Multi-user detector based on improved binary artificial bee colony algorithm

doi:10.3724/SP.J.1087.2013.00171

Journal of Computer Applications ›› 2013, Vol. 33 ›› Issue (01): 171-174.DOI: 10.3724/SP.J.1087.2013.00171

• Artificial intelligence • Previous Articles Next Articles

Multi-user detector based on improved binary artificial bee colony algorithm

LIU Ting¹,²,ZHANG Liyi¹,²,BAO Weiwei³,ZOU Kang³

1. School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China
2. School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China
3. School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China

Received:2012-06-26 Revised:2012-08-01 Online:2013-01-09 Published:2013-01-01
Contact: LIU Ting

基于改进二进制人工蜂群算法的多用户检测器

刘婷¹,²,张立毅¹,²,鲍韦韦³,邹康³

1. 天津大学电子信息工程学院，天津 300072
2. 天津商业大学信息工程学院，天津 300134
3. 天津工业大学电子与信息工程学院，天津 300387

通讯作者: 刘婷
作者简介:刘婷(1981-)，女，山西绛县人，讲师，博士研究生，主要研究方向：信号与信息处理；张立毅(1963-)，男，山西忻州人，教授，博士生导师，博士，主要研究方向：信号与信息处理；鲍韦韦(1987-)，女，河北邯郸人，硕士研究生，主要研究方向：人工智能；邹康(1988-)，男，湖北武汉人，硕士研究生，主要研究方向：人工智能。

Abstract

Abstract: Optimum Multi-user Detection (OMD) technique can achieve the theoretical minimum error probability, but it has been proven to be a Non-deterministic Polynomial (NP) problem. As a new swarm intelligence algorithm, Artificial Bee Colony (ABC) algorithm has been widely used in various optimization problems. However, the traditional Binary Artificial Bee Colony (BABC) algorithm has the shortcomings of slower convergence speed and falling into local optimum easily. Concerning the shortcomings, an improved binary artificial bee colony algorithm was proposed and used for optimum multi-user detection. The initialization process was simplified. The one-dimensional-reversal neighborhood search strategy was adopted. Compared with optimum multi-user detection, the computation complexity of the improved algorithm declines obviously. The simulation results show that the proposed scheme has significant performance improvement over the conventional detection in anti-multiple access interference and near-far resistance.

Key words: Optimum Multi-user Detection (OMD), Binary Artificial Bee Colony (BABC) algorithm, neighborhood search

摘要： 最优多用户检测(OMD)技术可以达到理论上的最小错误概率，但已经证明它是一个非确定多项式(NP)问题。作为一种新型的群智能算法，人工蜂群(ABC)算法已被广泛用于各种优化问题，但传统二进制人工蜂群算法具有收敛速度过慢、易陷入局部最优等缺点。针对这一缺点，提出了一种改进二进制人工蜂群算法并将其用于求解最优多用户检测问题。算法简化了初始化的过程，采用单维求反的邻域搜索策略，计算量与最优多用户检测相比明显降低。仿真结果表明，提出的多用户检测方案在抗多址干扰和抗“远近”效应能力方面与传统检测方案相比，都有显著提高。

关键词: 最优多用户检测, 二进制人工蜂群算法, 邻域搜索

CLC Number:

TP18

LIU Ting ZHANG Liyi BAO Weiwei ZOU Kang. Multi-user detector based on improved binary artificial bee colony algorithm[J]. Journal of Computer Applications, 2013, 33(01): 171-174.

刘婷张立毅鲍韦韦邹康. 基于改进二进制人工蜂群算法的多用户检测器[J]. 计算机应用, 2013, 33(01): 171-174.

References

[1]VERDU S. Minimum probability of error for asynchronous Gaussian multiple-access channels ［J］. IEEE Transactions on Information Theory, 1986, 32(1): 85-96.

[2]YEN K, HANZO L. Genetic-algorithm-assisted multi-user detection in asynchronous CDMA communications ［J］. IEEE Transactions on Vehicular Technology, 2004, 53(5): 1413-1422.

[3]DU Y G, CHAN K T. Feasibility of applying genetic algorithms in space-time block coded multi-user detection systems ［J］. Digital Signal Processing, 2005, 15(2): 161-170.

[4]XU C, MAUNDER R G, YANG L L, et al. Near-optimum multi-user detectors using soft-output ant-colony-optimization for the DS-CDMA uplink ［J］. IEEE Signal Processing Letters, 2009, 16(2): 137-140.

[5]XU C, YANG L L, HANZO L. Ant-colony-based multi-user detection for MC DS-CDMA systems ［C］// IEEE Vehicular Technology Conference. Piscataway: IEEE Press, 2007: 960-964.

[6]LIU HONGWU, LI JI. A particle swarm optimization-based multi-user detection for receive-diversity-aided STBC systems ［J］. IEEE Signal Processing Letters, 2008, 15: 29-32.

[7]SOO K K, SIU Y M, CHAN W S, et al. Particle-swarm-optimization-based multi-user detector for CDMA communications ［J］. IEEE Transactions on Vehicular Technology, 2007, 56(5): 3006-3013.

[8]KARABOGA D. An idea based on honey bee swarm for numerical optimization ［R］. Kayseri, Turkey: Erciyes University, 2005.

[9]苏国韶，钱坤.人工蜂群算法在重力坝断面优化设计中的应用［J］.计算机工程与应用，2011，47（11）：223-226.

[10]暴励，曾建潮.一种双种群差分蜂群算法［J］.控制理论与应用，2011，28（2）：266-272.

[11]KARABOGA D, BASTURK B. On the performance of Artificial Bee Colony (ABC) algorithm ［J］. Applied Soft Computing, 2008, 8(1): 687-697.

[12]KARABOGA D. A new design method based on artificial bee colony algorithm for digital IIR filters ［J］. Journal of the Franklin Institute, 2009, 346(4): 328-348.

[13]KARABOGA D, GORKEMLI B. A combinatorial artificial bee colony algorithm for traveling salesman problem ［C］// International Symposium on Innovations in Intelligent Systems and Applications. Piscataway: IEEE Press, 2011: 50-53.

[14]胡中华，赵敏.基于人工蜂群算法的机器人路径规划［J］.电焊机，2009，39（4）：93-96.

[15]VERDU S. Computational complexity of optimum multi-user detection ［J］. Algorithmica, 1989, 4(3): 303-312.

[16]KARABOGA D, BASTURK B. A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) algorithm ［J］. Journal of Global Optimization, 2007, 39(3): 459-471.

[17]MARINAKIS Y, MARINAKI M, MATSATSINIS N. A hybrid discrete artificial bee colony-GRASP algorithm for clustering ［C］// International Conference on Computers Industrial Engineering. Piscataway: IEEE Press, 2009: 548-553.

[18]KENNEDY J, EBERHART R C. A discrete binary version of the particle swarm algorithm ［C］// Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. Piscataway: IEEE Press, 1997: 4104-4109.

Multi-user detector based on improved binary artificial bee colony algorithm

基于改进二进制人工蜂群算法的多用户检测器

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