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To cope with growing wireless bandwidth demand, millimeter wave (mmWave) communication has been identified as a promising technology to deliver Gbps throughput. However, due to the susceptibility of mmWave signals to blockage, applications can experience significant performance variability as users move around due to rapid and significant variation in channel conditions. In this context, proactive schedulers that make use of future data rate prediction have potential to bring a significant performance improvement as compared to traditional schedulers. In this work, we explore the possibility of proactive scheduling that uses mobility prediction and some knowledge of the environment to predict future channel conditions. We present both an optimal proactive scheduler, which is based on an integer linear programming formulation and provides an upper bound on proactive scheduling performance, and a greedy heuristic proactive scheduler that is suitable for practical implementation. Extensive simulation results show that proactive scheduling has the potential to increase average user data rate by up to 35% over the classic proportional fair scheduler without any loss of fairness and incurring only a small increase in jitter. The results also show that the efficient proactive heuristic scheduler achieves from 60% to 75% of the performance gains of the optimal proactive scheduler. Finally, the results show that proactive scheduling performance is sensitive to the quality of mobility prediction and, thus, use of state-of-the-art mobility prediction techniques will be necessary to realize its full potential. 

To cope with growing wireless bandwidth demand, millimeter wave (mmWave) communication has been identified as a promising technology to deliver Gbps throughput. However, due to the susceptibility of mmWave signals to blockage, applications can experience significant performance variability as users move around due to rapid and significant variation in channel conditions. In this context, proactive schedulers that make use of future data rate prediction have potential to bring a significant performance improvement as compared to traditional schedulers. In this work, we propose an efficient proactive algorithm that prioritizes the scheduling of scarce resources to achieve better performance than traditional schedulers. The results show that our scheduler can increase average data rate by up to 20% compared to non-proactive scheduling and achieves from 60% to 75% of the performance gain of an optimal proactive scheduler. 

Millimeter-wave (mmWave) communications have been regarded as one of the most promising solutions to deliver ultra-high data rates in wireless local-area networks. A significant barrier to delivering consistently high rate performance is the rapid variation in quality of mmWave links due to blockages and small changes in user locations. If link quality can be predicted in advance, proactive resource allocation techniques such as link-quality-aware scheduling can be used to mitigate this problem. In this paper, we propose a link quality prediction scheme based on knowledge of the environment. We use geometric analysis to identify the shadowed regions that separate LoS and NLoS scenarios, and build LoS and NLoS link-quality predictors based on an analytical model and a regression-based approach, respectively. For the more challenging NLoS case, we use a synthetic dataset generator with accurate ray tracing analysis to train a deep neural network (DNN) to learn the mapping between environment features and link quality. We then use the DNN to efficiently construct a map of link quality predictions within given environments. Extensive evaluations with additional synthetically generated scenarios show a very high prediction accuracy for our solution. We also experimentally verify the scheme by applying it to predict link quality in an actual 802.11ad environment, and the results show a close agreement between predicted values and measurements of link quality. 

To address the needs of emerging bandwidth-intensive applications in 5G and beyond era, the millimeter-wave (mmWave) band with very large spectrum availability have been recognized as a promising choice for future wireless communications. In particular, IEEE 802.11ad/ay operating on 60 GHz carrier frequency is a highly anticipated wireless local area network (WLAN) technology for supporting ultra-high-rate data transmissions. In this paper, we describe additions to the ns-3 802.11ad simulator that include 3D obstacle specifications, line-of-sight calculations, and a sparse cluster-based channel model, which allow researchers to study complex mmWave Wi-Fi network deployments under more realistic conditions. We also study the performance accuracy and simulation efficiency of the implemented statistical channel model as compared to a deterministic ray-tracing based channel model. Through extensive ns-3 simulations, the results show that the implemented channel model has the potential to achieve good accuracy in performance evaluation while improving simulation efficiency. We also provide a detailed parametric analysis on the statistical channel model, which yields insight on how to properly tune the model parameters to further improve performance accuracy.