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This paper presents a stochastic geometry-based analysis of propagationstatistics for 5G millimeter wave (mm-wave) cellular. In particular, thetime-of-arrival (TOA) and angle-of-arrival (AOA) distributions of thefirst-arriving multipath component (MPC) are derived. These statistics findtheir utility in many applications such as cellular-based localization, channelmodeling, and link establishment for mm-wave initial access (IA). Leveragingtools from stochastic geometry, a Boolean model is used to statisticallycharacterize the random locations, orientations, and sizes of reflectors, e.g.,buildings. Assuming non-line-of-sight (NLOS) propagation is due to first-order(i.e., single-bounce) reflections, and that reflectors can either facilitate orblock reflections, the distribution of the path length (i.e., absolute timedelay) of the first-arriving MPC is derived. This result is then used to obtainthe first NLOS bias distribution in the localization literature that is basedon the absolute delay of the first-arriving MPC for outdoor time-of-flight(TOF) range measurements. This distribution is shown to match exceptionallywell with commonly assumed gamma and exponential NLOS bias models in theliterature, which were only attainted previously through heuristic or indirectmethods. Continuing under this analytical framework, the AOA distribution ofthe first-arriving MPC is derived, which gives novel insight into howenvironmental obstacles affect the AOA and also represents the first AOAdistribution derived under the Boolean Model.

Characterizing the First-Arriving Multipath Component in 5G Millimeter Wave Networks: TOA, AOA, and Non-Line-of-Sight Bias