Abstract
The increased demand for spectrum resources for multimedia communications and a limited licensed spectrum have led to widespread concern regarding the operation of long term evolution (LTE) in the unlicensed (LTE-U) band for internet of things (IoT) systems. Because Wi-Fi and LTE are diverse with dissimilar physical and link layer configurations, several solutions to achieve an efficient and fair coexistence have been proposed. Most of the proposed solutions facilitate a fair coexistence through a discontinuous transmission using a duty cycling or contention mechanism and an efficient coexistence through a clean channel selection. However, they are constrained only by fairness or efficient coexistence but not both. Herein, we propose joint adaptive duty cycling (ADC) and dynamic channel switch (DCS) mechanisms. The ADC mechanism supports a fair channel access opportunity by muting certain numbers of subframes for Wi-Fi users whereas the DCS mechanism offers more access opportunities for LTE-U and Wi-Fi users by preventing LTE-U users from occupying a crowded channel for a longer time. To support these mechanisms in a dynamic environment, LTE-U for IoT applications is enhanced using Q-learning techniques for an automatic selection of the appropriate combination of muting period and channel. Simulation results show the fair and efficient coexistence achieved from using the proposed mechanism.
Highlights
An exponential increase in demand for wireless multimedia data and the limited nature of a licensed spectrum for cellular networks have inspired the use of unlicensed bands for long term evolution (LTE) in internet of things (IoT)
This paper considers the number of different channels in the unlicensed band and creates a utility function by considering the network efficiency and fairness factor
Mk =1 where Mk represents the total number of LTE-U user equipment (UE) assisted by the LTE-U base stations (BSs) exploiting the supplemental downlink capacity in channel k, SINRmk is the signal-to-noise and interference ratio perceived by the mth UE when downlink data are conveyed on the kth channel, and Θ is the portion
Summary
An exponential increase in demand for wireless multimedia data and the limited nature of a licensed spectrum for cellular networks have inspired the use of unlicensed bands for long term evolution (LTE) in internet of things (IoT). Wi-Fi systems, whereas LTE uses continuous traffic generation with the smallest time gaps even in the absence of data traffic Considering these operating characteristics in both systems, Wi-Fi seems to have a minimal opportunity to use the channel compared with LTE under a coexistence scenario, resulting in a performance degradation for Wi-Fi [4]. Q-learning techniques for an autonomous selection of the appropriate combination of best duty cycles in various channels through iterations of the learning process This process escalates the spectrum efficiency of the network while assuring the fairness among these LTE and Wi-Fi systems. A Q-learning mechanism used for an ideal and autonomous selection of an LTE-U operational channel muting duration toward fair and efficient spectrum sharing under a dynamic environment;.
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