Dynamic Channel Access Scheme for Interference Mitigation in Relay-assisted Intra-WBANs

This work addresses problems related to interference mitigation in a single wireless body area network (WBAN). In this paper, We propose a distributed \textit{C}ombined carrier sense multiple access with collision avoidance (CSMA/CA) with \textit{F}lexible time division multiple access (\textit{T}DMA) scheme for \textit{I}nterference \textit{M}itigation in relay-assisted intra-WBAN, namely, CFTIM. In CFTIM scheme, non interfering sources (transmitters) use CSMA/CA to communicate with relays. Whilst, high interfering sources and best relays use flexible TDMA to communicate with coordinator (C) through using stable channels. Simulation results of the proposed scheme are compared to other schemes and consequently CFTIM scheme outperforms in all cases. These results prove that the proposed scheme mitigates interference, extends WBAN energy lifetime and improves the throughput. To further reduce the interference level, we analytically show that the outage probability can be effectively reduced to the minimal.Comment: 2015 IEEE International Conference on Protocol Engineering (ICPE) and International Conference on New Technologies of Distributed Systems (NTDS), Paris, France. arXiv admin note: text overlap with arXiv:1602.0865

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig