## 3 Routing in Satellite Networks
### Routing Protocols in LEOs
#### 3.3.5 Dynamic Detection Routing Algorithm (DDRA)
However, while it is an excellent option for single-layer satellite networks, it can be enhanced to increase compatibility with multilayer satellite networks [112]. In 2011, [PERSON] et al. proposed a new dynamic, distributed, and adaptive routing algorithm to overcome the problem of poor adaptability of centralized, static, and non-adaptive routing algorithms in LEO satellite networks to frequently changing situations and traffic loads. While the proposed TPDRA algorithm makes prediction decisions with the data collected from the ground networks, it also provides the routing decision using the mobile agent on the satellite network side. For traffic prediction, the traffic density of the Earth's surface, which they divide into grids and classify according to population density, is estimated through a radial basis function neural network. On the side of the satellite networks, the final routing decision is made based on both the current and future status of the satellite. It was compared to ACO in the simulation results and showed better performance regarding conduction delay and congestion [109]. In 2019, [PERSON] et al. centered on refining routing algorithms on LEO satellite networks. The fluctuating topology of LEO satellite networks causes difficulties in routing algorithm design. They recommended an adaptive satellite communication routing algorithm based on SDN architecture for communication with good communication quality. The proposed model is promising both in finding the shortest path and optimizing this path instantly, adjusting to the movement of satellites. Nonetheless, owing to the heightened intricacy of the algorithm, this approach is appropriate for limited-scale networks rather than extensive, dynamic networks [116]. In 2019, [PERSON] et al. focused on memory-efficient routing approaches in LEO networks. During the movements of LEO satellite networks, the topology of the satellite network changes periodically. One of the challenges posed by this situation is how to construct a fault-tolerant and durable routing protocol. For this, a new and memory-conserving routing technique, 'OPSPF', which takes the upside of the predictability of the satellite array, is proposed in the study. The OPSPF routing approach provides zero route convergence expense amid frequent topology modifications. It achieves a 57% reduction in communication costs amid infrequent, unpredictable topology shifts. It also achieves an 82% reduction in route convergence time during erratic topology changes. These advantages show that OPSPF is a more efficient and cost-effective routing protocol than conventional OSPF. Despite this, the algorithm is only effective for a certain small network with eight satellites, and the algorithm cannot optimize connection latency [117]. In 2021, [PERSON] et al. worked on routing processes in the LEO satellite network. Their goals were to optimize and improve traditional and centralized routing processes in LEO satellite networks with high node mobility, topology change, and connection congestion, which will hold significant importance in 6G networks. For this purpose, they proposed an intelligent decentralized routing algorithm based on deep reinforcement for LEO networks. The proposed algorithm uses the DQN table for decision-making. The algorithm receives data, such as the current channel bandwidth, the signal-to-noise ratio (SNR) of the channel, spacing, queue delay, and spatial location, that it collects from its neighbors as input. With the received data and its own state data, the DQN model gives the predicted output. In the simulation results of the model trained in the Keras environment, it was seen that the latency improved compared to traditional algorithms. However, it is necessary to expand the study with different deep learning networks in terms of feasibility for real scenarios. DQN tables have scalability issues as the network grows [118]. In 2022, [PERSON] et al. worked on load-balancing for LEO satellite networks.