Peak-to-average Power Ratio Reduction Algorithm Research Articles

A BACTERIA FORAGING ALGORITHM-BASED HYBRID A-LAW AND PTS PAPR REDUCTION METHOD FOR BEYOND 5G WAVEFORM

One of the most advanced waveforms available in the beyond-fifth-generation radio (B5GR) framework is nonorthogonal multiple access (NOMA). The power amplifier (PA) of the NOMA structure performs less efficiently when the signal is strong and the peak-to-average power ratio (PAPR) is high. This study applies a hybrid algorithm to the NOMA structure, combining fractal partial transmit sequence (PTS), A-law companding, and the bacterial foraging algorithm (BFA). Fractals are known for their self-repeating structures at different scales, allowing for efficient coverage and exploration of space. Fractals enhance the bacteria foraging algorithm (BFA) by improving search efficiency, enabling better exploration of complex, multi-dimensional optimization landscapes. Similarly, BFA balances exploring the search in promising areas. We utilized BFA to achieve optimal phase factors for the PTS algorithm and applied A-Law companding to the NOMA symbols to further enhance the structure’s performance. The intermediate computational complexity in the Rician and Rayleigh channels improves PAPR, bit error rate (BER), and power spectral density performance. Simulation results reveal that the performance of the proposed hybrid method is superior to that of existing PAPR reduction algorithms and substantially enhances the efficiency of NOMA.

A Novel Alternating μ-Law Companding Algorithm for PAPR Reduction in OFDM Systems

Orthogonal frequency division multiplexing (OFDM) inherits multi-carrier systems’ inevitable high peak-to-average power ratio (PAPR) problem. In this paper, a novel alternating companding technique is proposed to combat the harassment of high PAPR. The sequential μ-law companding (SULC) and a tone with a lower PAPR result in only partial tones needing companding. The SULC scheme’s PAPR and bit error rate (BER) performance has been balanced and improved. However, the computational complexity is still too high to be implemented. Therefore, this study sorted the transmission signals according to their amplitudes. Then, all the tones are divided into two groups by estimating the rough companding amount (around 54% of the subcarriers), using traditional parallel companding for the first group and the other group only by partial μ-law companding. This alternating μ-law companding (AULC) is proposed to improve the PAPR performance and simultaneously reduce complexity. Simulation results show that the proposed AULC method appreciably reduces the PAPR by about 5 dB (around 45%) compared with the original μ-law at complementary cumulative distribution function (CCDF) equal to 10−4. Moreover, it only requires a moderate complexity to outperform the other companding schemes without sacrificing the BER performance in the OFDM systems.

PEAK-TO-AVERAGE POWER RATIO OF SPECTRALLY EFFICIENT FDM SIGNALS

The change of the number of subcarriers in SEFDM signal from 5 to 1024 leads to the growth of the PAPR (peak-to-average power ratio) from 4.5 to 9 dB. PAPR of SEFDM signals stays about constant (instability is no more than 5%) while normalized subcarriers frequency spacing increase from 1/2 to 1. It indicates no additional energy loss during the transition from OFDM signals to SEFDM signals, caused by an increase in the PAPR. It is proposed to use a part of the resulting gain in the frequency band to reduce PAPR. The method, based on modification of Tone Insertion method, was developed. Developed method consists in adding additional subcarriers with randomly generated manipulation symbols to the SEFDM signal. SEFDM symbol with minimal PAPR is chosen to be send. According to this method the PAPR can be reduced in average of 1.4 dB. The degree of reduction of the PAPR depends on the size of the channel alphabet of additional subcarriers (changing of the average win is 0.6 dB for BPSK to 1.4 dB for QAM-64) and the number of combinations of symbols of additional subcarriers (1.6 dB for 1000 combinations for QAM-64). According to estimation of the FPGA resources for implementation of PAPR reduction algorithm it is required relatively small resource costs compared to the formation of SEFDM signal without reduction.

Evaluation of 5G techniques affecting the deployment of smart hospital infrastructure: Understanding 5G, AI and IoT role in smart hospital

Smart Hospital will play a key role in improving the quality of services (QoS) related to the health sector. In the present scenario, it is impossible to accommodate an ageing population with the current infrastructure and facilities provided by conventional hospitals. However, the integration of technologies with the conventional hospital is enhancing the medical services and also making it easier for the public, who can save time and money by getting all the necessary medical facilities while sitting at home. The deployment of the fifth generation (5G) is taking place all over the world. The 5G network offers a better service that can be used in hospitals to make it a smart health service provider with online facilities such as video monitoring, suggesting a proper medical prescription based on medical data, remote surgery, and so on. High bandwidth, a fast data rate, low latency, and a low peak-to-average power ratio (PAPR) are some requirements that need to be fulfilled by the 5G radio. In this work, we focus on improving the power savings, spectral access, latency, and Bit error rate (BER) of the advanced 5G waveforms such as non-orthogonal multiple access (NOMA), filter bank multi carrier (FBMC), and orthogonal frequency division multiplexing (OFDM). In the first part of the article, we proposed a PAPR reduction algorithm to improve the power efficiency of the power amplifier (PA) for 5G waveforms. Several algorithms are applied to the 5G waveforms, and their performances are estimated in terms of PAPR curves. In the second part, we have utilised advanced detection algorithms to improve the signal detection of the 5G waveforms. The simulation results reveal that the proposed algorithms efficiently enhance the throughput of the framework. In the third part, we improve the spectral efficiency of the 5G waveforms by applying spectrum sensing (SS) algorithms. It is seen that the SS methods efficiently reduce the spectrum leakage of the multi-carrier waveforms (MCW). Finally, we examined the role and challenges of the Internet of Things (IoT) and artificial intelligence (AI) in smart hospitals. Overall, it is concluded that hospitals can become more effective, cut expenses, and provide better patient care with the aid of AI and IoT. Hospitals can improve patient outcomes and the state of the healthcare system as a whole by utilising these technologies.

A novel PAPR reduction method for hybrid beamforming transmitter

In orthogonal frequency division multiplexing (OFDM) systems, peak-to-average power ratio (PAPR) is one of the major factors that affect the power efficiency of transmitter. There exist many PAPR reduction methods for the conventional fully digital multiple-input multiple-output (MIMO) systems but there is little discussion on the PAPR reduction for the hybrid beamforming systems. When some output signals of the digital to analogue converter (DAC) are combined by the analogue beamforming network before power amplifier (PA), the one to one mapping between the digital signal and the signal passing through PAs is destroyed. In such hybrid beamforming architectures, the existing methods can only reduce the PAPR of DAC input signal but not PAPR of PA input signal. In this paper, we investigate whether and how we can extend existing PAPR reduction methods to the hybrid beamforming systems. We propose a novel PAPR reduction method, which extends the iteratively clipping and filtering (ICF) to the hybrid beamforming systems. To the best of our knowledge, our proposed algorithm is the first PAPR reduction algorithm dedicated for the hybrid beamforming systems. From the simulation, our proposed algorithm can achieve a smaller PAPR than the improved selective mapping (SLM) algorithm at the cost of minor signal distortion.

A LOW-COMPLEXITY PTS TECHNIQUE–BASED IMPROVED PSO ALGORITHM FOR PAPR REDUCTION IN OFDM SYSTEMS

Partial transmit sequence (PTS) is a potent and distortionless scheme frequently used to lower the high peak-to-average power ratio (PAPR) levels in orthogonal frequency division multiplexing (OFDM) signaling. Although this method effectively reduces the PAPR, its main limitation is the exponential growth in the computational expense caused by the exhaustive search for the optimal complex of rotation factors. This work proposes a suboptimal PTS scheme based on an improved particle swarm optimization (PSO) algorithm. The latter exploits heuristics to find the best-matched weighting factors complex in fewer computations. A comparison against existing techniques ascertains the superior efficiency of the proposed scheme in terms of PAPR mitigation and computational burden.

A hybrid technique for the PAPR reduction of NOMA waveform

SummaryNon‐orthogonal multiple access (NOMA) is a great contender for future cellular modulation due to its desirable properties like massive connectivity, high data rate transmission, and high spectral efficiency. However, its peak‐to‐average power ratio (PAPR) is significant, which becomes a significant disadvantage for the efficient operability of the NOMA waveform compared to current techniques. Several PAPR reduction algorithms like selective mapping (SLM), partial transmission sequence (PTS), and companding techniques have been proposed to lower the PAPR of multicarrier waveforms (MCWs). PTS reduces the PAPR but has high complexity. On the other hand, SLM has a less complex framework, but its PAPR performance is not as efficient as PTS. Companding methods reduce the PAPR by compressing the signals at the transmitter, which unfortunately reduces the dynamic range of the signal. In this work, we propose a hybrid algorithm (SLM + PTS) with a companding method for the first time for the NOMA waveform, which efficiently reduces the PAPR with low computational complexity. Furthermore, we compare the performances of a host of candidate algorithms like SLM, PTS, hybrid (SLM + PTS), hybrid + A law (SLM–PTS–A law), and hybrid + Mu law (SLM–PTS–Mu law). The results of the experiments show that the hybrid + Mu law did a better job than the existing PAPR reduction algorithms.

Analysis of Compressing PAPR-Reduced OFDM IQ Samples for Cloud Radio Access Network

A common problem of the virtualized cloud radio access network architecture (C-RAN) is the compression of the time-domain IQ samples before transmission over the fronthaul link. Considering a multicarrier waveform such as OFDM, whose IQ samples follow a quasi-Gaussian distribution, the conventional Gaussian quantizer may be used as the optimal solution to the compression problem. However, since the high peak-to-average power ratio (PAPR) of OFDM signals remains a serious problem, various techniques may be employed to reduce the time-domain fluctuations of the IQ samples in the OFDM, resulting in a change in its distribution. The latter fact makes the Gaussian quantizer suboptimal. The literature lacks a performance analysis of the conventional OFDM-based compression techniques when the PAPR of the OFDM signal is reduced. Therefore, in this paper, we study for the first time the impact of reducing the PAPR of the OFDM signal before compression in the C-RAN architecture through rate-distortion analysis. We consider clipping and tone reservation PAPR reduction algorithms. The former is the simplest PAPR reduction approach, while the latter is one of the most effective algorithms used in broadcasting standards such as DVB-T2 and ATSC 3.0. We first derive the distribution of the PAPR-reduced OFDM IQ samples. This is used to optimize the thresholds and codebook levels of a non-uniform scalar quantizer and the number of quantization bits allocated for each quantized level in the entropy coding stage, along with the MER performance analysis. The simulation results show that the conventional Gaussian-based compression techniques applied to a PAPR-reduced signal is not very robust to the statistical changes in the signal unless the signal distribution at the input of the Gaussian quantizer is not significantly affected. However, a significant gain is obtained when the quantizer is optimized with respect to the true distribution of the PAPR-reduced IQ samples.

A Closed Form Selected Mapping Algorithm for PAPR Reduction in OFDM Multicarrier Transmission

Nowadays, the demand for communication multi-carriers’ channels, where the sub-channels are made mutually independent by using orthogonal frequency division multiplexing (OFDM), is widespread both for wireless and wired communication systems. Even if OFDM is a spectrally efficient modulation scheme, due to the allowed number of subcarriers, high data rate, and good coverage, the transmitted signal can present high peak values in the time domain, due to inverse fast Fourier transform operations. This gives rise to high peak-to-average power ratio (PAPR) with respect to single carrier systems. These peaks can saturate the transmitting amplifiers, modifying the shape of the OFDM symbol and affecting its information content, and they give rise to electromagnetic compatibility issues for the surrounding electric devices. In this paper, a closed form PAPR reduction algorithm is proposed, which belongs to selected mapping (SLM) methods. These methods consist in shifting the phases of the components to minimize the amplitude of the peaks. The determination of the optimal set of phase shifts is a very complex problem; therefore, the SLM approaches proposed in literature all resort to iterative algorithms. Moreover, as this calculation must be performed online, both the computational cost and the effect on the bit rate (BR) cannot be established a priori. The proposed analytic algorithm finds the optimal phase shifts of an approximated formulation of the PAPR. Simulation results outperform unprocessed conventional OFDM transmission by several dBs. Moreover, the complementary cumulative distribution function (CCDF) shows that, in most of the packets, the proposed algorithm reduces the PAPR if compared with randomly selected phase shifts. For example, with a number of shifted phases U=8, the CCFD curves corresponding to the analytical and random methods intersect at a probability value equal to 10−2, which means that in 99% of cases the former method reduces the PAPR more than the latter one. This is also confirmed by the value of the gain, which, at the same number of shifted phases and at the probability value equal to 10−1, changes from 2.09 dB for the analytical to 1.68 dB for the random SLM.

Partially-Reserved Cyclic Algorithm For OFDM-Based RadCom PAPR Reduction

With the proliferation of internet of vehicles (IoV) applications, the information exchange and anti-collision radar become increasingly important for vehicular safety. The fusion of radar and wireless communication, called RadCom, has been proposed for future intelligent transportation. Traditional orthogonal frequency division multiplexing (OFDM)-based RadCom systems, however, suffer from impractical bandwidth allocation and high peak-to-average power ratio (PAPR), which results in not only severe nonlinear distortion but also low efficiency of high power amplifier (HPA). Motivated by these research problems, we propose a new partially-reserved cyclic algorithm for OFDM-based RadCom fusion systems. Our proposed RadCom algorithm is able to allocate appropriate communication bandwidth on the request of actural transmission by assuming that the entire bandwidth can be used for radar detection. In the frequency-domain, when the spectral sub-band for communication is reserved, we optimize over the remaining spectral sub-band by the proposed algorithm for PAPR reduction. The evaluation of the system with the proposed algorithm shows significantly improved performance over traditional OFDM-based RadCom systems in both the communication and radar functions when HPA is taken into account.

A Novel Peak-to-Average Power Ratio Reduction for 5G Advanced Waveforms

Multi and single carrier waveforms are utilized in cellular systems for high-speed data transmission. In The Fifth Generation (5G) system, several waveform techniques based on multi carrier waveforms are proposed. However, the Peak to Average Power Ratio (PAPR) is seen as one of the significant concerns in advanced waveforms as it degrades the efficiency of the framework. The proposed article documents the study, progress, and implementation of PAPR reduction algorithms for the 5G radio framework. We compare the PAPR algorithm performance for advanced and conventional waveforms. The simulation results reveal that the advanced Partial Transmission Sequence (PTS) and Selective Mapping (SLM) methods enhanced the throughput and gain of the 5G waveforms. Furthermore, we have also analyzed the performance of Orthogonal Time Frequency Space Modulation (OTFSM) based on a single carrier system and found that PAPR is significantly low and is best suited to fading environments. It is seen that the conventional algorithms lower the PAPR but increase the complexity. The proposed PTS and SLM have shown good performance with low computational complexity.

Novel PAPR Reduction Algorithms for OFDM Signals

This paper proposes two efficient peak-to-average power ratio (PAPR) reduction algorithms for OFDM signals based on the principle of tone reservation. The first algorithm is performed in the time domain, whereas the second algorithm is a new clipping-and-filtering method. Both algorithms consist of two stages. The first stage, which is done off-line, precomputes a set of canceling signals based on the settings of the OFDM system. In particular, these signals are constructed to cancel signals at different levels of maximum instantaneous power that are above a predefined threshold. The second stage, which is online and iterative, reduces the signal peaks using modified versions of the canceling signals constructed in the first stage. When the reserved tones are distributed among the data tones, analysis and simulation results obtained with Data Over Cable Service Interface Specifications (DOCSIS) parameters show that the proposed algorithms achieve slightly better PAPR reduction performance and with significantly lower complexity when compared to the conventional algorithms.

An efficient and improved PTS algorithm for PAPR reduction in OFDM system

ABSTRACT In this research, an improved PTS algorithm is projected for diminishing peak to average power ratio (PAPR) of well accepted and effective orthogonal frequency division multiplexing (OFDM) system. OFDM is also considered as trustworthy method for several wireless communication applications and obtained a lot of research interests but unfortunately, one of the most important concern is very high PAPR in OFDM signals, which deteriorates system performance thus this is the most important drawback. To address this problem, a novel partial transmit sequence (PTS) algorithm is projected based upon centering phase sequences matrix (PTS-CPSM-Algo). These phase sequences are used for optimization of data so that minimum PAPR can be obtained. This algorithm obtains significant reduction in PAPR which leads to the enhancement in performance of OFDM system. From the developed analysis and simulation results, supremacy can be clearly seen for proposed PTS-CPSM-Algo over already existing OFDM system, selected mapping (SLM) and partial transmit sequence (PTS).

A Model-Driven DL Algorithm for PAPR Reduction in OFDM System

Deep learning (DL) has dramatically improved the peak-to-average power ratio (PAPR) performance. However, the high computational complexity and excessive training data constitute a significant hurdle. In this letter, a model-driven deep learning algorithm is proposed for PAPR reduction in orthogonal frequency division multiplexing (OFDM) system. Precisely, an iterative peak-canceling signal generation scheme is unfolded as a layer structure of the DL network. The scheme falls into the category of tone reservation technique. A set of trainable parameters, which optimizes the clipping threshold and weights time-domain kernel function, has been designed and introduced into the iterative scheme. Compared with the existing approaches, the simulation results demonstrate that the proposed algorithm achieves comparable PAPR performance with low complexity and training costs.

EVM Closed-Form Expression for OFDM Signals With Tone Reservation-Based PAPR Reduction

Peak to average power ratio (PAPR) reduction of OFDM signals has extensively been studied in the literature. Tone Reservation (TR) is one of the most famous algorithms and has already been included in some digital television standards such as DVB-T2 and ASTC3.0. However, the literature is still lacking theoretical analysis and performance bounds for the PAPR reduction of OFDM signals, especially using TR algorithms. For the first time, this paper provides fundamental results establishing the links between TR-PAPR reduction and the remaining signal distortions at the output of a non-linear high power amplifier (HPA) with and without memory effects. We first derive a generic EVM expression relying on the statistical characteristics of the samples composing the time domain signal and for any PAPR reduction technique. Computing these moments in the case of the quadratically constrained quadratic problem (QCQP) algorithm known as the optimal solution to the TR-PAPR reduction problem allows us to get the lower bound of the EVM of OFDM signals after TR-PAPR reduction and non-linear HPA. As a reference, we also provide the EVM expression using a clipping-based PAPR reduction method. The obtained EVM expressions have direct practical meaning since they are in function of the input power back-off (IBO) applied to the signal before the HPA. They also consist in a general analytical framework for OFDM PAPR reduction since they can be further exploited to analyze the performance of sub-optimal TR-based PAPR reduction algorithms.

A novel PAPR reduction algorithm for DCO-OFDM/OQAM system in underwater VLC

In the underwater visible light communication (VLC), the photon propagation scattering effect is obvious and the multipath effect is enhanced as the link distance increases. This paper uses orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) of filter bank multicarrier technology (FBMC), which is based on time frequency localization (TFL) filter, to overcome inter symbol interference (ISI) and inter carrier interference (ICI). Since light-emitting diode (LED) modulation needs positive modulation signal, we use the direct current (DC) bias to add on OFDM/OQAM signal so that the signal is positive. But, as the OFDM/OQAM is also multicarrier modulation system, the instantaneous fluctuation of OFDM/OQAM signal is bigger than single carrier modulation system and the nonlinear distortion is very serious. Therefore, this paper puts forward the modified overlapped selective mapping (M-OSLM) suppression algorithm to restrain the peak to average power ratio (PAPR), and optimizes the range of data symbol which affects on current data symbol by taking the characteristic of filter bank into consideration so that decrease PAPR. Thus, the instantaneous volatility of OFDM/OQAM signals is reduced in order to select the appropriate DC bias and reduce the clipping distortion. The simulation results show that compared with DC-biased cyclic prefix optical OFDM (DCO-CP-OFDM), DC-biased optical OFDM/OQAM (DCO-OFDM/OQAM) has a better overcoming ability on ISI of underwater wireless optical scattered channel. Meanwhile, the performance of PAPR reduction is greatly improved by M-OSLM algorithm. The signal-to-noise ratio (SNR) is improved by 3 dB averagely compared to DCO-CP-OFDM at the same bit error rate (BER).

Blind Algorithm Development for Peak to Average Power Ratio Reduction in OFDM Systems under Frequency Selective Channels

One major drawback of orthogonal frequency division multiplexing (OFDM) system is peak to average power ratio (PAPR). This effect causes high power amplifier (HPA) to introduce intermodulation and out of band radiation as the signal goes through, thus degrades the performance of OFDM systems. This paper proposes blind algorithms which takes advantage of signal transformation technique and signal distortion technique. Simulation results show that at complementary cumulative distribution function (CCDF) level of 10-3 , the proposed algorithm achieved 3.2 dB PAPR improvement compared to discrete Fourier transform with interleaved frequency division multiple access (DFT-IFDMA) based algorithm. The bit error rate (BER) performance has degraded by 2 dB compared to the original OFDM signal with no distortion under frequency selective channel (FCS) at BER of 10-4 . These presented results, mark this algorithm as a better candidate for PAPR reduction algorithm in long term evolution (LTE) network. Under AWGN channels, the proposed algorithm performs better both in low and high signal power values. Under frequency selective channels, the existing and proposed algorithm converges after 10 dB of signal to noise power values. The low BER transmissions at low signal power values signify energy efficiency, ideal for portable wireless devices with limited battery power.

A Novel PAPR Reduction Scheme for Polar-Coded OFDM Systems

A novel joint channel coding and peak-to-average power ratio (PAPR) reduction technique for polar-coded orthogonal frequency-division multiplexing (OFDM) systems is proposed. The main concept is to introduce shaping indices to polar encoder, and generate multiple OFDM symbol candidates according to various shaping bit patterns and then select the one with the lowest PAPR as a transmitted signal. Since information and shaping bits can be jointly decoded using the conventional decoding algorithm of polar codes, transmission of side information is not required. The polar code design algorithm for efficient PAPR reduction is also discussed. It is demonstrated that the proposed shaping method offers a gain of more than 4.5dB in terms of PAPR performance without affecting the decoding complexity.

Convex constant modulus algorithm for PAPR reduction in MIMO-OFDM systems

ABSTRACT This paper addresses the issue of high peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM)-based multiple-input multiple-output (MIMO) systems. A PAPR reduction technique is developed based on the cost function used in well-known iterative constant modulus algorithm (CMA). We devise a convex optimisation based solution to the CMA cost function for estimating the PAPR precoding weights. The resulting convex CMA solution not only accelerates convergence but also avoids local minima to ensure global optimality. As illustrated by the simulation results, the proposed convex CMA presents superior performance in terms of PAPR reduction with respect to existing techniques.

Distortion-less PAPR reduction algorithm for multi-user MIMO system with linear precoding

High peak-to-average power ratio (PAPR) is a major factor degrading power efficiency of communication systems. In multi-user (MU) multiple-input multiple-output (MIMO) systems, the PAPR becomes even worse since the power efficiency of the system is determined by the transmission stream with the highest PAPR. In this paper, we propose two PAPR reduction algorithms based on linear precoding, which exploit extra degree-of-freedom of the channel state matrix in MU-MIMO systems. The basic idea is to generate candidate output signals by multiplying the input signal with a set of equivalent linear precoding matrices, and find the signal with the lowest PAPR for transmission. The proposed algorithms are distortion-less PAPR reduction algorithms that do not sacrifice the system performance. Comparing to other distortion-less PAPR reduction algorithms, the proposed algorithms do not need to transmit side information, thus comply with existing MU-MIMO systems. Numerical results further validate our designs.