Implementation of Quasi-Newton Method Based on BFGS Algorithm for Identification and Optimization of Signal Propagation Loss Model Parameters

Reliable and precise predictive modelling of signal losses along the communications paths and channels of propagated radio frequency waves is fundamental to the proper design, modelling, operation, and management of mobile broadband cellular networks. As such, the identification and tuning-based estimation of the signal propagation loss parameters has advanced into a recurrent task in the field of radio frequency and telecommunication engineering. Amongst the critical challenges known with identification and predictive estimation signal propagation loss parameters, the generic model-empirical data tuning approach is very vital, yet a most often disregarded and tough optimization problem. Here, a robust and fast computation capacity of Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm Quasi-Newton (QN) method based on the BFGS algorithm is presented for precise identification and optimization of generic log-distance propagation loss model parameters. The proposed QN based BFGS algorithm has been implemented for prognostic analysis of three sets of real-time signal propagation loss data obtained over a Long Term Evolution (LTE) mobile broadband network. When compared with the most popular LevenbergMarquardt (LM), QN, and Gradient Descent (GD) methods, the proposed method achieved the 30-46% precision accuracies over other methods using three different statistical indicators, particularly in two study locations. The indicators are root mean square error, correlation coefficient and mean absolute error. The awesome precision performance of the proposed method can be explored to overcome premature convergence and poor predictive fitting issues often experienced in the identification and tuningbased estimation of the signal propagation loss parameters during or after cellular network planning processes.