Lightweight Wi-Fi Frame Detection for Licensed Assisted Access LTE

Licensed assisted access LTE (LAA-LTE) aggregates 5 GHz unlicensed bands with LTE's licensed bands via carrier aggregation, and adopts energy detection (ED)-based clear channel assessment (CCA) for protection of coexisting Wi-Fi devices. Since LAA-LTE requires the ED threshold should be set conservatively in the potential presence of Wi-Fi, the spatial spectrum reuse of the LAA-LTE will be much impaired. Such non-flexible thresholding has been introduced mainly due to ED's incapability of differentiating Wi-Fi frames from LTE frames. As a remedy, this paper proposes a lightweight but effective Wi-Fi frame detection method with which the LAA-LTE devices can capture a Wi-Fi preamble by only using the LAA-LTE's own time domain samples while incurring very small latency. Built upon the proposed method, we also propose the Wi-Fi energy tracking algorithm to identify the duration of a Wi-Fi frame, and a dynamic ED threshold selection algorithm. The proposed schemes were evaluated via the MATLAB simulations and USRP-based experiments, through which their efficacy has been confirmed, e.g., Wi-Fi frame detection probability up to 98.7%. Moreover, via extensive NS-3 based simulations with a multi-cell coexistence topology, we further revealed that the proposed mechanism not only enhances the spatial efficiency of the LAA-LTE achieving up to 23.68% more throughput than the legacy LAA-LTE but also protects coexisting Wi-Fi better

Towards More Efficient 5G Networks via Dynamic Traffic Scheduling

Department of Electrical EngineeringThe 5G communications adopt various advanced technologies such as mobile edge computing and unlicensed band operations, to meet the goal of 5G services such as enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (URLLC). Specifically, by placing the cloud resources at the edge of the radio access network, so-called mobile edge cloud, mobile devices can be served with lower latency compared to traditional remote-cloud based services. In addition, by utilizing unlicensed spectrum, 5G can mitigate the scarce spectrum resources problem thus leading to realize higher throughput services. To enhance user-experienced service quality, however, aforementioned approaches should be more fine-tuned by considering various network performance metrics altogether. For instance, the mechanisms for mobile edge computing, e.g., computation offloading to the edge cloud, should not be optimized in a specific metric's perspective like latency, since actual user satisfaction comes from multi-domain factors including latency, throughput, monetary cost, etc. Moreover, blindly combining unlicensed spectrum resources with licensed ones does not always guarantee the performance enhancement, since it is crucial for unlicensed band operations to achieve peaceful but efficient coexistence with other competing technologies (e.g., Wi-Fi). This dissertation proposes a focused resource management framework for more efficient 5G network operations as follows. First, Quality-of-Experience is adopted to quantify user satisfaction in mobile edge computing, and the optimal transmission scheduling algorithm is derived to maximize user QoE in computation offloading scenarios. Next, regarding unlicensed band operations, two efficient mechanisms are introduced to improve the coexistence performance between LTE-LAA and Wi-Fi networks. In particular, we develop a dynamic energy-detection thresholding algorithm for LTE-LAA so that LTE-LAA devices can detect Wi-Fi frames in a lightweight way. In addition, we propose AI-based network configuration for an LTE-LAA network with which an LTE-LAA operator can fine-tune its coexistence parameters (e.g., CAA threshold) to better protect coexisting Wi-Fi while achieving enhanced performance than the legacy LTE-LAA in the standards. Via extensive evaluations using computer simulations and a USRP-based testbed, we have verified that the proposed framework can enhance the efficiency of 5G.clos