mmWave is emerging as an essential technology for next-generation wireless networks due to its capability of delivering multi-gigabit throughput performance. To achieve such a promising performance in mmWave communications, Line-of-sight (LOS) connectivity is a critical requirement. In this work, we explore the strategy of infrastructure mobility to alter the location of an access point (AP) in order to provide LOS connectivity to stations (STAs) in indoor mmWave WiFi networks. Through both simulation-based and theoretical analyses, we make a detailed case for infrastructure mobility by identifying the impact of AP mobile platforms configurations on network performance and propose a ceiling-mounted mobile (CMM) AP model. Then, we compare the performance of a CMM AP with multiple static APs, and we identify that the throughput and fairness performance of a CMM AP is better than as many as 5 ceiling-mounted static APs.
WiMove: Toward Infrastructure Mobility in mmWave WiFi
Line-of-sight (LOS) is a critical requirement for mmWave wireless communications. In this work, we explore the use of access point (AP) infrastructure mobility to optimize indoor mmWave WiFi network performance based on the discovery of LOS connectivity to stations (STAs).We consider a ceiling-mounted mobile (CMM) AP as the infrastructure mobility framework. Within this framework, we present a LOS prediction algorithm based on machine learning (ML) that addresses the LOS discovery problem. The algorithm relies on the available network state information (e.g., LOS connectivity between STAs and the AP) to predict the unknown LOS connectivity status between the reachable AP locations and target STAs. We show that the proposed algorithm can predict LOS connectivity between the AP and target STAs with an accuracy up to 91%. Based on the LOS prediction algorithm, we then propose a systematic solution WiMove, which can decide if and where the AP should move to for optimizing network performance. Using both ns-3 based simulation and experimental prototype implementation, we show that the throughput and fairness performance of WiMove is up to 119% and 15% better compared with single static AP and brute force search.
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- ACM Symposium on Mobility Management and Wireless Access
- Sponsoring Org:
- National Science Foundation
More Like this
mmWave communication in 60GHz band has been recognized as an emerging technology to support various bandwidth-hungry applications in indoor scenarios. To maintain ultra-high throughputs while addressing potential blockage problems for mmWave signals, maintaining line-of-sight (LoS) communications between client devices and access points (APs) is critical. To maximize LoS communications, one approach is to deploy multiple APs in the same room. In this paper, we investigate the optimal placement of multiple APs using both analytical methods and simulations. Considering the uncertainty of obstacles and clients, we focus on two typical indoor settings: random-obstacle-random-client (RORC) scenarios and fixed-obstacle-random-client (FORC) scenarios. In the first case, we analytically derive the optimal positions of APs by solving a thinnest covering problem. This analytical result is used to show that deploying up to 5 APs in a specific room brings substantial performance gains. For the FORC scenario, we propose the shadowing-elimination search (SES) algorithm based on an analytic model to efficiently determine the placement of APs. We show, through simulations, that with only a few APs, the network can achieve blockage-free operation in the presence of multiple obstacles and also demonstrate that the algorithm produces near-optimal deployments. Finally, we perform ns-3 simulations based on the IEEEmore »
Millimeter-wave communication is a highly promising technology to deliver multi-gigabit-per-second transmission rates for next-generation wireless LANs (WLANs). To achieve such ultra-high throughput performance in indoor scenarios, line-of-sight (LoS) connectivity becomes a critical requirement. Prior work has proposed access point (AP) mobility as an approach to improve LoS conditions and, thereby, approach optimum mmWave WLAN performance. In this work, we present a comprehensive simulation study of linear AP mobility that investigates various dimensions, including the number of mobile APs, the placement of the mobile AP platforms, and the length of the platforms. The results show how WLAN performance varies across these dimensions and also compares the results against a varying number of static APs to quantity the performance gains achievable from mobility. The results show that even 2 or 3 mobile APs can significantly outperform a much larger number of static APs and that deploying up to 3 mobile APs in a room brings substantial performance gains.
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 qualitymore »
Wireless x-haul networks rely on microwave and millimeter-wave links between 4G and/or 5G base-stations to support ultra-high data rate and ultra-low latency. A major challenge associated with these high frequency links is their susceptibility to weather conditions. In particular, precipitation may cause severe signal attenuation, which significantly degrades the network performance. In this paper, we develop a Predictive Network Reconfiguration (PNR) framework that uses historical data to predict the future condition of each link and then prepares the network ahead of time for imminent disturbances. The PNR framework has two components: (i) an Attenuation Prediction (AP) mechanism; and (ii) a Multi-Step Network Reconfiguration (MSNR) algorithm. The AP mechanism employs an encoderdecoder Long Short-Term Memory (LSTM) model to predict the sequence of future attenuation levels of each link. The MSNR algorithm leverages these predictions to dynamically optimize routing and admission control decisions aiming to maximize network utilization, while preserving max-min fairness among the base-stations sharing the network and preventing transient congestion that may be caused by re-routing. We train, validate, and evaluate the PNR framework using a dataset containing over 2 million measurements collected from a real-world city-scale backhaul network. The results show that the framework: (i) predicts attenuation with highmore »