Several recent publications have shown that jointsource-channel decoding could be a powerful technique to take advantage of residual source redundancy for fixed- and variable-length source codes. This letter gives an...
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Several recent publications have shown that jointsource-channel decoding could be a powerful technique to take advantage of residual source redundancy for fixed- and variable-length source codes. This letter gives an in-depth analysis of a low-complexity method recently proposed by Guivarch et al, where the redundancy left by a Huffman encoder is used at a bit level in the channel decoder to improve its performance. Several simulation results are presented, showing for two first-order Markov sources of different sizes that using a priori knowledge of the source statistics yields a significant improvement, either with a Viterbi channel decoder or with a turbo decoder.
This paper proposes an unequal error protection (UEP) scheme for transporting scalable video packets over packet-lossy peer-to-peer networks. In our scheme, given an estimated system uplink capacity, a receiver-driven...
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This paper proposes an unequal error protection (UEP) scheme for transporting scalable video packets over packet-lossy peer-to-peer networks. In our scheme, given an estimated system uplink capacity, a receiver-driven joint source-channel coding (JSCC) mechanism is proposed by which each child-peer minimizes the received visual distortion by subscribing to appropriate numbers of source and channelcoding packets. Because the bandwidth for inter-peer transmissions may fluctuate largely due to peer dynamics, in our method, a peer estimates the available system uplink capacity based on consensus propagation to avoid the fluctuating allocations of JSCC. To efficiently utilize the uplink bandwidth of peers, parent-peers utilize sender-driven contribution-guided peer selection to reject the low-contribution subscriptions requested from candidate child-peers. Simulation results demonstrate that our method significantly improves the visual quality, compared to other state-of-the-art schemes. (C) 2016 Elsevier Inc. All rights reserved.
We consider the problem of learned speech transmission. Existing methods have exploited joint source-channel coding (JSCC) to encode speech directly to transmitted symbols to improve the robustness over noisy channels...
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We consider the problem of learned speech transmission. Existing methods have exploited joint source-channel coding (JSCC) to encode speech directly to transmitted symbols to improve the robustness over noisy channels. However, the fundamental limit of these methods is the failure of identification of content diversity across speech frames, leading to inefficient transmission. In this paper, we propose a novel neural speech transmission framework named NST. It can be optimized for superior rate-distortion-perception (RDP) performance toward the goal of high-fidelity semantic communication. Particularly, a learned entropy model assesses latent speech features to quantify the semantic content complexity, which facilitates the adaptive transmission rate allocation. NST enables a seamless integration of the source content with channel state information through variable-length joint source-channel coding, which maximizes the coding gain. Furthermore, we present a streaming variant of NST, which adopts causal coding based on sliding windows. Experimental results verify that NST outperforms existing speech transmission methods including separation-based and JSCC solutions in terms of RDP performance. Streaming NST achieves low-latency transmission with a slight quality degradation, which is tailored for real-time speech communication.
Application Layer Forward Error Correction (AL-FEC) is becoming a popular addition to protocols for real-time video delivery over IP-based wireless networks. In particular, rateless codes are identified as suitable so...
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Application Layer Forward Error Correction (AL-FEC) is becoming a popular addition to protocols for real-time video delivery over IP-based wireless networks. In particular, rateless codes are identified as suitable solution for AL-FEC due to their flexibility and capacity-approaching performance. Since each part of video data is not equally important for video reconstruction, it is beneficial to group it based on its importance, and then provide different degree of protection using Unequal Error Protection (UEP). Data partitioning (DP) is one such low-cost feature in H.264/AVC enabling partitioning of video data based on its importance. We propose schemes for the DP H.264/AVC video transmission using Raptor and Random Linear Codes (RLC) and investigate their performance as AL-FEC solutions in Digital Video Broadcasting. We provide comparisons between optimized Non-Overlapping Window RLC and Expanding Window (EW) RLC, which are two effective UEP RLC strategies. The results using realistic channel traces show viability of the EW RLC as a promising solution for multimedia broadcast applications.
Two new schemes are proposed for transmitting a Gaussian source over a Gaussian channel. These schemes directly generalize previous results of Bross et al. and Puri et al. A scaled version of either the source itself ...
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Two new schemes are proposed for transmitting a Gaussian source over a Gaussian channel. These schemes directly generalize previous results of Bross et al. and Puri et al. A scaled version of either the source itself or the quantization error is superimposed on the digital information, and thus serves as effective channel state information (CSI) unknown to the receiver. It is shown that for any power allocation between the coded and uncoded components of transmission, optimal distortion can be achieved by a continuum of auxiliary random variables (rather than only Costa's) if the decoded auxiliary codeword is properly used. This observation provides a new degree of freedom in point-to-point transmission. This freedom, in turn, can be utilized in multiterminal scenarios, as is demonstrated with an example.
We study the estimation of a Gaussian source by a Gaussian wireless sensor network (WSN) where distributed sensors transmit noisy observations of the source through a fading Gaussian multiple access channel to a fusio...
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We study the estimation of a Gaussian source by a Gaussian wireless sensor network (WSN) where distributed sensors transmit noisy observations of the source through a fading Gaussian multiple access channel to a fusion center. In a recent work Gastpar, ["Uncoded transmission is exactly optimal for a Simple Gaussian Sensor Network," IEEE Trans. Inf. Theory, vol. 54, no. 11, pp. 5247-5251, Nov. 2008] showed that for a symmetric Gaussian WSN with no fading, uncoded (analog) transmission achieves the optimal performance theoretically attainable (OPTA). In this correspondence, we consider an asymmetric fading WSN in which the sensors have differing noise and transmission powers. We first present lower and upper bounds on the system's OPTA under random fading. We next focus on asymmetric networks with deterministic fading. By comparing the obtained lower and upper OPTA bounds under deterministic fading, we provide a sufficient condition for the optimality of the uncoded transmission scheme for a given power tuple P = (P-1, P-2, ... , P-L). Then, allowing the sensor powers to vary under a weighted sum constraint (this includes the sum-power constraint as a special case), we obtain a sufficient condition for the optimality of uncoded transmission and provide the system's corresponding OPTA.
An efficient policy allocation algorithm for the transmission of embedded bit streams over noisy channels with feedback is proposed. The transmission is based on the typeII hybrid ARQ/FEC protocol and uses a nested se...
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An efficient policy allocation algorithm for the transmission of embedded bit streams over noisy channels with feedback is proposed. The transmission is based on the typeII hybrid ARQ/FEC protocol and uses a nested sequence C of channel codes to protect the packets. There are also constraints on the total bit budget and on the allowed number of retransmissions per packet. The allocation algorithm assigns different protection policies, each policy being a subset of C, to different packets to maximize the average number of correctly received source bits. We study the performance and the complexity of the proposed scheme through the transmission of images encoded by JPEG2000 over mobile channels with correlated Rayleigh fading. We demonstrate by simulations that the proposed multiple-policy scheme provides significant improvements over a purely FEC scheme with no feedback and also the existing fixed-policy schemes. Our results show that feedback is particularly helpful for poor channel conditions and that the proposed scheme is very robust against changes in the channel signal-to-noise ratio (SNR) and the mobile speed.
In this paper, we consider progressive image transmission over differentially space-time coded orthogonal frequency-division multiplexing (OFDM) systems and treat the problem as one of optimal jointsource-channel cod...
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In this paper, we consider progressive image transmission over differentially space-time coded orthogonal frequency-division multiplexing (OFDM) systems and treat the problem as one of optimal joint source-channel coding (JSCC) in the form of unequal error protection (UEP), as necessitated by embedded sourcecoding (e.g., SPIHT and JPEG 2000). We adopt a product channel code structure that is proven to provide powerful error protection and employ low-complexity decision-feedback decoding for differentially space-time coded OFDM without assuming channel state information. For a given SNR, the BER performance of the differentially spacetime coded OFDM system is treated as the channel condition in the JSCC/UEP design via a fast product code optimization algorithm so that the end-to-end quality of reconstructed images is optimized in the average minimum MSE sense. Extensive image transmission experiments show that SNR/BER improvements can be translated into quality gains in reconstructed images. Moreover, compared to another non-coherent detection algorithm, i.e., the iterative receiver based on expectation-maximization algorithm for the space-time coded OFDM systems, differentially space-time coded OFDM systems suffer some quality loss in reconstructed images. With the efficiency and simplicity of decision-feedback differential decoding, differentially space-time coded OFDM is thus a feasible modulation scheme for applications such as wireless image over mobile devices (e.g., cell phones). Copyright (C) 2006 John Wiley & Sons, Ltd.
This paper considers the problem of simultaneous information and energy transmission, where the energy harvesting function is only known experimentally at sample points, e.g., due to nonlinearities and parameter uncer...
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This paper considers the problem of simultaneous information and energy transmission, where the energy harvesting function is only known experimentally at sample points, e.g., due to nonlinearities and parameter uncertainties in harvesting circuits. We investigate the performance loss due to this partial knowledge of the harvesting function in terms of transmitted energy and information. In particular, we assume that the harvesting function is a subclass of the Sobolev space and consider two cases, where the experimental samples are either taken noiselessly or in the presence of noise. Using constructive function approximation and regression methods for noiseless and noisy samples, respectively, we show that the worst loss in energy transmission vanishes asymptotically as the number of samples increase. Similarly, the loss in information rate vanishes in the interior of the energy domain;however, it does not always vanish at maximal energy. We further show that the same principle applies in multicast settings, such as medium access in the Wi-Fi protocol. We also consider the end-to-end source-channel communication problem under source distortion constraint and channel energy requirement, where both distortion and harvesting functions are known only at samples.
We study joint source-channel coding (JSCC) of compressed sensing (CS) measurements using vector quantizer (VQ). We develop a framework for realizing optimum JSCC schemes that enable encoding and transmitting CS measu...
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We study joint source-channel coding (JSCC) of compressed sensing (CS) measurements using vector quantizer (VQ). We develop a framework for realizing optimum JSCC schemes that enable encoding and transmitting CS measurements of a sparse source over discrete memoryless channels, and decoding the sparse source signal. For this purpose, the optimal design of encoder-decoder pair of a VQ is considered, where the optimality is addressed by minimizing end-to-end mean square error (MSE). We derive a theoretical lower bound on the MSE performance and propose a practical encoder-decoder design through an iterative algorithm. The resulting coding scheme is referred to as channel-optimized VQ for CS, coined COVQ-CS. In order to address the encoding complexity issue of the COVQ-CS, we propose to use a structured quantizer, namely low-complexity multistage VQ (MSVQ). We derive new encoding and decoding conditions for the MSVQ and then propose a practical encoder-decoder design algorithm referred to as channel-optimized MSVQ for CS, coined COMSVQ-CS. Through simulation studies, we compare the proposed schemes vis-a-vis relevant quantizers.
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