A New Technique of Removing Blind Spots to Optimize Wireless Coverage in Indoor Area

Abstract

Blind spots (or bad sampling points) in indoor areas are the positions where no signal exists (or the signal is too weak) and the existence of a receiver within the blind spot decelerates the performance of the communication system. Therefore, it is one of the fundamental requirements to eliminate the blind spots from the indoor area and obtain the maximum coverage while designing the wireless networks. In this regard, this paper combines ray-tracing (RT), genetic algorithm (GA), depth first search (DFS), and branch-and-bound method as a new technique that guarantees the removal of blind spots and subsequently determines the optimal wireless coverage using minimum number of transmitters. The proposed system outperforms the existing techniques in terms of algorithmic complexity and demonstrates that the computation time can be reduced as high as 99% and 75%, respectively, as compared to existing algorithms. Moreover, in terms of experimental analysis, the coverage prediction successfully reaches 99% and, thus, the proposed coverage model effectively guarantees the removal of blind spots.

A Coverage Prediction Technique for Indoor Wireless Campus Network

“…It can be identified from various parameters, such as type of the device, kind of materials around the APs, the wide range of the observed area, and the height of the AP placement [6]. There has been various research developed to find the coverage area, which can be covered by an AP [7]- [15]. An optimization has done by rearrangement AP placement using Monte Carlo.…”