Cyclone Codes

We introduce Cyclone codes which are rateless erasure resilient codes. They combine Pair codes with Luby Transform (LT) codes by computing a code symbol from a random set of data symbols using bitwise XOR and cyclic shift operations. The number of data symbols is chosen according to the Robust Soliton distribution. XOR and cyclic shift operations establish a unitary commutative ring if data symbols have a length of $p-1$ bits, for some prime number $p$. We consider the graph given by code symbols combining two data symbols. If $n/2$ such random pairs are given for $n$ data symbols, then a giant component appears, which can be resolved in linear time. We can extend Cyclone codes to data symbols of arbitrary even length, provided the Goldbach conjecture holds. Applying results for this giant component, it follows that Cyclone codes have the same encoding and decoding time complexity as LT codes, while the overhead is upper-bounded by those of LT codes. Simulations indicate that Cyclone codes significantly decreases the overhead of extra coding symbols

Reed-solomon forward error correction (FEC) schemes, RFC 5510

This document describes a Fully-Specified Forward Error Correction (FEC) Scheme for the Reed-Solomon FEC codes over GF(2^^m), where m is in {2..16}, and its application to the reliable delivery of data objects on the packet erasure channel (i.e., a communication path where packets are either received without any corruption or discarded during transmission). This document also describes a Fully-Specified FEC Scheme for the special case of Reed-Solomon codes over GF(2^^8) when there is no encoding symbol group. Finally, in the context of the Under-Specified Small Block Systematic FEC Scheme (FEC Encoding ID 129), this document assigns an FEC Instance ID to the special case of Reed-Solomon codes over GF(2^^8). Reed-Solomon codes belong to the class of Maximum Distance Separable (MDS) codes, i.e., they enable a receiver to recover the k source symbols from any set of k received symbols. The schemes described here are compatible with the implementation from Luigi Rizzo

Myths and Realities of Rateless Coding

Fixed-rate and rateless channel codes are generally treated separately in the related research literature and so, a novice in the field inevitably gets the impression that these channel codes are unrelated. By contrast, in this treatise, we endeavor to further develop a link between the traditional fixed-rate codes and the recently developed rateless codes by delving into their underlying attributes. This joint treatment is beneficial for two principal reasons. First, it facilitates the task of researchers and practitioners, who might be familiar with fixed-rate codes and would like to jump-start their understanding of the recently developed concepts in the rateless reality. Second, it provides grounds for extending the use of the well-understood code design tools — originally contrived for fixed-rate codes — to the realm of rateless codes. Indeed, these versatile tools proved to be vital in the design of diverse fixed-rate-coded communications systems, and thus our hope is that they will further elucidate the associated performance ramifications of the rateless coded schemes