Fixed-Rate Maximum-Runlength-Limited Codes From Variable-Rate Bit Stuffing
We introduce the fixed-rate bit stuff (FRB) algorithm for efficiently encoding and decoding maximum-runlength-limited (MRL) sequences. Our approach is based on a simple, variable-rate technique called bit stuffing . Bit stuffing produces near-capacity achieving codes for a wide range of constraints,...
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Veröffentlicht in: | IEEE transactions on information theory 2007-08, Vol.53 (8), p.2769-2790 |
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description | We introduce the fixed-rate bit stuff (FRB) algorithm for efficiently encoding and decoding maximum-runlength-limited (MRL) sequences. Our approach is based on a simple, variable-rate technique called bit stuffing . Bit stuffing produces near-capacity achieving codes for a wide range of constraints, but encoding is variable-rate, which is unacceptable in most applications. In this work, we design near-capacity fixed-rate codes using a three-step procedure. The fixed-length input data block first undergoes iterative preprocessing, followed by variable-rate bit stuffing, and finally dummy-bit padding to a fixed output length. The iterative preprocessing is key to achieving high encoding rates. We discuss rate computation for the proposed FRB algorithm and show that the asymptotic (in input block length) encoding rate is close to the average rate of the variable-rate bit stuff code. Then, we proceed to explore the effect of decreasing/increasing the number of preprocessing iterations. Finally, we derive a lower bound on the encoding rate with finite-length input blocks and tabulate the parameters required to design FRB codes with rate close to 100/101 and 200/201. |
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Our approach is based on a simple, variable-rate technique called bit stuffing . Bit stuffing produces near-capacity achieving codes for a wide range of constraints, but encoding is variable-rate, which is unacceptable in most applications. In this work, we design near-capacity fixed-rate codes using a three-step procedure. The fixed-length input data block first undergoes iterative preprocessing, followed by variable-rate bit stuffing, and finally dummy-bit padding to a fixed output length. The iterative preprocessing is key to achieving high encoding rates. We discuss rate computation for the proposed FRB algorithm and show that the asymptotic (in input block length) encoding rate is close to the average rate of the variable-rate bit stuff code. Then, we proceed to explore the effect of decreasing/increasing the number of preprocessing iterations. 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Our approach is based on a simple, variable-rate technique called bit stuffing . Bit stuffing produces near-capacity achieving codes for a wide range of constraints, but encoding is variable-rate, which is unacceptable in most applications. In this work, we design near-capacity fixed-rate codes using a three-step procedure. The fixed-length input data block first undergoes iterative preprocessing, followed by variable-rate bit stuffing, and finally dummy-bit padding to a fixed output length. The iterative preprocessing is key to achieving high encoding rates. We discuss rate computation for the proposed FRB algorithm and show that the asymptotic (in input block length) encoding rate is close to the average rate of the variable-rate bit stuff code. Then, we proceed to explore the effect of decreasing/increasing the number of preprocessing iterations. Finally, we derive a lower bound on the encoding rate with finite-length input blocks and tabulate the parameters required to design FRB codes with rate close to 100/101 and 200/201.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Asymptotic properties</subject><subject>Binary sequences</subject><subject>Binary system</subject><subject>Bit stuffing</subject><subject>Blocking</subject><subject>Codes</subject><subject>Coding, codes</subject><subject>constrained coding</subject><subject>Data encryption</subject><subject>Data mining</subject><subject>Data preprocessing</subject><subject>Data transmission</subject><subject>Decoding</subject><subject>Encoding</subject><subject>Exact sciences and technology</subject><subject>fixed-rate codes</subject><subject>Information theory</subject><subject>Information, signal and communications theory</subject><subject>Iterative algorithms</subject><subject>Iterative methods</subject><subject>Lower bounds</subject><subject>Magnetic materials</subject><subject>Magnetic properties</subject><subject>Magnetic recording</subject><subject>Magnetization</subject><subject>Preprocessing</subject><subject>Signal and communications theory</subject><subject>Systems, networks and services of telecommunications</subject><subject>Telecommunications</subject><subject>Telecommunications and information theory</subject><subject>Timing</subject><subject>Transmission and modulation (techniques and equipments)</subject><issn>0018-9448</issn><issn>1557-9654</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp90U2LFDEQBuAgCo6rZw9eGkE99Wyqk3Snjjo4ujIirKPXkE0qa5b-WJNuWP-9GXpR8OAphDxvQepl7DnwLQDH8-PFcdtw3m2RQ8PFA7YBpboaWyUfsg3noGuUUj9mT3K-KVepoNmwT_t4R76-tDNVn-1dHJahvlzGnsbr-Ud9iEOcyVe7yVOu9mkaqu82RXvV0xp5F-fq67yEEMfrp-xRsH2mZ_fnGfu2f3_cfawPXz5c7N4eaic0zHUQqIC8ttR6p33QvHEhOHflGmUtagFeo_aEAcnzABLBW1C8kQJ9q1GcsTfr3Ns0_Vwoz2aI2VHf25GmJRvdFayUbot8_V8ppETsQBT48h94My1pLL8wgApBQ3dC5ytyaco5UTC3KQ42_TLAzakCUyowpwrMWkFJvLofa7OzfUh2dDH_jemyCcW74l6sLhLRn2fZdC0KKX4DCMiNyQ</recordid><startdate>20070801</startdate><enddate>20070801</enddate><creator>Sankarasubramaniam, Y.</creator><creator>McLaughlin, S.W.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Our approach is based on a simple, variable-rate technique called bit stuffing . Bit stuffing produces near-capacity achieving codes for a wide range of constraints, but encoding is variable-rate, which is unacceptable in most applications. In this work, we design near-capacity fixed-rate codes using a three-step procedure. The fixed-length input data block first undergoes iterative preprocessing, followed by variable-rate bit stuffing, and finally dummy-bit padding to a fixed output length. The iterative preprocessing is key to achieving high encoding rates. We discuss rate computation for the proposed FRB algorithm and show that the asymptotic (in input block length) encoding rate is close to the average rate of the variable-rate bit stuff code. Then, we proceed to explore the effect of decreasing/increasing the number of preprocessing iterations. Finally, we derive a lower bound on the encoding rate with finite-length input blocks and tabulate the parameters required to design FRB codes with rate close to 100/101 and 200/201.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TIT.2007.901203</doi><tpages>22</tpages></addata></record> |
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subjects | Algorithms Applied sciences Asymptotic properties Binary sequences Binary system Bit stuffing Blocking Codes Coding, codes constrained coding Data encryption Data mining Data preprocessing Data transmission Decoding Encoding Exact sciences and technology fixed-rate codes Information theory Information, signal and communications theory Iterative algorithms Iterative methods Lower bounds Magnetic materials Magnetic properties Magnetic recording Magnetization Preprocessing Signal and communications theory Systems, networks and services of telecommunications Telecommunications Telecommunications and information theory Timing Transmission and modulation (techniques and equipments) |
title | Fixed-Rate Maximum-Runlength-Limited Codes From Variable-Rate Bit Stuffing |
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