Adaptive Activity-Aware Iterative Detection for Massive Machine-Type Communications

Renna, Roberto B. Di; Lamare, Rodrigo C. de

doi:10.1109/lwc.2019.2932674

Cited by 36 publications

(45 citation statements)

References 9 publications

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“…[15] reports a detection order method based on the activity probability of devices and the sorted QR decomposition (SQRD), in order to increase the efficiency of SA-SIC. The work in [16] investigates the reliability of each soft-estimate obtained by a regularized MMSE-SIC detector while [17] proposed an algorithm based on the direction method of multipliers (ADMM). The ones described in [18] and [19] are iterative and belong to the class of Bayesian inference algorithms.…”

Section: A Relevant Prior Artmentioning

confidence: 99%

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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 Relevant Prior Artmentioning

confidence: 99%

“…Rewriting the signal model in a way to increase the sparsity of the transmitted vector, the performance of the OLS is improved in [36]. By exchanging extrinsic information between active user detector and symbol detector, the schemes in [37]- [43] propose adaptive and iterative detectors that also employ channel coding.…”

Section: A Relevant Prior Artmentioning

confidence: 99%

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

et al. 2020

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“…The ML receiver calculates an estimate of the vector of symbols sent by the sourcesx[i]. Other suboptimal detection techniques could be considered in future work [17], [18], [19], [20], [21], [22], [54], [24], [25], [26], [27], [28], [31], [32], [33].…”

Section: B System Modelmentioning

confidence: 99%

Cloud-Driven Multi-Way Multiple-Antenna Relay Systems: Best-User-Link Selection and Joint Mmse Detection

Duarte

Lamare

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this work, we present a cloud-aided uplink framework for multi-way multiple-antenna relay systems which facilitates joint linear Maximum Likelihood (ML) symbol detection in the cloud and where users are selected to simultaneously transmit to each other aided by relays. We also investigate relay selection techniques for the proposed cloud-aided uplink framework that uses cloud-based buffers and physical-layer network coding. In particular, we develop a novel multi-way relay selection protocol based on the selection of the best link, denoted as Multi-Way Cloud-Aided Best-User-Link (MWC-Best-User-Link). We then devise the maximum minimum distance relay selection criterion along with the algorithm that is incorporated into the proposed MWC-Best-User-Link protocol. Simulations show that MWC-Best-User-Link outperforms previous works in terms of average delay, sum-rate and bit error rate.

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“…Considering perfect synchronization, we employ the ML receiver at the cluster-head processor: . Alternative suboptimal detection techniques could also be considered in future work [29], [30], [31], [32], [33], [34], [73], [36], [37], [38], [39], [40], [41], [46], [47], [48], [49].…”

Section: B System Descriptionmentioning

confidence: 99%

C-RAN-Type Cluster-Head-Driven UAV Relaying With Recursive Maximum Minimum Distance

Duarte

Lamare

2020

IEEE Commun. Lett.

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In this letter, a C-RAN-type cluster-head-driven uplink model for multiple-antenna Unmanned Aerial Vehicles (UAV) relaying schemes, which enables joint Maximum Likelihood (ML) symbol detection in the UAV cluster-head and the selection of UAV sources to communicate with each other aided by UAV-based relays, is presented. In this context, a relay selection technique, named Cluster-Head-Driven Best-Link (CHD-Best-Link), that employs cluster-head buffers and physical-layer network coding, is devised. Then, a recursive maximum minimum distance relay selection strategy that exploits time-correlated channels and equips the CHD-Best-Link scheme is developed. Simulations illustrate that CHD-Best-Link has superior average delay and bit error rate performances to that of previous schemes.

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Adaptive Activity-Aware Iterative Detection for Massive Machine-Type Communications

Cited by 36 publications

References 9 publications

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

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

Cloud-Driven Multi-Way Multiple-Antenna Relay Systems: Best-User-Link Selection and Joint Mmse Detection

C-RAN-Type Cluster-Head-Driven UAV Relaying With Recursive Maximum Minimum Distance

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