Joint Beamforming Design and Resource Allocation for Terrestrial-Satellite Cooperation System

In this paper, we investigate a multicast beamforming terrestrial-satellite cooperation system to optimize the communication capacity and quality of service. Different from traditional link-based terrestrial network, we design the terrestrial and satellite beamforming vectors cooperatively based on...

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Bibliographic Details
Published in:IEEE transactions on communications 2020-02, Vol.68 (2), p.778-791
Main Authors: Zhang, Yuandong, Yin, Liuguo, Jiang, Chunxiao, Qian, Yi
Format: Article
Language:English
Subjects:
Array signal processing
Base stations
Beamforming
beamforming design
Communications systems
Complexity
Computer simulation
Cooperation
Design optimization
Heuristic methods
Interference
Iterative algorithms
Lower bounds
multicast
Multicasting
Optimization
Quality of service
Quality of service architectures
Resource allocation
Resource management
Satellites
Stations
Terrestrial-satellite cooperation system
Upper bounds
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Summary:In this paper, we investigate a multicast beamforming terrestrial-satellite cooperation system to optimize the communication capacity and quality of service. Different from traditional link-based terrestrial network, we design the terrestrial and satellite beamforming vectors cooperatively based on the required contents of users in order to realize more reasonable resource allocation. Meanwhile, the backhaul links between content provision center and satellite and base stations are limited, and the users always need high quality of service, considering these, our object is maximizing the sum of user minimum ratio under the constraints of resource allocation, backhaul link and quality of service in reality. We first formulate the optimization problem and propose a joint optimization iterative algorithm to design the beamforming vectors of satellite and base stations cooperatively. Then, to obtain the global optimum solution, we propose a Bound-based algorithm and solve the optimization problem by shrinking the upper bound and lower bound of the optimization feasible region. To decrease the complexity, we then design a heuristic scheme to solve the problem. The simulation results show that, our proposed cooperative optimization algorithms have better performance than non-cooperative methods, and the heuristic scheme has little poor performance but has significant advantage in low complexity.
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2019.2950022