未来5G通信中基于协同感知的能量有效性优化算法

崔翠梅; 朱锡芳; 金石; 汪一鸣

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未来5G通信中基于协同感知的能量有效性优化算法

更新时间：2026-09-04

- 未来5G通信中基于协同感知的能量有效性优化算法
- Cooperative Spectrum-aware Energy-Efficiency Optimization for 5G Communications
- 微电子学与计算机 2017年34卷第12期页码：1-4
- 作者机构：
  
  1. 常州工学院机构电气与光电工程学院,江苏,常州,213022
  2. 东南大学移动通信国家重点实验室机构,江苏,南京,210096
  3. 苏州大学城市轨道交通学院,江苏,苏州,215021
- 作者简介：
- 基金信息：
  
  国家自然科学基金项目 61471252常州市应用基础研究计划项目 CJ20160054江苏高校文化创意协同创新中心重点项目 XYN1505常州工学院自然科学基金重点项目 YN1504
- DOI：
  中图分类号：
- 纸质出版：2017
- 稿件说明：
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崔翠梅, 朱锡芳, 金石, 汪一鸣. 未来5G通信中基于协同感知的能量有效性优化算法[J]. 微电子学与计算机, 2017, 34(12): 1-4.

CUI Cui-mei, ZHU Xi-fang, JIN Shi, et al. Cooperative Spectrum-aware Energy-Efficiency Optimization for 5G Communications[J]. 2017, 34(12): 1-4.
崔翠梅, 朱锡芳, 金石, 汪一鸣. 未来5G通信中基于协同感知的能量有效性优化算法[J]. 微电子学与计算机, 2017, 34(12): 1-4. DOI：

CUI Cui-mei, ZHU Xi-fang, JIN Shi, et al. Cooperative Spectrum-aware Energy-Efficiency Optimization for 5G Communications[J]. 2017, 34(12): 1-4. DOI：

摘要

针对未来5G通信网络频谱资源紧缺、高吞吐量和低功耗需求等难题

研究了如何提高频谱资源利用率和能量有效性.首先

基于协同频谱感知方法和动态时分多址接入机制构建了5G认知网络模型.其次

推导出能量有效性函数表达式

并给出多目标优化函数

利用帕累托最优化方法将非凸函数转化为凸函数求取全局最优解.最后

通过仿真验证所提能量有效性优化算法在保证感知准确性和时间敏捷性的同时

获得比其他两种方法更高的性能.

Abstract

To address the issues of spectrum inefficiency

high throughput

and low power consumption in 5G communications

we study the spectrum efficiency and energy efficiency issues. First

5G cognitive network model

where cognitive femtocell coexist with picocell

was built based on cooperative spectrum sensing scheme and dynamic time-division multiple-access scheduling mechanism. Then

formulate energy efficiency function and optimization problem with respect to transmission power. To resolve the non-convexity of the formulated optimization problem

Pareto-optimal solution was involved in global optimality. The simulation results show that the proposed schemes significantly enhance the energy efficiency of the cognitive femto user compared with another schemes

and ensure the accuracy and time agility of spectrum sensing.

关键词

Keywords

references

Park H, Hwang T. Energy-efficient power control of cognitive femto users for 5G communications[J]. IEEE Journal on Selected Areas in Communications, 2016, 34(4): 772-784. DOI: 772

Mitola J, Maguire G Q Jr. Cognitive radios: Making software radio more personal[J]. IEEE Personal Communications, 1999, 6(4): 13-18. DOI: 13

CUI C, WANG Y. Time agility optimization for dual-stage collaborative spectrum[J]. Chinese Journal of Electronics, 2014, 23(2): 399-402. DOI: 399

CUI C, WANG Y. Analysis and optimization of sensing reliability for relay-based dual-stage collaborative spectrum sensing in cognitive radio networks[J]. Wireless Personal Communications, 2013, 72(4): 2321-2337 DOI: 2321

Xu X, Yang W, Cai Y, et a; . On the secure spectral-energy efficiency tradeoff in random cognitive radio Networks[J]. IEEE Journal on Selected Areas in Communications, 2016, 34(10): 2706-2722. DOI: 2706

Wang C X. Cellular architecture and key technologies for 5G wireless communication networks[J]. IEEE Communication Magazine, 2014, 52(2): 122-130. DOI: 122

Ozcan G, Gursoy M C. Energy-efficient power adaptation for cognitive radio systems under imperfect channel sensing [C]//IEEE INFOCOM Workshops. IEEE, 2014: 706-711.

Park H, Hwang T. Optimal beamforming and power allocation for cognitive femto base stations based on soft decision[J]. IEEE Journal on Selected Areas in Communications, 2015, 3 (5): 878-895 DOI: 878

杨威, 管东林, 逯东辉, 等. 面向认知无线电网络能量高效协作感知的在线节点选择算法[J]. 通信学报, 2012, 33(7): 103-110. DOI: 103

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