Peak Power Reduction of OFDM Signals with Sign Adjustment

It has recently been shown that significant reduction in the peak to mean envelope power (PMEPR) can be obtained by altering the sign of each subcarrier in a multicarrier system with n subcarriers. However, finding the best sign not only requires a search over 2n possible signs but also may lead to a substantial rate loss for small size constellations. In this paper, we first propose a greedy algorithm to choose the signs based on p-norm minimization and prove that the resulting PMEPR is guaranteed to be less than c log n where c is a constant independent of n for any n. This approach has lower complexity in each iteration compared to the derandomization approach of while achieving similar PMEPR reduction. We further improve the performance of the proposed algorithm by enlarging the search space using pruning. Simulation results show that PMEPR of a multicarrier signal with 128 subcarriers can be reduced to within 1.6 dB of the PMEPR of a single carrier system. In the second part of the paper, we address the rate loss by proposing a block coding scheme in which only one sign vector is chosen for K different modulating vectors. The sign vector can be computed using the greedy algorithm in n iterations. We show that the multi-symbol encoding approach can reduce the rate loss by a factor of K while achieving the PMEPR of c logKn, i.e., only logarithmic growth in K. Simulation results show that the rate loss can be made smaller than %10 at the cost of only 1 db increase in the resulting PMEPR for a system with 128 subcarriers

A Low-Complexity SLM PAPR Reduction Scheme for OFDMA

In orthogonal frequency division multiplexing (OFDM) systems, selected mapping (SLM) techniques are widely used to minimize the peak to average power ratio (PAPR). The candidate signals are generated in the time domain by linearly mixing the original time-domain transmitted signal with numerous cyclic shift equivalents to reduce the amount of Inverse Fast Fourier Transform (IFFT) operations in typical SLM systems. The weighting factors and number of cyclic shifts, on the other hand, should be carefully chosen to guarantee that the elements of the appropriate frequency domain phase rotation vectors are of equal magnitude. A low-complexity expression is chosen from among these options to create the proposed low-complexity scheme, which only requires one IFFT. In comparison to the existing SLM technique, the new SLM scheme achieves equivalent PAPR reduction performance with significantly less computing complexity. MATLAB tool is used for simulating the proposed work

A Low-Complexity SLM PAPR Reduction Scheme for OFDMA

In orthogonal frequency division multiplexing (OFDM) systems, selected mapping (SLM) techniques are widely used to minimize the peak to average power ratio (PAPR). The candidate signals are generated in the time domain by linearly mixing the original time-domain transmitted signal with numerous cyclic shift equivalents to reduce the amount of Inverse Fast Fourier Transform (IFFT) operations in typical SLM systems. The weighting factors and number of cyclic shifts, on the other hand, should be carefully chosen to guarantee that the elements of the appropriate frequency domain phase rotation vectors are of equal magnitude. A low-complexity expression is chosen from among these options to create the proposed low-complexity scheme, which only requires one IFFT. In comparison to the existing SLM technique, the new SLM scheme achieves equivalent PAPR reduction performance with significantly less computing complexity. MATLAB tool is used for simulating the proposed work

Moving-object reconstruction from camera-blurred sequences using interframe and interregion constraints

Also issued as Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1988.Includes bibliographical references.Supported by the AT&TFoundation through a Bell Laboratories Ph.D. scholarship.Stephen Charles Hsu

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Low-Complexity Schemes for Class-III and CORR SLM in OFDM Systems

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 노종선.In this dissertation, orthogonal frequency division multiplexing (OFDM) system is studied. Since OFDM signal sequence undergoes high peak-to-average power ratio (PAPR), several schemes are proposed to mitigate the PAPR problem. PAPR reduction schemes such as selected mapping (SLM) and partial transmit sequence (PTS) are introduced. Due to the high computational complexity of the SLM scheme, low-complexity SLM schemes have been proposed by many researchers. Class-III SLM scheme [55] requires only one inverse fast Fourier transform (IFFT) operation, whereas the conventional scheme needs U IFFT operations. By randomly selecting the cyclic shift and rotation values, this scheme can generate up to N3 alternative OFDM signal sequences. The PAPR reduction performance of Class-III SLM scheme is little degraded compared to the conventional SLM scheme. Recently, instead of PAPR reduction, the different performance criteria for SLM scheme are proposed such as inter modulation distortion [38] and correlation (CORR) [56]. The objective of these schemes are enhancing the bit error rate (BER) performance instead of PAPR reduction performance. In the first part of this dissertation, a deterministic selection method of phase sequences is proposed for Class-III SLM scheme [55]. First, the optimal condition of cyclic shift values in the Class-III SLM scheme is proposed. Then, the cyclic shift values satisfying the optimal condition is also derived. Compared to the random selection method, the proposed selection method guarantees the optimal PAPR reduction performance. Second, two generation methods for good alternative OFDM signal sequences are proposed, one by using rotation values which do not have linear relation and the other with no rotation values. The advantages of the proposed selection schemes are: (a) The second proposed selection scheme does not need the rotation values. (b) Both of the proposed selection schemes require less side information than random selection scheme. (c) The first proposed selection scheme guarantees the optimal PAPR reduction performance in terms of variance of correlation. In the second part of this dissertation, the proper oversampling rate for the CORR SLMscheme is proposed. It is known that four times oversampling is enough to estimate the PAPR of the continuous OFDM signal. By calculating the correlation coefficient between the continuous and two times oversampled OFDMsignal sequences, it is found that two times oversampling is enough to achieve the same BER performance as four times oversampling case in the CORR SLM scheme. In the simulation results, the same BER performance can be achieved by the proposed two times oversampling rate as four times oversampling case.Abstract i Contents iii List of Tables vii List of Figures ix 1. Introduction 1 1.1. Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2. Overview of Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . 4 2. OFDM System Model 7 2.1. OFDM System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2. Modulation and Demodulation of OFDM Signal . . . . . . . . . . . . 9 2.2.1. Orthogonality Principle . . . . . . . . . . . . . . . . . . . . . . 9 2.2.2. OFDM Signal Modulation and Demodulation . . . . . . . . . . 10 2.3. Fast Fourier Transform . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.4. Guard Interval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.5. Peak-to-Average Power Ratio . . . . . . . . . . . . . . . . . . . . . . . 13 2.5.1. Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.5.2. The distribution of PAPR . . . . . . . . . . . . . . . . . . . . . 13 2.5.3. PAPR of Oversampled Signal . . . . . . . . . . . . . . . . . . 15 3. PAPR Reduction Schemes 17 3.1. Clipping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2. Tone Reservation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.3. Partial Transmit Sequence . . . . . . . . . . . . . . . . . . . . . . . . 19 3.4. Selected Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.5. Low-Complexity SLM Schemes . . . . . . . . . . . . . . . . . . . . . 24 3.5.1. SLM Scheme with Divided IFFT Stages . . . . . . . . . . . . . 24 3.5.2. Modified SLM Scheme . . . . . . . . . . . . . . . . . . . . . . 25 3.5.3. SLM Scheme with Conversion Matrices . . . . . . . . . . . . . 26 3.6. Considerations for PAPR Reduction Schemes . . . . . . . . . . . . . . 28 4. BER Reduction Schemes 30 4.1. PTS Scheme with PICR Metric . . . . . . . . . . . . . . . . . . . . . . 30 4.2. IMD Reduction Scheme . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.3. PTS Scheme with MSE Metric . . . . . . . . . . . . . . . . . . . . . . 33 4.4. DSR Reduction Scheme with Distortion Prediction . . . . . . . . . . . 34 5. Low-Complexity Class-III SLM Scheme 37 5.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 5.2. Overview of Class-III SLM Scheme . . . . . . . . . . . . . . . . . . . 39 5.3. Selection of Optimal Alternative OFDM Signal Sequences for Class-III SLM Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 5.3.1. Correlation Analysis . . . . . . . . . . . . . . . . . . . . . . . 41 5.3.2. Selection of Optimal Cyclic Shift Values . . . . . . . . . . . . 44 5.3.3. Maximum Number of Optimal Alternative OFDM Signal Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.3.4. Selection of Additional Alternative OFDM Signal Sequences . . 49 5.4. Side Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.5. Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.6. Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 5.7. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 6. Low-Complexity CORR SLM Scheme 61 6.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 6.2. Overview of SLM Scheme Using CORR Metric . . . . . . . . . . . . . 62 6.2.1. Overview of CORR Metric . . . . . . . . . . . . . . . . . . . . 62 6.2.2. BER Performance of SLM Scheme under HPA . . . . . . . . . 65 6.3. Oversampling Effect on SLM Scheme Using CORR Metric . . . . . . . 67 6.3.1. Expression of Oversampled Signal and CORR Metric . . . . . . 67 6.3.2. Correlation Coefficients between Coefficient Sequences Derived from CORR Metric Computation . . . . . . . . . . . . . . . . 70 6.4. Computational Complexity . . . . . . . . . . . . . . . . . . . . . . . . 72 6.5. Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.6. Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.6.1. Effect of 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.6.2. Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 6.6.2.1. Comparative CORR . . . . . . . . . . . . . . . . . . 82 6.6.2.2. Low Sampled CORR . . . . . . . . . . . . . . . . . 83 6.7. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 7. Conclusions 86 Bibliography 88 초록 96Docto