Optimized Scalable Image and Video Transmission for MIMO Wireless Channels

In this chapter, we focus on proposing new strategies to efficiently transfer a compressed image/video content through wireless links using a multiple antenna technology. The proposed solutions can be considered as application layer physical layer (APP-PHY) cross layer design methods as they involve optimizing both application and physical layers. After a wide state-of-the-art study, we present two main solutions. The first focuses on using a new precoding algorithm that takes into account the image/video content structure when assigning transmission powers. We showed that its results are better than the existing conventional precoders. Second, a link adaptation process is integrated to efficiently assign coding parameters as a function of the channel state. Simulations over a realistic channel environment show that the link adaptation activates a dynamic process that results in a good image/video reconstruction quality even if the channel is varying. Finally, we incorporated soft decoding algorithms at the receiver side, and we showed that they could induce further improvements. In fact, almost 5 dB peak signal-to-noise ratio (PSNR) improvements are demonstrated in the case of transmission over a Rayleigh channel

Analysis of Joint Source Channel LDPC Coding for Correlated Sources Transmission over Noisy Channels

In this paper, a Joint Source Channel coding scheme based on LDPC codes is investigated. We consider two concatenated LDPC codes, one allows to compress a correlated source and the second to protect it against channel degradations. The original information can be reconstructed at the receiver by a joint decoder, where the source decoder and the channel decoder run in parallel by transferring extrinsic information. We investigate the performance of the JSC LDPC code in terms of Bit-Error Rate (BER) in the case of transmission over an Additive White Gaussian Noise (AWGN) channel, and for different source and channel rate parameters. We emphasize how JSC LDPC presents a performance tradeoff depending on the channel state and on the source correlation. We show that, the JSC LDPC is an efficient solution for a relatively low Signal-to-Noise Ratio (SNR) channel, especially with highly correlated sources. Finally, a source-channel rate optimization has to be applied to guarantee the best JSC LDPC system performance for a given channel

Neural network controller for active demand side management with PV energy in the residential sector

In this paper, we describe the development of a control system for Demand-Side Management in the residential sector with Distributed Generation. The electrical system under study incorporates local PV energy generation, an electricity storage system, connection to the grid and a home automation system. The distributed control system is composed of two modules: a scheduler and a coordinator, both implemented with neural networks. The control system enhances the local energy performance, scheduling the tasks demanded by the user and maximizing the use of local generation

Arithmetic Coding for Length-constrained Joint Source Compression and Error Detection

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Joint Source-Channel Decoding for MDC-encoded Sources Transmitted Over Relay Systems

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Cooperative communication using turbo product codes with multiple-source spatial and temporal correlations

International audiencewe study a cooperative coding scheme for densely deployed wireless sensor networks (WSNs) where a number of sensors transmit data to a single destination with the help of a relay. The latter applies algebraic network coding to the source codewords and forwards only the additional redundancy to the destination that observes a product code matrix built based on source codewords and relay-generated redundancy. However, for such an application two types of correlation can be found between the different sensors' observations. The first type is due to the high density of the WSN that results in a correlation between observations delivered by neighbor sensors, and we call it spatial correlation. The nature of the measured physical phenomena induces also some correlation between successive observations of the same sensor, and this type is called temporal correlation. Spatial and temporal correlations represent extra source information that was neglected in previous contributions dealing with cooperative communications. In this contribution, we investigate a joint source channel decoding scheme that exploits the source memory structure to improve the product code iterative decoding performance. Significant performance improvements are demonstrated depending on the spatial and temporal correlation level. A performance gain achieving 0.8 dB for the additive white Gaussian noise (AWGN) channel, and 1.5 dB for the fast Rayleigh fading channel are demonstrated

LDPC-based multi-relay lossy forwarding for correlated source transmission over orthogonal Rayleigh fading channels

International audienceIn this paper, we design a communication and coding strategy for Lossy Forwarding (LF) systems with multiple relay nodes or helpers based on Low-Density Parity-check Codes (LDPC) with message passing decoding applied on a Tanner-graph that maps all the network. The system performance is investigated under the cases of a fixed number of helpers, and a random multiple shifted-Poisson distributed helpers for orthogonal Rayleigh fading channels. All the practical results will be also compared and validated with respect to theoretical outage probabilities

Joint Source-Channel Decoding for LDPC-coded Error-Corrupted Binary Markov Sources

We consider the problem of joint decoding and data fusion in data gathering for densely deployed sensor networks modeled by the Chief Executive Officer (CEO) problem. More specifically, we consider the binary CEO problem where all sensors observe the same time-correlated binary Markov source corrupted by independent binary noises. Hence, the observations are two-dimensionally (temporary and spatially) correlated. In the proposed scheme, every sensor apply a low-density parity-check (LDPC) code and transmit the corresponding codeword independently over additive white Gaussian noise (AWGN) channels. To reconstruct the original bit sequence, an iterative joint source-channel decoding (JSCD) technique is considered. To exploit the knowledge about the source correlations, we consider an iterative decoding between a sum-product (SP) decoder serially concatenated with BCJR decoder which is applied for every sensor as local iterations. Then, correlation between sensors' data is employed to update extrinsic information received from the SP-BCJR decoders of the different sensors during global iterations. We illustrate the performance of the joint decoder for different correlation setups and with different number of sensors. Simulation results, in terms of bit error rate show promising improvements compared with the separate decoding scheme where the correlation knowledge is not completely utilized in the decoder

Inter-node compression with LDPC joint source–channel coding for highly correlated sources

This paper investigates a new communication system where two nodes want to disseminate highly correlated contents to a single destination and can be applied for densely deployed wireless sensors networks applications. Motivated by their capacity-achieving performance and existing practical implementations, the proposed communication scheme is fully based on Low-Density Parity-Check (LDPC) codes for data compression and channel coding. More specifically, we consider a network of two correlated binary sources with two orthogonal communication phases. Data are encoded at the first source with an LDPC channel code and broadcast in the first phase. Based on the first source received data, the second source computes the correlation vector and applies a Joint Source–Channel (JSC) LDPC code, which output is communicated in the second phase. At the receiver, the whole network is mapped on a joint factor graph over which an iterative message-passing joint decoder is proposed. The aim of the joint decoder is to exploit the residual correlation between the sources for better estimation. Simulation results are investigated and compared to the theoretical limits and to an LDPC-based distributed coding system where no inter-node compression is applied

Low-complexity joint source/channel turbo decoding of arithmetic codes with image transmission application

International audienceIn this paper a novel joint source channel (JSC) decoding technique is presented. The proposed approach enables iterative decoding for serially concatenated arithmetic codes and convolutional codes. Iterations are performed between Soft In Soft Out (SISO) component decoders. For arithmetic decoding, we proposed to employ a low complex trellis search technique to estimate the best transmitted codewords and generate soft outputs. Performance of the presented system are evaluated in terms of PER, in the case of transmission across the AWGN channel. Simulation results show that the proposed JSC iterative scheme leads to significant gain in comparison with a traditional separated decoding. Finally, the practical relevance of the proposed technique is validated under an image transmission system using the SPIHT codec