Abstract
High data throughput is desired in the wireless communication system design. Rate adaptation is an efficient way to update the data rate in the dynamic wireless environment. Conventional rate adaptation algorithms rely on the feedback of acknowledgment/negative acknowledgment (ACK/NACK) messages or signal to noise ratio (SNR). Existing rate adaptation algorithms can not achieve satisfactory transmission rates in time-varying environments. In this paper, we model the rate selection problem as a multi-armed bandit (MAB) problem and propose an online learning rate adaptation algorithm that learns the channel status from both RSSI and ACK/NACK signals. Compared with existing rate adaptation algorithms, the proposed algorithm can adapt to the time-varying channel better and achieve near-optimal transmission rate performance.
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