Reinforcement Learning for Efficient and Fair Coexistence Between LTE-LAA and Wi-Fi

Abstract

Long-Term Evolution (LTE) over unlicensed spectrum extends LTE technology to the spacious unlicensed spectrum with readily available bandwidth. The provided capacity surge makes it one of the most high-profile technologies to meet the explosive growth of mobile traffic demand. Among its different variants, Licensed Assisted Access (LAA) is considered as a promising global solution attributed to its mandatory listen-before-talk (LBT) procedure. Nevertheless, although LBT effectively maintains transmission fairness between LTE and other unlicensed systems (e.g., Wi-Fi), the current LAA protocol specified in 3GPP Release 13 is far from perfect to achieve harmony coexistence. To this end, in this paper, we first develop an analytical model to evaluate the throughput performance of Category 4 (Cat 4) algorithm agreed in 3GPP release 13. Subject to the system fairness constraint, the aggregate throughput of LTE-LAA and Wi-Fi networks is maximized based on a semi branch and bound algorithm. To make the complex optimization tractable, reinforcement learning techniques are introduced to intelligently tune the contention window size for both LTE-LAA and Wi-Fi nodes. Specifically, a cooperative learning algorithm is developed assuming that the information between different systems is exchangeable. A non-cooperative version is subsequently developed to remove the previous assumption for better practicability. Extensive simulations are conducted to demonstrate the performance of the proposed learning algorithms in contrast to the analytical upper bound under various conditions. It is shown that both proposed learning algorithms can significantly improve the total throughput performance while satisfying the fairness constraints. Particularly, the proposed cooperative learning algorithm can closely approach the analytical bound.