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A random linear network coding approach to multicast

IEEE TRANSACTIONS ON INFORMATION THEORY 2006年第10期52卷 4413-4430页

作者： Ho, Tracey Medard, Muriel Koetter, Ralf Karger, David R. Effros, Michelle Shi, Jun Leong, Ben MIT LIDS Cambridge MA 02139 USA Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA MIT CSAIL Cambridge MA 02139 USA

We present a distributed random linear network coding approach for transmission and compression of information in general multisource multicast networks. Network nodes independently and randomly select linear mappings from inputs onto output links over some field. We show that this achieves capacity with probability exponentially approaching I with the code length. We also demonstrate that random linear coding performs compression when necessary in a network, generalizing error exponents for linear Slepian-Wolf coding in a natural way. Benefits of this approach are decentralized operation and robustness to network changes or link failures. We show that this approach can take advantage of redundant network capacity for improved success probability and robustness. We illustrate some potential advantages of random linear network coding over routing in two examples of practical scenarios: distributed network operation and networks with dynamically varying connections. Our derivation of these results also yields a new bound on required field size for centralized network coding on general multicast networks.

关键词： distributed compression distributed networking multicast network coding random linear coding

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Reduced-Complexity Optimization of distributed Quantization Using the Information Bottleneck Principle

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2021年 2卷 1267-1278页

作者： Steiner, Steffen Kuehn, Volker Stark, Maximilian Bauch, Gerhard Univ Rostock Inst Commun Engn D-18119 Rostock Germany Hamburg Univ Technol Inst Commun D-21073 Hamburg Germany

This paper addresses the optimization of distributed compression in a sensor network. A direct communication among the sensors is not possible so that noisy measurements of a single relevant signal have to be locally compressed in order to meet the rate constraints of the communication links to a common receiver. This scenario is widely known as the Chief Executive Officer (CEO) problem and represents a long-standing problem in information theory. In recent years significant progress has been achieved and the rate region has been completely characterized for specific distributions of involved processes and distortion measures. While algorithmic solutions of the CEO problem are principally known, their practical implementation quickly becomes challenging due to complexity reasons. In this contribution, an efficient greedy algorithm to determine feasible solutions of the CEO problem is derived using the information bottleneck (IB) approach. Following the Wyner-Ziv coding principle, the quantizers are successively designed using already optimized quantizer mappings as side-information. However, processing this side-information in the optimization algorithm becomes a major bottleneck because the memory complexity grows exponentially with number of sensors. Therefore, a sequential compression scheme leading to a compact representation of the side-information and ensuring moderate memory requirements even for larger networks is introduced. This internal compression is optimized again by means of the IB method. Numerical results demonstrate that the overall loss in terms of relevant mutual information can be made sufficiently small even with a significant compression of the side-information. The performance is compared to separately optimized quantizers and a centralized quantization. Moreover, the influence of the optimization order for asymmetric scenarios is discussed.

关键词： Distortion measurement Distortion Optimization Loss measurement Noise measurement Source coding Receivers Chief executive officer distributed compression distributed source coding information bottleneck

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Lossless Coding of Correlated Sources With Actions

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IEEE TRANSACTIONS ON INFORMATION THEORY 2017年第2期63卷 1237-1252页

作者： Sabag, Oron Permuter, Haim H. Cohen, Asaf Ben Gurion Univ Negev Dept Elect & Comp Engn IL-8410501 Beer Sheva Israel Ben Gurion Univ Negev Dept Commun Syst Engn IL-8410501 Beer Sheva Israel

This paper studies the problem of the distributed compression of correlated sources with an action-dependent joint distribution. This class of problems is, in fact, an extension of the Slepian-Wolf model, but where cost-constrained actions taken by the encoder or the decoder affect the generation of one of the sources. The purpose of this paper is to study the impact of actions on the achievable rates. In particular, two cases where transmission occurs over a rate-limited link are studied;case A for actions taken at the decoder and case B where actions are taken at the encoder. A complete single-letter characterization of the set of achievable rates is given in both cases. Furthermore, a network coding setup for the case where actions are taken at the encoder is investigated. The sources are generated at different nodes of the network and are required at a set of terminal nodes, yet transmission occurs over a general, acyclic, directed network. For this setup, generalized cut-set bounds are derived, and a full characterization of the set of achievable rates using single-letter expressions is provided. For this scenario, random linear network coding is proved to be optimal, even though this is not a classical multicast problem. In addition, two binary examples are investigated and demonstrate how actions taken at different nodes of the system have a significant effect on the achievable rate region, when compared with a naive time-sharing strategy.

关键词： Actions correlated sources distributed compression network coding random linear network coding Slepian-wolf source coding

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Gaussian Primitive Diamond Channel: Correlated Noise at Relays and Relevant Applications

Gaussian Primitive Diamond Channel: Correlated Noise at Rela...

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IEEE International Conference on Microwaves, Communications, Antennas, Biomedical Engineering and Electronic Systems (COMCAS)

作者： Katz, Asif Peleg, Michael Poor, H. Vincent Shamai, Shlomo (Shitz Technion Israel Inst Technol Haifa Israel Rafael Ltd Haifa Israel Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350348194;9798350348187

We investigate the special case of a symmetric Gaussian primitive diamond relay channel comprising a correlated Gaussian noise channel, and fronthaul links with limited rate from the relays to the destination. We use a combination of oblivious compress and forward (CF) with distributed Wyner-Ziv compression, and decode and forward (DF) techniques, where each relay decodes the whole message and sends half of its bits to the destination. We derive the CF achievable rate and investigate the effect of positive noise correlation on the system performance. The optimal time-sharing proportion between CF and DF is calculated for positive noise correlation.

关键词： Diamond Relay Channel Information Bottleneck Compress and Forward Decode and Forward distributed compression Correlated Gaussian noise channel

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The Filtered Gaussian Primitive Diamond Channel

The Filtered Gaussian Primitive Diamond Channel

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IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems (COMCAS)

作者： Katz, Asif Peleg, Michael Shamai (Shitz), Shlomo Technion Israel Inst Technol Haifa Israel Rafael Ltd Haifa Israel

ISBN: (纸本)9780738146720

We investigate the special case of diamond relay comprising a Gaussian channel with identical frequency response between the user and the relays and fronthaul links with limited rate from the relays to the destination. We use the oblivious compress and forward (CF) with distributed compression and decode and forward (DF) where each relay decodes the whole message and sends half of its bits to the destination. We derive the achievable rate by using optimal time-sharing between DF and CF, which is advantageous over superposition of CF and DF. The optimal time sharing proportion between DF and CF and power and rate allocations are different at each frequency and are fully determined.

关键词： Diamond Relay Channel Information Bottleneck Compress and Forward Decode and Forward distributed compression

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Kolmogorov Complexity Version of Slepian-Wolf Coding 2017

Kolmogorov Complexity Version of Slepian-Wolf Coding

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49th Annual ACM-SIGACT Symposium on Theory of Computing (STOC)

作者： Zimand, Marius Towson Univ Baltimore MD 21252 USA

ISBN: (纸本)9781450345286

Alice and Bob are given two correlated n-bit strings x1 and, respectively, x2, which they want to losslessly compress and send to Zack. They can either collaborate by sharing their strings, or work separately. We show that there is no disadvantage in the second scenario: Alice and Bob, without knowing the other party's string, can compress their strings to almost minimal description length in the sense of Kolmogorov complexity. Furthermore, compression takes polynomial time and can be made at any combination of lengths that satisfy some necessary conditions (modulo additive polylogarithmic terms). More precisely, there exist probabilistic algorithms E-1,E-2, and deterministic algorithm D, with E-1 and E-2 running in polynomial time, having the following behavior: if n(1), n(2) are two integers satisfying n(1) + n(2) >= C(x(1),x2), n(1) >= C(x(1) vertical bar x(2)), n(2) >= C(x(2) vertical bar x(1)), then for i is an element of{1,2}, E-i on input x(i) and n(i) outputs a string of length n(i) + O (log(3) n) such that D on input E-1 (x(1)), E-2 (x(2)) reconstructs (x(1), x(2)) with high probability (where C(x) denotes the plain Kolmogorov complexity of x, and C(x vertical bar y) is the complexity of x conditioned by y). Our main result is more general, as it deals with the compression of any constant number of correlated strings. It is an analog in the framework of algorithmic information theory of the classic Slepian-Wolf Theorem, a fundamental result in network information theory, in which x(1) and x(2) are realizations of two discrete random variables representing n independent draws from a joint distribution. In the classical result, the decompressor needs to know the joint distribution of the sources. In our result no type of independence is assumed and the decompressor does not have any prior information about the sources that are compressed.

关键词： distributed compression Kolmogorov complexity Slepian-Wolf theorem extractors

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Achievable Rate Optimization for Coordinated Multi-Point Transmission (CoMP) in Cloud-Based RAN Architecture

Achievable Rate Optimization for Coordinated Multi-Point Tra...

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IEEE International Conference on Communications (ICC)

作者： Qi, Yinan Imran, Muhammad Ali Quddus, Atta Tafazolli, Rahim Univ Surrey Ctr Commun Syst Res Guildford GU2 5XH Surrey England

ISBN: (纸本)9781479920037

In this paper, we consider Coordinated Multi-Point (CoMP) in a cloud-based radio access network (RAN), where each coordinating access point compresses its observation using distributed Wyner-Ziv compression and forwards the compressed signal to the receiving processing unit. We map this architecture to the multiple-relay compress-and-forward (CF) problem, derive the achievable rate of such a system and then show that the achievable rate can be maximized by optimizing the distributed compression rate at each individual coordinating point in a joint manner. An iterative optimization algorithm is proposed and numerical results indicate that compared with other coordinating schemes, when the channel between the coordinating points and the processing unit is strong, using distributed compression can effectively improve the spectrum efficiency.

关键词： CoMP distributed compression

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Optimal Backhaul Resource Allocation with User Pairing in Uplink Coordinated Multicarrier Networks 4

Optimal Backhaul Resource Allocation with User Pairing in Up...

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International Conference on Identification, Information, and Knowledge in the Internet of Things (IIKI)

作者： Gao, Yuan Hong, Ao Zhou, Quan Jian, Chu Zhou, Bo Zhou, Weigui Li, Yi Xichang Satellite Launch Ctr Xichang 615000 Peoples R China Tsinghua Univ Natl Lab Informat Sci & Technol State Key Lab Microwave & Digital Commun Beijing 100084 Peoples R China China Def Sci & Technol Informat Ctr Beijing 100030 Peoples R China

ISBN: (纸本)9781467386371

With the increasing demand of transmission speed and more robust quality of service, the capacity constrained backhaul gradually becomes bottleneck in cooperative wireless networks, e.g. in Joint Processing mode of LTE-Advanced Pro. This paper focuses the fact of resource allocation with limited backhaul capacity in uplink cooperative networks, where two base stations (BS) equipped with single-antenna each serving multiple users with single-antenna via multi-carrier transmission mode. We propose a novel cooperative scheme based on compress-and-forward with user pairing to solve the problem. To maximize the system capacity under the limited backhaul capacity constraint, we formulate the joint optimization problem of user pairing, subcarrier mapping and backhaul capacity sharing among different pairs (subcarriers). A novel efficient algorithm based on alternating optimization strategy and perfect mapping is proposed to solve this mixed integer programming problem. Simulations show that our novel method can improve the system capacity significantly under the constraint of the backhaul capacity compared with the blind alternatives.

关键词： distributed compression backhaul optimization user pairing uplink CoMP virtual MIMO

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compression Schemes for Time-Varying Sparse Signals 48th

Compression Schemes for Time-Varying Sparse Signals

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48h Asilomar Conference on Signals, Systems and Computers

作者： Chepuri, Sundeep Prabhakar Leus, Geert Delft Univ Technol Fac Elect Engn Math & Comp Sci NL-2600 AA Delft Netherlands

ISBN: (纸本)9781479982974

In this paper, we will investigate an adaptive compression scheme for tracking time-varying sparse signals with possibly varying sparsity patterns and/or order. In particular, we will focus on sparse sensing, which enables a completely distributed compression and simplifies the sampling architecture. The sensing matrix is designed at each time step based on the entire history of measurements and known dynamics such that the information gain is maximized. We illustrate the developed theory with a target tracking example. Finally, we provide a few extensions of the proposed framework to include a richer class of sparse signals, e.g., structured sparsity and smoothness.

关键词： Structured sensing sensor selection sparsity-aware Kalman filter sparse sensing adaptive compressed sensing distributed compression big data

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Reducing Communication Overhead via CEO in distributed Training 20

Reducing Communication Overhead via CEO in Distributed Train...

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20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Abdi, Afshin Fekri, Faramarz Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)9781538665282

One of the main challenges in distributed training, especially in sensor networks, is the communication cost due to the transmission of the parameters of the model and synchronization across nodes, a.k.a. workers. We frame this problem as a central estimation officer (CEO) problem;starting from an initial guess for the model shared by all workers and the server, at each iteration of training, the workers refine the model parameters by training over their available dataset and send them to the server. The server merges the received information from the workers to get a better estimate of the optimum parameter of the model and sends it back to the workers. We propose using CEO framework to tackle the communication issue in distributed training. The proposed method consists of three major blocks: 1) dithered and nested quantization at the workers, 2) distributed source coding to incorporate the correlation among workers for further reduction in communication bit rate, and 3) decoding the data received from the workers and estimating the optimum parameters at the server. We have verified the effectiveness of our proposed approach via simulations. We have shown that our proposed method reduces the communication bit-rate compared to the other existing methods while it can achieve nearly the same convergence speed as of the baseline training (no quantization of the parameters) in our experiments.

关键词： distributed Learning CEO Problem Nested Quantization distributed compression

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