An efficient and lightweight predictive channel assignment scheme for multiband b5g-enabled massive iot: A deep learning approach

Multihop device-to-device (D2D)-enabled relay networks are envisaged to be utilized by the Internet of Things (IoT) and massive machine-type communication (mMTC) traffic for the purpose of offloading data in beyond fifth-generation (B5G) networks. The emerging challenge of spectrum scarcity and overloading of cellular base stations can be addressed using such relay nodes in terms of spectrum and energy efficiency. In order to improve spectral efficiency, in this article, we intend to employ several frequency bands concurrently in the relay node rather than the traditional concept of specifying one channel on a specific band at a time. A deep learning-based predictive channel selection method is leveraged to unravel the potential challenges associated with the dynamic channel conditions in the multiband relay networks. For predicting the most appropriate channel based on its quality, signal-to-interference-plus-noise-ratio (SINR) is adopted as the metric, which is predicted by the proposed convolutional neural network (CNN) model. The best modulation and coding rates of the predicted band are attained in order to transmit the packets received from the source or previous relay node to the successive relay node/destination. Two proactive channel assignment strategies, referred to as controlled and smart prediction schemes, are employed to exhibit the performance of the shallow and deep-CNN models. The proposed model is evaluated on multiple publicly available data sets from diverse network systems and compared with several machine/deep learning methods. Our proposal leads to encouraging results for proactively predicting the conditions of the channels and choosing the most suitable ones in multiband relay systems.