Partial transmit sequences based on artificial bee colony algorithm for peak-to-average power ratio reduction in multicarrier code division multiple access systems
ABSTRACT Multicarrier code division multiple access (MC-CDMA) is a very promising system for wireless communication. However, MC-CDMA signals have a high peak-to-average power ratio (PAPR), which causes signal distortion because of the use of a high-power amplifier (HPA) in the transmitter. Partial transmit sequences (PTSs) represent one of the most attractive PAPR reduction methods, but its high computational complexity in finding the optimal phase vector impedes practical implementation. In this paper, we propose a PTS based on an artificial bee colony (ABC) algorithm scheme (ABC-PTS) to reduce the computational complexity of the PTS in the MC-CDMA systems. Simulation results prove that the proposed ABC-PTS scheme shows a significant improvement in PAPR reduction performance, with a low computational complexity. In addition, the bit-error-rate performance of the MC-CDMA with the ABC-PTS and the conventional PTS is compared when the HPA and the linear amplifier are used.
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ABSTRACT: In recent years, swarm intelligence has proven its importance for the solution of those problems that cannot be easily dealt with classical mathematical techniques. The foraging behaviour of honey bees produces an intelligent social behaviour and falls in the category of swarm intelligence. Artificial bee colony ABC algorithm is a simulation of honey bee foraging behaviour, established by Karaboga in 2005. Since its inception, a lot of research has been carried out to make ABC more efficient and to apply it on different types of problems. This paper presents a review on ABC developments, applications, comparative performance and future research perspectives.International Journal of Advanced Intelligence Paradigms 01/2013; 5(1):123-159.
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ABSTRACT: How to design the pilot tones that are used in channel estimation has a significant effect on the estimation performance. To achieve good performance in least square (LS) algorithm, we propose the artificial bee colony (ABC) algorithm for optimizing the placement of pilot tones in MIMO–OFDM systems. We also derive the upper bound of mean square error of LS estimation with the help of Gerschgorin disc theorem for fitness function of ABC algorithm. The results show that designing pilot tones using the ABC algorithm outperforms other considered placement strategies in terms of high system performance and low computational complexity.Wireless Personal Communications 07/2013; 71(1). · 0.43 Impact Factor
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ABSTRACT: Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.Artificial Intelligence Review · 1.57 Impact Factor