Energy-efficient optimal power allocation in integrated wireless sensor and cognitive satellite terrestrial networks
journal contributionposted on 2017-09-28, 16:09 authored by Shengchao Shi, Guangxia Li, Kang An, Bin Gao, Gan Zheng
This paper proposes novel satellite-based wireless sensor networks (WSNs), which integrate the WSN with the cognitive satellite terrestrial network. Having the ability to provide seamless network access and alleviate the spectrum scarcity, cognitive satellite terrestrial networks are considered as a promising candidate for future wireless networks with emerging requirements of ubiquitous broadband applications and increasing demand for spectral resources. With the emerging environmental and energy cost concerns in communication systems, explicit concerns on energy efficient resource allocation in satellite networks have also recently received considerable attention. In this regard, this paper proposes energy-efficient optimal power allocation schemes in the cognitive satellite terrestrial networks for non-real-time and real-time applications, respectively, which maximize the energy efficiency (EE) of the cognitive satellite user while guaranteeing the interference at the primary terrestrial user below an acceptable level. Specifically, average interference power (AIP) constraint is employed to protect the communication quality of the primary terrestrial user while average transmit power (ATP) or peak transmit power (PTP) constraint is adopted to regulate the transmit power of the satellite user. Since the energy-efficient power allocation optimization problem belongs to the nonlinear concave fractional programming problem, we solve it by combining Dinkelbach’s method with Lagrange duality method. Simulation results demonstrate that the fading severity of the terrestrial interference link is favorable to the satellite user who can achieve EE gain under the ATP constraint comparing to the PTP constraint.
The work of Shengchao Shi, Guangxia Li, Kang An and Bin Gao was supported by National Natural Science Foundation of China (No. 61571464, 61601511, 91338201, 91438109 and 61401507). The work of Gan Zheng was supported by the UK EPSRC under grant number EP/N007840/1.
- Mechanical, Electrical and Manufacturing Engineering