Convolutional Coding

The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.

It is known that the frame error rate of turbo codes on quasi-static fading channels can be accurately approximated using the convergence threshold of the corresponding iterative decoder. This paper considers quasi-static fading channels and demonstrates that non-iterative schemes can also be characterized by a similar threshold based on which their frame error rate can be readily estimated. In particular,

The hybrid ARQ scheme with parity retransmission for error control, recently proposed by Lin and Yu [1], [2], is quite robust. This scheme provides both high system throughput and high system reliability. In this paper, a modified Lin-Yu hybrid ARQ scheme is presented. The modified scheme provides a slightly better throughput performance than the original Lin-Yu scheme; however, it is

The performances of a multicarrier modulation (COFDM) system in an urban mobile radio channel is analyzed using a computer-aided analysis tool called SPW. This multicarrier modulation system, with convolutional coding and interleaving, provides an extremely bandwidth efficient and multipath resistant transmission method. COFDM gives a remarkable performance in selective Rayleigh channels at a much higher bit rate than conventional systems

Abstract In this paper, we study joint channel estimation and maximum likelihood decoding method for coded orthogonal frequency division multiplexing (COFDM) systems. Using this method in conjunction with convolutional coding, robust and nearly optimal coherent detection can be achieved in rapid dispersive fading channels. Significant performance gain in packet data throughput is realized in a system with aggressive frequency reuse

The paper presents the study of an effective classification method for traffic signs on the basis of a convolutional neural network with various dimension filters. Every model of convolutional neural network has the same architecture but different dimension of filters for convolutional layer. The studied dimensions of the convolution layer filters are: 3 × 3, 5 × 5, 9 × 9, 13 × 13, 15 × 15, 19 × 19, 23 × 23, 25 × 25 and 31 ×31. In each experiment, the input image is convolved with the filters of certain dimension and with certain processing depth of image borders, which depends directly on the dimension of the filters and varies from 1 to 15 pixels. Performances of the proposed methods are evaluated with German Traffic Sign Benchmarks (GTSRB). Images from this dataset were reduced to 32 × 32 pixels in dimension. The whole dataset was divided into three subsets: training, validation and testing. The effect of the dimension of the convolutional layer filters on the extracted feature maps is analyzed in accordance with the classification accuracy and the average processing time. The testing dataset contains 12000 images that do not participate in convolutional neural network training. The experiment results have demonstrated that every model shows high testing accuracy of more than 82%. The models with filter dimensions of 9 × 9, 15 × 15 and 19 × 19 achieve top three with the best results on classification accuracy equal to 86.4 %, 86 % and 86.8 %, respectively. The models with filter dimensions of 5 × 5, 3 × 3 and 13 × 13 achieve top three with the best results on the average processing time equal to 0.001879, 0.002046 and 0.002364 seconds, respectively. The usage of convolutional layer filter with middle dimension has shown not only the high classification accuracy of more than 86 %, but also the fast classification rate, that enables these models to be used in real-time applications.

In coding theory, concatenated codes form a class of error-correcting codes that are derived by combining an inner code with an outer code. This is a solution to the problem of finding a code that has both exponentially decreasing error probability with increasing block length and polynomial-time decoding complexity. The work presented in this paper, provides link level performance analysis of non-line of sight (NLOS) QPSK-OFDM data transmission over Rayleigh fading channels. Two scenarios have been considered in this paper. Firstly, the performance of un-coded and coded 1024-pointOFDM over the AWGN channel was obtained. Secondly, using Jake's sum of sinusoids model, the link level performance of the system was obtained over the COST 207 Rayleigh fading channels (Typical Urban and Bad Urban) and the Winner vehicular NLOS (RS-MS-NLOS) channel. To the best of the authors' knowledge, this work is first in presenting the individual/compared contributions of CC, RS and RS-CC coded version of QPSK-OFDM over the COST 207 TU/BU channels.