This paper proposes a path loss model for crowded outdoor environments that can consider the density of people. Measurement results in an anechoic chamber with three blocking persons showed that multiple human body shadowing can be calculated by using finite width screens. As a result, path loss in crowded environments can be calculated by using the path losses of the multipath and the multiple human body shadowing on those paths. The path losses of the multipath are derived from a ray tracing simulation, and the simulation results are then used to predict the path loss in crowded environments. The predicted path loss of the proposed model was examined through measurements in the crowded outdoor station square in front of Shibuya Station in Tokyo, and results showed that it can accurately predict the path loss in crowded environments at the frequencies of 4.7GHz and 26.4GHz under two different conditions of antenna height and density of people. The RMS error of the proposed model was less than 4dB.

A joint deployment of base stations (BSs) and RGB-depth (RGB-D) cameras for camera-assisted millimeter-wave (mmWave) access networks is discussed in this paper. For the deployment of a wide variety of devices in heterogeneous networks, it is crucial to consider the synergistic effects among the different types of nodes. A synergy between mmWave networks and cameras reduces the power consumption of mmWave BSs through sleep control. A purpose of this work is to optimize the number of nodes of each type, to maximize the average achievable rate within the constraint of a predefined total power budget. A stochastic deployment problem is formulated as a submodular optimization problem, by assuming that the deployment of BSs and cameras forms two independent Poisson point processes. An approximate algorithm is presented to solve the deployment problem, and it is proved that a (1-e-1)/2-approximate solution can be obtained for submodular optimization, using a modified greedy algorithm. The numerical results reveal the deployment conditions under which the average achievable rate of the camera-assisted mmWave system is higher than that of a conventional system that does not employ RGB-D cameras.

To substantially alleviate the human blockage problem in mmWave communications, this paper proposes a proactive handover system based on human blockage prediction using RGB and depth (RGB-D) cameras. The proposed scheme uses RGB-D camera images to estimate the mobility of pedestrians and to predict when blockage will occur. On the basis of this information, the proposed system transfers a mobile station (STA) communicating with one wireless BS (base station) to another BS before human blockage occurs and thus avoids blockage-induced throughput degradation. This paper presents performance modeling of both proactive handover scheme and reactive handover scheme which is based on the received power level. A numerical evaluation reveals conditions under which the proactive handover scheme achieves higher spectral efficiency compared to reactive scheme. In addition, using IEEE 802.11ad-based wireless local area network (WLAN) devices, a testbed for implementing the proposed system is built. The innovative experimental results demonstrate that the proactive handover system can considerably reduce the duration of human blockage-induced degradation of throughput performance relative to the reactive scheme.