To accommodate the increasing number of networked devices and rapidly increasing application bandwidth demands, mmWave WLAN has been identified as a promising technology with the potential to achieve multi-Gbps throughput.
However, the small wavelength of mmWave brings the innate problem of having both high penetration loss and weak diffraction abilities around objects. Directional high gain antennas are therefore adopted for mmWave communication, making mmWave extremely sensitive to any blockage that lies between the access point (AP) and user equipment (UE).
To address this challenge, in this dissertation, we focus on using proactive approaches to mitigate potential network outage caused by dynamic blockages. Our approach thus far consists of two aspects. The first part considers maximizing signal coverage in the the spatial aspect in the interior planning phase after router deployment, and the second part studies the possibility of temporally maximizing best case transmission conditions with proactive scheduling approaches using predicted blockage information. For the first part, we study the use of dedicated flat passive reflectors to improve coverage in indoor mmWave WLANs through a reflector placement scheme that accommodates any general indoor scenario with pre-deployed ceiling-mounted APs. For the second part, we first formulate and solve an optimal scheduling problem in order to investigate the potential performance improvements of proactive schedulers that make use of blockage prediction. The formulation takes form of a binary integer linear programming (BILP) problem, preserving fairness constraints.  Then, we follow up the scheduling problem with an efficient approximate or heuristic algorithm to give a practical solution to the proactive scheduling problem. Combined with an integrated approach to utilize mobility prediction and known indoor environments to produce longer term blockage predictions, we extensively evaluate the proactive schedulers' performance under various influencing factors. Through these results, we show that proactive scheduling achieves significant performance gains compared to traditional schedulers.