12 research outputs found
Multi-Factor Pruning for Recursive Projection-Aggregation Decoding of RM Codes
The recently introduced recursive projection aggregation (RPA) decoding
method for Reed-Muller (RM) codes can achieve near-maximum likelihood (ML)
decoding performance. However, its high computational complexity makes its
implementation challenging for time- and resource-critical applications. In
this work, we present a complexity reduction technique called multi-factor
pruning that reduces the computational complexity of RPA significantly. Our
simulation results show that the proposed pruning approach with appropriately
selected factors can reduce the complexity of RPA by up to for
while keeping the comparable error-correcting performance
Hardware Implementation of Iterative Projection-Aggregation Decoding of Reed-Muller Codes
In this work, we present a simplification and a corresponding hardware
architecture for hard-decision recursive projection-aggregation (RPA) decoding
of Reed-Muller (RM) codes. In particular, we transform the recursive structure
of RPA decoding into a simpler and iterative structure with minimal
error-correction degradation. Our simulation results for RM(7,3) show that the
proposed simplification has a small error-correcting performance degradation
(0.005 in terms of channel crossover probability) while reducing the average
number of computations by up to 40%. In addition, we describe the first fully
parallel hardware architecture for simplified RPA decoding. We present FPGA
implementation results for an RM(6,3) code on a Xilinx Virtex-7 FPGA showing
that our proposed architecture achieves a throughput of 171 Mbps at a frequency
of 80 MHz
Pipelined Architecture for Soft-decision Iterative Projection Aggregation Decoding for RM Codes
The recently proposed recursive projection-aggregation (RPA) decoding
algorithm for Reed-Muller codes has received significant attention as it
provides near-ML decoding performance at reasonable complexity for short codes.
However, its complicated structure makes it unsuitable for hardware
implementation. Iterative projection-aggregation (IPA) decoding is a modified
version of RPA decoding that simplifies the hardware implementation. In this
work, we present a flexible hardware architecture for the IPA decoder that can
be configured from fully-sequential to fully-parallel, thus making it suitable
for a wide range of applications with different constraints and resource
budgets. Our simulation and implementation results show that the IPA decoder
has 41% lower area consumption, 44% lower latency, four times higher
throughput, but currently seven times higher power consumption for a code with
block length of 128 and information length of 29 compared to a state-of-the-art
polar successive cancellation list (SCL) decoder with comparable decoding
performance
Short Codes with Near-ML Universal Decoding: Are Random Codes Good Enough?
Short blocklength codes have an important role in machine-type and ultra-low-latency communications. Unfortunately, reducing the blocklength makes it very challenging to achieve good error-correcting performance. There exist near-ML decoding algorithms with manageable complexity for short blocklength codes, such as ordered statistics decoding and the more recent guessing random additive noise decoding algorithm. These algorithms have the additional advantage that they are universal, in the sense that they can decode any linear block code. For this reason, some recent works have attempted to construct unstructured linear codes for use with universal decoders using sophisticated techniques, such as reinforcement learning. In this work, we first describe a genetic-algorithm-aided (GA-aided) construction method for unstructured codes and we then compare a very simple random construction to both the GA-aided construction and the reinforcement learning construction. Our simulation results indicate that, while some care should be taken when selecting an unstructured code, sophisticated and complex code construction methods may not be necessary in the sense that they lead to minimal improvements
Hardware Implementation of Iterative Projection-Aggregation Decoding of Reed-Muller Codes
In this work, we present a simplification and a corresponding hardware architecture for hard-decision recursive projectionaggregation (RPA) decoding of Reed-Muller (RM) codes. In particular, we transform the recursive structure of RPA decoding into a simpler and iterative structure with minimal error-correction degradation. Our simulation results for RM(7; 3) show that the proposed simplification has a small error-correcting performance degradation (0:005 in terms of channel crossover probability) while reducing the average number of computations by up to 40%. In addition, we describe the first fully parallel hardware architecture for simplified RPA decoding. We present FPGA implementation results for an RM(6; 3) code on a Xilinx Virtex-7 FPGA showing that our proposed architecture achieves a throughput of 171 Mbps at a frequency of 80 MHz
Multi-Factor Pruning for Recursive Projection-Aggregation Decoding of RM Codes
The recently introduced recursive projection aggregation (RPA) decoding method for Reed-Muller (RM) codes can achieve near-maximum likelihood (ML) decoding performance. However, its high computational complexity makes its implementation challenging for time- and resource-critical applications. In this work, we present a complexity reduction technique called multi-factor pruning that reduces the computational complexity of RPA significantly. Our simulation results show that the proposed pruning approach with appropriately selected factors can reduce the complexity of RPA by up to 92% for RM(8, 3) while keeping a comparable error-correcting performance
Hardware Implementation of Iterative Projection Aggregation Decoding for Reed-Muller Codes
The recently proposed recursive projection-aggregation (RPA) decoding algorithm for Reed-Muller codes has received significant attention as it provides near-ML decoding performance at reasonable complexity for short codes. However, its complicated structure makes it unsuitable for hardware implementation. Iterative projection-aggregation (IPA) decoding is a modified version of RPA decoding that simplifies the hardware implementation. In this work, we present a flexible hardware architecture for the IPA decoder that can be configured from fully-sequential to fully-parallel, thus making it suitable for a wide range of applications with different constraints and resource budgets. Our simulation and implementation results show that the IPA decoder has 41% lower area consumption, 44% lower latency, and four times higher throughput for a code with block length of 128 and information length of 29 compared to a state-of-the-art polar successive cancellation list (SCL) decoder with comparable decoding performance
Recursive/Iterative Unique Projection-Aggregation Decoding of Reed-Muller Codes
We describe recursive unique projection-aggregation (RUPA) decoding and iterative unique projection-aggregation (IUPA) decoding of Reed-Muller (RM) codes, which remove non-unique projections from the recursive projection-aggregation (RPA) and iterative projection-aggregation (IPA) algorithms respectively. We show that these algorithms have competitive error-correcting performance while requiring up to 95% projections lower than the baseline RPA algorithm.</p
Recursive/Iterative unique Projection-Aggregation of RM codes
We describe recursive unique projection-aggregation (RUPA) decoding and iterative unique projection-aggregation (IUPA) decoding of Reed-Muller (RM) codes, which remove non-unique projections from the recursive projection-aggregation (RPA) and iterative projection-aggregation (IPA) algorithms respectively. We show that these algorithms have competitive error-correcting performance while requiring up to 95% projections less than the baseline RPA algorithm
Pipelined Architecture for Soft-Decision Iterative Projection Aggregation Decoding for RM Codes
The recently proposed recursive projection-aggregation (RPA) decoding algorithm for Reed-Muller codes has received significant attention as it provides near-ML decoding performance at reasonable complexity for short codes. However, its complicated structure makes it unsuitable for hardware implementation. Iterative projection-aggregation (IPA) decoding is a modified version of RPA decoding that simplifies the hardware implementation. In this work, we present a flexible hardware architecture for the IPA decoder that can be configured from fully-sequential to fully-parallel, thus making it suitable for a wide range of applications with different constraints and resource budgets. Our simulation and implementation results show that the IPA decoder has 41% lower area consumption, 44% lower latency, four times higher throughput, but currently seven times higher power consumption for a code with a block length of 128 and information length of 29 compared to a state-of-the-art polar successive cancellation list (SCL) decoder with comparable decoding performance.</p