1,069 research outputs found
Multi-level Turbo Decoding Assisted Soft Combining Aided Hybrid ARQ
Hybrid Automatic Repeat reQuest (ARQ) plays an essential role in error control. Combining the incorrectly received packet replicas in hybrid ARQ has been shown to reduce the resultant error probability, while improving the achievable throughput. Hence, in this contribution, multi-level turbo codes have been amalgamated both with hybrid ARQ and efficient soft combining techniques for taking into account the Log- Likelihood Ratios (LLRs) of retransmitted packet replicas. In this paper, we present a soft combining aided hybrid ARQ scheme based on multi-level turbo codes, which avoid the capacity loss of the twin-level turbo codes that are typically employed in hybrid ARQ schemes. More specifically, the proposed receiver dynamically appends an additional parallel concatenated Bahl, Cocke, Jelinek and Raviv (BCJR) algorithm based decoder in order to fully exploit each retransmission, thereby forming a multi-level turbo decoder. Therefore, all the extrinsic information acquired during the previous BCJR operations will be used as a priori information by the additional BCJR decoders, whilst their soft output iteratively enhances the a posteriori information generated by the previous decoding stages. We also present link- level Packet Loss Ratio (PLR) and throughput results, which demonstrate that our scheme outperforms some of the previously proposed benchmarks
Iterative Soft Input Soft Output Decoding of Reed-Solomon Codes by Adapting the Parity Check Matrix
An iterative algorithm is presented for soft-input-soft-output (SISO)
decoding of Reed-Solomon (RS) codes. The proposed iterative algorithm uses the
sum product algorithm (SPA) in conjunction with a binary parity check matrix of
the RS code. The novelty is in reducing a submatrix of the binary parity check
matrix that corresponds to less reliable bits to a sparse nature before the SPA
is applied at each iteration. The proposed algorithm can be geometrically
interpreted as a two-stage gradient descent with an adaptive potential
function. This adaptive procedure is crucial to the convergence behavior of the
gradient descent algorithm and, therefore, significantly improves the
performance. Simulation results show that the proposed decoding algorithm and
its variations provide significant gain over hard decision decoding (HDD) and
compare favorably with other popular soft decision decoding methods.Comment: 10 pages, 10 figures, final version accepted by IEEE Trans. on
Information Theor
Stable adaptive control with gain constraints
Stable adaptive control with gain constraint
DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression
We propose a new architecture for distributed image compression from a group
of distributed data sources. The work is motivated by practical needs of
data-driven codec design, low power consumption, robustness, and data privacy.
The proposed architecture, which we refer to as Distributed Recurrent
Autoencoder for Scalable Image Compression (DRASIC), is able to train
distributed encoders and one joint decoder on correlated data sources. Its
compression capability is much better than the method of training codecs
separately. Meanwhile, the performance of our distributed system with 10
distributed sources is only within 2 dB peak signal-to-noise ratio (PSNR) of
the performance of a single codec trained with all data sources. We experiment
distributed sources with different correlations and show how our data-driven
methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding
(DSC). To the best of our knowledge, this is the first data-driven DSC
framework for general distributed code design with deep learning
A New Protocol for Cooperative Spectrum Sharing in Mobile Cognitive Radio Networks
To optimize the usage of limited spectrum resources, cognitive radio (CR) can be used as a viable solution. The main contribution of this article is to propose a new protocol to increase throughput of mobile cooperative CR networks (CRNs). The key challenge in a CRN is how the nodes cooperate to access the channel in order to maximize the CRN's throughput. To minimize unnecessary blocking of CR transmission, we propose a so-called new frequency-range MAC protocol (NFRMAC). The proposed method is in fact a novel channel assignment mechanism that exploits the dependence between signal's attenuation model, signal's frequency, communication range, and interference level. Compared .to the conventional methods, the proposed algorithm considers a more realistic model for the mobility pattern of CR nodes and also adaptively selects the maximal transmission range of each node over which reliable transmission is possible. Simulation results indicate that using NFRMAC leads to an increase of the total CRN's throughput by 6% and reduces the blocking rate by 10% compared to those of conventional methods
Evaluation of the effect of vibration nonlinearity on convergence behavior of adaptive higher harmonic controllers
Effect of nonlinearity on convergence of the local linear and global linear adaptive controllers is evaluated. A nonlinear helicopter vibration model is selected for the evaluation which has sufficient nonlinearity, including multiple minimum, to assess the vibration reduction capability of the adaptive controllers. The adaptive control algorithms are based upon a linear transfer matrix assumption and the presence of nonlinearity has a significant effect on algorithm behavior. Simulation results are presented which demonstrate the importance of the caution property in the global linear controller. Caution is represented by a time varying rate weighting term in the local linear controller and this improves the algorithm convergence. Nonlinearity in some cases causes Kalman filter divergence. Two forms of the Kalman filter covariance equation are investigated
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