A Self-Scrutinized Backoff Mechanism for IEEE 802.11ax in 5G Unlicensed Networks

Abstract

The IEEE 802.11ax high-efficiency wireless local area network (HEW) is promising as a foundation for evolving the fifth-generation (5G) radio access network on unlicensed bands (5G-U). 5G-U is a continued effort toward rich ubiquitous communication infrastructures, promising faster and reliable services for the end user. HEW is likely to provide four times higher network efficiency even in highly dense network deployments. However, the current wireless local area network (WLAN) itself faces huge challenge of efficient radio access due to its contention-based nature. WLAN uses a carrier sense multiple access with collision avoidance (CSMA/CA) procedure in medium access control (MAC) protocols, which is based on a binary exponential backoff (BEB) mechanism. Blind increase and decrease of the contention window in BEB limits the performance of WLAN to a limited number of contenders, thus affecting end-user quality of experience. In this paper, we identify future use cases of HEW proposed for 5G-U networks. We use a self-scrutinized channel observation-based scaled backoff (COSB) mechanism to handle the high-density contention challenges. Furthermore, a recursive discrete-time Markov chain model (R-DTMC) is formulated to analyze the performance efficiency of the proposed solution. The analytical and simulation results show that the proposed mechanism can improve user experience in 5G-U networks.

Q-learning-enabled channel access in next-generation dense wireless networks for IoT-based eHealth systems

“…Therefore, for the systems that need to deal interactively, it is often impractical to obtain a sample training dataset of anticipated behavior that is equally precise and descriptive regarding all the states in which the device has to perform actions in the future. In an unexplored environment, wherein ML is expected to be most valuable, a device must be able to learn from its own experience of interaction with the environment [18,19]. Examples of supervised ML algorithms are regression models [20], k-nearest neighbor (KNN) [21], support vector machine (SVM) [22], and Bayesian learning (BL) [18].…”

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Sum Utilization of Spectrum with Spectrum Handoff and Imperfect Sensing in Interweave Multi-Channel Cognitive Radio Networks

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