We present coding methods for generating l-symbol constrained codewords taken from a set, S, of allowed codewords. In standard practice, the size of the set S, denoted by M = vertical bar S vertical bar, is truncated ...
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We present coding methods for generating l-symbol constrained codewords taken from a set, S, of allowed codewords. In standard practice, the size of the set S, denoted by M = vertical bar S vertical bar, is truncated to an integer power of two, which may lead to a serious waste of capacity. We present an efficient and low-complexity coding method for avoiding the truncation loss, where the encoding is accomplished in two steps: first, a series of binary input (user) data is translated into a series of M-ary symbols in the alphabet M = {0,..., M - 1}. Then, in the second step, the M-ary symbols are translated into a series of admissible l-symbol words in S by using a small look-up table. The presented construction of Pearson codes and fixed-weight codes offers a rate close to capacity. For example, a 255B320B balanced code, where 255 source bits are translated into 32 10-bit balanced codewords, has a rate 0.1% below capacity.
Energy-harvesting sliding-window constrained blockcodes guarantee that within any prescribed window of l consecutive bits the constrained sequence has at least t, t >= 1, 1's. Prior art code design methods bui...
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Energy-harvesting sliding-window constrained blockcodes guarantee that within any prescribed window of l consecutive bits the constrained sequence has at least t, t >= 1, 1's. Prior art code design methods build upon the finite-state machine description of the (l, t) constraint, but as the number of states equals l choose t, a code design becomes prohibitively complex for mounting l and t. We present a new blockcode construction that circumvents the enumeration of codewords using a finite-state description of the (l, t)-constraint. The codewords of the blockcode are encoded and decoded using a single look-up table. For (l = 4, t = 2), the new blockcodes are maximal, that is, they have the largest possible number of codewords for its parameters.
A set of heuristic algorithms to numerically search for binary unit-memory convolutional codes (UMC) are presented along with a large number of new codes for 2 less than or equal to k less than or equal to 8 and code ...
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A set of heuristic algorithms to numerically search for binary unit-memory convolutional codes (UMC) are presented along with a large number of new codes for 2 less than or equal to k less than or equal to 8 and code rate 1/4 less than or equal to R < 1, Combinatorial optimization is used which involves selecting and then pairwise-matching column vectors of the two (n, k) UMC tap weight matrices. The column selection problem is that of finding the best (2n, k) binary, linear blockcode (BC, In this correspondence, the best BC generator matrix G is found by successively refining G using directed local exhaustive searches, In particular, the set of minimum-weight codewords are used to find a subset of G to exhaustively search. The UMC starch strategy (pairwise matching problem) uses a directed local exhaustive search similar to the BC directed search by using the concept of the terminated BC of the UMC. The heuristic algorithms developed in this correspondence are very robust and converge relatively quickly to the optimal or near-optimal UMC. In addition, although it is generally possible to achieve the blockcode upper bound for free distance, we give a class of UMC's which cannot achieve this bound.
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