Sequential decoding can achieve high throughput convolutional decoding with much lower computational complexity when compared with the Viterbi algorithm (VA) at a relatively high signal-to-noise ratio (SNR). A parallel bidirectional Fano algorithm (BFA) decoding architecture is investigated in this paper. In order to increase the utilisation of the parallel BFA decoders, and thus improve the decoding throughput, a state estimation method is proposed which can effectively partition a long codeword into multiple short sub-codewords. The parallel BFA decoding with state estimation architecture is shown to achieve 30–55% decoding throughput improvement compared with the parallel BFA decoding scheme without state estimation. Compared with the VA, the parallel BFA decoding only requires 3–30% computational complexity of that required by the VA with a similar error rate performance.

In this paper, a bidirectional Fano algorithm (BFA) is proposed, in which a forward decoder (FD) and a backward decoder (BD) search in the opposite direction in the code tree simultaneously. It is shown that the proposed BFA can achieve more than twice the decoding throughput compared to the conventional unidirectional Fano algorithm (UFA) and there is higher throughput improvement at low signal-to-noise ratio (SNR). This new BFA decoding technique is applied in the parallel convolutional decoding architecture in very high throughput systems, such as the WirelessHD system. Due to the variability in the decoding delays of the parallel codewords, a scheduler is introduced in the parallel Fano decoding architecture which can dynamically allocate the idle decoders to assist with decoding the other parallel codewords in a bidirectional manner. It is shown that the proposed parallel Fano decoding with scheduling can dramatically increase the decoding throughput compared to the parallel Fano decoding without scheduling, and its computational complexity is much lower than that of parallel Viterbi decoding, especially at high SNR. The performance of the parallel Fano decoding with different scheduling schemes is also compared and analyzed in detail in the paper.

The bidirectional Fano algorithm (BFA) can achieve at least two times decoding throughput compared to the conventional unidirectional Fano algorithm (UFA). In this paper, bidirectional Fano decoding is examined from the queuing theory perspective. A Discrete Time Markov Chain (DTMC) is employed to model the BFA decoder with a finite input buffer. The relationship between the input data rate, the input buffer size and the clock speed of the BFA decoder is established. The DTMC based modelling can be used in designing a high throughput parallel BFA decoding system. It is shown that there is a trade-off between the number of BFA decoders and the input buffer size, and an optimal input buffer size can be chosen to minimize the hardware complexity for a target decoding throughput in designing a high throughput parallel BFA decoding system.

Recently, we introduced a bidirectional Fano algorithm (BFA) [10] which can achieve much higher decoding throughput compared to the regular unidirectional Fano algorithm (UFA), especially at low signal-to-noise-ratio (SNR). However, the decoding throughput improvement of the conventional BFA with respect to the UFA reduces as the SNR increases and converges to 100% at high SNR. In this paper, two parameters in the BFA, which are known as the number of merged states (NMS) and the threshold increment value Δ, are exploited to improve the decoding throughout of the conventional BFA. The improved BFA can achieve much higher decoding throughput compared to the UFA and the conventional BFA, especially at high SNR. For example at Eb/N0=5dB, the throughput improvement achieved by the improved BFA is about 280% compared to the UFA and about 80% compared to the conventional BFA, and its computational complexity is only 4% of the Viterbi algorithm.

Various techniques, such as bidirectional search, have been employed in sequential decoding to reduce the decoding delay. In this paper, a bidirectional Fano algorithm (BFA) is proposed, in which a forward decoder (FD) and a backward decoder (BD) search in the opposite direction simultaneously. It is shown that the proposed BFA can reduce the average decoding delay by at least 50% compared to the unidirectional Fano algorithm (UFA). Due to the reduction in the variability of the computational effort by using bidirectional search, there is even higher decoding throughput improvement at low signal-to-noise-ratio (SNR). For example at Eb/No=3dB, there is 300% throughput improvement by using the BFA decoding compared to the conventional UFA decoding. The proposed BFA decoding technique can be employed in very high throughput wireless communication systems with low hardware complexity and power consumption.