A hardware spinal decoder

Abstract

Spinal codes are a recently proposed capacity-achieving rateless code. While hardware encoding of spinal codes is straightforward, the design of an efficient, high-speed hardware decoder poses significant challenges. We present the first such decoder. By relaxing data dependencies inherent in the classic M-algorithm decoder, we obtain area and throughput competitive with 3GPP turbo codes as well as greatly reduced latency and complexity. The enabling architectural feature is a novel "α-β " incremental approximate selection algorithm. We also present a method for obtaining hints which anticipate successful or failed decoding, permitting early termination and/or feedback-driven adaptation of the decoding parameters.We have validated our implementation in FPGA with on-air testing. Provisional hardware synthesis suggests that a near-capacity implementation of spinal codes can achieve a throughput of 12.5 Mbps in a 65 nm technology while using substantially less area than competitive 3GPP turbo code implementations.

Design of segmented CRC‐aided spinal codes for IoT applications

“…Within this range, we take 5 km as the basis of analysis, then the delay caused by the transmission distance can be calculated as . According to [6], spinal codes can achieve a maximum throughput of 12.5 Mbps in field programmable gate array with on‐air testing. Therefore, when the information length , the delay caused by decoding is .…”

“…The key to the spinal codes is the implementation of hash functions over the information bits. Based on that, the Bubble decoder is designed in [6]. The existing spinal codes can achieve capacity both in the additive white Gaussian noise channel and the binary symmetric channel.…”

SoftLight: Adaptive visible light communication over screen-camera links

“…Rateless codes for AWGN (Additive White Gaussian Noise) channels, like Spinal codes [22] and soft decoding [2], avoid the block size setting by bit-level coding. Their decoding process, however, involves intensive floating-point iteration operations which are computationally infeasible for VLC on smartphones [13].…”