Four reliability-based high-efficient dynamic schedules (RHDS) for belief propagation (BP) decoding of low-density parity-check codes are proposed. The RHDS only processes one variable (or check) node, which has the maximum relative residual among all the variable (or check) nodes in each decoding iteration, thus keeping less greediness and decreasing complexity in comparison with egde-wise variable-to-check residual belief propagation (VC-RBP) algorithm. Simulation results show that RHDS's convergence rate is higher than VC-RBP while still keeping very low computational complexity.
Four reliability-based high-efficient dynamic schedules (RHDS) for belief propagation (BP) decoding of low-density parity-check codes are proposed. The RHDS only processes one variable (or check) node, which has the maximum relative residual among all the variable (or check) nodes in each decoding iteration, thus keeping less greediness and decreasing complexity in comparison with egde-wise variable-to-check residual belief propagation (VC-RBP) algorithm. Simulation results show that RHDS's convergence rate is higher than VC-RBP while still keeping very low computational complexity.
An efficient dynamic schedule for layered belief-propagation (LBP) decoding of low-density parity-check (LDPC) codes is presented, in which the check nodes connecting to a variable node with maximum relative message residual are chosen in each dynamic decoding iteration, and then LBP is performed for these check nodes. In this schedule we combined the features of lazy schedule and node-wise residual belief propagation (NWRBP) together while keeping the extra computational complexity very low. Simulation results show that the new schedule speeds up the convergence rate and greatly improves error performance at medium to high signal-to-noise ratio (SNR) when compared to the standard LBP decoding algorithm.
Reliability-Based-Layered Belief Propagation (RBL-BP) decoding algorithm for LDPC codes is proposed in this paper. Being an informed dynamic schedule (IDS), it measures convergence and selects iterations by the absolute value of logarithmic likelihood ratio information obtained from channel (ABS-LLR-CH). Each variable node can select its own iterations by estimating its own convergence, and finish iterative decoding. Simulation results show that the proposed RBL-BP algorithm has better performance than those of flooding schedule, layered belief propagation (LBP) and variable-to-check residual belief propagation (VC-RBP). For the convergence property of IDS, the proposed algorithm has faster speed than those of flooding schedule and LBP with a little increase in complexity while it has almost no speed decrease with much lower computational complexity than VC-RBP.
In order to improve the error correction performance of Low-Density Parity-Check (LDPC) codes, In this paper, we propose a Reliability Profile (RP) Based Low-Complexity Dynamic Schedule, called RPD. In terms of dynamic scheduling, the new RPD method is distinct from the existing residual belief propagation (RBP) and its variations, since RPD is based on residual criterion. Reliability divides nodes message into two categories: reliable and unreliable. By using as many messages as possible from the latest available current iteration instead of the previous iteration to update unreliable messages multiple times to speed up the convergence speed and increase the total number of updates of effective nodes message, so as to improve the Bit Error Rate (BER) performance. By not updating the reliable message, the number of ineffective nodes message updates is reduced, thereby reducing the decoding complexity. Besides, the more iterations increase, the fewer calculations of these reliable nodes. Moreover, based on reliability profile, RPD has an advantage over RBP for dynamic scheduling in that the former only needs integer operations, while the latter requires float operations. Therefore, the RPD algorithm not only accelerates the convergence speed of LDPC decoding and but also improves the BER performance while reducing the complexity.The analysis and simulation results show that the RPD strategy not only retains the fast-convergence advantage of RBP, but also has a better convergence BER performance compared to that of node-wise residual belief propagation (NW RBP). Furthermore, the simulation results show that the RPD can significantly improve the convergence speed of protograph LDPC decoding.
Belief-propagation (BP) algorithm and its variants are well-established methods for iterative decoding of LDPC codes. Among them, residual belief-propagation (RBP), which is the most primitive and representative informed dynamic scheduling (IDS) strategy, can significantly accelerate the convergence speed. However, RBP decoding suffers from a poor convergence error-rate performance due to its greedy property, which is one of the challenging issues in the design of IDS strategies. To tackle this problem, a novel IDS scheme, namely residual-decaying-based residual belief-propagation (RD-RBP) algorithm, is presented in this paper. In RD-RBP, a decaying mechanism is introduced to manipulate the residuals of those check-to-variable messages, preventing the decoding resources from being unreasonably occupied by a small group of edges in the Tanner graph. The greediness is therefore alleviated and better performance of convergence error-rate is achieved. Besides, a two-stage scheduling scheme combining prior-art variable-node and variable-to-check-edge RBP (V-VCRBP) with RD-RBP, named V-VC-RD-RBP, is proposed for achieving both fast convergence speed and a low convergence error-rate. The simulation results validate the advantages of the proposed schemes.
We researched BP decoding algorithm based on variable-to-check information residual for LDPC code (VC-RBP) in this paper. It is a dynamic scheduling belief propagation using residuals, and has some advantages, such as fast decoding, good performance, and low complexity. It is similar to residual belief propagation (RBP),but has some difference in computing the residual message. Simulation shows that it outperforms with only a maximum of ten iterations by about 0.28 dB compared with RBP at an BER of 10-4.
Compared to classic channel coding,Low-density parity-check(LDPC) codes have proved to be very powerful channel coding schemes with a broad range of applications.However,as maximum-likelihood decoding is too complex to implement,suboptimal decoders have to be employed.One of the most popular decoding algorithms of LDPC codes is belief propagation(BP) decoding.Informed Dynamic Scheduling(IDS) can provide a better BER performance,but hard to implement due to high computational complexity increased.In order to improve the convergence,a simple low-complexity sequential check-node-based scheduling strategy is proposed.A new check-node-based reliability measure is defined,which represents the degree of reliability approximately.The reliability measure is used for ordering while the actual message updates still use the exact BP equations.During an iteration,the schedule strategy finds an update sequence,which is arranged by the new defined reliability measure in ascending order.Complexity is compared among those algorithms.Mechanism why the new strategy works effective is presented.Simulation shows that the new approach significantly improves the BER performance using short-length block LDPC codes and reduces iterations.