Can Punctured Rate-1/2 Turbo Codes Achieve a Lower Error Floor than their Rate-1/3 Parent Codes?

In this paper we concentrate on rate-1/3 systematic parallel concatenated convolutional codes and their rate-1/2 punctured child codes. Assuming maximum-likelihood decoding over an additive white Gaussian channel, we demonstrate that a rate-1/2 non-systematic child code can exhibit a lower error floor than that of its rate-1/3 parent code, if a particular condition is met. However, assuming iterative decoding, convergence of the non-systematic code towards low bit-error rates is problematic. To alleviate this problem, we propose rate-1/2 partially-systematic codes that can still achieve a lower error floor than that of their rate-1/3 parent codes. Results obtained from extrinsic information transfer charts and simulations support our conclusion.Comment: 5 pages, 7 figures, Proceedings of the 2006 IEEE Information Theory Workshop, Chengdu, China, October 22-26, 200

Block-based feature adaptive compressive sensing for video

This paper focuses on the problem of feature adaptive reconstruction of Compressive Sensing (CS) captured video. In CS, sparse signals can be recovered with high probability of success from very few random samples. Utilizing the temporal correlations between video frames, it is possible to exploit improved CS reconstruction algorithms. Features that relate to the changes between frames are one of the options to benefit reconstruction. However, to choose the optimal feature for every particular region in each frame is difficult, as the true images are unknown in a CS framework. In this paper, we propose two systems for block-based feature adaptive CS video reconstruction, i.e., a Cross Validation (CV) based system and a classification based system. The CV based system achieves the selection of the optimal feature by applying the techniques of CV to the results of extra reconstructions and the classification based system reduces complexity by classifying the CS samples directly, where the optimal feature for the particular class is employed for the reconstruction. Simulations demonstrate that both of our systems work appropriately and their performance is better than uniformly using any single feature for the whole video reconstruction.This work is supported by EPSRC Research Grant (EP/K033700/1); the Natural Science Foundation of China (61401018); Beijing Jiaotong University; the Fundamental Research Funds for the Central Universities (2014JBM149).This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.25

Sparsity-fused Kalman filtering for reconstruction of dynamic sparse signals

This article focuses on the problem of reconstructing dynamic sparse signals from a series of noisy compressive sensing measurements using a Kalman Filter (KF). This problem arises in many applications, e.g., Magnetic Resonance Imaging (MRI), Wireless Sensor Networks (WSN) and video reconstruction. The conventional KF does not consider the sparsity structure presented in most practical signals and it is therefore inaccurate when being applied to sparse signal recovery. To deal with this issue, we derive a novel KF procedure which takes the sparsity model into consideration. Furthermore, an algorithm, namely Sparsity-fused KF, is proposed based upon it. The method of iterative soft thresholding is utilized to refine our sparsity model. The superiority of our method is demonstrated by synthetic data and the practical data gathered by a WSN.This work is supported by EPSRC Research Grant (EP/K033700/1); the Natural Science Foundation of China (61401018, U1334202); the State Key Laboratory of Rail Traffic Control and Safety (RCS2014ZT08), Beijing Jiaotong University; the Fundamental Research Funds for the Central Universities (2014JBM149); the Key Grant Project of Chinese Ministry of Education (313006); the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ICC.2015.724938

Assessment of a low-profile planar antenna for a wireless sensor network monitoring the local water distribution network

This paper presents an assessment on the suitability of a low-profi le planar antenna for a Wireless Sensor Network (WSN) application monitoring the water supply at Fire Hydrants (FHs). The antenna must have a low pro le so that it can be mounted on the FH lid; it must have an omnidirectional radiation pattern so that it can communicate with base stations at low elevations; and it must operate in the 2.4 GHz Industrial, Scienti c and Medical (ISM) band. Measurements show that for the majority of the 2.4 GHz ISM band, the antenna has a return loss of at least -10 dB and e ciency greater than 60 %. For the FH WSN assessment, the antenna was deployed as a transmitter mounted on the FH lid above the underground FH chamber and a vertically polarised monopole antenna mounted on a mast at various speci ed heights above ground level was used to measure the received power as a function of distance. The path loss results were compared with those from a previous deployment, where the FH antenna was located in the FH chamber, and it is found that using the low-pro le antenna reduced the path loss by at least 10 dB over the measured transmitter and receiver separation.This paper is a postprint of a paper submitted to and accepted for publication in IET Wireless Sensor Systems and is subject to Institution of Engineering and Technology Copyright. The final version is available the IET Digital Library

A Novel Multistage Equalization Algorithm

A novel equalization algorithm utilizing improper nature of the intersymbol interference (ISI) is introduced in this paper. We show that full exploitation of the available information on the second-order statistics of the observed signal entails widely linear processing and that previously known linear minimum mean square error (MMSE) equalizers represent sub-optimum solutions. The proposed scheme is generally applicable for both real and complex signal constellations. The results show that accounting for the improper nature of the ISI leads to significant performance gain compared to conventional equalization schemes

Deep network for image super-resolution with a dictionary learning layer

The aim of single image super-resolution (SR) is to gener- ate a high-resolution (HR) image from a low-resolution (LR) observable image. In this paper, we address this task by inte- grating sparse coding and dictionary learning schemes into an end-to-end deep architecture. More specifically, we propose a new non-linear dictionary learning layer composed of a fi- nite number of recurrent units to solve the sparse codes and also to yield the relevant gradients to update the dictionary. In addition, we present a new deep network architecture using the proposed non-linear layers, where two separate parallel dictionaries are adopted to represent the LR and HR images respectively. The whole network is optimized by back prop- agation, constraining not only reconstruction errors between the restored and the ground truth HR images but also between the sparse codes of the LR and HR image pairs. Various datasets are used to evaluate the performance of the proposed approach and it is shown to outperform many state-of-the-art single image super-resolution algorithms

A Simple Recursively Computable Lower Bound on the Noncoherent Capacity of Highly Underspread Fading Channels

Real-world wireless communication channels are typically highly underspread: their coherence time is much greater than their delay spread. In such situations it is common to assume that, with sufficiently high bandwidth, the capacity without Channel State Information (CSI) at the receiver (termed the noncoherent channel capacity) is approximately equal to the capacity with perfect CSI at the receiver (termed the coherent channel capacity). In this paper, we propose a lower bound on the noncoherent capacity of highly underspread fading channels, which assumes only that the delay spread and coherence time are known. Furthermore our lower bound can be calculated recursively, with each increment corresponding to a step increase in bandwidth. These properties, we contend, make our lower bound an excellent candidate as a simple method to verify that the noncoherent capacity is indeed approximately equal to the coherent capacity for typical wireless communication applications. We precede the derivation of the aforementioned lower bound on the information capacity with a rigorous justification of the mathematical representation of the channel. Furthermore, we also provide a numerical example for an actual wireless communication channel and demonstrate that our lower bound does indeed approximately equal the coherent channel capacity.The work of T. H. Loh was supported by the 2013 - 2017 Electromagnetics and Time Metrology Programme of the National Measurement Office, an Executive Agency of the U.K. Department for Business, Innovation and Skills, under Projects EMT13018This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TWC.2016.253167

Generalized-KFCS: Motion estimation enhanced Kalman filtered compressive sensing for video

In this paper, we propose a Generalized Kalman Filtered Compressive Sensing (Generalized-KFCS) framework to reconstruct a video sequence, which relaxes the assumption of a slowly changing sparsity pattern in Kalman Filtered Compressive Sensing [1, 2, 3, 4]. In the proposed framework, we employ motion estimation to achieve the estimation of the state transition matrix for the Kalman filter, and then reconstruct the video sequence via the Kalman filter in conjunction with compressive sensing. In addition, we propose a novel method to directly apply motion estimation to compressively sensed samples without reconstructing the video sequence. Simulation results demonstrate the superiority of our algorithm for practical video reconstruction.This work was partially supported by EPSRC Research Grant EP/K033700/1, the Fundamental Research Funds for the Central Universities (No. 2014JBM149), and the Scientific Research Foundation for the Returned Overseas Chinese Scholars (of State Education Ministry).This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ICIP.2014.702525

On the analogy between vehicle and vehicle-like cavities with reverberation chambers

Deploying wireless systems in vehicles is an area of current interest. Often, it is implicitly assumed that the electromagnetic environment in vehicle cavities is analogous to that in reverberation chambers, it is therefore important to assess to what extent this analogy is valid. Specifically, the cavity time constant, electromagnetic isolation and electric field uniformity are investigated for typical vehicle and vehicle-like cavities. It is found that the time constant is a global property of the cavity (i.e., it is the same for all links). This is important, as it means that the root mean square delay spread for any link is also a property of the cavity, and thus so is the coherence bandwidth. These properties could be exploited by wireless sytems deployed in vehicles. It is also found that the field distribution is not homogeneous (and is therefore not uniform), but can be isotropic. For situations where the field distribution is isotropic, the spatial coherence is well defined, and therefore Multiple-Input-Multiple-Output antenna arrays can be used to improve performance of wireless systems. For situations where the field distribution is not isotropic, the angular spread is not uniform, and therefore beam-forming can be used to improve performance of wireless systems.This is the author's accepted manuscript and will be under embargo until publication. The final version is available from IEEE at http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=692843