Evolutionary Random Access Game With Objective and Subjective Players

Abass, Ahmed A. Alabdel; Mandayam, Narayan B.; Gajić, Zoran

doi:10.1109/access.2021.3062212

Cited by 5 publications

(3 citation statements)

References 41 publications

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“…On the other hand, a classical noncooperative game has found applications in wireless networks interference management and resource allocation such as the work in refs. [25][26][27][28][29][30][31]. The seminal work in ref.…”

Section: Related Workmentioning

confidence: 99%

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A game theoretic approach to wireless body area networks interference control

Alabdel Abass,

Alshaheen,

Takruri

2024

IET Wireless Sensor Systems

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In this paper we consider a scenario where there are two wireless body area networks (WBANs) interfere with each other from a game theoretic perspective. In particular, we envision two WBANs playing a potential game to enhance their performance by decreasing interference to each other. Decreasing interference extends the sensors' batteries life time and reduces the number of re‐transmissions. We derive the required conditions for the game to be a potential game and its associated the Nash equilibrium (NE). Specifically, we formulate a game where each WBAN has three strategies. Depending on the payoff of each strategy, the game can be designed to achieve a desired NE. Furthermore, we employ a learning algorithm to achieve that NE. In particular, we employ the Fictitious play (FP) learning algorithm as a distributed algorithm that WBANs can use to approach the NE. The simulation results show that the NE is mainly a function of the power cost parameter and a reliability factor that we set depending on each WBAN setting (patient). However, the power cost factor is more dominant than the reliability factor according to the linear cost function formulation that we use throughout this work.

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Section: Related Workmentioning

confidence: 99%

“…The authors in ref. [29] propose a new evolutionary game model played on a graph where the number of connected players can affect the game outcomes. The work in ref.…”

Section: Related Workmentioning

confidence: 99%

A game theoretic approach to wireless body area networks interference control

Alabdel Abass,

Alshaheen,

Takruri

2024

IET Wireless Sensor Systems

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“…Shi, D. et al [23] presented a concept that both sides of the game, regardless of the game process, would eventually reach non-cooperative equilibrium. In [24], the conditions to achieve a locally asymptotically stable Nash equilibrium (NE) and the necessary conditions to achieve an evolutionary stable strategy (ESS) were derived. Yan, PY et al [25] developed an iterative algorithm that solves the production decision and scheduling problem based on a noncooperative equilibrium strategy with cyclic bi-value graphs.…”

Section: Introductionmentioning

confidence: 99%

An Interactive Self-Learning Game and Evolutionary Approach Based on Non-Cooperative Equilibrium

Zhao

Zhang

et al. 2021

Electronics

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Most current studies on multi-agent evolution based on deep learning take a cooperative equilibrium strategy, while interactive self-learning is not always considered. An interactive self-learning game and evolution method based on non-cooperative equilibrium (ISGE-NCE) is proposed to take the benefits of both game theory and interactive learning for multi-agent confrontation evolution. A generative adversarial network (GAN) is designed combining with multi-agent interactive self-learning, and the non-cooperative equilibrium strategy is well adopted within the framework of interactive self-learning, aiming for high evolution efficiency and interest. For assessment, three typical multi-agent confrontation experiments are designed and conducted. The results show that, first, in terms of training speed, the ISGE-NCE produces a training convergence rate of at least 46.3% higher than that of the method without considering interactive self-learning. Second, the evolution rate of the interference and detection agents reaches 60% and 80%, respectively, after training by using our method. In the three different experiment scenarios, compared with the DDPG, our ISGE-NCE method improves the multi-agent evolution effectiveness by 43.4%, 50%, and 20%, respectively, with low training costs. The performances demonstrate the significant superiority of our ISGE-NCE method in swarm intelligence.

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