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The RA problem in wireless communication systems is considered as one of the most challenging tasks. The RA problem is formulated as an optimization problem and usually solved online with available information [32]. It is difficult to obtain a real-time optimal solution for most RA problems due to their nonconvex nature. To solve these problems, Lagrangian and greedy methods are employed which results in performance degradation [33]. The nonlinear programming (NLP) methods were used to solve the RA problem, due to their cubic complexity, the implementation of these methods were also targeted on graphics processing units (GPUs) for faster processing [34]. Hence, the traditional algorithms for RA are facing great challenges in achieving the QoS requirement of the users in scarce wireless scenarios. RA has a great ability to provide a guaranteed user’s QoS by optimizing the available facilities to minimize operational cost and maximize the operator’s revenue. Therefore, the efficient RA is always a trending topic for future wireless communication networks.

In recent years, there has been a drastic increase in internet traffic and expected to grow in future wireless systems [2, 35]. This traffic growth contributed by the various applications such as wide variety of user equipment (UE), smartphones, automatic vehicles, and IoT sensors. Due to this enormous growth in internet traffic, radio RA in future wireless networks (5G and beyond) is becoming more challenging. Therefore, RA resurfaced as a trending topic in the wireless communication area [36]. DL methods have a great potential to efficiently optimize the radio resource in future wireless systems. Recently, Zhou et al. [37] proposed a DL-based radio RA in ultra-dense 5G networks. In [37], authors have proposed LSTM method for RA problem in 5G scenario and achieved low packet loss along with high throughput. Wang et al. [38] and Zhang et al. [39] presented ML-based RA problems assisted with cloud computing. DL has shown great potential and provided a break-through in a variety of research areas [21].