Performance of Millimeter Wave Dense Cellular Network Using Stretched Exponential Path Loss Model

Abstract

Future wireless networks are expected to be dense and employ a higher frequency spectrum such as millimeter wave (mmwave) to support higher data rates. In a dense urban environment, the presence of obstructions causes the transmissions between the user equipment and base stations to transit from line-of-sight (LOS) to non-LOS (NLOS). This transit hence emphasizes the significance of NLOS links for reliable mmwave communication. The work presented in this paper investigates the downlink performance of a mmwave cellular system by modeling the NLOS channel using stretched exponential path loss model (SEPLM) and employing a 3GPP distance-dependent LOS probability function. This path loss model has the inherent ability to define short ranges as well as obstructions in the environment as a function of its parameter resulting in a more realistic performance analysis. The path loss model is first validated for NLOS link using a data set from an open-source mmwave channel simulator. Then, a mathematical model incorporating LOS and NLOS transmissions is developed to study the impact of path loss on signal-to-interference-plus-noise (SINR) coverage probability and area spectral efficiency (ASE). The proposed framework can provide coverage performance indication over various blockage environments. Our results demonstrate that SINR coverage probability decreases exponentially with increasing base station density. Moreover, ASE initially increases with increasing BS density and is maximized for a particular density value, after which it converges to zero for higher densities. The results are also benchmarked with the existing path loss model of mmwave cellular system with different exponents for LOS and NLOS paths. It was observed that as the base station density increases, the SINR degrades more rapidly when using SEPLM as compared to the existing mmwave path loss model.

Modeling medium dense cellular network and evaluation of probability of coverage

“…Wang et al have analyzed the power allocation problem and joint subcarrier assignment for the uplink NOMA in multi‐cell dense networks 27 . Mariam et al have investigated the performance of downlink mmWave dense cellular networks by modeling nonline of sight channels with 3GPP path loss function 28 . Imène Trigui et al have analyzed the coverage probability of ultra‐dense cellular networks by considering the small‐scale fading and incorporating network statistics such as carrier frequency, antenna height, and so forth 29 .…”

“…27 Mariam et al have investigated the performance of downlink mmWave dense cellular networks by modeling nonline of sight channels with 3GPP path loss function. 28 Imène Trigui et al have analyzed the coverage probability of ultra-dense cellular networks by considering the small-scale fading and incorporating network statistics such as carrier frequency, antenna height, and so forth. 29 Yu et al have analyzed the multi-antenna cellular network using stochastic geometry.…”

“…Based on our observations at UDC extremity, coverage probability of an idempotent MDC network at UDC extremity (at 𝜎 2 = −5 dB) is given by Equation (28) and Coverage probability of a Congruent MDC Network at UDC Extremity (at 𝜎 2 = −5 dB) is given by Equation (29).…”

“…VLDC extremity plots and Ideal VLDC model are plotted using the same parameters likewise Figure6. Figure7plots also strengthen our VLDC extremity validation.Based on our observations at UDC extremity, coverage probability of an idempotent MDC network at UDC extremity (at 𝜎 2 = −5 dB) is given by Equation(28) and Coverage probability of a Congruent MDC Network at UDC Extremity (at 𝜎 2 = −5 dB) is given by Equation(29).…”

Remarks on a stochastic geometric model for interference-limited cellular communications

“…Stochastic geometry (SG) has been used heavily as a modeling tool in the field of wireless communications, e.g., ad hoc networks, cellular networks and IoT device deployment. For example, SG has been used in [1] to model heterogeneous cellular networks, in [2] to model narrow band internet of things (IoT), in [3] to model D2D communications underlaying cellular systems, and in [4] to model millimeter wave dense cellular networks. The SG model is based on treating the nodes as a realization (snapshot) of a spatial point process in the Euclidean plane, then averaging over all node locations to obtain useful performance metrics.…”