Indoor empirical path loss prediction model for 2.4 GHz 802.11n network

The purpose of this study is to develop an indoor empirical path loss prediction model for IEEE 802.11n network at 2.4 GHz. As 802.11n features Multiple-Input Multiple-Output (MIMO) which is not present in any previous wireless local area network (WLAN) standards, it is considered imperative to figure out a suitable prediction model for 802.11n network. Signal predictions using empirical models such as Dual-Slope Model, Partitioned Model, Log-Normal Shadowing Model, ITU-R Recommendation P.1238-1 Model, Adjusted Motley-Keenan Model and COST 231-Multi-Wall Model were carried out at an academic building to determine the best prediction model. Analysis showed that Partitioned Model is the best signal estimation model and is chosen as the reference model for optimization. The optimization process involves modification of the Partitioned Model through three selected steps. Prediction results of the optimized model showed a further increase in signal prediction accuracy. This new model is named as Solah's Model and is recommended for predicting indoor signal loss in 802.11n WLAN, especially in assisting network deployment, migration and management in office or academic buildings.