# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
## 2 Related Work  
As was mentioned earlier, the performance of traditional loss-based congestion-control solutions is severely hindered by channel variations that characterizes wireless communications. To overcome this limitation, new delay-based algorithms have recently loomed. For instance, the authors of [5] proposed Versus, which is built upon learning the relationship between the perceived delay and transmission window in cellular networks. Similarly, [PERSON] [6] proposed to infer wireless channel variation in cellular networks from packet inter-arrival times, and to adapt the transmission rate accordingly. Following a different approach, Performance-oriented Congestion Control (PCC) Vivace [7] exploits online convex optimization to optimize the sender rate. Although all these schemes have been shown to offer good performance over cellular networks, their behavior worsens [8] over certain highly variable 5G scenarios, like mmWave. On the other hand, there are other works that have analyzed the performance of congestion-control solutions over mmWave channels, either through simulations or using real platforms. For instance, [PERSON] et al. analyzed in [9] the performance of different TCP algorithms exploiting a ns-3 mmWave module. This study evinced a bad adaptation of the congestion-control solutions, which led to frequent slow-start phases and buffer-ploat situations. This work was further extended in [10], were more congestion-control algorithms were analyzed, including Bottleneck Bandwidth and Round-trip propagation time (BBR). This latter paper discusses the tight interplay between transport layer performance and the particular network configuration (for instance, packet size, 3 GPP stack parameters, etc.). Other works have tried to analytically model this relationship. For instance, authors in [11] analyze the performance of TCP-Cubic over 5G mmWave channels, using finite-state Markov Chains to model the system behavior. To complement the results obtained by simulators and analytical models, the performance of congestion-control solutions over mmWave has been also studied using real hardware. In this sense, the measurement campaign depicted in [12] also revealed a bad adaptation of both Cubic and BBR to mmWave channel variations. The performance of BBR was also analyzed in [13], which shows a severe throughput degradation over mmWave channels. The authors even reported what they referred to as \"throughput collapse\", for bad Round-Trip Time (RTT) estimations. The authors of [14] thoroughly analyze bufferbloat in 5G mmWave scenarios and they point out that existing congestion-control solutions are not able to adapt to rapid rate variations. In addition, their work shows that Active Queue Management (AQM) techniques can mitigate this phenomenon. Altogether, all previously mentioned papers foster the idea of developing adaptive solutions, able to learn the congestion level, for instance exploiting machine learning techniques. In this sense, the adoption of machine learning-based solutions, in the context of 5G networks, is becoming quite popular, and the scientific community is fostering the development of data-driven algorithms, which may complement traditional model driven approaches [15; 16]. More importantly, artificial intelligence in general, and machine learning in particular, is believed to be one of the key enablers for future 6G networks [17; 18; 19]. Another category of works aim to propose new transport layer solutions (clean state approach) or to adapt existing congestion-control algorithms to improve their performance over mmWave networks. For instance, [PERSON] et al. propose in [20] a deep-learning-based solution for TCP congestion control in disaster scenarios with high-mobility drones. Although they share some of our goals, they are focused on a very particular scenario and its solution, while our main goal is to ascertain the potential performance of different machine learning algorithms. Similarly, the impact of mobility over TCP in mmWave scenarios has been addressed in [21].