Energy Efficient LTE/Wi-Fi Coexistence in the Unlicensed Spectrum

Abstract

The dramatic growth in demand for wireless services has fueled a severe shortage in RF spectrum, especially in the overcrowded licensed bands. The regulatory approach for meeting this galloping demand is to allow the coexistence of competing wireless technologies (e.g., LTE Unlicensed and Wi-Fi coexisting in the 5GHz U-NII band). This shared spectrum paradigm poses novel challenges for secure, efficient, and fair resource access. Many of these challenges stem from the heterogeneity of the coexisting systems, the system scale, and the lack of explicit coordination mechanisms between them. The fundamentally different spectrum access mechanisms and PHY-layer capabilities–dynamic vs. fixed access, schedule-based vs. random access, interference-avoiding vs. interference-mitigating, etc.–create a complex and interdependent ecosystem, without a unified control plane Motivated by the shared spectrum paradigm, we address the problem of implicit coordination between coexisting wireless systems that do not share a common control plane. We consider the coexistence of LTE and Wi-Fi and study mechanisms for conserving energy when the wireless channel is occupied. In a Wi-Fi only system, the network allocation vector (NAV) included in the preamble of IEEE 802.11 frames, advertises the duration of an eminent transmission. Nearby Wi-Fi terminals decode the frame preamble and transition to sleep mode to conserve energy. However, when heterogeneous systems co-exist (e.g., LTE and Wi-Fi), frames that belong to other systems are not decodable, leading to continuous channel sensing, even when the channel is to be occupied for long duration. In this thesis, we study mechanisms to achieve coordination between LTE and Wi-Fi operating on the same band, without relying on explicit messaging. We develop and implement several implicit techniques for monitoring the operational10 parameters of LTE on the Wi-Fi side. We use these parameters to conserve energy at Wi-Fi terminals by transitioning them to sleep mode whenever the channel is occupied by an LTE station. We exploit the unique backoff characteristics of each priority traffic class to predict the length of an imminent LTE transmission. We propose two class estimation mechanisms. The first is a conservative mechanism that maximizes the Wi-Fi sleep time without missing an opportunity to contend for the channel. In the second mechanism, we apply Bayesian estimation to get a more accurate prediction of the priority class and avoid waking up the receiver too early. This comes at the expense of oversleeping when a high priority class is misclassified, thus leading to a small loss in transmission opportunities. Although we present our work from the Wi-Fi perspective, the same methodology can be applied to conserve energy on the LTE side.