Critical Analysis of Learning Algorithms in Random Neural Network Based Cognitive Engine for LTE Systems

In this paper, we critically analyze the performance of an intelligent Long-Term Evolution-Uplink (LTE-UL) system having a cognitive engine (CE) embedded in e-NodeB. Performance characterization, optimal radio parameters prediction, and inter-cell-interference coordination (ICIC) are studied. The embedded CE allocates the optimal radio parameters to serving users and suggests the acceptable transmit power to users served by adjacent cells for ICIC. The desired cognition has been achieved with a novel random neural network (RNN) based CE architecture. To achieve the best learning performance, we critically analyzed three learning algorithms, gradient descent (GD), adaptive inertia weight particle swarm optimization (AIW-PSO) and differential evolution (DE). The analysis showed that AIW-PSO was 10.57% better than GD and 8.012% better than DE in terms of learning accuracy (based on MSE), but with considerable compromise on computational time as compared to GD. Moreover, in terms of convergence time (to achieve the MSE less than 1.04E-03), AIW-PSO took 60% less iterations than GD and 50% less than DE.