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Deep Learning-Constructed joint Transmission-Recognition for Internet of Things

IEEE ACCESS 2019年 7卷 76547-76561页

作者： Lee, Chia-Han Lin, Jia-Wei Chen, Po-Hao Chang, Yu-Chieh Natl Chiao Tung Univ Inst Commun Engn Hsinchu 30010 Taiwan

The widely deployed Internet-of-Things (IoT) devices provide intelligent services with its cognition capability. Since the IoT data are usually transmitted to the server for recognition (e.g., image classification) due to low computational capability and limited power supply, achieving recognition accuracy under limited bandwidth and noisy channel of wireless networks is a crucial but challenging task. In this paper, we propose a deep learning-constructed joint transmission-recognition scheme for the IoT devices to effectively transmit data wirelessly to the server for recognition, jointly considering transmission bandwidth, transmission reliability, complexity, and recognition accuracy. Compared with other schemes that may be deployed on the IoT devices, i.e., a scheme based on JPEG compression and two compressed sensing-based schemes, the proposed deep neural network-based scheme has much higher recognition accuracy under various transmission scenarios at all signal-to-noise ratios (SNRs). In particular, the proposed scheme maintains good performance at the very low SNR. Moreover, the complexity of the proposed scheme is low, making it suitable for IoT applications. Finally, a transfer learning-based training method is proposed to effectively mitigate the computing burden and reduce the overhead of online training.

关键词： Internet of things (IoT) recognition transmission joint source-channel coding deep learning deep neural networks transfer learning JPEG compressed sensing

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Fast algorithm for rate-based optimal error protection of embedded codes

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IEEE TRANSACTIONS ON COMMUNICATIONS 2003年第11期51卷 1788-1795页

作者： Stankovic, V Hamzaoui, R Saupe, D Texas A&M Univ Dept Elect Engn College Stn TX 77843 USA Univ Konstanz Dept Comp & Informat Sci D-78457 Constance Germany

Embedded image codes are very sensitive to channel noise because a single bit error can lead to an irreversible loss of synchronization between the encoder and the decoder. Sherwood and Zeger introduced a powerful system that protects an embedded wavelet image code with a concatenation of a cyclic redundancy check coder for error detection and a rate-compatible punctured convolutional coder for error correction. For such systems, Chande and Farvardin proposed an unequal error protection strategy that maximizes the expected number of correctly received source bits subject to a target transmission rate. Noting that an optimal strategy protects successive source blocks with the same channel code, we give an algorithm that accelerates the computation of the optimal strategy of Chande and Farvardin by finding an explicit formula for the number of occurences of the same channel code. Experimental results with two competitive channel coders and a binary symmetric channel showed that the speed-up factor over the approach of Chande and Farvardin ranged from 2.82 to 44.76 for transmission rates between 0.25 and 2 bits per pixel.

关键词： forward error correction (FEC) image coding image communication joint source-channel coding

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Robust transmission of multistage vector quantized sources over noisy communication channels-applications-to MELP speech codec

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2006年第6期55卷 1805-1811页

作者： Lahouti, Farshad Khandani, Amir K. Saleh, Aladdin Univ Tehran Sch Elect & Comp Engn Coll Engn Tehran 14395 Iran Univ Waterloo Dept Elect & Comp Engn Coding & Signal Transmiss Lab Waterloo ON N2L 3G1 Canada Wireless Technol Mississauga ON L4W 5N2 Canada

joint source-channel coding is an effective approach for the design of bandwidth efficient and error resilient communication systems with manageable complexity. An interesting research direction within this framework is the design of source decoders that exploit the residual redundancy for effective signal reconstruction at the receiver. Such source decoders are expected to replace the traditionally heuristic error concealment units that are elements of most multimedia communication systems. In this paper, we consider the reconstruction of signals encoded with a multistage vector quantizer (MSVQ) and transmitted over a noisy communications channel. The MSVQ maintains a moderate complexity and, due to its successive refinement feature, is a suitable choice for the design of layered (progressive) source codes. An approximate minimum mean squared error source decoder for MSVQ is presented, and its application to the reconstruction of the linear predictive coefficient (LPC) parameters in mixed excitation linear prediction (MELP) speech codec is analyzed. MELP is a low-rate standard speech codec suitable for bandwidth-limited communications and wireless applications. Numerical results demonstrate the effectiveness of the proposed schemes.

关键词： error concealment joint source-channel coding linear predictive coding (LPC) line spectral frequencies (LSFs) Markov model MELP minimum mean squared error (MMSE) estimation multistage vector quantization (MSVQ) residual redundancy source decoding

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Optimal quantization for finite-state channels

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IEEE TRANSACTIONS ON INFORMATION THEORY 1997年第2期43卷 758-765页

作者： Duman, TM Salehi, M Laboratory of Chromatography DEPg.Fac.Quimica Universidad Nacional Autonoma de Mexico Circuito interior Cd Universitaria/CP 04510 Mexico D.F.Mexico

Optimal scalar quantizer design for transmission over a finite-state channel is considered. The objective is to minimize the mean-squared error when the channel is in the normal mode of operation, while guaranteeing a minimum fidelity when the channel is in the ''bad'' state. An optimal quantizer design algorithm for the general case where noisy state information is available both at the receiver and at the transmitter is derived. It is shown that using mixed strategies is necessary in order to achieve the optimal performance. Finally, the case where the observation is noisy is considered and it is shown that the optimal scheme in this case is to apply the algorithm for the ''no observation noise'' to the mean-squared estimate of the desired random variable from the noisy data.

关键词： finite-state channels quantization joint source-channel coding data compression

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The Achievable Distortion Region of Sending a Bivariate Gaussian source on the Gaussian Broadcast channel

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IEEE TRANSACTIONS ON INFORMATION THEORY 2011年第10期57卷 6419-6427页

作者： Tian, Chao Diggavi, Suhas Shamai, Shlom (Shitz) AT&T Labs Res Florham Pk NJ 07932 USA Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA Technion Israel Inst Technol Dept Elect Engn IL-32000 Haifa Israel

We provide a complete characterization of the achievable distortion region for the problem of sending a bivariate Gaussian source over bandwidth-matched Gaussian broadcast channels, where each receiver is interested in only one component of the source. This setting naturally generalizes the simple single Gaussian source bandwidth-matched broadcast problem for which the uncoded scheme is known to be optimal. We show that a hybrid scheme can achieve the optimum for the bivariate case, but neither an uncoded scheme alone nor a separation-based scheme alone is sufficient. We further show that in this joint source channel coding setting, the Gaussian scenario is the worst scenario among the sources and channel noises with the same covariances.

关键词： Gaussian source joint source-channel coding squared error distortion measure

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channels With Cost Constraints: Strong Converse and Dispersion

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IEEE TRANSACTIONS ON INFORMATION THEORY 2015年第5期61卷 2415-2429页

作者： Kostina, Victoria Verdu, Sergio CALTECH Pasadena CA 91125 USA Princeton Univ Princeton NJ 08544 USA

This paper shows the strong converse and the dispersion of memoryless channels with cost constraints and performs a refined analysis of the third-order term in the asymptotic expansion of the maximum achievable channel coding rate, showing that it is equal to (1/2)((log n)/n) in most cases of interest. The analysis is based on a nonasymptotic converse bound expressed in terms of the distribution of a random variable termed the b-tilted information density, which plays a role similar to that of the d-tilted information in lossy source coding. We also analyze the fundamental limits of lossy joint-source-channel coding over channels with cost constraints.

关键词： Converse finite blocklength regime channels with cost constraints joint source-channel coding strong converse dispersion memoryless sources memoryless channels Shannon theory

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joint source-channel-authentication resource allocation and unequal authenticity protection for multimedia over wireless networks

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IEEE TRANSACTIONS ON MULTIMEDIA 2007年第4期9卷 837-850页

作者： Li, Zhi Sun, Qibin Lian, Yong Chen, Chang Wen Natl Univ Singapore Dept Elect & Comp Engn Singapore 117548 Singapore ASTAR Inst Infocomm Res Singapore 119613 Singapore Florida Inst Technol Dept Elect & Comp Engn Melbourne FL 32901 USA

There have been increasing concerns about the security issues of wireless transmission of multimedia in recent years. Wireless networks, by their nature, are more vulnerable to external intrusions than wired ones. Many applications demand authenticating the integrity of multimedia content delivered wirelessly. In this work, we describe a framework for jointly coding and authenticating multimedia to be delivered over heterogeneous wireless networks. We firstly introduce a novel concept called unequal authenticity protection (UAP), which unequally allocate resources to achieve an optimal authentication result. We then consider integrating UAP with specific source and channel-coding models, to obtain optimal end-to-end quality by the means of joint source-channel-authentication analysis. Lastly, we present an implementation of the proposed joint coding and authentication system on a progressive JPEG coder. Experimental results demonstrate that the proposed approach is indeed able to achieve the desired authentication of multimedia over wireless networks.

关键词： authentication digital signatures joint source-channel coding rate-distortion optimization unequal authenticity protection

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channel capacity and state estimation for state-dependent Gaussian channels

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IEEE TRANSACTIONS ON INFORMATION THEORY 2005年第4期51卷 1486-1495页

作者： Sutivong, A Chiang, M Cover, TM Kim, YH Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Dept Stat Stanford CA 94305 USA

We formulate a problem of state information transmission over a state-dependent channel with states known at the transmitter. In particular, we solve a problem of minimizing the mean-squared channel state estimation error E parallel to S-n - S-n parallel to for a state-dependent additive Gaussian channel Y-n = X-n + S-n + Z(n) with an independent and identically distributed (i.i.d.) Gaussian state sequence S-n = (S1,..., S-n) known at the transmitter and an unknown i.i.d. additive Gaussian noise Z(n). We show that a simple technique of direct state amplification (i.e., X-n = alpha S-n), where the transmitter uses its entire power budget to amplify the channel state, yields the minimum mean-squared state estimation error. This same channel can also be used to send additional independent information at the expense of a higher channel state estimation error. We characterize the optimal tradeoff between the rate R of the independent information that can be reliably transmitted and the mean-squared state estimation error D. We show that any optimal (R, D) tradeoff pair can be achieved via a simple power-sharing technique, whereby the transmitter power is appropriately allocated between pure information transmission and state amplification.

关键词： additive Gaussian noise channels channels with state-information joint source-channel coding state amplification state estimation

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Effective Communications: A joint Learning and Communication Framework for Multi-Agent Reinforcement Learning Over Noisy channels

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2021年第8期39卷 2590-2603页

作者： Tung, Tze-Yang Kobus, Szymon Roig, Joan Pujol Gunduz, Deniz Imperial Coll London Dept Elect & Elect Engn Informat Proc & Commun Lab IPC Lab London SW7 2AZ England Samsung Elect Res & Dev Inst UK Staines Upon Thames TW18 4QE England

We propose a novel formulation of the "effectiveness problem" in communications, put forth by Shannon and Weaver in their seminal work "The Mathematical Theory of Communication", by considering multiple agents communicating over a noisy channel in order to achieve better coordination and cooperation in a multi-agent reinforcement learning (MARL) framework. Specifically, we consider a multi-agent partially observable Markov decision process (MA-POMDP), in which the agents, in addition to interacting with the environment, can also communicate with each other over a noisy communication channel. The noisy communication channel is considered explicitly as part of the dynamics of the environment, and the message each agent sends is part of the action that the agent can take. As a result, the agents learn not only to collaborate with each other but also to communicate "effectively" over a noisy channel. This framework generalizes both the traditional communication problem, where the main goal is to convey a message reliably over a noisy channel, and the "learning to communicate" framework that has received recent attention in the MARL literature, where the underlying communication channels are assumed to be error-free. We show via examples that the joint policy learned using the proposed framework is superior to that where the communication is considered separately from the underlying MA-POMDP. This is a very powerful framework, which has many real world applications, from autonomous vehicle planning to drone swarm control, and opens up the rich toolbox of deep reinforcement learning for the design of multi-user communication systems.

关键词： Noise measurement Protocols channel coding Semantics Reinforcement learning Modulation Wireless communication Learning to communicate reinforcement learning (RL) multi-agent systems joint source-channel coding error correction coding

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source-channel rate allocation for progressive transmission of images

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IEEE TRANSACTIONS ON COMMUNICATIONS 2003年第2期51卷 186-196页

作者： Nosratinia, A Lu, J Aazhang, B Univ Texas Dept Elect Engn Richardson TX 75080 USA Sony Elect Inc San Jose CA 95134 USA Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA

Progressive image transmission is difficult in the presence of a noisy channel, mainly due to the propagation of errors during the decoding of a progressive bitstream. Excellent results for this problem are made possible through combined source-channel coding, a method that matches the channel code to the source operational rate distortion as well as channel conditions. This paper focuses on the key component of combined source-channel coding: rate allocation. We develop a parametric methodology for rate allocation, in progressive source-channel coding. The key to this technique is an empirical model of decoded bit-error rate as a function of the channel code rate. We investigate several scenarios. In the case of the memoryless channel, we present closed-form expressions. For the fading channel and channels with feedback, where closed-form results are elusive, our analysis leads to low-complexity algorithms.. The results presented in this paper are applicable to any progressive source code, and any family of channel codes.

关键词： embedded coding image compression joint source-channel coding progressive transmission rate allocation

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