Frequency-Selective PAPR Reduction for OFDM

We study the peak-to-average power ratio (PAPR) problem in orthogonal frequency-division multiplexing (OFDM) systems. In conventional clipping and filtering based PAPR reduction techniques, clipping noise is allowed to spread over the whole active passband, thus degrading the transmit signal quality similarly at all active subcarriers. However, since modern radio networks support frequency-multiplexing of users and services with highly different quality-of-service expectations, clipping noise from PAPR reduction should be distributed unequally over the corresponding physical resource blocks (PRBs). To facilitate this, we present an efficient PAPR reduction technique, where clipping noise can be flexibly controlled and filtered inside the transmitter passband, allowing to control the transmitted signal quality per PRB. Numerical results are provided in 5G New Radio (NR) mobile network context, demonstrating the flexibility and efficiency of the proposed method.Comment: Accepted for publication as a Correspondence in the IEEE Transactions on Vehicular Technology in March 2019. This is the revised version of original manuscript, and it is in press at the momen

PAPR Reduction Solutions for 5G and Beyond

The latest fifth generation (5G) wireless technology provides improved communication quality compared to earlier generations. The 5G New Radio (NR), specified by the 3rd Generation Partnership Project (3GPP), addresses the modern requirements of the wireless networks and targets improved communication quality in terms of for example peak data rates, latency and reliability. On the other hand, there are still various crucial issues that impact the implementation and energy-efficiency of 5G NR networks and their different deployments. The power-efficiency of transmitter power amplifiers (PAs) is one of these issues. The PA is an important unit of a communication system, which is responsible from amplifying the transmit signal towards the antenna. Reaching high PA power-efficiency is known to be difficult when the transmit waveform has a high peak-to-average power ratio (PAPR). The cyclic prefix (CP)-orthogonal frequencydivision multiplexing (OFDM) that is the main physical-layer waveform of 5G NR, suffers from such high PAPR challenge. There are generally many PAPR reduction methods proposed in the literature, however, many of these have either very notable computational complexity or impose substantial inband distortion. Moreover, 5G NR has new features that require redesigning the PAPR reduction methods. In line with these, the first contribution of this thesis is the novel frequencyselective PAPR reduction concept, where clipping noise is shaped in a frequencyselective manner over the active passband. This concept is in line with the 5G NR, where aggressive frequency-domain multiplexing is considered as an important feature. Utilizing the frequency-selective PAPR reduction enables the realization of the heterogeneous resource utilization within one passband. The second contribution of this thesis is the frequency-selective single-numerology (SN) and mixed-numerology (MN) PAPR reduction methods. The 5G NR targets utilizing different physical resource blocks (PRBs) and bandwidth parts (BWPs) within one passband flexibly. Yet, existing PAPR reduction methods do not exploit these features. Based on this, novel algorithms utilizing PRB and BWP level control of clipping noise are designed to meet error vector magnitude (EVM) limits of the modulations while reducing the PAPR. TheMNallocation has one critical challenge as inter numerology interference (INI) emerges after aggregation of subband signals. Proposed MN PAPR reduction algorithm overcomes this issue by cancelling INI within the PAPR reduction loop, which has not been considered earlier. The third contribution of this thesis is the proposal of two novel non-iterative PAPR reduction methods. First method utilizes the fast-convolution filteredOFDM (FC-F-OFDM) that has excellent spectral containment, and combines it with clipping. Moreover, clipping noise is also allocated to guard bands by filter passband extension (FPE) and clipping noise in out-of-band (OOB) regions is essentially filtered through FC filtering. The second method is the guard-tone reservation (GTR) which is applied to discrete Fourier transform-spread-OFDM (DFT-s-OFDM). Uniquely, GTR estimates the time domain peaks in data symbol domain before inverse fast Fourier transform (IFFT), and uses guard band tones for PAPR reduction. The fourth contribution of the thesis is the design of two novel machine learning (ML) algorithms that improve the drawbacks of frequency-selective PAPRreduction. The first ML algorithm, PAPRer, models the nonlinear relation between the PAPR target and the realized PAPR value. Then, it auto-tunes the optimal PAPR target and this way minimizes the realized PAPR. The second ML algorithm, one-shot clipping-and-filtering (OSCF), solves the complexity problem of iterative clipping and filtering (ICF)-like methods by generating proper approximated clipping noise signal after running only one iteration, leading to very efficient PAPR reduction. Finally, an over-arching contribution of this thesis is the experimental validation of the performance benefits of the proposed methods by considering realistic 5GNR uplink (UL) and downlink (DL) testbeds that include realistic PAs and associated hardware. It is very important to confirm the practical benefits of the proposed methods and, this is realized with the conducted experimental work

Machine Learning Based Tuner for Frequency-Selective PAPR Reduction

Frequency-selective peak-to-average power ratio (PAPR) reduction is essential in networks such as 5 G New Radio (NR) that support frequency-domain multiplexing of users and services. However, stemming from the frequency-selective shaping of the involved clipping noise, the relation between the intended PAPR target and the actually realized PAPR is known to be heavily nonlinear, which complicates the PAPR reduction. In this article, a novel machine learning (ML)-based solution, called PAPRer , is proposed to automatically and accurately tune the optimal PAPR target for frequency-selective PAPR reduction. This is achieved by utilizing the features related to the used clipping noise filter and minimization of the defined loss function, through supervised learning, which quantifies the PAPR target estimation accuracy. An analytical clipping noise power-based method is also devised for reference purposes. Extensive numerical evaluations in 5 G NR context are provided and analyzed, showing that PAPRer can very accurately predict and tune the optimal PAPR target. These results, together with the provided complexity assessment, demonstrate that the proposed PAPRer offers a favorable performance-complexity tradeoff in choosing the optimal PAPR target for frequency-selective PAPR reduction.acceptedVersionPeer reviewe