An optimization technique for reducing the PAPR in MIMO-OFDM technique by using FPPC scheme

Ramakrishnan, P. S.; Sivagurunathan, P. T.; Kumar, Nitesh

doi:10.1109/iceice.2017.8191917

Cited by 1 publication

(1 citation statement)

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“…To deal with this problem, several solutions have been proposed to mitigate the high PAPR of OFDM [3,4] and MIMO-OFDM signals [5,6], as clipping [7], clipping and filtering [8], coding [9], tone injection [10], peak windowing [11], selected mapping [12], and partial transmit sequence (PTS) [13][14][15]. All these methods have their own advantages and disadvantages, but the PTS technique is still the most attractive one due to its efficiency in PAPR reduction.…”

Section: Introductionmentioning

confidence: 99%

PAPR Reduction Using Fireworks Search Optimization Algorithm in MIMO-OFDM Systems

Amhaimar

Ahyoud

Yaakoubi

et al. 2018

Journal of Electrical and Computer Engineering

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The transceiver combination technology, of orthogonal frequency division multiplexing (OFDM) with multiple-input multiple-output (MIMO), provides a viable alternative to enhance the quality of service and simultaneously to achieve high spectral efficiency and data rate for wireless mobile communication systems. However, the high peak-to-average power ratio (PAPR) is the main concern that should be taken into consideration in the MIMO-OFDM system. Partial transmit sequences (PTSs) is a promising scheme and straightforward method, able to achieve an effective PAPR reduction performance, but it requires an exhaustive search to find the optimum phase factors, which causes high computational complexity increased with the number of subblocks. In this paper, a reduced computational complexity PTS scheme is proposed, based on a novel swarm intelligence algorithm, called fireworks algorithm (FWA). Simulation results confirmed the adequacy and the effectiveness of the proposed method which can effectively reduce the computation complexity while keeping good PAPR reduction. Moreover, it turns out from the results that the proposed PTS scheme-based FWA clearly outperforms the hottest and most important evolutionary algorithm in the literature like simulated annealing (SA), particle swarm optimization (PSO), and genetic algorithm (GA).

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