Zero-Delay Source-Channel Coding With a Low-Resolution ADC Front End

Varasteh, Morteza; Rassouli, Borzoo; Simeone, Osvaldo; Gündüz, Deniz

doi:10.1109/tit.2017.2719708

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…Unlike conventional NOMA (see, e.g., [37]- [39]), in TBMA, the communication protocol is tailored to the information being transmitted and to the detection task. It can hence be interpreted as an example of joint source-channel coding, which is more generally receiving renewed interest for its potential spectral and power efficiency in IoT systems (see, e.g., [40]- [42]). A recent related work is [43] that introduces a novel Bayesian Message Passing technique with joint sourcechannel coding via a non-orthogonal generalization of TBMA; while in [44] a hybrid orthogonal and non-orthogonal multiple access channel based on TBMA was introduced with an optimized decision rule.…”

Section: Related Workmentioning

confidence: 99%

Information-Centric Grant-Free Access for IoT Fog Networks: Edge vs. Cloud Detection and Learning

Kassab

Simeone

Popovski

2020

IEEE Trans. Wireless Commun.

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A multi-cell Fog-Radio Access Network (F-RAN) architecture is considered in which Internet of Things (IoT) devices periodically make noisy observations of a Quantity of Interest (QoI) and transmit using grant-free access in the uplink. The devices in each cell are connected to an Edge Node (EN), which may also have a finite-capacity fronthaul link to a central processor. In contrast to conventional informationagnostic protocols, the devices transmit using a Type-Based Multiple Access (TBMA) protocol that is tailored to enable the estimate of the field of correlated QoIs in each cell based on the measurements received from IoT devices. In this paper, this form of information-centric radio access is studied for the first time in a multi-cell F-RAN model with edge or cloud detection. Edge and cloud detection are designed and compared for a multi-cell system. Optimal model-based detectors are introduced and the resulting asymptotic behavior of the probability of error at cloud and edge is derived. Then, for the scenario in which a statistical model is not available, data-driven edge and cloud detectors are discussed and evaluated in numerical results.

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Section: Related Workmentioning

confidence: 99%

Information-Centric Grant-Free Access for IoT Fog Networks: Edge vs. Cloud Detection and Learning

Kassab

Simeone

Popovski

2020

IEEE Trans. Wireless Commun.

Self Cite

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show abstract

“…Unlike conventional NOMA (see, e.g., [24] [25] [26]), in TBMA, the communication protocol is tailored to the information being transmitted and to the detection task. It can hence be interpreted as an example of joint sourcechannel coding, which is more generally receiving renewed interest for its potential spectral and power efficiency in IoT systems (see, e.g., [27] [28] [29]). To the best of our knowledge, TBMA has not been studied in multi-cell F-RAN systems.…”

Section: Related Workmentioning

confidence: 99%

Information-Centric Grant-Free Access for IoT Fog Networks: Edge vs Cloud Detection and Learning

Kassab¹,

Simeone²,

Popovski³

2019

Preprint

Self Cite

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A multi-cell Fog-Radio Access Network (F-RAN) architecture is considered in which Internet of Things (IoT) devices periodically make noisy observations of a Quantity of Interest (QoI) and transmit using grant-free access in the uplink. The devices in each cell are connected to an Edge Node (EN), which may also have a finite-capacity fronthaul link to a central processor. In contrast to conventional information-agnostic protocols, the devices transmit using a Type-Based Multiple Access (TBMA) protocol that is tailored to enable the estimate of the field of correlated QoIs in each cell based on the measurements received from IoT devices. TBMA has been previously introduced in the single-cell scenarios as a bandwidth-efficient data collection method, and is here studied for the first time in a multicell F-RAN model as an instance of information-centric access protocols. To this end, in this paper, edge and cloud detection are designed and compared for a multi-cell system. In the former case, detection of the local QoI is done locally at each EN, while, with the latter, ENs forward the received signals, upon quantization, over the fronthaul links to the central processor that carries out centralized detection of all QoIs. Optimal model-based detectors are introduced and the resulting asymptotic behavior of the probability of error at cloud and edge is derived. Then, for the scenario in which a statistical model is not available, data-driven edge and cloud detectors are discussed and evaluated in numerical results.

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“…Although these separated systems are not very robust to the channel variation, optimality can be achieved given that no constraints are considered in terms of complexity and delay. However, these systems have become unsuitable for certain emerging applications that require transmission in extreme latency constraints, such as those involving the internet of things (IoT) technologies [ 2 ] or wireless sensor networks (WSNs) [ 3 ]. Based on these applications scenarios, strict delay constraints are present owing to the near real-time monitoring and feedback between users and the underlying physical systems.…”

Section: Introductionmentioning

confidence: 99%

Zero-Delay Joint Source Channel Coding for a Bivariate Gaussian Source over the Broadcast Channel with One-Bit ADC Front Ends

Zhao

Chen

2021

Entropy

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In this work, we consider the zero-delay transmission of bivariate Gaussian sources over a Gaussian broadcast channel with one-bit analog-to-digital converter (ADC) front ends. An outer bound on the conditional distortion region is derived. Focusing on the minimization of the average distortion, two types of methods are proposed to design nonparametric mappings. The first one is based on the joint optimization between the encoder and decoder with the use of an iterative algorithm. In the second method, we derive the necessary conditions to develop the optimal encoder numerically. Using these necessary conditions, an algorithm based on gradient descent search is designed. Subsequently, the characteristics of the optimized encoding mapping structure are discussed, and inspired by which, several parametric mappings are proposed. Numerical results show that the proposed parametric mappings outperform the uncoded scheme and previous parametric mappings for broadcast channels with infinite resolution ADC front ends. The nonparametric mappings succeed in outperforming the parametric mappings. The causes for the differences between the performances of two nonparametric mappings are analyzed. The average distortions of the parametric and nonparametric mappings proposed here are close to the bound for the cases with one-bit ADC front ends in low channel signal-to-noise ratio regions.

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Zero-Delay Source-Channel Coding With a Low-Resolution ADC Front End

Cited by 4 publications

References 37 publications

Information-Centric Grant-Free Access for IoT Fog Networks: Edge vs. Cloud Detection and Learning

Information-Centric Grant-Free Access for IoT Fog Networks: Edge vs. Cloud Detection and Learning

Information-Centric Grant-Free Access for IoT Fog Networks: Edge vs Cloud Detection and Learning

Zero-Delay Joint Source Channel Coding for a Bivariate Gaussian Source over the Broadcast Channel with One-Bit ADC Front Ends

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