## 2 Background on ML-Based Optimisation of Wireless Networks
\begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline
**Network Functions** & **Examples of Key Research Studies** \\ \hline Symbol detection & DNN-based receiver design for joint channel equalisation and symbol detection in OFDM systems [27]. \\ \cline{2-3}  & DNN-based MIMO detector combining deep unfolding with linear-MMSE for channel equalisation [28]. \\ \cline{2-3}  & LSTM-based learning of the log-likelihood ratios (LLRs) in Viterbi decoding [26]. \\ \hline Channel estimation & Estimating the channel state information (CSI) under combined time and frequency selective fading channels using DNNs [Yang2019] and convolutional neural networks (CNNs) [29]. \\ \cline{2-3}  & Adaptive channel equalisation using recurrent neural networks (RNNs) [30]. \\ \cline{2-3}  & Channel estimation using meta-learning [31]. \\ \cline{2-3}  & Reducing the CSI feedback in FDD massive MIMO systems using autoencoders in the feedback channel [32]. \\ \hline Channel prediction & DNN-based prediction of the downlink CSI based on the measured uplink CSI in FDD MIMO systems [33,34]. \\ \cline{2-3}  & RNN-based downlink channel prediction leveraging correlations in space and time to alleviate the issue of outdated CSI [35]. \\ \hline Channel coding & DNN-based decoding of short polar codes of rate \% and block length N = 16 [36]. \\ \cline{2-3}  & DNN-based decoding of polar codes with length N = 128 leveraging the structure of belief propagation decoding algorithm [37]. \\ \cline{2-3}  & Integrating supervised learning for estimating the extrinsic LLRs into the max-log-map turbo decoder [38]. \\ \hline Link adaptation & Deep reinforcement learning (DRL)-based selection of the modulation and coding scheme (MCS) using the measured SNR as the environmental state and the experienced throughput as the reward [39]. \\ \cline{2-3}  & Supervised learning techniques for adaptive modulation and coding (AMC) including k-nearest-neighbours and support vector machines (SVMs) [40]. \\ \hline Reflecting intelligent surfaces (RIS) & DNN-based design and control of phase shifters [41]. \\ \cline{2-3}  & Using supervised learning to estimate the direct and the cascade (base station to RIS and RIS to the user) channels in RIS-based communication [42]. \\ \cline{2-3}  & Combining compressive sensing with deep learning (DL) to reduce the training overhead (due to the large number of reflecting elements) in the design of phase-shifts in RIS [43]. \\ \hline Spectrum sensing & Unsupervised (k-means, Gaussian mixture models) and supervised (k-nearest-neighbours and SVMs) learning methods for binary spectrum sensing [44]. \\ \cline{2-3}  & CNN-based multi-band cooperative binary spectrum sensing leveraging spatial and spectral correlations [45]. \\ \cline{2-3}  & Image-based automatic modulation identification based on the received constellation diagrams, signal distributions and spectrograms [46]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example key studies utilising model-based ML techniques for improving the performance of wireless networks in real time (less than 10 ms). The summary of research works included in the tables reveals that the majority concentrate on real-time RIC, while, as we will shortly see in the next section, the industry and standardisation efforts have been mostly focused on the non-real-time RIC, data analytics at the core and the edge networks, AI-based E2E network slicing and vendor inter-operability issues. In our view, the joint creation of RAN and network slices, and the optimal proactive allocation of resources for E2E network slicing using AI/ML is a promising research topic worth pursuing.