Search for: All records

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.