## 2 Challenges and Routing in 6G Networks
### Routing in 6G Networks
#### 2.2.2 Review of 6G Routing
Proposals exist for both land-based and satellite network solutions. While attention was drawn to what should be the criteria for evaluating the performance of ML models, no answer was given to the question of what is the promising solution to address this problem [55]. In 2021, [PERSON] et al. worked on energy-efficient routing for resource-scar massive-IoT networks. In 6G-enabled massive-IoT networks consisting of many devices, traditional stochastic routing algorithms use a lot of energy and time due to the computational load needed to find the best node for routing. They proposed deep learning-based, smart stochastic routing to improve this situation. Using deep neural networks and the Matlab simulation program as the methodology, they obtained a faster, energy-efficient, low-latency, and reliable model [84]. In 2021, [PERSON] et al. worked on dynamically driven congestion control in 6G networks. With the maturation of mobile networks, it is becoming increasingly difficult to ensure network efficiency and system performance in incredibly growing communication technologies. In this study, dynamically driven congestion control and segment redirection (DD-CCSR) using Deleroi superposed principles and onward-backward interfacing based on reducing congestion control to reduce transmission delay, maximize bandwidth utilization, and balance network loads are presented. With this method, the aim is to reduce data flow and over-crowding and boost network traffic observation and network trail rectification. As a result, better bandwidth utilization and packet arrival rates have been achieved. However, it should be tested on larger-scale networks. The additional loads that this method can bring to the network should be detailed [29]. In 2022, [PERSON] et al. proposed a new routing strategy aimed at minimizing overall latency in a 6G wide area network (WAN). In this proposition, they used a mixture of fiber  
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optic and cordless architecture for the WAN, featuring a spine grid network established by high-speed optical fibers, complemented by local cordless network admission points. The initial and final routings are achieved via the wireless channel, and the intermediary points are achieved via the backbone optical network. In the theoretical and simulation results, the average throughout delay was found to be less than 1 millisecond (ms) [58]. In 2022, [PERSON] et al. introduced a new prognostic QoS routing algorithm to increase QoS in beyond-5G networks by using Auto Regressive Integrated Moving Average (ARIMA). The proactive model they recommended in their study, which focused on eMBB flows, outperformed the reactive model [85]. In 2022, [PERSON] et al. suggested a novel model called P-HEUR to address the integrated issue of optimizing energy-efficient user assignment, traffic routing in backhaul, and switching of base station/backhaul links. They aimed to solve problems such as high path loss, antenna gain, cost, and the unstable links of the small cells densification concept, which will be brought about by structures such as mmWave that will be needed to support high-capacity and low-latency future networks such as 5G and 6G. The proposed P-HEUR model is capable of achieving energy-efficient solutions with 83% optimality while requiring 200 k times lower execution times compared to the state-of-the-art models. P-HEUR demonstrated higher feasibility and lower unsatisfied user probability compared to the optimal case due to an increased number of active BSs and BH links accommodating rate uncertainty, with up to 57% and 64% improvements, respectively. More information could be provided on the applicability of the proposed methods in real-world scenarios. Additionally, future research could discuss how this method might be further improved [86]. In 2022, [PERSON] et al.