In order to efficiently transfer data, rate control and route scheduling are critical in deep space scenarios. The major challenge is the long-haul and intermittent links, which easily leads to low link utilization. However, Traditional backpressure algorithms can not acquire accurate queue information and maintain long queues at each node. Therefore, we devise a system model for deep space networks, which divides the network into different clusters according to different distance scales. Then, we propose a two-scale geographic back-pressure algorithm whose goal is to improve throughput and decrease propagation delays. In one cluster, we introduce a delay cost function with geographic location information of nodes. And we implement two types of queues at each node between different clusters. The simulation results demonstrate that our algorithm can get smaller average queue lengths and reduce end-to-end delays by 23% compared to original back-pressure algorithms.

For deep space networks, the major challenge is that transmission power consumption is serious while the power resource is limited. In this paper, we proposed a Temporal Graph Model based Power Aware Routing algorithm using a weighted metric of remaining power level, data rate and distance of nodes for determining routes. The traditional standards and techniques of static networks is hard to solve routing problem in our networks. And the temporal graph model contributes to our scenario. Because the future contact information between nodes can be predicted accurately and we can use temporal graphs to model it. Our algorithm finds the route that obtains the minimal transmission power cost although these routes may be different. The numerical results demonstrate that our routing algorithm selects the path with low total transmission power in the first transmission round and achieves the maximum support transmission opportunities, making the network live longer.

To maximize the volume of data available for use on ground in near real time, many solutions have been proposed to perform data collection through satellite networks. Inspired by the backpressure algorithm, we are interested in the design of a network algorithm that not only exhibits long-term throughput optimality and but also delivers packets in reasonable end-to-end delays. Thus, we investigate the feasibility of backpressure algorithm on satellite networks and develop a throughput-optimal solution based on the utility maximize framework. For delay performance, we embed distance factor into the objective and derive a geographic-location-aware backpressure algorithm. The simulation results show that our algorithm is able to deliver packets in time and scale well as traffic load varies.

For deep space networks, the major challenge is that transmission paths have to be built among long-haul links. These paths demand considerable transmission power. Since all nodes' energy is limited, it is essential to choose paths with low power costs. For reducing power consumption, we explore joint relay node selection and dynamic power control. First, we build up the routing cost function and the state dynamics. Then, a non-cooperative differential game model for power control is established. Based on the game equilibrium, we propose a joint power control and routing strategy, which selects the path with the minimum total transmission power. In addition, a routing algorithm of polynomial time complexity is devised for the optimal path search. The numerical results demonstrate that our routing algorithm can save power up to 48% compared to the previous solution and to 84% compared to the shortest path routing algorithm.