Realistic Indoor Path Loss Modeling for Regular WiFi Operations in India

TL;DR

This paper introduces a new statistical indoor path loss model tailored for Indian environments, validated through extensive drive tests, to improve WiFi network design for IoT and robotics applications.

Contribution

The paper presents the Tata Indoor Path Loss Model (T-IPLM), a novel model specifically designed for Indian indoor scenarios, validated against real-world drive test data.

Findings

T-IPLM closely matches drive test results compared to existing models.

T-IPLM is effective for designing indoor WiFi networks in Indian environments.

The model accounts for local building materials and layouts.

Abstract

Indoor wireless communication using Wireless Fidelity (WiFi) is becoming a major need for the success of Internet of Things (IoT) and cloud robotics in both developed and developing countries. With different operating conditions, interference, obstacles and type of building materials used, it is difficult to predict the path loss components in an indoor environment, which are crucial for the network design. It has been observed that the indoor path loss models proposed for western countries cannot be directly used in Indian scenarios due to variations in building materials utilized, floor plans, etc. In this paper, we have proposed a non-deterministic statistical indoor path loss model- Tata Indoor Path Loss Model (T-IPLM) which can be used for the 2.4 - 2.5 GHz, Industrial Scientific and Medical (ISM) band. To propose and validate, we have conducted several drive tests with different conditions such as busy office premise with obstacles, open office premise, corridor, canteen, and multi-storey office locations, etc. We have also compared T-IPLM with popular path loss models such as ITU-R and Log-distance; T-IPLM matches closely with the drive test results as compared to other models. We believe that T-IPLM model can be used extensively to design accurate indoor communication networks required for regular WiFi communications and deployment and operations of IoT and cloud robotics.