Nonlinear Mobile Link Adaptation Using Modified FLNN and Channel Sounder Arrangement

Abstract

In a typical mobile environment, the varying speeds of transmit–receive pairs make traditional channel estimation methods inefficient due to continuously altering requirement of high density reference symbols. It has been largely instrumental in driving efforts to formulate innovative solutions, which are appropriate for such situations. Previously, with high computational cost, autoregressive moving average (ARMA) models of the stochastic wireless channels though appeared to be effective but could not efficiently incorporate the true nonlinearities observed in a practical situation. Therefore, nonlinear ARMA models based on artificial neural networks gained popularity. Yet certain challenges continue to exist, which are related to approximating all aspects of a real time situation, encompassing the non-linearities observed in a stochastic wireless channel, reducing training latency, enhancing processing capability, and deriving appropriate neuro-computational topologies. Modified functional link neural network with linearized activation function (FLNNLA) with nonlinear functional expansion is found to be more suitable for modeling stochastic wireless channels and removing the above-mentioned shortcomings. The proposed FLNNLA models the nonlinear tap gain process efficiently, reduces computational complexity, and enhances receiver performance with less learning cycles, better spectral efficiency and emerges as a strong candidate for being a part of upcoming receiver designs.