Channel Access Scheme With Alignment Reference Interval Adaptation (ARIA) for Frequency Reuse in Unlicensed Band LTE: Fuzzy Q-Learning Approach

In licensed-assisted access using the LTE (LAA) standard, carrier sensing via the listen-before-talk (LBT) procedure is a vital feature for fair sharing with the Wi-Fi systems. Furthermore, it has been designed to support frequency reuse-1 operation among all cells by the virtue of licensed spectrum. As opposed to the two existing channel-access schemes for frequency reuse-1, transmission start time alignment (TSTA) and energy detection threshold adaptation (EDTA), which may not be able to maximize the LAA system throughput without violating the requirement of fair coexistence, we propose a new frequency-reuse-1 scheme, referred to as the alignment reference interval adaptation-based LAA (ARIA-LAA). It attempts to combine the advantages of TSTA and EDTA into a unified access framework, in which the alignment reference interval (ARI) is adaptively adjusted to control the channel-access probability for LAA and Wi-Fi systems. Meanwhile, to operate the ARIA-LAA effectively toward our design objective, we design the fuzzy Q-learning system that adapts the continuous variable ARI to the dynamically changing wireless network environment. Based on the analytical system models and formation of the optimization problem, it employs a model-free learning algorithm that interacts with the state, defined as the current level of fairness achieved by adaptation, and the Q-learning function to determine the ARI as a global action. Our simulation results demonstrate that the ARIA-LAA is a novel scheme of spectrum sharing with spatial reuse for LAA that enhances the overall system capacity while satisfying the fair-coexistence requirement in the unlicensed spectrum.

[1]  Sunghyun Choi,et al.  FACT: Fine-Grained Adaptation of Carrier Sense Threshold in IEEE 802.11 WLANs , 2017, IEEE Transactions on Vehicular Technology.

[2]  Meng Joo Er,et al.  Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[3]  Arafet Ben Makhlouf,et al.  Practical Rate Adaptation for Very High Throughput WLANs , 2013, IEEE Transactions on Wireless Communications.

[4]  Zhu Han,et al.  A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless Networks , 2015, IEEE Communications Surveys & Tutorials.

[5]  Jeffrey G. Andrews,et al.  User Association for Load Balancing in Heterogeneous Cellular Networks , 2012, IEEE Transactions on Wireless Communications.

[6]  Geoffrey Ye Li,et al.  LBT-Based Adaptive Channel Access for LTE-U Systems , 2016, IEEE Transactions on Wireless Communications.

[7]  Youngnam Han,et al.  Coexistence of Wi-Fi and Cellular With Listen-Before-Talk in Unlicensed Spectrum , 2016, IEEE Communications Letters.

[8]  Raquel Barco,et al.  Fuzzy Rule-Based Reinforcement Learning for Load Balancing Techniques in Enterprise LTE Femtocells , 2013, IEEE Transactions on Vehicular Technology.

[9]  A. Girotra,et al.  Performance Analysis of the IEEE 802 . 11 Distributed Coordination Function , 2005 .

[10]  J. Mendel Fuzzy logic systems for engineering: a tutorial , 1995, Proc. IEEE.

[11]  Hiroki Takahashi,et al.  A Listen before Talk Algorithm with Frequency Reuse for LTE Based Licensed Assisted Access in Unlicensed Spectrum , 2016, 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring).

[12]  Yuan Li,et al.  Enhanced listen-before-talk scheme for frequency reuse of licensed-assisted access using LTE , 2015, 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[13]  Arafat J. Al-Dweik,et al.  QoS-Aware Power-Efficient Scheduler for LTE Uplink , 2014, IEEE Transactions on Mobile Computing.

[14]  Sangheon Pack,et al.  A Fair Listen-Before-Talk Algorithm for Coexistence of LTE-U and WLAN , 2016, IEEE Transactions on Vehicular Technology.