Abstract
5G millimeter wave (mmWave) application in mobile connectivity to realize high-speed, reliable communication is attributed with high path loss. This paper presents a detailed 3D ray-tracing technique at 28 GHz for Lagos Island to investigate five unique path loss scenarios: path loss, free space path loss with antenna pattern, free space path loss without antenna pattern, excess path loss with antenna pattern, and the excess path loss without antenna pattern for an urban environment. The Close-In (CI) model, Floating Intercept (FI) path loss model, and a root mean square error (RMSE) are used to model and evaluate the best path loss model for Lagos Island. The average achieved FI ( $\alpha,\beta, \sigma $ ) parameters were 189.92352, 0.1654, and 0.66948, While the average CI ( $\eta,X\sigma $ ) parameters were 2.309355 and 56.236425. From all the scenarios evaluated, the lowest path loss exponent achieved was 0.45, while the highest path loss exponent was 3.8. We have established that the FI path loss model accurately characterizes path loss for the Lagos Island environment with the lowest RMSE of 0.0359 dB and the highest RSME of 0.0997 dB. In contrast, the CI model over-predict the path loss at 28 GHz with the lowest RMSE of 0.0495 dB and the highest RMSE of 2.2547 dB. This work opens up a new area of research on mm-Wave at 28 GHz in Lagos Island, and the results obtained from this work can be used to benchmark future studies on mmWave in a similar environment.
Highlights
T HE exponential growth in data demand, communication infrastructure, mobile subscription, and high mobile and IoT devices penetration has significantly stretched the cellular bandwidth requirements
Future communication technology needs to improve spectral efficiency, increase the bandwidth and improve the spectrum reuse technology to overcome the current technology limitations. 5G mmWave communication promises diverse user applications like smart cities, IoT, industrial automation, and vehicular communication, this is due to its ultra-low latency and massive network capacity associated with higher performance and improved efficiency
Bao, et al [2] proposed a novel deep convolutional neural network (CNN) model for precoding channel parameters to optimize a combiner neural network architecture aimed at maximizing the spectral efficiency per small cell
Summary
T HE exponential growth in data demand, communication infrastructure, mobile subscription, and high mobile and IoT devices penetration has significantly stretched the cellular bandwidth requirements. One possible solution to compensate for the high path loss and penetration losses is to use many antenna elements in a small cell setup. To overcome this challenge, Bao, et al [2] proposed a novel deep CNN model for precoding channel parameters to optimize a combiner neural network architecture aimed at maximizing the spectral efficiency per small cell. The excess path loss causes an additional effect on the propagation channel by adding attenuation in the link. Throughout this work, we refer to NLoS environment as path loss scenario, LoS environment as FSPL, which evaluate the effect of antenna pattern on path loss. An LoS environment is characterized by a direct non-obstructed path between the transmitter and receiver; an NLoS environment is when the channel has no direct unobstructed path between receiver and transmitter
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