A potentially useful Cyber-Physical Systems element is a modern forward error correction (FEC) coding system, utilizing a code selected from the broad class of Low-Density Parity-Check (LDPC) codes. In this paper, development of a hardware implementation in an FPGAs of the decoder for Quasi-Cyclic (QC-LDPC) subclass of codes is presented. The decoder can be configured to support the typical decoding algorithms: Min-Sum or Normalized Min-Sum (NMS). A novel method of normalization in the NMS algorithm is proposed, one that utilizes combinational logic instead of arithmetic units. A comparison of decoders with different bit-lengths of data (beliefs that are messages propagated between computing units) is also provided. The presented decoder has been implemented with a distributed control system. Experimental studies were conducted using the Intel Cyclone V FPGA module, which is a part of the developed testing environment for LDPC coding systems.
The article presents an implementation of a low power Quasi-Cyclic Low-Density Parity-Check (QC-LDPC) decoder in a Field Programmable Gate Array (FPGA) device. The proposed solution is oriented to a reduction in dynamic energy consumption. The key research concepts present an effective technology mapping of a QC-LDPC decoder to an LUT-based FPGA with many limitations. The proposed decoder architecture uses a distributed control system and a Token Ring processing scheme. This idea helps limit the clock skew problem and is oriented to clock gating, a well-established concept for power optimization. Then the clock gating of the decoder building blocks allows for a significant reduction in energy consumption without deterioration in other parameters of the decoder, particularly its error correction performance. We also provide experimental results for decoder implementations with different QC-LDPC codes, indicating important characteristics of the code parity check matrix, for which an energy-saving QC-LDPC decoder with the proposed architecture can be designed. The experiments are based on implementations in the Intel Cyclone V FPGA device. Finally, the presented architecture is compared with the other solutions from the literature.
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