Joint parameter optimization for perpetual nanonetworks and maximum network capacity
journal contributionposted on 22.09.2016, 10:58 authored by Xin-Wei Yao, Wan-Liang Wang, Shuang-Hua Yang
One of the major bottlenecks in nanonetworks is the very limited energy that can be accessed by nanodevices. To achieve perpetual data transmission, it is required to investigate in-depth the relationship between energy harvesting and consumption, and the underlying constraints in nanonetworks. In this paper, the tradeoff between energy harvesting and consumption is analyzed by considering the peculiarities of THz communication. First, based on the TS-OOK scheme and constrained energy in nanodevices, the upper bound of the transmitted pulse amplitude is presented. Second, given the proposed mathematical expression of the signal-to-interference-noise ratio (SINR) in multi-user nanonetworks, the lower bound of pulse amplitude is presented to satisfy the required SINR threshold. Third, the minimum spreading factor is derived to guarantee the perpetual nanonetworks by considering the energy harvesting-consumption tradeoff. Finally, the maximization of network capacity is investigated by jointly optimizing the parameters of spreading factor, transmission distance, amplitude of the transmitted pulse, pulse probability, and node density for perpetual nanonetworks. The simulation results demonstrate short transmission distance and small spreading factor are recommended to improve the network capacity. Moreover, pulse probability, pulse amplitude, spreading factor, and node density are required to be comprehensively manipulated to achieve the maximum network capacity and perpetual communication.
This work was supported in part by the National Natural Science Foundation of China under Grant 61379123 and Grant 61402414, in part by the Natural Science Foundation of Zhejiang Province, China, under Grant LQ14F020005 and Grant LQ15E050006, in part by the Public Project of Science Technology Department of Zhejiang Province under Grant 2015C31007, and in part by the Research Program of Educational Commission of Zhejiang Province of China under Grant Y201431815.
- Computer Science