Deep Cognitive Perspective: Resource Allocation for NOMA-Based Heterogeneous IoT With Imperfect SIC

TLDR: A deep recurrent neural network-based algorithm is proposed to solve the energy efficient resource allocation (RA) problem for the NOMA-based heterogeneous IoT with fast convergence and low computational complexity.

Abstract: The Internet of Things (IoT) has attracted significant attentions in the fifth generation mobile networks and the smart cities. However, considering the large numbers of connectivity demands, it is vital to improve the spectrum efficiency (SE) of the IoT with an affordable power consumption. To improve the SE, the nonorthogonal multiple access (NOMA) technology is newly proposed through accommodating multiple users in the same spectrums. As a result, in this paper, an energy efficient resource allocation (RA) problem is introduced for the NOMA-based heterogeneous IoT. At first, we assume the successive interference cancelation (SIC) is imperfect for practical implementations. Then, based on the analyzing method for cognitive radio networks, we present a stepwise RA scheme for the mobile users and the IoT users with the mutual interference management. Third, we propose a deep recurrent neural network-based algorithm to solve the problem optimally and rapidly. Moreover, a priorities and rate demands-based user scheduling method is supplemented, to coordinate the access of the heterogeneous users with the limited radio resource. At last, the simulation results verify that the deep learning-based scheme is able to provide optimal RA results for the NOMA heterogeneous IoT with fast convergence and low computational complexity. Compared with the conventional orthogonal frequency division multiple access system, the NOMA system with imperfect SIC yields better performance on the SE and the scale of connectivity, at the cost of high power consumption and low energy efficiency.