Energy-Efficient Resource Allocation for OFDMA-based Wireless-Powered Backscatter Communications

被引:6
作者
Gu, Bowen [1 ]
Xu, Yongjun [1 ,2 ]
Huang, Chongwen [3 ]
Hu, Rose Qingyang [4 ]
机构
[1] Chongqing Univ Posts & Telecommun, Sch Commun & Informat Engn, Chongqing, Peoples R China
[2] Xian Univ Posts & Telecommun, Shaanxi Key Lab Informat & Commun Network & Secur, Xian, Peoples R China
[3] Zhejiang Univ, Zhejiang Prov Key Lab Informat Proc Commun & Netw, Hangzhou 310027, Peoples R China
[4] Utah State Univ, Dept Elect & Comp Engn, Logan, UT 84341 USA
来源
IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021) | 2021年
基金
中国国家自然科学基金;
关键词
Wireless-powered backscatter communications; OFDMA; resource allocation; energy efficiency;
D O I
10.1109/ICC42927.2021.9500839
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Energy efficiency (EE) is a crucial performance metric in wireless-powered backscatter communication networks (WP-BackComNets) for achieving a good tradeoff between data rates and the overall energy consumption, which however has not been sufficiently exploited by the existing works. In this paper, an EE-based maximization resource allocation (RA) problem is studied in a downlink orthogonal frequency division multiple access-based WP-BackComNet, where the circuit power consumption of the backscatter device, the minimum energy harvesting (EH) constraint, and the maximum transmit power constraint of the power station are considered. To deal with the non-convex problem, we firstly transform it into an equivalently subtractive form via Dinkelbach's method. Then, we apply a variable substitution approach to transform the non-convex problem into a convex one, where the closed-form solutions of the reflection coefficient, the transmit power, and the EH time are deduced by using Lagrange dual method. Simulation results demonstrate that the proposed algorithm can achieve better EE performance than other benchmark algorithms.
引用
收藏
页数:6
相关论文
共 20 条
[1]  
Boyd S., 2009, Convex Optimization, DOI DOI 10.1017/CBO9780511804441
[2]   ALGORITHMS FOR GENERALIZED FRACTIONAL-PROGRAMMING [J].
CROUZEIX, JP ;
FERLAND, JA .
MATHEMATICAL PROGRAMMING, 1991, 52 (02) :191-207
[3]  
Dinkelbach W., 1967, Management Science, V13, P492, DOI [242488, DOI 10.1287/MNSC.13.7.492]
[4]  
Grant M., Cvx: Matlab software for disciplined convex programming
[5]   Hybrid Active and Passive Antenna Selection for Backscatter-Assisted MISO Systems [J].
Li, Dong .
IEEE TRANSACTIONS ON COMMUNICATIONS, 2020, 68 (11) :7258-7269
[6]   Backscatter Communication Powered By Selective Relaying [J].
Li, Dong .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2020, 69 (11) :14037-14042
[7]   Optimal Time Scheduling Scheme for Wireless Powered Ambient Backscatter Communications in IoT Networks [J].
Liu, Xiaolan ;
Gao, Yue ;
Hu, Fengye .
IEEE INTERNET OF THINGS JOURNAL, 2019, 6 (02) :2264-2272
[8]   User Cooperation in Wireless-Powered Backscatter Communication Networks [J].
Lyu, Bin ;
Dinh Thai Hoang ;
Yang, Zhen .
IEEE WIRELESS COMMUNICATIONS LETTERS, 2019, 8 (02) :632-635
[9]   Time- and Power-Splitting Strategies for Ambient Backscatter System [J].
Ma, Zhe ;
He, Chen ;
Rao, Yanyi ;
Jiang, Jing ;
Ma, Shaodan ;
Gao, Feifei ;
Xing, Ling .
IEEE ACCESS, 2019, 7 :40068-40077
[10]   Ambient Backscatter Communications: A Contemporary Survey [J].
Nguyen Van Huynh ;
Dinh Thai Hoang ;
Lu, Xiao ;
Niyato, Dusit ;
Wang, Ping ;
Kim, Dong In .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2018, 20 (04) :2889-2922