A new multilevel coding method using error-correcting codes
TL;DR: A new multilevel coding method that uses several error-correcting codes that makes effective use of soft-decisions to improve the performance of decoding and is superior to other multileVEL coding systems.
Abstract: A new multilevel coding method that uses several error-correcting codes is proposed. The transmission symbols are constructed by combining symbols of codewords of these codes. Usually, these codes are binary error-correcting codes and have different error-correcting capabilities. For various channels, efficient systems can be obtained by choosing these codes appropriately. Encoding and decoding procedures for this method are relatively simple compared with those of other multilevel coding methods. In addition, this method makes effective use of soft-decisions to improve the performance of decoding. The decoding error probability is analyzed for multiphase modulation, and numerical comparisons to other multilevel coding systems are made. When equally complex systems are compared, the new system is superior to other multilevel coding systems.
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Proceedings Article•
01 Jan 2005
TL;DR: This book aims to provide a chronology of key events and individuals involved in the development of microelectronics technology over the past 50 years and some of the individuals involved have been identified and named.
Abstract: Alhussein Abouzeid Rensselaer Polytechnic Institute Raviraj Adve University of Toronto Dharma Agrawal University of Cincinnati Walid Ahmed Tyco M/A-COM Sonia Aissa University of Quebec, INRSEMT Huseyin Arslan University of South Florida Nallanathan Arumugam National University of Singapore Saewoong Bahk Seoul National University Claus Bauer Dolby Laboratories Brahim Bensaou Hong Kong University of Science and Technology Rick Blum Lehigh University Michael Buehrer Virginia Tech Antonio Capone Politecnico di Milano Javier Gómez Castellanos National University of Mexico Claude Castelluccia INRIA Henry Chan The Hong Kong Polytechnic University Ajit Chaturvedi Indian Institute of Technology Kanpur Jyh-Cheng Chen National Tsing Hua University Yong Huat Chew Institute for Infocomm Research Tricia Chigan Michigan Tech Dong-Ho Cho Korea Advanced Institute of Science and Tech. Jinho Choi University of New South Wales Carlos Cordeiro Philips Research USA Laurie Cuthbert Queen Mary University of London Arek Dadej University of South Australia Sajal Das University of Texas at Arlington Franco Davoli DIST University of Genoa Xiaodai Dong, University of Alberta Hassan El-sallabi Helsinki University of Technology Ozgur Ercetin Sabanci University Elza Erkip Polytechnic University Romano Fantacci University of Florence Frank Fitzek Aalborg University Mario Freire University of Beira Interior Vincent Gaudet University of Alberta Jairo Gutierrez University of Auckland Michael Hadjitheodosiou University of Maryland Zhu Han University of Maryland College Park Christian Hartmann Technische Universitat Munchen Hossam Hassanein Queen's University Soong Boon Hee Nanyang Technological University Paul Ho Simon Fraser University Antonio Iera University "Mediterranea" of Reggio Calabria Markku Juntti University of Oulu Stefan Kaiser DoCoMo Euro-Labs Nei Kato Tohoku University Dongkyun Kim Kyungpook National University Ryuji Kohno Yokohama National University Bhaskar Krishnamachari University of Southern California Giridhar Krishnamurthy Indian Institute of Technology Madras Lutz Lampe University of British Columbia Bjorn Landfeldt The University of Sydney Peter Langendoerfer IHP Microelectronics Technologies Eddie Law Ryerson University in Toronto
7,826 citations
Cites background from "A new multilevel coding method usin..."
...Other empirical studies [17, 52, 53] have obtained power falloff with distance proportional to d−γ , where γ lies anywhere between two and six....
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TL;DR: An up-to-date survey on FSO communication systems is presented, describing FSO channel models and transmitter/receiver structures and details on information theoretical limits of FSO channels and algorithmic-level system design research activities to approach these limits are provided.
Abstract: Optical wireless communication (OWC) refers to transmission in unguided propagation media through the use of optical carriers, i.e., visible, infrared (IR), and ultraviolet (UV) bands. In this survey, we focus on outdoor terrestrial OWC links which operate in near IR band. These are widely referred to as free space optical (FSO) communication in the literature. FSO systems are used for high rate communication between two fixed points over distances up to several kilometers. In comparison to radio-frequency (RF) counterparts, FSO links have a very high optical bandwidth available, allowing much higher data rates. They are appealing for a wide range of applications such as metropolitan area network (MAN) extension, local area network (LAN)-to-LAN connectivity, fiber back-up, backhaul for wireless cellular networks, disaster recovery, high definition TV and medical image/video transmission, wireless video surveillance/monitoring, and quantum key distribution among others. Despite the major advantages of FSO technology and variety of its application areas, its widespread use has been hampered by its rather disappointing link reliability particularly in long ranges due to atmospheric turbulence-induced fading and sensitivity to weather conditions. In the last five years or so, there has been a surge of interest in FSO research to address these major technical challenges. Several innovative physical layer concepts, originally introduced in the context of RF systems, such as multiple-input multiple-output communication, cooperative diversity, and adaptive transmission have been recently explored for the design of next generation FSO systems. In this paper, we present an up-to-date survey on FSO communication systems. The first part describes FSO channel models and transmitter/receiver structures. In the second part, we provide details on information theoretical limits of FSO channels and algorithmic-level system design research activities to approach these limits. Specific topics include advances in modulation, channel coding, spatial/cooperative diversity techniques, adaptive transmission, and hybrid RF/FSO systems.
1,749 citations
Cites background from "A new multilevel coding method usin..."
...An example is multilevel coding (MLC) [259] which is a powerful coded modulation scheme [260]....
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...BER Bit-Error-Rate BICM Bit-Interleaved Coded Modulation BPSK Binary PSK CAP Carrier-less Amplitude and Phase modulation CSI Channel State Information DAPSK Differential Amplitude-Phase-Shift Keying DetF Detect-and-Forward DF Decode-and-Forward DFB Distributed Feedback DOF Degree-Of-Freedom Double GG Double Generalized Gamma DPIM Digital Pulse Interval Modulation DPIWM Digital Pulse Interval and Width Modulation DPolPSK Differential Polarization-Phase-Shift Keying DPPM Differential PPM DPSK Differential PSK EDFA Erbium-Doped Fiber Amplifier EDRS European Data Relay System EGC Equal-Gain Combining EO Electrical-to-Optical ESA European Space Agency FSO Free Space Optics FOV Field Of View FP Fabry-Perot HARQ Hybrid ARQ HDTV High Definition Television IM/DD Intensity-Modulation Direct-Detection IR Infra-Red ISI Inter-Symbol Interference JPL Jet Propulsion Laboratory LAN Local Area Network LCRD Communication Relay Demonstration LD Laser Diode LDPC Low-Density Parity Check codes LED Light Emitting Diode LO Local Oscillator LT Luby Transform code MIMO Multiple-Inputs Multiple-Outputs MISO Multiple-Inputs Single-Output ML Maximum Likelihood MLC Multi-Level Coding MLCD Mars Laser Communications Demonstration MLD Maximum Likelihood Detection MMW Milli-Meter Wave MPPM Multipulse PPM MRC Maximal-Ratio Combining MTBF Mean Time Between Failures OE Optical-to-Electrical OFDM Orthogonal Frequency Division Multiplexing OOK On-Off Keying OPPM Overlapping PPM OSM Optical Spatial Modulation OSTBC Orthogonal ST Block Code OWC Optical Wireless Communication PAM Pulse Amplitude Modulation PAPR Peak-to-Average Power Ratio PD Photo-Diode PHY PHYsical layer PolSK Polarization Shift Keying PPM Pulse Position Modulation PSK Phase Shift Keying PWM Pulse Width Modulation QAM Quadrature Amplitude Modulation QPSK Quadrature PSK RC Repetition Coding RF Radio-Frequency RIN Relative Intensity Noise RMS Root Mean Square RS Reed Solomon code SI Scintillation Index SIM Subcarrier Intensity Modulation SIMO Single-Input Multiple-Outputs SISO Single-Input Single-Output SMux Spatial Multiplexing SNR Signal-to-Noise Ratio SOA Semiconductor Optical Amplifier ST Space-Time UV Ultra-Violet VCSEL Vertical-Cavity Surface-Emitting Laser VLC Visible Light Communication WBAN Wireless Body Area Network WLAN Wireless Local Area Network WPAN Wireless Personal Area Network...
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...For instance, LDPC coding with bit-interleaved coded modulation (BICM) [313] or MLC is considered in [314]....
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...In particular, the Mars Laser Communications Demonstration (MLCD) aims at demonstrating optical communications from Mars to the Earth at data rates between 1 and 10 Mbps [14]....
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...An example is multilevel coding (MLC) [257] which is a powerful coded modulation scheme [258]....
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TL;DR: The theoretical foundations of BICM are reviewed under the unified framework of error exponents for mismatched decoding, which allows an accurate analysis without any particular assumptions on the length of the interleaver or independence between the multiple bits in a symbol.
Abstract: The principle of coding in the signal space follows directly from Shannon's analysis of waveform Gaussian channels subject to an input constraint. The early design of communication systems focused separately on modulation, namely signal design and detection, and error correcting codes, which deal with errors introduced at the demodulator of the underlying waveform channel. The correct perspective of signal-space coding, although never out of sight of information theorists, was brought back into the focus of coding theorists and system designers by Imai's and Ungerbock's pioneering works on coded modulation. More recently, powerful families of binary codes with a good tradeoff between performance and decoding complexity have been (re-)discovered. Bit-Interleaved Coded Modulation (BICM) is a pragmatic approach combining the best out of both worlds: it takes advantage of the signal-space coding perspective, whilst allowing for the use of powerful families of binary codes with virtually any modulation format. BICM avoids the need for the complicated and somewhat less flexible design typical of coded modulation. As a matter of fact, most of today's systems that achieve high spectral efficiency such as DSL, Wireless LANs, WiMax and evolutions thereof, as well as systems based on low spectral efficiency orthogonal modulation, feature BICM, making BICM the de-facto general coding technique for waveform channels.
The theoretical characterization of BICM is at the basis of efficient coding design techniques and also of improved BICM decoders, e.g., those based on the belief propagation iterative algorithm and approximations thereof. In this text, we review the theoretical foundations of BICM under the unified framework of error exponents for mismatched decoding. This framework allows an accurate analysis without any particular assumptions on the length of the interleaver or independence between the multiple bits in a symbol. We further consider the sensitivity of the BICM capacity with respect to the signal-to-noise ratio (SNR), and obtain a wideband regime (or low-SNR regime) characterization. We review efficient tools for the error probability analysis of BICM that go beyond the standard approach of considering infinite interleaving and take into consideration the dependency of the coded bit observations introduced by the modulation. We also present bounds that improve upon the union bound in the region beyond the cutoff rate, and are essential to characterize the performance of modern randomlike codes used in concatenation with BICM. Finally, we turn our attention to BICM with iterative decoding, we review extrinsic information transfer charts, the area theorem and code design via curve fitting. We conclude with an overview of some applications of BICM beyond the classical coherent Gaussian channel.
1,245 citations
TL;DR: This paper deals with 2/sup l/-ary transmission using multilevel coding (MLC) and multistage decoding (MSD) and shows that capacity can in fact be closely approached at high bandwidth efficiencies.
Abstract: This paper deals with 2/sup l/-ary transmission using multilevel coding (MLC) and multistage decoding (MSD). The known result that MLC and MSD suffice to approach capacity if the rates at each level are appropriately chosen is reviewed. Using multiuser information theory, it is shown that there is a large space of rate combinations such that MLC and full maximum-likelihood decoding (MLD) can approach capacity. It is noted that multilevel codes designed according to the traditional balanced distance rule tend to fall in the latter category and, therefore, require the huge complexity of MLD. The capacity rule, the balanced distances rules, and two other rules based on the random coding exponent and cutoff rate are compared and contrasted for practical design. Simulation results using multilevel binary turbo codes show that capacity can in fact be closely approached at high bandwidth efficiencies. Moreover, topics relevant in practical applications such as signal set labeling, dimensionality of the constituent constellation, and hard-decision decoding are emphasized. Bit interleaved coded modulation, proposed by Caire et al. (see ibid., vol.44, p.927-46, 1998), is reviewed in the context of MLC. Finally, the combination of signal shaping and coding is discussed. Significant shaping gains are achievable in practice only if these design rules are taken into account.
1,030 citations
Cites methods from "A new multilevel coding method usin..."
...For finite , the labeling strategy introduced by Ungerboeck and Imai in an intuitive manner shows the best performance, but the gain compared to the other labelings is relatively small....
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...Imai’s idea ofmultilevel coding (MLC) is to protect each address bit of the signal point by an individual binary code at level ....
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...A straightforward generalization of Imai’s approach is to use -ary, , component codes based on a nonbinary partitioning of the signal set....
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...In terms of capacity the loss with regard to Imai’s MLC scheme is surprisingly small if and only if Gray labeling of signal points is employed....
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...For finite code length, the labeling introduced by Ungerboeck and Imai, leads to the most power-efficient schemes....
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References
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TL;DR: This tutorial paper begins with an elementary presentation of the fundamental properties and structure of convolutional codes and proceeds with the development of the maximum likelihood decoder, which yields for arbitrary codes both the distance properties and upper bounds on the bit error probability.
Abstract: This tutorial paper begins with an elementary presentation of the fundamental properties and structure of convolutional codes and proceeds with the development of the maximum likelihood decoder. The powerful tool of generating function analysis is demonstrated to yield for arbitrary codes both the distance properties and upper bounds on the bit error probability for communication over any memoryless channel. Previous results on code ensemble average error probabilities are also derived and extended by these techniques. Finally, practical considerations concerning finite decoding memory, metric representation, and synchronization are discussed.
1,040 citations
TL;DR: In this paper, the authors gave a tabulation of binary convolutional codes with maximum free distance for rates of 1/2, 1/3, and 1/4 for all constraint lengths up to and including nu = 14.
Abstract: This paper gives a tabulation of binary convolutional codes with maximum free distance for rates \frac{1}{2}, \frac{1}{3} , and \frac{1}{4} for all constraint lengths (measured in information digits)
u up to and including nu = 14 . These codes should be of practical interest in connection with Viterbi decoders.
188 citations
TL;DR: In this paper, a search procedure was developed to find good short binary (N,N - 1) convolutional codes using simple rules to discard from the complete ensemble of codes a large fraction whose free distance d{free} either cannot achieve the maximum value or is equal to d_{free} of some code in the remaining set.
Abstract: A search procedure is developed to find good short binary (N,N - 1) convolutional codes. It uses simple rules to discard from the complete ensemble of codes a large fraction whose free distance d_{free} either cannot achieve the maximum value or is equal to d_{free} of some code in the remaining set. Farther, the search among the remaining codes is started in a subset in which we expect the possibility of finding codes with large values of d_{free} to be good. A number of short, optimum (in the sense of maximizing d_{free} ), rate-2/3 and 3/4 codes found by the search procedure are listed.
97 citations
TL;DR: Certain classes of low-rate binary codes that have simple decoding algorithms can be used as underlying codes in the construction of high-rate easily decodable i -compressed codes, which have higher rates than binary codes of comparable length and number of correctable errors.
Abstract: In this paper we present a new error-control technique intended for use in 2^l -level data-transmission systems that employ Gray coding to transform a binary source sequence into the 2^l -ary transmitted sequence. The codes, which we call i -compressed codes, make use of the structure of binary codes and have the property that for some integer i , 1 \leq i \leq l , transmission errors can be corrected if the erroneously received signals lie less than 2^{i-1} levels from the corresponding correct, or nominal signal levels. The number of such errors that can be corrected is related to the error-correcting capability of the underlying binary code used in the construction. In return for this restriction on the magnitude of correctable errors in the received signal, these codes have higher rates than binary codes of comparable length (in bits) and number of correctable errors. Hence in applications where it can be assumed that the fraction of errors exceeding a certain magnitude is negligible (or at least tolerable), this technique is more efficient than the conventional practice of placing a binary encoder between the data source and modulator and a binary decoder between the demodulator and data sink. Furthermore, although the i -compressed codes are nonbinary, the decoding algorithm is that of the underlying binary code plus a small amount of additional processing; hence it is generally simpler to implement than other nonbinary decoding algorithms. It is also observed that the rate of an i -compressed code is always greater than that of the underlying binary code. Thus certain classes of low-rate binary codes that have simple decoding algorithms can be used as underlying codes in the construction of high-rate easily decodable i -compressed codes. Finally, for the case i = 1 , encoding and decoding becomes exceptionally simple and for this case it is possible to make use of "soft decisions" at the receiver to improve the performance.
17 citations