Universal Theoretical Wireless Signal Propagation Prediction Modelling

p {margin: 0; padding: 0;} .ft00{font-size:21px;font-family:NimbusSansBold;color:#000000;} .ft01{font-size:18px;font-family:NimbusSansBold;color:#000000;} .ft02{font-size:15px;font-family:OpenSymbol;color:#000000;} .ft03{font-size:18px;font-family:NimbusSans;color:#000000;} UNIVERSAL THEORETICAL WIRELESS SIGNAL PROPAGATION PREDICTION MODELLING Introduction  Wireless networks becoming ubiquitous with growing demand  Various methods used for modeling wireless signal propagation  Traditional empirical models include One Slope, Dual Slope, Hata etc.  AI methods like ANN, ANFIS being explored for prediction accuracy Literature Review  Obstacles affect indoor/outdoor signal propagation differently  Neural networks transform inputs like distance, obstacles to RSSI output  ANFIS combines neural networks and fuzzy logic for function approximation  Has learning capability of neural networks and knowledge base of fuzzy logic  ANFIS architecture has 5 layers - input, 3 hidden, output  Premise and consequent parameters modified during training  PSO optimizes ANFIS parameters based on swarm intelligence Objectives  Compare PSO trained LOG10D-ANFIS, LOG10D-ANFIS and ANFIS  Obtain RSSI using One Slope and Two Ray Ground Reflection models  Analyze performance using RMSE and graphical comparisons Conclusion  AI methods like PSO-ANFIS useful for accurate wireless signal prediction  Enhances modeling of complex real-world radio propagation environments  More research needed on integrating AI with empirical models Key Highlights  ANFIS combines strengths of neural networks and fuzzy logic systems  PSO provides efficient training of ANFIS parameters  Comparative study analyzes different ANFIS frameworks for RSSI modeling  AI and computational intelligence crucial for future wireless networks