana

2020

ETRI Journal

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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.

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Section: Literature Surveymentioning

confidence: 99%

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

Harikala

Narayana

2020

ETRI Journal

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“…Game theory is viewed as a feasible analysis theory, which is also introduced into radar countermeasure field. Accordingly, cooperative and non‐cooperative game theories are used to study the problems of radar resource allocation in response to different complex environments, so that the radar systems can reduce their own power consumptions and achieve maximum benefits (Chen et al., 2015; Deligiannis & Lambotharan, 2017; Deligiannis et al., 2017; Feng et al., 2016; Godrich et al., 2011; He & Su, 2019; Shi et al., 2017; Shi, Qiu, et al., 2019; Sun et al., 2014; Wang et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

Game Theoretic Countermeasure Analysis for Multistatic Radars and Multiple Jammers

Bin

2021

Radio Science

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Radar, target, and jammer are considered as the major participants in electronic warfare (EW), and all of them hope to win (Stephens, 1996;Xu et al., 2015). In order to mitigate the interferences of jammer, different kinds of new radar systems are developed, including cognitive radar (Haykin, 2006), multistatic radar (Chernyak, 1998) and multiple-input multiple-output (MIMO) radar (Li & Stoica, 2009). As a research hotpot, multistatic radar system has been paid much attention by a lot of countries. Whereas, multistatic radar system is interfered by jammers when it is detecting, tracking, and locating the targets at high-altitude, far-distance and high-speed. To get superior performances in the aspects of target detection and anti-jamming, multistatic radar system needs to optimize the scheduling of radar resources. Furthermore, for cooperative or non-cooperative resource allocation problems, game theory is considered to solve them (Persiano, 2009). Nowadays, the communication network has been studied by many researchers using game theory, which is mainly about the development of flexible, anonymous and autonomous mobile networks. In reality, network devices can establish a low-complexity distributed algorithm, which makes independent and reasonable strategic decisions on the cooperative and non-cooperative behaviors of network participants (Saad et al., 2009). Furthermore, resource allocation problems including power control, spectrum allocation, antenna placement and beamforming in the communication system have cooperative and non-cooperative relations. To assist decision-maker to formulate proper strategies and improve communication efficiency, game theoretic methods are considered as effective strategies to deal with these resource allocation problems

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“…In resource allocation, game theory is introduced into the fields of communication and radar as an effective resource allocation optimization theory [32][33][34][35][36][37][38]. In the field of communication, literature [32] studies the uplink power control of code division multiple access (CDMA) using the non-cooperative game theory, and proposes two algorithms to update strategies and converge to a stable NE point.…”

Section: Introductionmentioning

confidence: 99%

Multiple Power Allocation Game Schemes for Spectrum Coexistence Model Between Multistatic MIMO Radar Sensors and MU Communication

Bin

2020

Sensors

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The normal operations of radar systems and communication systems under the condition of spectrum coexistence are facing a huge challenge. This paper uses game theory to study power allocation problems between multistatic multiple-input multiple-output (MIMO) radars and downlink communication. In the case of spectrum coexistence, radars, base station (BS) and multi-user (MU) have the working state of receiving and transmitting signals, which can cause unnecessary interferences to different systems. Therefore, when they work together, they should try to suppress mutual interferences. Firstly, the signal from BS is considered as interference when radar detects and tracks targets. A supermodular power allocation game (PAG) model is established and the existence and uniqueness of the Nash equilibrium (NE) in this game are proved. In addition, the power allocation problem from BS to MU is also analyzed, and two Stackelberg PAG models are constructed. It is proved that the NE of each game exists and is unique. Simultaneously, two Stackelberg power allocation iterative algorithms converge to the NEs. Finally, numerical results verify the convergence of the proposed PAG algorithms.

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Power control scheme for spectral coexisting multistatic radar and massive MIMO communication systems under uncertainties: A robust Stackelberg game model

Cited by 21 publications

References 28 publications

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

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

Game Theoretic Countermeasure Analysis for Multistatic Radars and Multiple Jammers

Multiple Power Allocation Game Schemes for Spectrum Coexistence Model Between Multistatic MIMO Radar Sensors and MU Communication

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