ISI Free Channel Equalization for Future 5G Mobile Terminal Using Bio-inspired Neural Networks
This article presents the concept of future 5th generation (5G) wireless communications as an existing beyond 4G systems like long term evolution (LTE) which means the indicate of 5G scenarios can be introduced in near future. Therefore, this paper deals of a future 5G mobile terminal for applications to uplink transmission in a multiuser LTE scheme. Unfortunately, LTE-uplink inherently generates significant inter-symbol interference especially high bandwidth scenarios. The result is a rise to mutual interference among active users with an increased error rate. This incidence eventually causes non-orthogonal user spreading codes. Moreover, this drawback is known as the multiple access interference episodes which demonstrate high computational complexity and enhances symbol error rate at the receiving end and degrades the communication quality. Most of the related work has been claimed iterative linear minimum mean square error (LMMSE) detection requires a matrix inversion role which has a high computational complexity and contains a combinatorial optimization problem. Consequently, the LMMSE does not meet the requirement to implement real-time detection with low complexity and thus limiting its application. Therefore, we propose an acceptable bio-inspired neural network (NN) in the case of single and multilayer NNs with supervised learning particularly LevenbergMarquardt backpropagation learning algorithm to improve the convergence speed. Simulation results performed with highest approaches highlights a better act for the proposed system.
isi free channel equalization future 5g mobile terminal lte single and multilayer nns bio-inspired nns supervised lmb error learning algorithm