Vehicular Named Data Networking (VNDN) is a promising information-centric network architecture to achieve efficient data delivery among vehicles. With a subscription-based communication paradigm, it can disseminate event-triggered data like traffic updates or road accident notifications in a timely way. However, due to vehicle mobility and network dynamics, the data subscription path is normally fragile and consequently the data dissemination efficiency would be diminished. To address this problem, we propose SegSub, an adaptive segmented subscription mechanism which can maintain robust subscription path for efficient data dissemination in mobile scenarios. SegSub divides the whole subscription path into several segments and different strategies are employed to maintain subscription status for each segment path. The frequency of subscription update on each segment is calculated based on the prediction of vehicle mobility and network status to reach a trade-off between subscription updating overhead and expected data dissemination delay. Furthermore, a subscription migration mechanism is proposed to alleviate the data loss and redundant pending overhead caused by subscription failure when vehicles move. Evaluation results demonstrate that SegSub can decrease dissemination delay by 91.9% and 58.7% compared to native VNDN and the state-of-the-art subscription method.

In this paper, we investigate the use of most reliable basis (MRB) based decoding algorithms for the Markov modulated Gaussian noise (MMGN) channel, with an interleaver used at the transmitter. The decoder first finds a most reliable column basis (MRCB), which is a common reliable basis for each row of the interleaver (each row of the interleaver is a codeword). Based on this common reliable basis, each row is decoded with a MRB based decoding algorithm. This new decoding method is denoted as joint column decoding (JCD). Simulation results show that this new method can achieve better performance than the straightforward MRB based decoding, where each codeword is decoded independently with a smaller complexity. An upper bound of the decoding error performance of JCD is derived based on the theory of order statistics and Markov chain.

In this paper, a new reliability-based soft-decision decoding algorithm is presented. This algorithm repeatedly uses biased reliability values to construct the most-reliable-basis (MRB). As a result, this new method makes use of multiple information sets in a stochastic way. Compared to previously proposed competitive approaches, this new method produces a more efficient MRB reprocessing type algorithm to achieve near maximum-likelihood decoding (MLD) performance with a proper choice of the bias value. It can be combined with any MRB reprocessing type algorithm and in each case, a tight performance analysis can be derived

In this paper, an efficient method to improve the performance of the box and matching algorithm (BMA) is presented. By constructing a control band which is error free with high probability, we enhance the matching capability of the BMA. More precisely, the performance of BMA of order (i + 1) is nearly achieved with a linear increase in complexity and no increase in memory with respect to BMA of order i. Simulation results show that the performance of the enhanced BMA with a finite number of random biasing iterations for the decoding of the RS(255,239) code is about 0.1 dB away from that of maximum likelihood decoding (MLD) at the word error rate (WER) 10 3. A tight performance analysis is derived based on the theory of ordered statistics for this new approach.

In this work, an efficient approach is introduced for reliability-based list decoding of a linear block code. This method terminates the decoding if a local optimal candidate satisfies a probabilistic sufficient condition. The average computation complexity is greatly reduced with this method. The false alarm probability associated with the use of the probabilistic sufficient condition is also derived. Simulation results confirm the analysis with no performance degradation and important computation savings on average for soft decision decoding of the (255,239) RS code (reduction by a factor between 2 and 20)