論文ID: 2021XBL0226
This letter proposes a practical transmission datarate adaptation (TDA) scheme using Q-learning applicable to IEEE 802.11ax wireless local area networks. In the proposed scheme, each basic service set (BSS) selects an appropriate transmission datarate according to the buffer statuses of adjacent BSSs which are periodically collected and the transmission results of DATA frames in the BSS. Then, the BSS conducts underlay transmissions based on the framework of spatial reuse defined in IEEE 802.11ax. The performance of the proposed scheme is evaluated through system-level computer simulation assuming downlink full-buffer traffic. It is confirmed that the proposed scheme can achieve higher average area throughput than conventional TDA schemes such as Robust Rate Adaptation Algorithm (RRAA) and adaptive modulation and coding (AMC) based on the received power.
