Although many stopping methods of iterative decoding have been discussed in the literature extensively, many of them only focus on the solvable decoding (information is enough for successful decoding). In this paper, we discuss the limitation of the decoding ability based on the extrinsic information transform (EXIT) chart. Then, we propose a new information measurement by using cross correlation to predict the decoding threshold. Moreover, we propose two early termination (ET) schemes (ET-I and ET-II) based on the predicted decoding threshold. The iterative decoding can stop in either high-signal-to-noise ratio (SNR) situations where the decoded bits are highly reliable (solvable decoding), or low-SNR situations where the decoder already has no capability to decode (unsolvable decoding). The simulation results show that the reduced iterations due to the ET-I scheme almost will not affect the SNR performance, and the ones due to the ET-II scheme can still satisfy the requirement of the specification. Based on our analysis and simulation results, we can further modify the conventional GENIE chart by considering the decoding threshold. By using our new ET concepts, the previous stopping techniques can also be modified to stop in low-SNR situations. The ET property for the iterative decoding can help reduce the unnecessary iterations, so as to save computational complexity and power consumptions in digital signal processors (DSPs) or application-specific integrated circuits (ASICs) in mobile handsets.
To satisfy the advanced forward-error-correction (FEC) standards, in which the Convolutional code and Turbo code may co-exit, a prototype design of a unified Convolutional/Turbo decoder is proposed. In this paper, we systematically analyze the timing charts of both the Viterbi algorithm and the MAP algorithm. Then, three techniques, including Distribution, Pointer, and Parallel schemes, are introduced; they can be used as flexible tools in timing-chart analysis to either reduce memory size or to increase throughput rate. Furthermore, we propose a tile-based methodology to analyze the key features of timing charts, such as computing/memory units and hardware utilization. On the basis of the timing analysis, we developed a VA/MAP timing chart that has three modes (VA mode, MAP mode, and concurrent VA/MAP mode) by complementing the idle time of both VA and MAP decoding procedures. The new combined timing analysis helps us for constructing a unified component decoder with near 100% utilization rate of the processing element (PE) in both VA/MAP decoding functions. According to the triple-mode VA/MAP timing chart, we construct a triple-mode FEC kernel that can perform both Convolutional/Turbo decoding functions seamlessly for different communication systems. By integrating the FEC kernel with different size of memory, we can construct four types of FEC decoders for different application scenarios, such as 1) standalone Convolutional decoder (VA mode); 2) standalone Turbo decoder (MAP mode); 3) dualmode Convolutional/Turbo decoder (VA mode and MAP mode); and 4) triple-mode Convolutional/Turbo decoder (VA mode, MAP mode, and concurrent VA/MAP mode). Finally, a prototyping FEC kernel processor that is compliant to 3GPP standard is verified in TSMC 0.18-m CMOS process in the type of triple-mode FEC decoder.
Although many stopping methods of iterative decoding have been discussed in the literatures extensively, many of them only focus on the solvable decoding. In this paper, we propose a new early termination (ET) scheme based on the decoding threshold. Moreover, we proposed the cross-correlation measurement to predict the decoding threshold. The iterative decoding can stop in either high-SNR situation where the decoded bits are highly reliable (solvable decoding), or in low-SNR situation where the decoder already has no capability to decode (unsolvable decoding). At the same time, the receiver can send an automatic-repeat-request (ARQ) message to the transmitter in noise-seriously-coupling situation. Through simulations, the reduced iterations due to the ET scheme almost will not affect the SNR performance. Based on our analysis and simulation results, we can further modify the conventional GENIE chart. The ET property can help to either reduce the computational complexity or power consumptions in implementing the iterative decoding in DSP processors or VLSIs in mobile handsets.
Although many stopping methods of iterative decoding have been discussed in the literatures, many of them only focus on the solvable decoding. In this paper, we proposed a new rule, called Measurement ofReliability (MOR) that can be used to define the decoding boundary. With two pre-simulated threshold values, the iterative decoding can stop in either high-SNR situation where the decoded bits are highly reliable, or in low-SNR situation where the decoder already has no capability to decode. Through the simulations, the reduced iterations due to early termination (ET) will not affect the SNR performance. Based on our analysis and simulation results, we can further modify the popularly used GENIE chart. Hence, the iterative decoding can be terminated based on two pre-determined threshold values. The ET property can help to either reduce the computational complexity or power consumptions in implementing the iterative decoding in DSP processors or VLSIs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.