A PAPR Reduction for OFDM Signals Based on Self-Adaptive Multipopulation DE algorithm

One of major drawbacks of orthogonal frequency division multiplexing (OFDM) systems is the high peak-to-average power ratio (PAPR). A signal with high PAPR leads to nonlinear distortion caused mainly by power amplifiers in wireless transmitters. Partial transmit sequence (PTS) is one of the most attractive methods to reduce the PAPR in OFDM systems. It achieves considerable PAPR reduction without distortion, but it requires an exhaustive search over all the combinations of the given phase factors, which results in a computational complexity that increases exponentially with the number of partitions. For this optimization problem, we propose in this paper a suboptimal PTS method based on the self-adaptive multipopulation differential evolution algorithm (SAMDE). The self adaptation of control parameters and structured population, is able to obtain high quality solutions with low computational cost by evolving each sub-population of individuals over successive generations

On PAPR Reduction of OFDM using Partial Transmit Sequence with Intelligent Optimization Algorithms

In recent time, the demand for multimedia data services over wireless links has grown up rapidly. Orthogonal Frequency Division Multiplexing (OFDM) forms the basis for all 3G and beyond wireless communication standards due to its efficient frequency utilization permitting near ideal data rate and ubiquitous coverage with high mobility. OFDM signals are prone to high peak-to-average-power ratio (PAPR). Unfortunately, the high PAPR inherent to OFDM signal envelopes occasionally drives high power amplifiers (HPAs) to operate in the nonlinear region of their characteristic leading out-of-band radiation, reduction in efficiency of communication system etc. A plethora of research has been devoted to reducing the performance degradation due to the PAPR problem inherent to OFDM systems. Advanced techniques such as partial transmit sequences (PTS) and selected mapping (SLM) have been considered most promising for PAPR reduction. Such techniques are seen to be efficient for distortion-less signal processing but suffer from computational complexity and often requires transmission of extra information in terms of several side information (SI) bits leading to loss in effective data rate. This thesis investigates the PAPR problem using Partial Transmit Sequence (PTS) scheme, where optimization is achieved with evolutionary bio-inspired metaheuristic stochastic algorithms. The phase factor optimization in PTS is used for PAPR reduction. At first, swarm intelligence based Firefly PTS (FF-PTS) algorithm is proposed which delivers improved PAPR performance with reduced searching complexity. Following this, Cuckoo Search based PTS (CS-PTS) technique is presented, which offers good PAPR performance in terms of solution quality and convergence speed. Lastly, Improved Harmony search based PTS (IHS-PTS) is introduced, which provides improved PAPR. The algorithm has simple structure with a very few parameters for larger PTS sub-blocks. The PAPR performance of the proposed technique with different parameters is also verified through extensive computer simulations. Furthermore, complexity analysis of algorithms demonstrates that the proposed schemes offer significant complexity reduction when compared to standard PAPR reduction techniques. Findings have been validated through extensive simulation tests

New technique combining the Tone Reservation method with Clipping technique to reduce the Peak-to-Average Power Ratio

Nonlinear distortions and impairments appear in multicarrier signal with high fluctuations when amplified by a Radio Frequency Power Amplifier (RF PA). Clipping (CL) technique offers a simple way to reduce these fluctuations in Orthogonal Frequency Division Multiplexing (OFDM) Technique, but may degrade seriously the transmission quality. This is why the new mobile standards propose other methods, like the Tone Reservation (TR) technique in the Digital Video Broadcasting-Terrestrial (DVB-T), that reduce the Peak-to-Average Power Ratio (PAPR) without reaching optimal performances. This paper deals with how we can use the TR technique, which exploits null sub-carriers for generating corrective signal, in combining to CL technique in order to improve PAPR reduction without data loss. Also, we show some comparison results on the PAPR reduction obtained with proposed scheme and other techniques. Experiments using a simulated example on a complete WiMax 802.16e transmitter have been made in order to investigate the PAPR reduction performances on presence of the non-linear Power Amplifier model based on gain compression response and phase distortion

Preamble design using embedded signalling for OFDM broadcast systems based on reduced-complexity distance detection

The second generation digital terrestrial television broadcasting standard (DVB-T2) adopts the so-called P1 symbol as the preamble for initial synchronization. The P1 symbol also carries a number of basic transmission parameters, including the fast Fourier transform size and the single-input/single-output as well as multiple-input/single-output mode, in order to appropriately configure the receiver for carrying out the subsequent processing. In this contribution, an improved preamble design is proposed, where a pair of training sequences is inserted in the frequency domain and their distance is used for transmission parameter signalling. At the receiver, only a low-complexity correlator is required for the detection of the signalling. Both the coarse carrier frequency offset and the signalling can be simultaneously estimated by detecting the above-mentioned correlation. Compared to the standardised P1 symbol, the proposed preamble design significantly reduces the complexity of the receiver while retaining high robustness in frequency-selective fading channels. Furthermore, we demonstrate that the proposed preamble design achieves a better signalling performance than the standardised P1 symbol, despite reducing the numbers of multiplications and additions by about 40% and 20%, respectively

Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe