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3 results on '"low-complexity algorithms"'

1. Full Diversity Multidimensional Codebook Design for Fading Channels: The Combinatorial Approach

Author

Juliana Camilo Inacio, Samuel Montejo-Sanchez, Didier Le Ruyet, Bartolomeu F. Uchoa-Filho, Universidade Federal de Santa Catarina = Federal University of Santa Catarina [Florianópolis] (UFSC), CEDRIC. Traitement du signal et architectures électroniques (CEDRIC - LAETITIA), Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), and Universidad Tecnológica Metropolitana (UTEM)

Subjects
ensemble average performance, Computer science, combinatorial approach, 02 engineering and technology, Communications system, Precoding, low-complexity algorithms, 0203 mechanical engineering, signal constellation, full diversity multidimensional codebook design, 0202 electrical engineering, electronic engineering, information engineering, error event analysis, Fading, fading channels, signal space diversity, Electrical and Electronic Engineering, Gaussian-shaped grids, precoding, Computer Science::Information Theory, SSD, full diversity multidimensional codebooks, Codebook, 020302 automobile design & engineering, 020206 networking & telecommunications, Constellation diagram, transmission techniques, Antenna diversity, fixed grid, Transmission (telecommunications), Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, permutation sets, Communication channel
Abstract

International audience; Previous transmission techniques for fading channels that exploit signal space diversity (SSD) are based on rotation or precoding of an original signal constellation. In this work, we follow a completely different approach to design full diversity multidimensional codebooks whose implementation has low complexity and requires little storage. The design consists of finding good permutations of the labels of points in a fixed grid. We consider both the uniform and the Gaussian-shaped grids. An ensemble average performance based on an enumerative analysis of the error events is presented, which brings some insight into the problem. Three low-complexity algorithms are then proposed to find good permutations sets. Both analytical and simulation results confirm the good performance of the proposed codebooks, which can be considered as good candidates for practical communications systems.

Published
2020
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2. Optimum Low-Complexity Decoder for Spatial Modulation

Author

Octavia A. Dobre, Salama S. Ikki, Ibrahim Al-Nahhal, Ertugrul Basar, Başar, Ertuğrul (ORCID 0000-0001-5566-2392 & YÖK ID 149116), Al-Nahhal, İbrahim, Dobre, Octavia A., Ikki, Salama, College of Engineering, and Department of Electrical and Electronics Engineering

Subjects
FOS: Computer and information sciences, Engineering, electrical and electronic, Telecommunications, Computer Networks and Communications, Computer science, Information Theory (cs.IT), Computer Science - Information Theory, Monte Carlo method, 020206 networking & telecommunications, 02 engineering and technology, Computational Complexity (cs.CC), Multiple-input multiple-output (MIMO) systems, Spatial modulation (SM), Maximum likelihood (ML) decoder, Sphere decoder (SD), Low-complexity algorithms, Complexity analysis, Variable (computer science), Computer Science - Computational Complexity, Channel state information, 0202 electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Random variable, Algorithm, Decoding methods, Communication channel, Computer Science::Information Theory
Abstract

In this paper, a novel low-complexity detection algorithm for spatial modulation (SM), referred to as the minimum-distance of maximum-length (m-M) algorithm, is proposed and analyzed. The proposed m-M algorithm is a smart searching method that is applied for the SM tree-search decoders. The behavior of the m-M algorithm is studied for three different scenarios: 1) perfect channel state information at the receiver side (CSIR); 2) imperfect CSIR of a fixed channel estimation error variance; and 3) imperfect CSIR of a variable channel estimation error variance. Moreover, the complexity of the m-M algorithm is considered as a random variable, which is carefully analyzed for all scenarios, using probabilistic tools. Based on a combination of the sphere decoder (SD) and ordering concepts, the m-M algorithm guarantees to find the maximum-likelihood (ML) solution with a significant reduction in the decoding complexity compared with SM-ML and existing SM-SD algorithms; it can reduce the complexity up to 94% and 85% in the perfect CSIR and the worst scenario of imperfect CSIR, respectively, compared with the SM-ML decoder. The Monte Carlo simulation results are provided to support our findings as well as the derived analytical complexity reduction expressions., Natural Sciences and Engineering Research Council of Canada (NSERC); Turkish Academy of Sciences (TÜBA), GEBIP Programme

Published
2019

3. Polar codes are optimal for lossy source coding

Author

Rudiger Urbanke and Satish Babu Korada

Subjects
FOS: Computer and information sciences, Source code, Theoretical computer science, media_common.quotation_subject, Computer Science - Information Theory, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Binary number, Data_CODINGANDINFORMATIONTHEORY, 02 engineering and technology, Library and Information Sciences, Lossy compression, Topology, 01 natural sciences, lossy source coding, polar codes, low-complexity algorithms, Rate–distortion theory, Channel capacity, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, 010306 general physics, media_common, Computer Science::Information Theory, Mathematics, Polar code, NCCR-MICS, Information Theory (cs.IT), Variable-length code, Signal compression, 020206 networking & telecommunications, NCCR-MICS/CL1, Coding theory, Linear code, Computer Science Applications, Binary code, Hamming code, Algorithm, Decoding methods, Information Systems
Abstract

We consider lossy source compression of a binary symmetric source using polar codes and the low-complexity successive encoding algorithm. It was recently shown by Arikan that polar codes achieve the capacity of arbitrary symmetric binary-input discrete memoryless channels under a successive decoding strategy. We show the equivalent result for lossy source compression, i.e., we show that this combination achieves the rate-distortion bound for a binary symmetric source. We further show the optimality of polar codes for various problems including the binary Wyner-Ziv and the binary Gelfand-Pinsker problem, Comment: 15 pages, submitted to Transactions on Information Theory

Published
2009
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