# Performance of Millimeter Wave Dense Cellular Network Using Stretched Exponential Path Loss Model
## 4 Simulation Results and Discussions
Figure 7 shows that ASE is not a monotonic function; instead, it initially increases with increasing BS densities and reaches a peak value at a certain BS density, after which it decreases and falls to zero. For each value of \(\zeta\), the ASE curve has a similar trend but different maximum ASE values and decay rates. In an environment with high obstructions, ASE quickly drops to zero as BS densities are increased with low achievable ASE, whereas, in an environment with medium to sparse obstructions, sufficient ASE can be attained before it drops to zero. Consequently, network designers can decide the maximum BS density value that yields adequate ASE in different obstructive environments. As in a highly obstructive environment, deploying more BSs may not be beneficial. Similar insights are obtained in [33] for the downlink microwave network. However, the obtainable data rates in the case of an mmwave network are significantly higher than that of a microwave network. For ASE comparison purposes, a microwave network operating at 2 GHz is also simulated. It can be observed that due to its high bandwidth, the ASE obtained from the mmwave NLOS links are also higher than the microwave network, which is further increased by the addition of LOS links. To emphasize the practicality of our work, in Figure 8, the methodology present in this paper is compared with the existing widely adopted path loss model for mmwave in [38] under the same system assumptions (i.e., ignoring the noise and assuming Rayleigh fading) and antenna gain. The presence of thermal noise in the system is ignored to study the  
Figure 6: SINR coverage probability \(P_{c}(T)\) vs. SINR threshold (T) for different BS antenna parameters at \(r_{c}=100\) m. (**a**) obstacle scaling factor \(\zeta=1\) (**b**) obstacle scaling factor \(\zeta=2\). Figure 7: Impact of BS density (\(\lambda\)) on area spectral efficiency. impact of path loss alone on coverage performance. Moreover, in dense cellular networks, noise power has negligible impact as compared to interference. Hence noise can be ignored. It can be observed that at a high cell radius (\(r_{c}>100\) m), our proposed model shows that the coverage probability increases with the increasing cell radii due to weaker interference power as the distances between BS increase. In line with the findings in [38], at \(r_{c}>100\) m, the SIR coverage probability is high, due to weaker interference power and/or blocked interference components leading to improved performance. However, when employing SEPLM, the coverage probability shows significant improvement in coverage as the cell radius increases from 100 m to 200 m, while the mmwave system with traditional path loss does not show such variations. HMThis work is also compared with mmwave small cell network in [25; 26], which employs an inter-cell interference coordination (ICIC) scheme to mitigate interference in the dense network for improved coverage and capacity. It can be observed that SEPLM provides better coverage performance as compared to the conventional path loss model with opportunistic interference cancellation mechanism at different blockage densities. Similarly, it can also be observed that when using SEPLM as BS density is increased the coverage probability is reduced due to increasing interference power from nearby BS as illustrated in Figure 5. On the contrary, the existing path loss model shows a slow decline in SIR when increasing BS densities.