Machine Learning Based Scheme For Contention Window Size Adaptation In Lte-Laa

Zoraze Ali, Lorenza Giupponi, Josep Mangues-Bafallluy, Biljana Bojovic
2017 Zenodo  
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
more » ... 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-theart approaches, provides the best trade-off between the fairness to Wi-Fi and the LAA performance in terms of both throughput and latency.
doi:10.5281/zenodo.1038024 fatcat:sjcmdbf73jfadj6kkokhu4j4k4