摘要 : Operating LTE networks in unlicensed bands together with legacy WiFi systems is deemed as a promising technique to support explosively growing mobile traffic. In conventional LTE/WiFi spectrum sharing schemes, LTE systems need to ... 展开 Operating LTE networks in unlicensed bands together with legacy WiFi systems is deemed as a promising technique to support explosively growing mobile traffic. In conventional LTE/WiFi spectrum sharing schemes, LTE systems need to know WiFi traffic demands for optimizing system parameters to protect WiFi systems, for which the two systems are required to cooperate with each other via signallings exchanges. However, it is difficult to establish a dedicated channel among the two independent systems for exchanging signallings. Hence, in this paper, we propose an intelligent duty-cycle medium access control protocol to realize the effective and fair spectrum sharing between LTE and WiFi systems without requiring signalling exchanges. Specifically, we first design a duty-cycle spectrum sharing framework, which allows an LTE system to share the spectrum with a WiFi system by using time sharing. After that, we develop deep reinforcement learning (DRL)-based algorithms to learn WiFi traffic demands by analyzing WiFi channel activity, e.g., the idleness/business of WiFi channels, which can be observed by the LTE system via monitoring WiFi channels. Based on the learnt knowledge, the LTE system can adaptively optimize LTE transmission time to maximize its own throughput and meanwhile to provide sufficient protection to the WiFi system. Simulation results show that, in terms of LTE throughput and WiFi protection, the performance of the proposed intelligent scheme can approach that of the genie-aided exhaustive search algorithm, which needs the perfect knowledge of WiFi traffic demands through massive signalling exchanges and is of high computational complexity. 收起