Fethi Tlili

Fast Converging ADMM-Penalized Algorithm for LDPC Decoding

The alternate direction method of multipliers (ADMM) approach has been recently considered for LDPC decoding. It has been approved to enhance the error rate performance compared with conventional message passing (MP) techniques in both the waterfall and error floor regions at the cost of a higher computation complexity. In this letter, a formulation of the ADMM decoding algorithm with modified computation scheduling is proposed. It increases the error correction performance of the decoding algorithm and reduces the average computation complexity of the decoding process thanks to a faster convergence. Simulation results show that this modified scheduling speeds up the decoding procedure with regards to the ADMM initial formulation while enhancing the error correction performance. This decoding speed-up is further improved when the proposed scheduling is teamed with a recent complexity reduction method detailed in Wei et al.IEEE Commun. Lett., 2015.

Multicore implementation of LDPC decoders based on ADMM algorithm

Alternate direction method of multipliers (ADMM) technique has recently been proposed for LDPC decoding. Even though it improves the error rate performance compared with traditional message passing (MP) techniques, it shows a higher computation complexity. In this article, the ADMM decoding algorithm is first described. Then, its computation complexity is analyzed. Finally, an optimized version which benefits from the multi-core processors architecture as well as the ADMM algorithms parallelism is presented. The optimized version of the ADMM decoder can achieve up to 30 Mbps for standardized LDPC codes on a laptop x86 processor. Therefore, it could guide an efficient GPU implementation for real-time and high-throughput decoding systems requiring correction performances beyond MP-Sum Product Algorithm (SPA) capabilities.

Evaluation of the hardware complexity of the ADMM approach for LDPC decoding

Linear Programming (LP) is a novel technique for LDPC decoding. With the advance of the Alternate Direction Method of Multipliers (ADMM) approach, a significant step towards LP LDPC decoding scalability and optimization is made possible. Yet, this innovative decoding technique has not been implemented in hardware. Its hardware complexity has neither been estimated nor compared with traditional techniques. In this paper, an overview of the ADMM approach and its error correction performances for LDPC decoding is provided. Then, its computation complexity is evaluated to show the hardware feasibility of ADMM-based LDPC decoders. Our analysis is mainly carried at two levels. First, a quantitative complexity analysis is reported and a comparison with traditional LDPC decoders is given. Second, a proposal of a partially parallel architecture is described and its hardware complexity is evaluated then compared with state-of-the-art LDPC decoders.

Hardware complexity reduction of LDPC-CC decoders based on message-passing approaches

LDPC convolutional codes (LDPC-CC) are a family of error-correcting codes (ECC) used in digital communication systems like the IEEE 1901 standard. High throughput and low complexity hardware architectures were designed for real time systems. In this article we demonstrate that an efficient selection of the message passing (MP) algorithm for LDPC-CC decoding improves the architecture features of related works. In fact, considering the LDPC-CC decoders proposed for the IEEE 1901 standard, we show that an appropriate Min-Sum approximation selection can significantly improve the error correction performance by 0.1 to 0.2 dB in terms of Bit Error Ratio. It can also reduce the hardware complexity by 10% to 20% with no impact on the Bit Error Ratio performance.

Real Time LP Decoding of LDPC Codes for High Correction Performance Applications

The alternate direction method of multipliers is a recent linear programming error correcting approach which improves the decoding performance of LDPC codes compared with the best BP decoding techniques. In this letter, an efficient implementation of the ADMM LP decoding algorithm on a multicore architecture is presented. Its throughput performance level is about one order of magnitude higher than related works on the same multicore target. The proposed decoders throughput reaches up to 100 Mb/s which makes it viable for real time applications with tough error correction requirements.

Analysis of ADMM-LP algorithm for LDPC decoding, a first step to hardware implementation

The recent interest in linear programming techniques for LDPC decoding showed that these methods are too complex for real applicability. Alternating direction method of multipliers is a classic technique in convex optimization theory. When applied to the linear programming decoding of LDPC codes, the ADMM algorithm acts as a message passing decoding method. In this work, we present a complexity analysis of the ADMM LDPC decoder compared with the sum product approach and we explain the parallelism levels that are explored in the ADMM algorithm. A software implementation by taking advantage of the architectural features of the multi-core processors parallelism is presented. The overall analysis provides a better understanding of the ADMM approach complexity which makes a start to possible hardware implementations.

Ultra Wide Band Audio Visual PHY IEEE 802.15.3c for WMSNs

In order to retrieve multimedia content like video and audio at indoor environments, wireless multimedia sensor networks have recently emerged as a good solution. Moreover, Ultra Wide Band technology (UWB) can provide high data rates to transfer the multimedia content in a point-to-point mode. In this paper, we explain the features of UWB technology and investigate the performance of the IEEE 802.15.3c standard in terms of Bit Error Rate (BER) against Signal to Noise Ratio (SNR). The Audio Visual Physical (AV PHY) mode has been implemented and simulated and our results show that encoded AV PHY outperforms the uncoded AV PHY for high and low Convolutional encoder rates. Finally, we emphasized the quality improvements with different UWB standard parameters (coding and modulation) in the context of JPEG image compression.

Visual Odometry and Place Recognition Fusion for Vehicle Position Tracking in Urban Environments

In this paper, we address the problem of vehicle localization in urban environments. We rely on visual odometry, calculating the incremental motion, to track the position of the vehicle and on place recognition to correct the accumulated drift of visual odometry, whenever a location is recognized. The algorithm used as a place recognition module is SeqSLAM, addressing challenging environments and achieving quite remarkable results. Specifically, we perform the long-term navigation of a vehicle based on the fusion of visual odometry and SeqSLAM. The template library for this latter is created online using navigation information from the visual odometry module. That is, when a location is recognized, the corresponding information is used as an observation of the filter. The fusion is done using the EKF and the UKF, the well-known nonlinear state estimation methods, to assess the superior alternative. The algorithm is evaluated using the KITTI dataset and the results show the reduction of the navigation errors by loop-closure detection. The overall position error of visual odometery with SeqSLAM is 0.22% of the trajectory, which is much smaller than the navigation errors of visual odometery alone 0.45%. In addition, despite the superiority of the UKF in a variety of estimation problems, our results indicate that the UKF performs as efficiently as the EKF at the expense of an additional computational overhead. This leads to the conclusion that the EKF is a better choice for fusing visual odometry and SeqSlam in a long-term navigation context.

Multicore and Manycore Implementations of ADMM-based Decoders for LDPC Decoding

The alternate direction method of multipliers (ADMM) algorithm has recently been proposed for LDPC decoding based on linear programming (LP) techniques. Even though it improves the error rate performance compared with usual message passing (MP) techniques, it shows a higher computation complexity. However, a significant step towards LP LDPC decoding scalability and optimization is made possible since the ADMM algorithm acts as an MP decoding one. In this paper, an overview of the ADMM approach and its error correction performances is provided. Then, its computation and memory complexities are evaluated. Finally, optimized software implementations of the decoder to take advantage of multi/many-core device features are described. Optimization choices are discussed and justified according to execution profiling figures and the algorithm’s parallelism levels. Experimentation results show that this LP based decoding technique can reach WiMAX and WRAN standards real time throughput requirements on mid-range devices.

Reduced complexity ADMM-based schedules for LP decoding of LDPC convolutional codes

The ADMM based linear programming (LP) technique shows interesting error correction performance when decoding binary LDPC block codes. Nonetheless, its applicability to decode LDPC convolutional codes (LDPC-CC) has not been yet investigated. In this paper, a first flooding based formulation of the ADMM-LP for decoding LDPC-CCs is described. In addition, reduced complexity decoding schedules to lessen the storage requirements and improve the convergence speed of an ADMM-LP based LDPC-CC decoder without significant loss in error correction performances are proposed and assessed from an algorithmic and computational/memory complexity perspectives.