Machine learning based scheme for contention window size adaptation in LTE-LAA

Abstract

License Assisted Access (LAA) is the technology introduced by the Third Generation Partnership Project (3GPP) that enables the deployment of LTE networks in the unlicensed 5 GHz spectrum. To ensure a fair coexistence of LAA in the unlicensed spectrum with other technologies, e.g., with Wi-Fi, 3GPP has standardized the use of Listen Before Talk (LBT) as the default channel-access scheme for LAA. However, the performance of Wi-Fi when coexisting with LAA mainly relies on how the LBT parameters are configured by the LAA. In this paper, we focus on the Contention Window (CW) size parameter of LBT in LAA. We propose a Neural Network (NN) based scheme that adapts the CW size based on the predicted number of Negative Acknowledgments (NACKs) for all the subframes in a Transmit Opportunity (TXOP) of LAA. In particular, our proposed scheme learns from the past experience how many NACKs per subframe of a TXOP were received under certain channel conditions. The performance evaluation shows that our proposed scheme, when compared to the state-of-the-art approaches, provides the best trade-off between the fairness to Wi-Fi and the LAA performance in terms of both throughput and latency.