Low Latency Parallel Turbo Decoding Implementation for Future Terrestrial Broadcasting Systems

Luo, Hui; Zhang, Yue; Li, Wei; Huang, Li-Ke; Cosmas, John; Li, Dayou; Maple, Carsten; Zhang, Xun

doi:10.1109/tbc.2017.2704425

Cited by 13 publications

(8 citation statements)

References 25 publications

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“…However, the forward and backward recursions impose data dependencies, which limit the achievable degree of parallel processing, resulting in high processing latency. Several approaches have been proposed to improve the throughput and latency of the Log-BCJR turbo decoder, most of which focus on increasing the parallelism of the conventional turbo decoder [33,[38][39][40]. To be more specific, we have previously proposed a FPTD algorithm [33,41], which dramatically increases the parallelism of the decoding process and achieves significantly lower latency, by dispensing with the recursions of the Log-BCJR algorithm.…”

Section: B Fully-parallel Turbo Decodermentioning

confidence: 99%

Concurrent OFDM Demodulation and Turbo Decoding for Ultra Reliable Low Latency Communication

Xiang

Maunder

Hanzo

2020

IEEE Trans. Veh. Technol.

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The Ultra-Reliable Low Latency Communication (URLLC) applications have been proposed in recent years, targeting a round-trip end-to-end latency less than 1 ms with high reliability. Therefore, an order of magnitude improvements are needed in all layers of the wireless communication stack. This is a particular challenge for the physical layer, where typically a processing time of the order of microseconds is required for the computationally intensive demodulation and error correction processing, among other operations. Conventionally, the reception of signals, the demodulation processing and the error correction processing are performed consecutively at the receiver. However, this approach is associated with processing times on the order of hundreds of microseconds, preventing URLLC. Therefore, this paper proposes a novel processing architecture, which is capable of performing reception, Orthogonal Frequency Division Multiplexing (OFDM) demodulation and turbo decoding concurrently, rather than consecutively, hence significantly reducing the processing time. In order to achieve concurrent operation, the OFDM demodulation is performed using a novel cumulative Fast Fourier Transform (FFT), which produces successively more reliable estimates of all transmitted symbols in each successive clock cycle. At the same time, a Fully-Parallel Turbo Decoder (FPTD) is used to recover successively more reliable estimates of all bits in each successive clock cycle.

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Section: B Fully-parallel Turbo Decodermentioning

confidence: 99%

Concurrent OFDM Demodulation and Turbo Decoding for Ultra Reliable Low Latency Communication

Xiang

Maunder

Hanzo

2020

IEEE Trans. Veh. Technol.

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“…This will enable the user to design complex hardware designs [10]. The researchers in [1] using parallelism level 64 for frame size 2048-6144 and parallelism level 8 for frame size 256-2048 and using an interleaver proposed by them, and they conclude that the result of performance and latency that could use turbo code in future terrestrial broadcasting (TB) systems. And in research [2] a comparison between turbo code, LDPC, polar code was done the result showed that the turbo code made the best performance than the others in error correction and flexibility in using different block sizes and different code rates.…”

Section: Fig 1 General Block Diagram Of Digital Communication Systemsmentioning

confidence: 99%

Design Analysis of Turbo Decoder Based on One MAP Decoder Using High Level Synthesis Tool

Ali¹,

Alneema²

2020

(AREJ)

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High Level Synthesis (HLS) tool does not only simplify the designing operation and rapid prototyping but also allows the designers to explore large number of design's techniques such as parallelism, pipeline, memory partitioning and many other techniques. Turbo decoder based on Maximum APosterior Probability (MAP) algorithm is designed in this work using Vivado HLS. The normal turbo decoder with two MAP decoders were implemented with and without parallelism and proposed a new design of turbo decoder with one MAP decoder and it was designed with and without parallelism using different window technique in HLS tool which it is not explored previously. These designs were implemented for different frame size in this work. A comparison in latency and resource utilization where done and how a tradeoff done between these two parameters to reach the specific design that we need. The new design produces better results.

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“…Baseband signal processing such as channel estimation, channel equalization, channel encoding, etc, are the important parts of wireless communication systems to resist fading channel [8][9][10][11], where channel estimation is especially critical for massive MIMO systems. In massive MIMO systems, accurate and efficient channel estimation is a challenging problem and an open research issue, because the number of channel parameters to be estimated is very large as the antennas increase, while the number of pilots adopted by channel estimation is limited to make sure a high spectrum efficiency.…”

Section: Introductionmentioning

confidence: 99%

Compression-Based LMMSE Channel Estimation With Adaptive Sparsity for Massive MIMO in 5G Systems

Zhang

Chen

et al. 2019

IEEE Systems Journal

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Massive multi-input multi-output (MIMO) has been regarded as one of the key technologies for fifth generation (5G) mobile communication systems, as it can significantly enhance the system capacity with high spectrum and energy efficiency. For massive MIMO systems, accurate channel estimation is a challenging problem, especially when the number of parameters to be estimated is large and the number of pilots is limited. In this paper, a compression based linear minimum mean square error (CLMMSE) channel estimation algorithm is proposed for massive MIMO in 5G systems. Compared with the traditional linear minimum mean square error (LMMSE) algorithm, the proposed approach calculates the channel autocorrelation matrix by investigating the channel prior information based on compressive sensing (CS) theory, utilizing the block sparsity of massive MIMO channels, to reduce the complexity for obtaining autocorrelation matrix. Then it substitutes matrix inverse operation by singular value decomposition (SVD) to further reduce the computational complexity. In addition, a block sparsity adaptive matching pursuit (BSAMP) method is also proposed to adaptively estimate the block sparsity of the channel in the first step of the proposed CLMMSE algorithm, which can make it more efficient. The sparsity-adaptive processing is achieved by setting a threshold and finding the position of the maximum backward difference, then using the regularized method to solve channel estimation as a convex optimization problem. Analyses and simulations indicate that with slight performance degradation, the proposed algorithm reduces the computational complexity significantly compared with the traditional LMMSE algorithm. And compared with pure CS methods, CLMMSE has an obviously better performance, which is benefit to solve the pilot pollution problem of massive MIMO in 5G systems. Furthermore, the BSAMP based CLMMSE algorithm has better performance and lower time complexity than the algorithm based on other CS methods, which further improves the system performance.

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Low Latency Parallel Turbo Decoding Implementation for Future Terrestrial Broadcasting Systems

Abstract: Xun. (2017) Low latency parallel turbo decoding implementation for future terrestrial broadcasting systems. IEEE Transactions on Broadcasting. pp. 1-9.

Cited by 13 publications

References 25 publications

Concurrent OFDM Demodulation and Turbo Decoding for Ultra Reliable Low Latency Communication

Concurrent OFDM Demodulation and Turbo Decoding for Ultra Reliable Low Latency Communication

Design Analysis of Turbo Decoder Based on One MAP Decoder Using High Level Synthesis Tool

Compression-Based LMMSE Channel Estimation With Adaptive Sparsity for Massive MIMO in 5G Systems

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