Decentralized Inter-User Interference Suppression in Body Sensor Networks with Non-cooperative Game

TL;DR

This paper introduces a decentralized algorithm called DISG that uses game theory and learning to reduce inter-user interference in Body Sensor Networks, ensuring better QoS.

Contribution

The paper proposes a novel interference suppression algorithm for BSNs that is decentralized, adaptive, and based on non-cooperative game theory and no regret learning.

Findings

DISG effectively reduces inter-user interference in BSNs.

Theoretical proof of DISG's correctness and effectiveness.

Experimental results confirm improved QoS in BSNs.

Abstract

Body Sensor Networks (BSNs) provide continuous health monitoring and analysis of physiological parameters. A high degree of Quality-of-Service (QoS) for BSN is extremely required. Inter-user interference is introduced by the simultaneous communication of BSNs congregating in the same area. In this paper, a decentralized inter-user interference suppression algorithm for BSN, namely DISG, is proposed. Each BSN measures the SINR from other BSNs and then adaptively selects the suitable channel and transmission power. By utilizing non-cooperative game theory and no regret learning algorithm, DISG provides an adaptive inter-user interference suppression strategy. The correctness and effectiveness of DISG is theoretically proved, and the experimental results show that DISG can reduce the effect of inter-user interference effectively.