Two-Stage Multiple Access for Many Devices of Unique Identifications over Frequency-Selective Fading Channels

Choi, Jinho

doi:10.1109/jiot.2016.2636162

Cited by 35 publications

(27 citation statements)

References 26 publications

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“…In the literature, there are works that address the GFRA in different ways. In [89], sparse sequences were used instead of binary sequences for data signal spreading in order to increase the number of MTC devices and allow device identification. With the aim to reduce the metadata collision and improve the GFRA throughput, the work in [90] suggests the usage of multiple resource blocks.…”

Section: A General Assumptionsmentioning

confidence: 99%

Detection Techniques for Massive Machine-Type Communications: Challenges and Solutions

et al. 2020

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Massive machine-type communications (mMTC) is one of the key application scenarios of fifth generation (5G) and beyond cellular networks. Bringing the unique technical challenge of supporting a huge number of MTC devices (MTCD) in cellular networks, how to efficiently estimate the channel, detect the active users and data in this scenario is an open research topic. In this regard, this paper aims to present an overview of different techniques to address the problem of channel estimation, activity and data detection specifically for the mMTC scenario. In order to highlight potential solutions and to propose new research directions, we discuss the performance of the state-of-the-art techniques in the literature using a unified evaluation framework. INDEX TERMS 5G, channel estimation, detection, massive access, mMTC, random access.

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Section: A General Assumptionsmentioning

confidence: 99%

Detection Techniques for Massive Machine-Type Communications: Challenges and Solutions

et al. 2020

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“…In [117,118] a CS-based two-stages framework for joint data identification and detection is proposed for IoT applications using multi-carrier CDMA (MC-CDMA) with data transmitting scheme based on sparse index multiple access (SIMA) [119]. First, CS is exploited to estimate the channel state information (CSI) to determine the number of the devices/users that are actually transmitting data using OMP.…”

Section: Multi-user Detection and Identificationmentioning

confidence: 99%

“…CS-based Routing [90][91][92]140] Extend nodes lifetime Better signal quality • Build routing path by choosing sensing matrix that minimize the coherence with sparsyfing basis [90] • Utilize BP algorithm to recover the ensemble measurements [92] • Build the Sensing matrix by minimizing the coherence • Hybrid routing approach using PF and DCS [91] • Random walk (RN)with DCS [140] Processing • User detection in networks with CDMA based transmission [114][115][116] • Joint identification and detection in massive IoT with MC-CDMA [117,118,120,121] Cloud data Storing Efficient storage…”

Section: Iot Layer Approach Features Main Attributementioning

confidence: 99%

Compressive Sensing-Based IoT Applications: A Review

Djelouat

Amira

Bensaali

2018

JSAN

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The Internet of Things (IoT) holds great promises to provide an edge cutting technology that enables numerous innovative services related to healthcare, manufacturing, smart cities and various human daily activities. In a typical IoT scenario, a large number of self-powered smart devices collect real-world data and communicate with each other and with the cloud through a wireless link in order to exchange information and to provide specific services. However, the high energy consumption associated with the wireless transmission limits the performance of these IoT self-powered devices in terms of computation abilities and battery lifetime. Thus, to optimize data transmission, different approaches have to be explored such as cooperative transmission, multi-hop network architectures and sophisticated compression techniques. For the latter, compressive sensing (CS) is a very attractive paradigm to be incorporated in the design of IoT platforms. CS is a novel signal acquisition and compression theory that exploits the sparsity behavior of most natural signals and IoT architectures to achieve power-efficient, real-time platforms that can grant efficient IoT applications. This paper assesses the extant literature that has aimed to incorporate CS in IoT applications. Moreover, the paper highlights emerging trends and identifies several avenues for future CS-based IoT research.

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“…For example, in [19], CRA is used for the RACH procedure where joint channel estimation and preamble detection is carried out. In [14] [20] [21] [22] [23], CRA is used for grant-free transmission. In particular, in [14] [20] [21], joint channel estimation and data detection for CRA under a frequencyselective fading environment is considered.…”

Section: Introductionmentioning

confidence: 99%

NOMA-Based Compressive Random Access Using Gaussian Spreading

Choi

2019

IEEE Trans. Commun.

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Compressive random access (CRA) is a random access scheme that has been considered for massive machine-type communication (MTC) with non-orthogonal spreading sequences, where the notion of compressive sensing (CS) is used for lowcomplexity detectors by exploiting the sparse device activity.In this paper, we study the application of power-domain nonorthogonal multiple access (NOMA) to CRA in order to improve the performance of CRA (or increase the number of devices to be supported). We consider Gaussian spreading sequences and derive design criteria through a large-system analysis to determine the power levels for power-domain NOMA. From simulation results, we can confirm that the number of incorrectly detected device activity can be reduced by applying NOMA to CRA.

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Two-Stage Multiple Access for Many Devices of Unique Identifications over Frequency-Selective Fading Channels

Cited by 35 publications

References 26 publications

Detection Techniques for Massive Machine-Type Communications: Challenges and Solutions

Detection Techniques for Massive Machine-Type Communications: Challenges and Solutions

Compressive Sensing-Based IoT Applications: A Review

NOMA-Based Compressive Random Access Using Gaussian Spreading

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