Random Neural Network Based Cognitive-eNodeB Deployment in LTE Uplink

Artificial intelligence (AI)/machine learning (ML) based cognitive solutions have widely been applied to deal with downlink inter-cell interference coordination (ICIC) in long-term evolution (LTE) systems. This paper presents a random neural network (RNN) based novel framework to improve ICIC and radio resource management (RRM) in LTE-Uplink system. The RNN based cognitive engine (CE) is embedded within the eNodeB which allocates optimal radio parameters to attached users and also suggests acceptable transmit power to users served by adjacent cells, in order to reduce inter-cell interference (ICI). The proposed CE concurrently achieves long-term learning, fast decision making, and less computational complexity. These three CE design features are essential for the development and practical deployment of any real-time cognitive communication system and most of the existing AI/ML based cognitive solutions in literature lack them. The performance of RNN based optimization framework is compared with artificial neural network (ANN) and state-of-the-art fractional power control (FPC) scheme. In six different test-cases, simulation results have shown an improvement of 53.88%-87.53% in decision making accuracy and a decrease of 44.22% in scheduling delay as compared to ANN. In addition, throughput gain of 16.13% and 18.62% has been achieved as compared to ANN and FPC schemes respectively.