# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### Discussions  
As presented in this systematic review, all the selected papers are very recent as most of them were published in the year 2019 (57.1%). The oldest paper we examined is from the year 2015. This reflects the novelty and hotness of the technologies 5G and deep learning, and of course their integration. 5G is a technology in development and is set to solve several limitations present in the previous generations of cellular communication systems. It offers services, so far limited, such as massive connectivity, security, trust, large coverage, ultra-low latency (in the range of 1 ms over the air interface), throughput, and ultra-reliability, (99.999% of availability). On the other side of the spectrum, deep learning has received a lot of attention in the last few years as it has surpassed several state-of-the-art solutions in several fields, such as computer vision, text recognition, robotics, etc. The many reviewed recent publications attest the benefits that 5G technology would enjoy by making use of deep learning advances. For the purpose of illustration only, as commented in [11], resource allocation in real cellular wireless networks can be formulated and solved using tools from optimization theory. However, the solutions often used have a high computational complexity. deep learning models may be a surrogate solution keeping the same performance but with reduced computational complexity. We also noted that many works (a total of 25 to be precise) were published in conferences with few pages (around six pages). We believe that they represent works in progress, as they only show initial results. It reinforces the general view that the the integration between 5G and deep learning is still an evolving area of interest with many advances and contributions expected soon. By observing the different scenarios considered in the examined articles, they generally do not focus on a real application (30 out of 57 articles found). However, a project called Mobile and wireless communications Enablers for the Twenty-twenty Information Society (METIS) published a document that explained several 5G scenarios and their specific requirements [100]. Nine use cases are presented: gaming, marathon, media on demand, unnamed aerial vehicles, remote tactile interaction, e-health, ultra-low cost 5G network, remote car sensing and control, and forest industry on remote control. Each of these scenarios have different characteristics and different requirements regarding 5G networks. For instance, remote tactile iterations scenarios can be considered a critical application (e.g., remote surgeries) and demand ultra-low latency (not be greater than 2 ms) and high reliability (99.999%). On the other hand, in the marathon use case, the participants commonly use attached tracing devices. This scenario must handle thousands of users simultaneously requiring high signaling efficiency and user capacity. As result, we believe that in order to achieve high impact results, deep learning solutions need to be targeted towards addressing use cases with specific requirements instead of trying to deal with the more general picture. Planning deep learning models for dynamic scenarios can be a complex task, since deep learning models need to capture the patterns present in the dataset. Thus, if the data varies widely between scenarios, it can certainly impact the performance of the models. One approach that can be used to deal with this limitation is the use of reinforcement learning. As presented in Section 3.2, seven works considered this paradigm in their solutions. Indeed, this approach considers training software agents to react to the environment in order to maximize (or minimize) a metric of interest. This paradigm can be a good approach to train software agents to dynamically adapt according to changes in the environment, and thus meet the different requirements of the use cases presented above. However, reinforcement learning requires an environment where the software agent needs to be inserted during their training.