Data Recovery Performance Research Articles

Joint Interference Mitigation and Data Recovery in Compressive Domain: A Sparse MLE Approach

We consider the problem where a receiver acquires information (data) corrupted by interference and noise. Both the information and interference are assumed to have a sparse structure. This problem occurs in many applications such as data demodulation in cellular systems. The joint interference mitigation and data recovery is formulated as a sparse maximum likelihood estimation (MLE) problem which maximizes the associated likelihood function under individual sparsity levels (ISLs) constraints. This sparse MLE framework can fully exploit the individual sparse structure of the information and interference to improve the data recovery performance at the receiver. We propose an alternating optimization (AO) recovery algorithm to solve the non-convex sparse MLE problem. To analyze the performance of the proposed AO algorithm, we introduce a new kind of restricted isometry property (RIP) called the ISLs-RIP. Under the ISLs-RIP conditions, we show that the proposed AO algorithm converges to the optimal solution of the sparse MLE problem. We also derive an upper bound of the corresponding estimation error for the information. Finally, we extend the above results and algorithms to the case when the receiver only has statistical knowledge of the ISLs. Simulations show that the proposed solution achieves significant gain over various baselines.

Modeling and Analysis of Frame-Level Forward Error Correction for MPEG Video over Burst-Loss Channels

Forward error correction (FEC) is a common error control technique to improve the quality of video streaming over lossy channels. To optimize the data recovery performance, frame-level FEC schemes have been proposed for streaming video to maximize playable frame rate (PFR) within transmission rate constraint on a random binary symmetric channel (BSC). However, burst loss is a commonplace in current Internet architecture, and the FEC efficacy c an be degraded since the burst data losses easily exceed the error correction capacity of FEC. Accordingly, an estimated video quality model over burst loss channels is proposed in this paper to evaluate the impact of burst loss for FEC-based video applications. In addition to th e model analysis, the simulation experiments on the NS-2 network simulator are conducted at a given estimate of the packet loss probability and average burst length. The results suggest a useful reference in designing the FEC scheme for video applications, and as the video coding and channel parameters are given, the proposed model can provide the optimal FEC solution to achieve a better reconstructed video quality than the FEC model based on a random BSC channel.

Low‐complexity data decoding using binary phase detection in SLM‐OFDM systems

High computational complexity associated with maximum-likelihood (ML) data decoding in orthogonal frequency division multiplexing (OFDM) systems that use selected mapping (SLM) for the reducing peak-to-average power ratio (PAPR) can be significantly reduced using modified SLM and data decoding techniques presented in this Letter. Simulations show that the proposed method achieves similar PAPR reduction capability and data recovery performance compared with standard SLM (with perfect data recovery) and an ML detection scheme.

Wireless sensor networks data recovery algorithm based on quadratic programming

For improving the real-time performance of recovery algorithm in Compressed Sensing(CS) of Wireless Sensor Networks(WSN) data,a quadratic programming based network data recovery algorithm was proposed in this paper.The CS recovery was transformed to bound-constrained quadratic programming,and then the network data was recovered by solving the quadratic programming problem based on the Armijo rule.The analysis and experimental results demonstrate that the proposed algorithm can significantly reduce the complexity and ensure the accuracy of recovery,thus improving the real-time performance of data recovery in WSN.

Seismic data reconstruction via matrix completion

In seismic processing, one goal is to recover missing traces when the data is sparsely and incompletely sampled. We present a method which treats this reconstruction problem from a novel perspective. By utilizing its connection with the general matrix completion (MC) problem, we build an approximately low-rank matrix, which can be reconstructed through solving a proper nuclear norm minimization problem. Two efficient algorithms, accelerated proximal gradient method (APG) and low-rank matrix fitting (LMaFit) are discussed in this paper. The seismic data can then be recovered by the conversion of the completed matrix into the original signal space. Numerical experiments show the efficiency and high performance of data recovery for our model compared with other models.

Error resiliency measure for RVLC codes

Video signals transmitted over error-prone wireless channels are often corrupted by bit errors. Due to their superior data recovery performance, the reversible variable length coding (RVLC) has been used as an error resiliency scheme in the MPEG-4 and H.263 video coding standards. In order to evaluate and compare the error resiliency performance of RVLC codes, we develop an error resiliency measure in this letter. Using this measure, different RVLC schemes are compared for MPEG-4 video sequences. The effects of propagating and nonpropagating errors are discussed as well.