4.7 Article

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

Journal

IEEE TRANSACTIONS ON COMMUNICATIONS
Volume 68, Issue 2, Pages 778-791

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCOMM.2019.2950022

Keywords

Base stations; Satellites; Array signal processing; Quality of service; Interference; Optimization; Resource management; Terrestrial-satellite cooperation system; multicast; resource allocation; beamforming design; quality of service

Funding

  1. National Natural Science Foundation of China [91538203, 61871257]
  2. new strategic industries development projects of Shenzhen City [JCYJ20170307145820484]
  3. Joint Research Foundation of the General Armaments Department
  4. Ministry of Education [6141A02033322]
  5. Beijing Innovation Center for Future Chips, Tsinghua University

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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.

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