物联网传感器网络中次级用户在不完美信道下的带宽和功率分配

doi:10.11772/j.issn.1001-9081.2018010133

计算机应用 ›› 2018, Vol. 38 ›› Issue (8): 2330-2336.DOI: 10.11772/j.issn.1001-9081.2018010133

物联网传感器网络中次级用户在不完美信道下的带宽和功率分配

文槿奕, 唐伦, 陈前斌

重庆邮电大学移动通信技术重庆市重点实验室, 重庆 400065

收稿日期:2018-01-16 修回日期:2018-03-15 出版日期:2018-08-10 发布日期:2018-08-11
通讯作者: 文槿奕
作者简介:文槿奕(1993-),女,重庆人,硕士研究生,主要研究方向:认知无线电、物联网;唐伦(1973-),男,重庆人,教授,博士,主要研究方向:新一代无线通信网络、异构蜂窝网络、软件定义无线网络;陈前斌(1967-),男,重庆人,教授,博士生导师,博士,主要研究方向:个人通信、多媒体信息处理与传输、下一代移动通信网络、异构蜂窝网络。
基金资助:
国家自然科学基金资助项目（61571073）；重庆市科委重点产业共性关键技术创新专项（cstc2015zdcy-ztzx40008）。

Secondary user bandwidth and power allocation in imperfect channel for Internet of things sensor networks

WEN Jinyi, TANG Lun, CHEN Qianbin

Chongqing Key Laboratory of Mobile Communication Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2018-01-16 Revised:2018-03-15 Online:2018-08-10 Published:2018-08-11
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61571073), the Key Technical Innovation Project of Chongqing Science and Technology Commission (cstc2015zdcy-ztzx40008).

摘要/Abstract

摘要： 针对物联网（IoT）传感器网络无线资源的稀缺和信道不完美带来的误差问题，提出了一种在不完美信道状态信息（CSI）下为次级IoT设备（SID）用户分配带宽和功率的方法。首先，基于次级系统和主级系统的不同情况，分别建立了次级系统发送端与主级系统接收端、次级系统发送端与接收端之间的不完美信道模型。其次，提出了为次级IoT设备用户分配带宽和功率的机制，并考虑了当前带宽不够分配时应采取的措施和相应的惩罚机制，目的是在充分利用现有无线资源的情况下，最大化整个次级系统的能量效率（EE）。最后，使用粒子群优化（PSO）算法和加权切比雪夫法进行分步求解，在降低问题复杂度的同时得到最优的分配方案。仿真结果表明，与均等分配和随机分配方法相比，所提方法在最大化系统总发送速率和最小化次基站平均发送功率方面提升了约75%，能有效提升整个网络的能量效率。

关键词: 物联网传感器网络, 不完美信道, 功率和频谱分配, 粒子群优化算法, 认知无线电网络, 加权切比雪夫法

Abstract: Aiming at the problem of wireless resource scarcity and errors caused by imperfect channel in Internet of Things (IoT) sensor network, a method for allocating bandwidth and power to Secondary IoT Device (SID), under the condition of imperfect Channel State Information (CSI), was proposed. Firstly, considering the secondary and primary situations, the imperfect CSI of links between secondary transmitter side and primary receiving end, secondary transmitter side and secondary receiving end were modeled. Secondly, a mechanism for allocating bandwidth and power to secondary IoT devices was proposed. Meanwhile, the measurement and corresponding penalty policy were considered when current bandwidth is not enough to allocate. The idea is to maximize the secondary Energy Efficiency (EE) while fully using resources in extent. Finally, Particle Swarm Optimization (PSO) algorithm and weighted Chebyshev method were implemented to solve the problem, which made it possible to obtain the optimal allocation policy while lowering the problem complexity. The simulation results show that compared with equal allocation method and random allocation method, the proposed method improves the overall system transmission rate and minimizes the average secondary IoT base station transmit power by 75%, which can effectively enhance the energy efficiency of the entire network.

Key words: Internet of Things (IoT) sensor network, imperfect channel, bandwidth and power allocation, Particle Swarm Optimization (PSO) algorithm, Cognitive Radio Network (CRN), weighted Chebyshev method

中图分类号:

TP393

文槿奕, 唐伦, 陈前斌. 物联网传感器网络中次级用户在不完美信道下的带宽和功率分配[J]. 计算机应用, 2018, 38(8): 2330-2336.

WEN Jinyi, TANG Lun, CHEN Qianbin. Secondary user bandwidth and power allocation in imperfect channel for Internet of things sensor networks[J]. Journal of Computer Applications, 2018, 38(8): 2330-2336.

参考文献

[1] LI W, ZHU C, LEUNG V C M, et al. Performance comparison of cognitive radio sensor networks for industrial IoT with different deployment patterns[J]. IEEE Systems Journal, 2017, 11(3):1456-1466.
[2] KHAN A A, REHMANI M H, RACHEDI A. cognitive-radio-based Internet of things:applications, architectures, spectrum related functionalities, and future research directions[J]. IEEE Wireless Communications, 2017, 24(3):17-25.
[3] SHAH M A, ZHANG S, MAPLE C. Cognitive radio networks for Internet of things:applications, challenges and future[C]//Proceedings of the 2013 International Conference on Automation and Computing. Piscataway, NJ:IEEE, 2013:1-6.
[4] 张佳民.基于认知无线电的物联网频谱共享策略研究[D].天津:河北工业大学,2013.(ZHANG J M. Research on IoT spectrum sharing strategy based on cognitive radio[D]. Tianjin:Hebei University of Technology, 2013.)
[5] 杜红,富爽,许杰,等.基于认知无线电的农业物联网架构的研究[J].电视技术,2015,39(20):31-34.(DU H, FU S, XU J, et al. Research on agricultural IoT architecture based on cognitive radio[J]. TV Technology, 2015, 39(20):31-34.)
[6] HONG J G, LEE S J, LIM J, et al. RF spectrum sensing receiver system with improved frequency channel selectivity for cognitive IoT sensor network applications[C]//Proceedings of the 2016 International Microwave Symposium. Piscataway, NJ:IEEE, 2016:1-3.
[7] ZHU J, SONG Y, JIANG D, et al. Multi-armed bandit channel access scheme with cognitive radio technology in wireless sensor networks for the Internet of things[J]. IEEE Access, 2016, 4:4609-4617.
[8] TIWARI P, SAHA S. Co-channel interference constrained spectrum allocation with simultaneous power and network capacity optimization using PSO in cognitive radio network[C]//Proceedings of the 2016 IEEE International Conference on Advanced Networks and Telecommuncations Systems. Piscataway, NJ:IEEE, 2016:1-3.
[9] SBOUI L, REZKI Z, ALOUINI M S. A unified framework for the ergodic capacity of spectrum sharing cognitive radio systems[J]. IEEE Transactions on Wireless Communications, 2012, 12(2):877-887.
[10] MALLICK S, DEVARAJAN R, LOODARICHEH R A, et al. Robust resource optimization for cooperative cognitive radio networks with imperfect CSI[J]. IEEE Transactions on Wireless Communications, 2015, 14(2):907-920.
[11] MILI M R, MUSAVIAN L, HAMDI K A, et al. How to increase energy efficiency in cognitive radio networks[J]. IEEE Transactions on Communications, 2016, 64(5):1829-1843.
[12] LI Z, GONG S, XING C, et al. Multi-objective optimization for distributed MIMO networks[J]. IEEE Transactions on Communications, 2017, 65(10):4247-4259.

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物联网传感器网络中次级用户在不完美信道下的带宽和功率分配

Secondary user bandwidth and power allocation in imperfect channel for Internet of things sensor networks

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