认知小蜂窝网络中基于能效的下行资源分配算法

doi:10.11959/j.issn.1000-436x.2016079

摘要/Abstract

摘要：

在认知小蜂窝网络框架下，对基于OFDMA技术的下行联合频谱资源块和功率分配问题进行了研究。小蜂窝基站在分布式结构下采用开放式接入方式共享空闲频谱资源以最大化其能量效率，基站间的竞争关系使系统资源的动态分配过程可建模为非合作博弈模型。由于最大化具有多个限制条件的分数形势的能量效用函数属于非凸最优问题，可通过将其转化为等价的减数形势，并从串行和并行迭代的角度进行求解。在给定资源块分配策略后，原有博弈模型可被重新建模为便于独立求解发射功率的等价子博弈模型。仿真结果表明，所提算法在干扰受限的通信环境下能收敛到纳什均衡，并有效提高了系统资源利用率和能量效率。

关键词: 认知小蜂窝网络, 能量效率, 博弈论, 纳什均衡, 频谱分配, 功率分配

Abstract:

Joint downlink spectrum resource block(SRB) and power allocation was studied based on OFDMA n cog-nitive small cell networks.In a distributed framework, small cell base stations (SCBS) could share free spectrum re-sources with open access to maximize their energy efficiency (EE) respectively. The competition among SCBS made the dynamic resources allocation problem be modeled as a non-cooperative game. It was non-convex optimal to op-timize the EE in a fractional form under multiple interference constraints. The maximization problem was transmitted into an equivalent problem in subtractive form which could be solved from the sequential and parallel iteration points of view. After obtaining the SRB allocation strategies, the master game could be re-modeled as equivalent sub-games for solving the transmission power more easily. Simulation results show the proposed algorithm can converge to a Nash equilibrium and effectively improve system resources utilization and EE in interference-limited communication environments.

Key words: cognitive small cell networks, energy efficiency, game theory,, Nash equilibrium, spectrum allocation, power allocation

贾亚男,岳殿武. 认知小蜂窝网络中基于能效的下行资源分配算法[J]. 通信学报, 2016, 37(4): 116-127.

Ya-nan JIA,Dian-wu YUE. Energy efficiency-based downlink resource allocation in cognitive small cell networks[J]. Journal on Communications, 2016, 37(4): 116-127.

图/表 10

图1

图2

图3

表1

仿真参数"

仿真对象	参数设置
SCBS数量L	9,36,1
CSCU数量Q_l	24~25每个SCBS
系统带宽B₀/MHz	5
SRB数量M	25
子载波数K	12
子载波带宽B/ kHz	15
带宽效率V	0.9
热噪声N_o,N_G 功率/dBm	-174+10lg(B)
阴影衰落标准差s /dB	4
记忆因子J	0.2, 0.6
单用户最大发射功率 $P_{q_{1}}^{0} / d B M$	30
SCBS的静态功率 $P_{E_{0}}^{1} / d B M$ 0	40

表1

图4

图5

图6

图7

图8

图9

参考文献 27

[1]	CHEN S , ZHAO J . The requirements, challenges, and tech logies for 5G of terrestrial mobile telecommunication[J]. IEEE Comm ications Magazine, 2014,52(5):36-43.
[2]	HOYDIS J , KOBAYASHI M , DEBBAH M E . Green small-cell net-works: a cost- and energy-efficient way of meeting the future traffic demands[J]. IEEE Vehicular Technology Magazine, 2011,6(1):37-43.
[3]	ANDREWS J G , BUZZI S , CHOI W , et al. What will 5G be?[J]. IEEE Journal on Selected Areas in Communications, 2014,32(6):1065-1082.
[4]	CHENG S M , LIEN S Y , CHU F S , et al. On exploiting cog itive radio to mitigate interference in macro/femto heterogeneous net-works[J]. IEEE Wireless Communications, 2011,18(3):40-47.
[5]	WILDEMEERSCH M , QUEK T Q S , SLUMP C H , et al. Cognitive small cell networks: energy efficiency and trade-offs[J]. IEEE Trans-actions on Communications, 2013,61(9):4016-4029.
[6]	JUNGNICKEL V , MANOLAKIS K , ZIRWAS W , et al. The role o small cells, coordinated multipoint, and massive MIMO in 5G[J]. IEEE Communications Magazine, 2014,52(5):44-51.
[7]	PANG J S , SCUTARI G , et al. Design of cognitive radio ystems under temperature-interference constraints: a variational inequali-ty approach[J]. IEEE Transactions on Signal Processing, 2010,58(6):3251-3271.
[8]	HAYKIN S . Cognitive radio: brain-empowered wireless communica-tions[J]. IEEE Journal on Selected Areas in Communications, 2005,23(2):201-220.
[9]	AKYILDIZ I F , LO B F , BALAKRISHNAN R . Cooperative spectrum sensing in cognitive radio networks: a survey[J]. Physical Communi-cation, 2010,48(1):40-62.
[10]	BENNIS M , PERLAZA S M , BLASCO P , et al. Self-organization in small cell networks: a reinforcement learning appro h[J]. IEEE Transactions on Wireless Communications, 2013,21(7):3202-3212.
[11]	FEHSKE A J , VIERING I , VOIGT J , et al. Small-cell self-organizing wireless networks[J]. Proceedings of the IE E, 2014,102(3):334-350.
[12]	SARDELLITTI S , BARBAROSSA S . Joint optimization of colla-borative sensing and radio resource allocation in small-cell net-works[J]. IEEE Transactions on Signal Processing, 2013,61(18):4506-4520.
[13]	FENG D , JIANG C , LIM G , et al. A survey of energy-efficient wire-less communications[J]. IEEE communications Surveys ＆ Tutorials, 2013,15(1):167-178.
[14]	RAO J B , FAPOJUWO A H O . A survey of energy efficient urce management techniques for multicell cellular networks IEEE Communications Surveys ＆ Tutorials, 2014,16(1):154-180.
[15]	HU R Q , QIAN Y . An energy efficient and spectrum efficient wireless heterogeneous network framework for 5G systems[J]. IEEE Commu-nications Magazine, 2014,52(5):94-101.
[16]	YU G , JIANG Y , XU L , et al. An energy efficient and spectrum effi-cient wireless heterogeneous network framework for 5G systems[J]. IEEE Journal on Selected Areas in Communications, 2015,33(10):2118-2127.
[17]	YU G , CHEN Q , YIN R , et al. Joint downlink and uplink allocation for energy-efficient carrier aggregation[J]. IEEE Transac-tions on Wireless Communications, 2015,14(6):3207-3218.
[18]	NG D W K , LO E S , SCHOBER R . Energy-efficient resource allocation in OFDMA systems with large numbers of base station antennas[J]. IEEE Transactions on Wireless Communications, 2012,11(9):3292-3304.
[19]	NG D W K , LO E S , SCHOBER R . Energy-efficient resource allocation in OFDMA systems with large numbers of base station antennas[J]. IEEE Transactions on Wireless Communications, 2012,11(10):3618-3631.
[20]	KIM S , LEE B G , PARK D . Energy-per-bit minimized radio resource allocation in heterogeneous networks[J]. IEEE Transactions on Wire-less Communications, 2014,13(4):1862-1873.
[21]	贾亚男，岳殿武．博弈论框架下认知小蜂窝网络的动态资源分配算法[J]．电子学报， 2015,43(10):1911-1917. JIA Y N , YUE D W . Dynamic resource allocation algorithm based on game theory in cognitive small cell networks[J]. Acta Electronica, Sinica, 2015,43(10):1911-1917.
[22]	SCUTARI G , PALOMAR D P , FACCHINEI F , et al. Convex opt miza-tion, game theory, and variatonal inequality theory[J]. IEEE Signal Processing Magazine, 2010,27(3):35-49.
[23]	DINKELBACH W . On nonlinear fractional programming[J]. Man-agement Science, 1967,13(7):492-498.
[24]	BOYD S , VANDENBERGHE L . Convex optimization[M]. Cam-bridge: Cambridge University Press, 2004.
[25]	PALOMAR D P , FONOLLOSA J R . Practical algorithms for a family of waterfilling solutions[J]. IEEE Transactions on Si nal Processing, 2005,53(2):686-695.
[26]	SCUTARI G , PALOMAR D P , BARBAROSSA S . Asynchronous iterative water-filling for gaussian frequency-selective interference channels: a unified framework[C]// IEEE 7th Workshop on Signal Processing Advances in Wireless Communications. Cannes, France, c2006:1-5.
[27]	CHO Y S , KIM J , YANG W Y , et al. MIMO-OFDM wireless commu-nications with matlab[M]. New York: John Wiley ＆ Sons (Asia) Pte Ltd, 2010.