认知网络中基于网络辅助的速率控制方法

doi:10.3969/j.issn.1000-436x.2013.05.015

通信学报 ›› 2013, Vol. 34 ›› Issue (5): 126-135.doi: 10.3969/j.issn.1000-436x.2013.05.015

认知网络中基于网络辅助的速率控制方法

杨春刚,盛敏,董延杰,李建东,李红艳,刘勤

西安电子科技大学综合业务数字网国家重点实验室，陕西西安 710071

出版日期:2013-05-25 发布日期:2017-06-27
基金资助:
国家自然科学基金资助项目;国家自然科学基金资助项目;国家自然科学基金资助项目;国家重点基础研究发展计划(973计划)基金资助项目;陕西省自然科学基金资助项目;陕西省科技研究与发展计划基金资助项目;中央高校基本业务费基金资助项目;综合业务数字网国家重点实验室基金资助项目;111基地专项基金资助项目;长江学者创新团队基金资助项目;国家科技重大专项基金资助项目

Network-assisted optimal rate control methods in cognitive networks

Chun-gang YANG,Min SHENG,Yan-jie DONG,Jian-dong LI,Hong-yan LI,Qin LIU

State Key Lab of Integrated Service Network,Xidian University,Xi'an 710071,China

Online:2013-05-25 Published:2017-06-27
Supported by:
The National Natural Science Foundation of China;The National Natural Science Foundation of China;The National Natural Science Foundation of China;The National Basic Re-search Program of China (973 Program);The Natural Science Program of Shaanxi Province;The Science and Technology Research and Development Program of Shaanxi Province;The Fundamental Research Funds for the Central Universities;The Program of State Key Lab of Integrated Service Network;111 Project;The Program for Changjiang Scholars and Innovative Research Team in University;The National Science and Technology Major Project of the Ministry of Science and Technology of China

摘要/Abstract

摘要：

摘要：面向多速率业务的认知网络，为了克服其动态性并实现速率控制的自主性，在改进IEEE 1900.4系统架构的基础上提出了速率控制框架。探讨不同层面不同尺度的速率控制方案。重点研究终端侧短期实时速率控制的问题。首先，基于非合作博弈提出分布式自主速率选择方法；进而，基于合作博弈提出基于网络辅助的中心式速率分配方法。仿真结果表明，后者较前者获得近60%的效能和一定的公平性改善，同时也验证了定价函数设计能够有效地改进公平性。

关键词: 认知网络, 速率控制, 博弈, IEEE1900.4

Abstract:

Orienting the multi-rate cognitive networks,overcoming the typical characteristics of dynamics to implement the autonomy and rationality of rate control,first the rate control framework based on the presented improved IEEE 1900.4 architecture was proposed.Meanwhile,different scaled rate control schemes on different levels were investigated.Then,the real-time rate control problem on the terminal we concentrate on.Most importantly,both the distributed rate selection of TRM towards RNRM and the centralized rate allocation of RNRM to TRM were investigated.Simulation results show that the latter can achieve 60% utility and certain fairness improvements,in addition,the rationality and fairness guaranteed by the newly-built pricing function is verified.

Key words: cognitive networks, rate control, game theory, IEEE 1900.4

杨春刚,盛敏,董延杰,李建东,李红艳,刘勤. 认知网络中基于网络辅助的速率控制方法[J]. 通信学报, 2013, 34(5): 126-135.

Chun-gang YANG,Min SHENG,Yan-jie DONG,Jian-dong LI,Hong-yan LI,Qin LIU. Network-assisted optimal rate control methods in cognitive networks[J]. Journal on Communications, 2013, 34(5): 126-135.

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参数	取值
系统带宽	5MHz
终端发射功率路径损耗	21dBmL= 128.1+37.6lg R，其中，R为距离，单位为km
噪声功率	?120 dBm
业务模型	FTP、HTTP、视频、VoIP

认知网络中基于网络辅助的速率控制方法

Network-assisted optimal rate control methods in cognitive networks

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