Interference Mitigation for Coexisting Wireless Body Area Networks: Distributed Learning Solutions
| Autor: | Lillykutty Jacob, Emy Mariam George |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
stochastic estimator learning algorithm
Mathematical optimization IEEE 802.15.6 General Computer Science Computer science Signal-to-interference-plus-noise ratio 02 engineering and technology Channel hopping symbols.namesake Interference (communication) Computer Science::Networking and Internet Architecture 0202 electrical engineering electronic engineering information engineering Wireless General Materials Science Fading interference mitigation 020203 distributed computing Network packet business.industry General Engineering stochastic learning algorithm 020206 networking & telecommunications Nash equilibrium potential game symbols lcsh:Electrical engineering. Electronics. Nuclear engineering Potential game business lcsh:TK1-9971 Communication channel |
| Zdroj: | IEEE Access, Vol 8, Pp 24209-24218 (2020) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2020.2970581 |
| Popis: | When multiple wireless body area networks (WBANs) exist in close proximity to each other, the inter-user interference considerably degrades the signal to interference plus noise ratio of the packets arriving at each WBAN coordinator. Also, the propagation paths within each WBAN experience fading due to the continuous changes in the body posture and mobility of the human body. The most preferred coexisting mechanisms specified in the IEEE 802.15.6 standard is the channel hopping mechanism, which fails to consider the varying radio environment and obtained reward in its channel selection. Thus, our paper investigates this channel selection problem for interference mitigation in a time-varying environment. We formulate this channel selection problem as a finite repeated potential game and propose two learning algorithms, Stochastic Learning Algorithm (SLA) and Stochastic Estimator Learning Algorithm (SELA) to achieve the Nash Equilibrium (NE) of the game. Numerical results show the convergence of the learning algorithms to the NE point of the game. The performance evaluation and impact of parameters on these two algorithms are also analyzed in our paper. |
| Databáze: | OpenAIRE |
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