با استفاده از تکنیک محاسبات تکاملی برای تجاری کردن بین عملکرد پیک به طور متوسط ​​کاهش سهمیه قدرت و پیچیدگی محاسباتی در سیستم های OFDM

A low-complexity partial transmit sequence (PTS) technique for reducing the peak-to-average power ratio (PAPR) of an orthogonal frequency division multiplexing (OFDM) system is presented. However, PTS technique requires an exhaustive search over all combinations of allowed phase weighting factors, and the search complexity increases exponentially with the number of sub-blocks in OFDM system. Hence, there has been a trade-off between performance PAPR reduction and computational complexity in PTS OFDM system. The proposed is a sub-optimum PTS for PAPR reduction of OFDM system. Simulation results demonstrate that the superiority of evolutionary computation technique-particle swarm optimization (PSO) based on PTS which can be utilized for finding the optimum phase weighting factors, and can achieve the lower PAPR and computational complexity of OFDM systems. In addition, our evolutionary computation technique can be used to reduce reduction PAPR with comparable performance to genetic algorithm-based PTS, with much less computation cost.

Orthogonal frequency division multiplexing (OFDM) technique is a very attractive technique for high bit transmission in a radio environment [1]. The high peak-to-average power ratio (PAPR) is the main drawback of the OFDM system, in which the OFDM transmitters require expensive linear amplifiers with wide dynamic range. Moreover, the amplifier non-linearity will cause inter modulation products resulting in unwanted out-of-band power and increased interference. Recently, many reductions PAPR have been proposed for OFDM system, as clipping [2] and peak windowing, block coding [3], scrambling [4], nonlinear commanding transform schemes [5] and [6]; OFDM is an attractive technique for achieving high bit rate selective mapping [7] and [8] and phase optimization [9], [10], [11], [12] and [13], and both are the most attractive ones due to their good system performance and low-complexity. Among these methods, partial transmit sequences (PTS) scheme is the most efficient approach and a least distortion-less scheme for PAPR reduction by optimally combining signal sub-blocks. In PTS technique, the input date block is broken up into disjoint sub-blocks. The sub-blocks are multiplied by phase weighting factors and then added together to produce alternative transmit containing the same information. The phase weighting factors, which amplitude is usually set to 1, are selected such that the resulting PAPR is minimized. The number of allowed phase weighting factors should not be excessively high, to keep the number of required side information bits and the search complexity within a reasonable limit. However, the exhaustive search complexity of the ordinary PTS technique increases exponentially with number of sub-blocks, so it is practically not realizable for a large number of sub-blocks. To find out a best weighting factor is a complex and difficulty problem. The advent of evolutionary computation has inspired new resources for optimization problem solving, such as the optimal design of code division multiple access (CDMA) and fuzzy system. In contrast to traditional computation systems which may be good at accurate and exact computation, but have brittle operations, evolutionary computation provides a more robust and efficient approach for solving complex real world problem. Many evolutionary algorithms, such as genetic algorithm (GA) [14] and [15], ant colony optimization (ACO) [16], simulated annealing (SA) [17] and particle swarm optimization (PSO) [18], [19], [20], [21], [22], [23] and [24], have been proposed. GAs is stochastic search procedures based on the mechanics of natural selection, genetics, and evolution. Since they simultaneously evaluate many points in the search space, they are more likely to find the global solution of a given problem. PSO is a population-based stochastic optimization technique based on the movement of swarms and inspired by social behavior of bird flocking or fish schooling. Compared with GA [14] and [15], PSO has some attractive characteristics. It has memory, so the knowledge of good solutions is retained by all particles; whereas in GA, previous knowledge of the problem is destroyed once the populations changed. It has constructive cooperation between particles, particles in the swarm share information between them. In this paper, we present a novel approach to tackle the PAPR problem to reduce the complexity based on the relationship between the phase weighting factors and the sub-block partition schemes. Specifically, we apply the PSO to search the optimal combination of phase factors with largely reduced complexity. Numerical results show that the proposed scheme can achieve better PAPR reduction with lower computational complexity compared with that of the GA approaches. The rest of this paper is organized as follow. In Section 2, definition of PAPR of OFDM system and the principles of PTS techniques are introduced. The particle swarm optimization algorithm-based PTS OFDM system has been examined in Section 3. Section 4 provides some performance results and a comparison of the proposed method with related work. Some conclusions are given in Section 5.

Comparisons of the PAPR reduction performance and complexity trade-offs for different PTS searching strategies have been considered in this study. In this paper, we introduce a novel PTS based on PSO is applied to search the optimal combination of phase weighting factors, which can achieve the OFDM system with low PAPR and reduce the computational complexity significantly. The performance of the proposed scheme is compared with different sub-optimum techniques proposed in the literature for the reduce PAPR. The complexity of the proposed detector is approximately O(M)2O(M)2 , and it is evident that its performance is significantly better and more robust compared to other examined evolutionary computation techniques. Simulations results demonstrate that the performance of the PSO-based PTS is an effective method to compromise a better trade-off between PAPR reduction and computation complexity. For many cased that we can check, the proposed detector has identical or almost identical performance to that of the optimum PTS in the range of PAPR of most practical interest. Finally, by appropriate selection of phase weighting factors according to the required performance and tolerable complexity, the proposed partition scheme can be adaptive to QOS requirement.