137 research outputs found
Reversed-Trellis Tail-Biting Convolutional Code (RT-TBCC) Decoder Architecture Design for LTE
Tail-biting convolutional codes (TBCC) have been extensively applied in communication systems. This method is implemented by replacing the fixed-tail with tail-biting data. This concept is needed to achieve an effective decoding computation. Unfortunately, it makes the decoding computation becomes more complex. Hence, several algorithms have been developed to overcome this issue in which most of them are implemented iteratively with uncertain number of iteration. In this paper, we propose a VLSI architecture to implement our proposed reversed-trellis TBCC (RT-TBCC) algorithm. This algorithm is designed by modifying direct-terminating maximum-likelihood (ML) decoding process to achieve better correction rate. The purpose is to offer an alternative solution for tail-biting convolutional code decoding process with less number of computation compared to the existing solution. The proposed architecture has been evaluated for LTE standard and it significantly reduces the computational time and resources compared to the existing direct-terminating ML decoder. For evaluations on functionality and Bit Error Rate (BER) analysis, several simulations, System-on-Chip (SoC) implementation and synthesis in FPGA are performed
Deep Ensemble of Weighted Viterbi Decoders for Tail-Biting Convolutional Codes
Tail-biting convolutional codes extend the classical zero-termination
convolutional codes: Both encoding schemes force the equality of start and end
states, but under the tail-biting each state is a valid termination. This paper
proposes a machine-learning approach to improve the state-of-the-art decoding
of tail-biting codes, focusing on the widely employed short length regime as in
the LTE standard. This standard also includes a CRC code.
First, we parameterize the circular Viterbi algorithm, a baseline decoder
that exploits the circular nature of the underlying trellis. An ensemble
combines multiple such weighted decoders, each decoder specializes in decoding
words from a specific region of the channel words' distribution. A region
corresponds to a subset of termination states; the ensemble covers the entire
states space. A non-learnable gating satisfies two goals: it filters easily
decoded words and mitigates the overhead of executing multiple weighted
decoders. The CRC criterion is employed to choose only a subset of experts for
decoding purpose. Our method achieves FER improvement of up to 0.75dB over the
CVA in the waterfall region for multiple code lengths, adding negligible
computational complexity compared to the circular Viterbi algorithm in high
SNRs
CRC-Aided High-Rate Convolutional Codes With Short Blocklengths for List Decoding
Recently, rate-1/n zero-terminated (ZT) and tail-biting (TB) convolutional
codes (CCs) with cyclic redundancy check (CRC)-aided list decoding have been
shown to closely approach the random-coding union (RCU) bound for short
blocklengths. This paper designs CRC polynomials for rate- (n-1)/n ZT and TB
CCs with short blocklengths. This paper considers both standard rate-(n-1)/n CC
polynomials and rate- (n-1)/n designs resulting from puncturing a rate-1/2
code. The CRC polynomials are chosen to maximize the minimum distance d_min and
minimize the number of nearest neighbors A_(d_min) . For the standard
rate-(n-1)/n codes, utilization of the dual trellis proposed by Yamada et al.
lowers the complexity of CRC-aided serial list Viterbi decoding (SLVD).
CRC-aided SLVD of the TBCCs closely approaches the RCU bound at a blocklength
of 128. This paper compares the FER performance (gap to the RCU bound) and
complexity of the CRC-aided standard and punctured ZTCCs and TBCCs. This paper
also explores the complexity-performance trade-off for three TBCC decoders: a
single-trellis approach, a multi-trellis approach, and a modified
single-trellis approach with pre-processing using the wrap around Viterbi
algorithm.Comment: arXiv admin note: substantial text overlap with arXiv:2111.0792
Underwater acoustic communications and adaptive signal processing
This dissertation proposes three new algorithms for underwater acoustic wireless communications. One is a new tail-biting circular MAP decoder for full tail-biting convolution (FTBC) codes for very short data blocks intended for Internet of Underwater Things (IoUT). The proposed algorithm was evaluated by ocean experiments and computer simulations on both Physical (PHY) and Media access control (MAC) layers. The ocean experimental results show that without channel equalization, the full tail-biting convolution (FTBC) codes with short packet lengths not only can perform similarly to zero-tailing convolution (ZTC) codes in terms of bit error rate (BER) in the PHY layer. Computer simulation results show that the FTBC codes outperform the ZTC codes in terms of MAC layer metrics, such as collision rate and bandwidth utilization, in a massive network of battery powered IoUT devices.
Second, this dissertation also proposes a new approach to utilizing the underwater acoustic (UWA) wireless communication signals acquired in a real-world experiment as a tool for evaluating new coding and modulation schemes in realistic doubly spread UWA channels. This new approach, called passband data reuse, provides detailed procedures for testing the signals under test (SUT) that change or add error correction coding, change bit to symbol mapping (baseband modulation) schemes from a set of original experimental data --Abstract, page iv
Configurable and Scalable Turbo Decoder for 4G Wireless Receivers
The increasing requirements of high data rates and quality of service (QoS) in fourth-generation (4G) wireless communication require the implementation of practical capacity approaching codes. In this chapter, the application of Turbo coding schemes that have recently been adopted in the IEEE 802.16e WiMax standard and 3GPP Long Term Evolution (LTE) standard are reviewed. In order to process several 4G wireless standards with a common hardware module, a reconfigurable and scalable Turbo decoder architecture is presented. A parallel Turbo decoding scheme with scalable parallelism tailored to the target throughput is applied to support high data rates in 4G applications. High-level decoding parallelism is achieved by employing contention-free interleavers. A multi-banked memory structure and routing network among memories and MAP decoders are designed to operate at full speed with parallel interleavers. A new on-line address generation technique is introduced to support multiple Turbo
interleaving patterns, which avoids the interleaver address memory that is typically necessary in the traditional designs. Design trade-offs in terms of area and power efficiency are analyzed for different parallelism and clock frequency goals
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