This paper proposes a stochastic geometry framework to evaluate the coverage and rate performance in mmWave cellular networks. The framework models the locations of LOS and NLOS base stations as two independent non-homogeneous Poisson point processes, with different path loss laws applied to each. The framework derives expressions for the signal-to-noise-and-interference ratio (SINR) and rate coverage probability. The results show that dense mmWave networks can achieve comparable coverage and much higher data rates than conventional UHF systems, despite blockage effects. The cell size to achieve optimal SINR scales with the average size of the area that is LOS to a user. The paper also proposes a simplified system model for dense mmWave networks, where the LOS region is approximated as a fixed LOS ball. This model simplifies the analysis and enables efficient computation of SINR and rate coverage. The results indicate that dense mmWave networks can achieve good coverage and significantly higher achievable rates than conventional systems. The paper also provides a systematic approach to approximate the LOS probability function as a step function, which simplifies the analysis and numerical evaluation. The results show that the SINR coverage in dense networks is largely determined by the relative density of the network. The paper also provides asymptotic analysis for ultra-dense networks, showing that increasing base station density beyond a certain threshold can hurt system performance. The optimal base station density is finite, and the SINR distribution in dense networks is primarily determined by the average number of LOS base stations to a user. The paper concludes that the proposed framework provides a general and efficient method for analyzing mmWave networks, and that the results have important implications for the design and deployment of mmWave cellular systems.This paper proposes a stochastic geometry framework to evaluate the coverage and rate performance in mmWave cellular networks. The framework models the locations of LOS and NLOS base stations as two independent non-homogeneous Poisson point processes, with different path loss laws applied to each. The framework derives expressions for the signal-to-noise-and-interference ratio (SINR) and rate coverage probability. The results show that dense mmWave networks can achieve comparable coverage and much higher data rates than conventional UHF systems, despite blockage effects. The cell size to achieve optimal SINR scales with the average size of the area that is LOS to a user. The paper also proposes a simplified system model for dense mmWave networks, where the LOS region is approximated as a fixed LOS ball. This model simplifies the analysis and enables efficient computation of SINR and rate coverage. The results indicate that dense mmWave networks can achieve good coverage and significantly higher achievable rates than conventional systems. The paper also provides a systematic approach to approximate the LOS probability function as a step function, which simplifies the analysis and numerical evaluation. The results show that the SINR coverage in dense networks is largely determined by the relative density of the network. The paper also provides asymptotic analysis for ultra-dense networks, showing that increasing base station density beyond a certain threshold can hurt system performance. The optimal base station density is finite, and the SINR distribution in dense networks is primarily determined by the average number of LOS base stations to a user. The paper concludes that the proposed framework provides a general and efficient method for analyzing mmWave networks, and that the results have important implications for the design and deployment of mmWave cellular systems.