An Enhanced PRACH Preamble Detector for Cellular IoT Communications

Kim, Taehoon; Bang, Inkyu; Sung, Dan Keun

doi:10.1109/lcomm.2017.2745543

Cited by 23 publications

(14 citation statements)

References 7 publications

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“…Generally, the preambles generated from the same root are completely orthogonal, and preambles generated from two different roots are nearly orthogonal [13]. To mitigate the interference among preambles generated from different root sequences, some published literature have specifically studied such correlations, and some results shown that their proposed preamble detectors can asymptotically achieve almost interference-free detection performance (i.e., nearly orthogonal) [40]. However, this is beyond the scope of this paper, and to focus on studying spatio-temporal model for RACH, we assume different preambles are completely orthogonal.…”

Section: B Contention-based Random Access Proceduresmentioning

confidence: 99%

Random Access Analysis for Massive IoT Networks Under a New Spatio-Temporal Model: A Stochastic Geometry Approach

Jiang

Deng

Kang

et al. 2018

IEEE Trans. Commun.

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Massive Internet of Things (mIoT) has provided an auspicious opportunity to build powerful and ubiquitous connections that faces a plethora of new challenges, where cellular networks are potential solutions due to their high scalability, reliability, and efficiency. The Random Access CHannel (RACH) procedure is the first step of connection establishment between IoT devices and Base Stations (BSs) in the cellular-based mIoT network, where modeling the interactions between static properties of physical layer network and dynamic properties of queue evolving in each IoT device are challenging. To tackle this, we provide a novel traffic-aware spatio-temporal model to analyze RACH in cellular-based mIoT networks, where the physical layer network is modeled and analyzed based on stochastic geometry in the spatial domain, and the queue evolution is analyzed based on probability theory in the time domain.For performance evaluation, we derive the exact expressions for the preamble transmission success probabilities of a randomly chosen IoT device with different RACH schemes in each time slot, which offer insights into effectiveness of each RACH scheme. Our derived analytical results are verified by the realistic simulations capturing the evolution of packets in each IoT device. This mathematical model and analytical framework can be applied to evaluate the performance of other types of RACH schemes in the cellular-based networks by simply integrating its preamble transmission principle.

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Section: B Contention-based Random Access Proceduresmentioning

confidence: 99%

Random Access Analysis for Massive IoT Networks Under a New Spatio-Temporal Model: A Stochastic Geometry Approach

Jiang

Deng

Kang

et al. 2018

IEEE Trans. Commun.

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“…The most computing-power is consumed during the calculation of correlations, so we consider the total number of correlation operations as a performance metric of the computational complexity. In our proposed channel estimator, the total number of correlation operations can be expressed as [24]:…”

Section: Algorithm 1 Pilot Signal Reconstructionmentioning

confidence: 99%

A Channel Estimator via Non-Orthogonal Pilot Signals for Uplink Cellular IoT

Kim

Chae

2019

IEEE Access

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Simultaneous acquisition of channel state information (CSI) of many devices with high accuracy is crucial to provide the Internet-of-Things (IoT) connectivity in cellular networks. A traditional channel estimator in cellular networks adopts the orthogonal pilot structure, which cannot provide high scalability for many IoT devices without a significant increase of pilot overhead. To overcome such a limitation, we propose a novel channel estimator based on non-orthogonal pilot signals for frequencydivision duplex (FDD) based uplink cellular IoT networks. The proposed scheme provides an interferencecanceled environment during the channel estimation procedure by reconstructing the non-orthogonal pilot signals and removing their effects. Consequently, the proposed scheme can increase the number of available pilot signals without any increase of pilot overhead, which makes the BS spectral-efficiently accommodate more IoT devices with full exploitation of antenna technique. The numerical results verify the effectiveness of the proposed scheme on supporting more IoT devices at the expense of a slight loss of the channel estimation accuracy, compared to the conventional discrete Fourier transform (DFT)-based channel estimator, from the viewpoint of a mean squared error, a bit error rate, and a network throughput.INDEX TERMS Channel estimation, non-orthogonal pilot signals, Zadoff-Chu sequence, the Internet-of-Things, cellular networks.

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“…Conventional grantbased random access (RA) protocols require control signaling and the scheduling of uplink access requests for granting of resources [3]- [6]. The physical random access channel (PRACH) protocol of Long-Term Evolution is one example of grant-based protocols, and can be classified into two categories: contention-free RA and contention-based RA [3]. For contention-free RA, the BS first allocates dedicated preambles to the active UEs, which are then transmitted by the UEs, and finally the BS responds to the requesting UEs without further contention resolution [4].…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing-Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO

Gao

et al. 2020

IEEE Trans. Signal Process.

315

302

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This paper considers massive access in massive multiple-input multiple-output (MIMO) systems and proposes an adaptive active user detection and channel estimation scheme based on compressive sensing. By exploiting the sporadic traffic of massive connected user equipments and the virtual angular domain sparsity of massive MIMO channels, the proposed scheme can support massive access with dramatically reduced access latency. Specifically, we design non-orthogonal pseudo-random pilots for uplink broadband massive access, and formulate the active user detection and channel estimation problems as a generalized multiple measurement vector compressive sensing problem. Furthermore, by leveraging the structured sparsity of the uplink channel matrix, we propose an efficient generalized multiple measurement vector approximate message passing (GMMV-AMP) algorithm to realize simultaneous active user detection and channel estimation based on a spatial domain or an angular domain channel model. To jointly exploit the channel sparsity presented in both the spatial and the angular domains for enhanced performance, a Turbo-GMMV-AMP algorithm is developed for detecting the active users and estimating their channels in an alternating manner. Finally, an adaptive access scheme is proposed, which adapts the access latency to guarantee reliable massive access for practical systems with unknown channel sparsity level. Additionally, the state evolution of the proposed GMMV-AMP algorithm is derived to predict its performance. Simulation results demonstrate the superiority of the proposed active user detection and channel estimation schemes compared to several baseline schemes.

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An Enhanced PRACH Preamble Detector for Cellular IoT Communications

Cited by 23 publications

References 7 publications

Random Access Analysis for Massive IoT Networks Under a New Spatio-Temporal Model: A Stochastic Geometry Approach

Random Access Analysis for Massive IoT Networks Under a New Spatio-Temporal Model: A Stochastic Geometry Approach

A Channel Estimator via Non-Orthogonal Pilot Signals for Uplink Cellular IoT

Compressive Sensing-Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO

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