Communication Theoretic Data Analytics

Chen, Kwang-Cheng; Huang, Shih-Chia; Zheng, Lizhong; Poor, H. Vincent

doi:10.1109/jsac.2015.2393471

Cited by 21 publications

(18 citation statements)

References 43 publications

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“…In [119], the authors mainly investigated the big data analytics over the communication system with discussions about statistical analysis and machine learning techniques. The authors pointed out that one of the critically associated challenges ahead was how to detect outliers in the context of big data.…”

Section: The Latest Research Progressmentioning

confidence: 99%

A survey of machine learning for big data processing

Qiu

Ding

et al. 2016

EURASIP J. Adv. Signal Process.

641

361

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There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them requires new ways of thinking and novel learning techniques to address the various challenges. In this paper, we present a literature survey of the latest advances in researches on machine learning for big data processing. First, we review the machine learning techniques and highlight some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning. Next, we focus on the analysis and discussions about the challenges and possible solutions of machine learning for big data. Following that, we investigate the close connections of machine learning with signal processing techniques for big data processing. Finally, we outline several open issues and research trends.

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Section: The Latest Research Progressmentioning

confidence: 99%

A survey of machine learning for big data processing

Qiu

Ding

et al. 2016

EURASIP J. Adv. Signal Process.

641

361

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“…For simplicity, in this paper, we do not allow any mixing between distinct messages (network coding [9]), focusing on the routing capacity. Then, for each set of δ (ℓ) ij that satisfies (19), one can obtain a layered network with fixed capacity δ (ℓ) ij σ 2,(ℓ) ij for each link (i, j) in the ℓ-th layer. This reduces to the traditional routing problem.…”

Section: Multi-hop Layered Networkmentioning

confidence: 99%

“…This reduces to the traditional routing problem. Hence, we can characterize the LIC capacity region of the 9 rate tuples by investigating achievable rate regions over all possible sets of δ (ℓ) ij subject to (19). Theorem 1: Consider a two-source two-destination multi-hop layered network illustrated in Fig.…”

Section: Multi-hop Layered Networkmentioning

confidence: 99%

“…5. Assume that δ (ℓ) ij 's satisfy the constraint of (19). Then, the feedback LIC capacity region of the full-feedback model is the same as that of the layeredfeedback model, and is given by…”

Section: B Multi-hop Layered Networkmentioning

confidence: 99%

“…It has been shown in [3] that the local geometric approach plays a crucial role in finding an investment strategy that maximizes an incremental growth rate in repeated investments [15]. The local geometric approach has also been exploited to a wide range of applications in machine learning: a learning problem in graphical models [16], an inference problem in hidden Markov models [17], [18], and big networked data analytics via communication and information theory [19], [20].…”

Section: Applications Of the Local Geometric Approachmentioning

confidence: 99%

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Euclidean Information Theory of Networks

Huang

Suh

Zheng

2015

IEEE Trans. Inform. Theory

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In this paper, we extend the information theoretic framework that was developed in earlier works to multi-hop network settings. For a given network, we construct a novel deterministic model that quantifies the ability of the network in transmitting private and common messages across users. Based on this model, we formulate a linear optimization problem that explores the throughput of a multi-layer network, thereby offering the optimal strategy as to what kind of common messages should be generated in the network to maximize the throughput. With this deterministic model, we also investigate the role of feedback for multi-layer networks, from which we identify a variety of scenarios in which feedback can improve transmission efficiency. Our results provide fundamental guidelines as to how to coordinate cooperation between users to enable efficient information exchanges across them.

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Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

You

Wang

Huang

et al. 2020

Sci. China Inf. Sci.

Self Cite

1,329

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The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

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Communication Theoretic Data Analytics

Cited by 21 publications

References 43 publications

A survey of machine learning for big data processing

A survey of machine learning for big data processing

Euclidean Information Theory of Networks

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

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