An adaptive low-power LDPC decoder using SNR estimation

Abstract

Owing to advancement in 4 G mobile communication and mobile TV, the throughput requirement in digital communication has been increasing rapidly. Thus, the need for efficient error-correcting codes is increasing. Furthermore, since most mobile devices operate with limited battery power, low-power communication techniques are attracting considerable attention lately. In this article, we propose a novel low-power, low-density parity check (LDPC) decoder. The LDPC code is one of the most common error-correcting codes. In mobile TV, SNR estimation is required for the adaptive coding and modulation technique. We apply the SNR estimation result to the proposed LDPC decoding to minimize power consumption due to unnecessary operations. The SNR estimation value is used for predicting the iteration count until the completion of the successful LDPC decoding. When the SNR value is low, we omit computing the parity check and the tentative decision. We implemented the proposed decoder which is capable of adaptively skipping unnecessary operations based on the SNR estimation. The power consumption was measured to show the efficiency of our approach. We verified that, by using our proposed method, power consumption is reduced by 10% for the SNR range of 1.5-2.5 dB.

Conditional termination check min-sum algorithm for efficient LDPC decoders

“…However, D_min32 can be said to take small area considering that 32 VN units are required for a 32-parallel LDPC decoder. Moreover, the SNR estimator in a conventional LDPC decoder [10] takes up 12.1% of the total decoder area, the hardware cost of the delta-minima module is relatively low because the proposed algorithm does not need the estimator.…”

“…Furthermore, even a simple modification of MSA can reduce the loss of the coding gain, so modified versions [6,8] of MSA have been widely adopted in LDPC decoders. Moreover, dynamic approaches of MSA have been discussed in several studies for better performance and throughput [8,10]. In [8], the scaling and offset factors were adjusted adaptively, and [10] utilized a look-up table of the minimum iteration number for adaptive scheduling.…”

“…Moreover, dynamic approaches of MSA have been discussed in several studies for better performance and throughput [8,10]. In [8], the scaling and offset factors were adjusted adaptively, and [10] utilized a look-up table of the minimum iteration number for adaptive scheduling. However, conventional works were based on estimation of signal-to-noise ratio (SNR) which required either complex computation or an external estimator.…”

“…When the SNR was under 1.5 dB, every termination check except the last iteration (line 13 of Algorithm 1) was skipped, and almost the half of the iterations were skipped when the SNR was over 1.8 dB. In [10], the authors reported that the termination check would account for 17% of the power consumption of the total LDPC decoder in each iteration, and our proposed method reduced power consumption of the LDPC decoder by up to 16.43% under bad channel conditions. The decoding time was also reduced by 7% in case of low SNRs.…”

Dynamic voltage and frequency scaling scheme for an adaptive LDPC decoder using SNR estimation

“…This new LDPC decoder is based on the adaptive LDPC decoder previously proposed in [16], to which we added a DVFS controller to provide dynamically adjustable operating frequencies and the corresponding supply voltages. To find the optimal operating point for CMMB codes, we carried out various analyses regarding the correlation between the operating frequency and the supply voltage with respect to a certain SNR value.…”