Handbook of Intelligent Computing and Optimization for Sustainable Development - Группа авторов

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system comprises of a transmitter, channel, and receiver. The design of the transmitter and receiver is described by the channel model and its underlying physical phenomenon [21]. Signaling schemes are developed corresponding to system’s requirements, such as ease of implementation, robustness to channel impairments, and transmit power minimization [6]. Likewise, detection algorithms are devised to enhance system performance subject to the constraints. Generally, a frequency below 10 GHz are utilized by the current wireless communication systems. In these systems, the channels adapt to flexible mathematical models that define electromagnetic propagation with rational accuracy. However, in other systems, the channel models are not easily described. ML concept mostly considered as a tool has created a revolution in modern day computing over the past years [22]. With easier to handle with today’s hardware and software solutions, ML has created a diverse range of algorithms that has enabled the development of intelligent, fast, and adaptive systems. Many applications such as unmanned armed vehicles, navigation systems, and big data analysis have efficiently proved the need and importance of DL, rather than using traditional strict and complex algorithms. DL is basically a sub decision of ML. Mostly DL is being used for prediction, processing of data, analysis of data, and classification of data [11]. Some of the fields where DL is immensely used are computer vision, image processing, and stock market data predictions which are very important part of our day-to-day life [23]. Our daily used products such as personal computers, laptops, mobile phones, and tablets use DL where every upgrade comes with a new technique used. Much research on DL and its application are being done every day and researchers are making use of all possible methods to utilize it efficiently for the modern wireless communication scenarios. The modern-day wireless communication deal with the need of increasing high data rates, demand of much more bandwidth, and many more radically used applications, which, in turn, requires novel signal processing techniques [2].

Schematic illustration of the ML application for communications.

      5.2.1 Automatic Modulation Classification

      A wide amount of work has been done in feature-based AMC and the application of DL algorithms directly on the received signal, thereby eliminating the feature extraction step and further reducing computational complexity. The application of CNN in AMC has shown promising accuracy which can ensure acceptable performance with much lower cost of computation. The next step would be to find effective hardware implementation of DL-based AMC classifiers [31].

      5.2.2 Resource Allocation (RA)

      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].

      The application of a neural network (NN) for channel estimation is influenced by the channels which are challenging to describe. This problem may ensue from a provision that inhibit the possession of CSI at the receiver (CSIR) or an unavailability of a well-known channel models. For instance, in MIMO systems with low-resolution analog-to-digital converters (ADCs), consistent CSIR cannot be achieved due

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