Over the past few decades, attempts had been made to build a suitable channel prediction model to optimize radio transmission systems. It is particularly essential to predict the path loss due to the blockage of the signal, in indoor radio system applications. This paper proposed a multiwall path-loss propagation model for an indoor environment, operating at a transmission frequency of 2.45 GHz in the industrial, scientific, and medical (ISM) radio band. The effects of the number of the walls to be traversed along the radio propagation path are considered in the model. To propose the model, the previous works on well-known indoor path loss models are discussed. Then, the path loss produced by the intervening walls in the propagation path is measured, and the terms representing the loss factors in the theoretical path-loss model are modified. The analyzed results of the path loss factors acquired at 2.45 GHz are presented. The proposed path-loss model simplifies the loss factor term with an admissible assumption of the indoor environment and predicts the path-loss factor accurately.
|Title of host publication||2020 20th International Conference on Control, Automation and Systems, ICCAS 2020|
|Publisher||IEEE Computer Society|
|Number of pages||4|
|Publication status||Published - 2020 Oct 13|
|Event||20th International Conference on Control, Automation and Systems, ICCAS 2020 - Busan, Korea, Republic of|
Duration: 2020 Oct 13 → 2020 Oct 16
|Name||International Conference on Control, Automation and Systems|
|Conference||20th International Conference on Control, Automation and Systems, ICCAS 2020|
|Country||Korea, Republic of|
|Period||20/10/13 → 20/10/16|
Bibliographical noteFunding Information:
This work was supported by Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (KNPA) (No. 2019-0-01291, LTE-based accurate positioning technique for emergency rescue).
© 2020 Institute of Control, Robotics, and Systems - ICROS.
Copyright 2020 Elsevier B.V., All rights reserved.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Science Applications
- Control and Systems Engineering
- Electrical and Electronic Engineering