# TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
## 4 Integrating TinyML with Network Technologies  
### 5G and TinyML  
At this point, we'd like to talk about another technical breakthrough that will transform how devices communicate with one another: 5G. With minimal latency, 5G connectivity can play a special role in managing huge amounts of data [147; 148]. 5G will usher in a new hyper-connected society in which everyone and everything will be linked. The relationship between the ML, the 5G, and the TinyML is depicted in Figure 16.  
As a result, the coming of 5G will usher in new paths and challenges for TinyML. We would want to suggest that, while TinyML is meant to handle ML challenges at the edge, predictions based on metadata or collective decisions made by thousands of devices will continue to be made in the cloud. As a result, it would be advantageous to distinguish between issues solved by devices and problems solved in the cloud. Furthermore, incorporating the benefits of both technologies while avoiding their drawbacks would result in a more resilient design. Researchers think that 5G will play an important role in more successfully linking these two frameworks because of the following vision [149]. * The real-time data rate is targeted at =1-10 Gbps [150], with a delay <10 ms [151]. * A significant number of devices are linked together [152]. * The goal is to minimize energy use by about 90% [147]. * It offers massive bandwidth, cheap cost, and extended battery life. In light of the development of machine learning and its benefits in wireless communications, each scientific community has made an attempt to assess the influence of ML  
Figure 16: Association of ML, 5G and TinyML. on 5G in their individual domains. As a result, multiple papers have been written about various themes like the effect of machine learning on the physical layer, privacy concerns, managing radio resource, and more. This makes it difficult to give a brief explanation of the importance and use of ML/AI in 5G. Due to the high complexity of 5G, deep learning has been identified by many leading papers as the most promising ML method. As a result, works that apply ML to 5G can be divided into two categories: \"deep learning-based classification\", in which we place a sole focus on deep learning and \"general ML classification\", where we take into account all feasible ML viewpoints from the literary works [153]. In the case of 5G, a broad ML classification follows the general ML structure shown in the previous sections, which employs three ML categories: Unsupervised learning, supervised learning and reinforcement learning (RL). An illustration of such categorization is provided in Table 6, providing the learning methodologies utilized in each category as well as a specific example of a 5G [154] application. Moreover, RL and 5G can exist in unmanned aerial vehicles (UAVs) as indicated in [155]. The next part will outline several 5G use cases and provide a method for mobile network operators to integrate AI/ML. Deep learning is the primary AI/ML learning technique that certain studies concentrate on since it is thought to be the most effective. As described in earlier case studies [156; 157], this gives 5G and its numerous components a very wide variety of applications. A number of studies have also concentrated on the use of several very promising deep learning subvariants, for instance traffic engineering/resource planning, network security and connection preservation, deep Q-learning for caching/offloading [158]. The advantages of using deep learning and TinyML to handle wireless communications issues, particularly in the context of 5G, are outlined in [156] and may be summed up as follows:  
* Unsupervised learning: Unsupervised learning is achievable due to the efficiency of deep learning in processing semi-labeled or unlabeled input. This is essential for dealing with the enormous amounts of unlabeled data that mobile systems frequently deal with.