## 3 AI/ML in the Standards and Industry  
The first public deployments of 5G were a natural extension of the services offered by 4G by boosting connectivity through the enhanced mobile broadband (eMBB) use case, but the realisation of the full potential of 5G and beyond also stems from the support of new requirements offering lower latency services and the support of higher numbers of user equipment (UE) per unit surface area. Two new supported use cases, ultra-reliable and low-latency communications (uRLLC) and massive machine-type communications (mMTC), open new horizons to various industry verticals by providing new opportunities and prospects. Some representative examples of such verticals are healthcare, manufacturing, automotive, harbours, and retail.  
The main technical enablers for providing customised services to verticals are mobile (or multi-access) edge computing (MEC) and network slicing [58]. British Telecom has calculated a 30% increase in revenue and a 40% decrease in OpEx by using only one physical infrastructure with network slicing instead of developing different physical networks to support various services. As we are gradually moving towards a standalone 5G network, a fully-fledged network slicing functionality brings a more granular control of the network resources. Capacity-based slices can be broken down into smaller pieces, offered to verticals and individual customers only for a few minutes or so. Network slicing also has an impact on the orchestration of the RAN, e.g., network and RAN slices may be jointly created for better E2E performance. The optimal operation of such a highly complex system across several geographical areas calls for an E2E automation with as little human intervention as possible.  
Several standardisation bodies have already recognised that a fully automated network operation is closely linked to the effective use of big data analytics and AI/ML. AI-driven self-organised networking solutions by commercial vendors have also become increasingly popular [59]. Data collection engines could gather an enormous volume of raw data from several geographical areas and network locations (RAN/Edge/Core). The collected data can be subsequently analysed and mapped onto key performance indicators (KPIs), which are converted into appropriate actions at the RAN (e.g., radio resource allocation) and the core (e.g., intelligent network slicing) for satisfying the service level agreements (SLAs).  
Figure 2: The Viterbi decoding algorithm has to estimate the log-likelihood ratio of the received symbol \(y\) for all possible transmitted sequences of symbols \(\mathbf{s}\) to propagate along the Trellis diagram. Viterbient uses a small-sized fully-connected NN with two hidden layers that is trained to estimate the likelihood for each possible sequence of transmitted symbols, given the channel output \(p(\mathbf{s}|y)\). This is subsequently combined with the estimated marginal probability of the channel output \(p(y)\) to calculate the desired log-likelihood ratios \(p(y|\mathbf{s})\). The NN applies a softmax activation function at the output layer to calculate conditional probabilities. In the depicted NN, the output size is equal to 16 corresponding to BPSK modulated symbols transmitted over an intersymbol interference AWGN channel with memory length equal to 4 [26].  
Hidden patterns of the network behaviour can also be unveiled using AI/ML and provide the foundation for predicting interesting events, such as sharp load variations, connectivity outages, and faults. Additionally, industry verticals would require access to data analytics for assessing the acquired benefits against the induced cost.  
Next, we review the existing works and future trends in the standardisation bodies and telecommunication industry related to the adoption of AI/ML and data analytics in 5G communication networks at the edge, the core and the RAN.