Aggregate Preamble Sequence Design and Detection for Massive IoT With Deep Learning

Mostafa, Ahmed Elhamy; Wong, Vincent W. S.; Zhou, Yong; Schober, Robert; Luo, Zhe; Liao, Shuri; Ding, Mengying

doi:10.1109/tvt.2021.3064868

Cited by 11 publications

(4 citation statements)

References 33 publications

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“…As AI is one of the key enabling technologies for 6G communication systems, many researchers have adapted it for improving the preamble detection process. In [8] a deep learning-based method was developed for the decoding of preambles. The study aggregated to ZC-sequences to mimic the effect of massive IoT devices for the use-case of 5G systems and designed two separate decoders.…”

Section: State-of-the-art Studiesmentioning

confidence: 99%

“…However, these techniques fail to generalize the performance on large number of devices due to the false peaks [4], [7]. Researchers have since then moved to machine learning approaches for preamble detection [8]. The problem with the existing methods employing machine learning is that they do not consider the random noise problem that can affect the data collection process itself.…”

Section: Introductionmentioning

confidence: 99%

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IIFNet: A Fusion based Intelligent Service for Noisy Preamble Detection in 6G

Khowaja¹,

Dev²,

Khuwaja³

et al. 2022

Preprint

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In this article, we present our vision of preamble detection in a physical random access channel for nextgeneration (Next-G) networks using machine learning techniques. Preamble detection is performed to maintain communication and synchronization between devices of the Internet of Everything (IoE) and next-generation nodes. Considering the scalability and traffic density, Next-G networks have to deal with preambles corrupted by noise due to channel characteristics or environmental constraints. We show that when injecting 15% random noise, the detection performance degrades to 48%. We propose an informative instance-based fusion network (IIFNet) to cope with random noise and to improve detection performance, simultaneously. A novel sampling strategy for selecting informative instances from feature spaces has also been explored to improve detection performance. The proposed IIFNet is tested on a real dataset for preamble detection that was collected with the help of a reputable commercial company.

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Section: State-of-the-art Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

IIFNet: A Fusion based Intelligent Service for Noisy Preamble Detection in 6G

Khowaja¹,

Dev²,

Khuwaja³

et al. 2022

Preprint

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“…By the year 2023, there will be 14.7 billion connected devices, with IoT devices making up half of that total [2]. It is envisioned that the number of concurrent connections increase from one million per square kilometre in 5G to ten million per square kilometre in 6G [3], [4]. Together with artificial intelligence (AI), the next generation of wireless technologies intends to enable the communication infrastructure required for the massive IoT (mIoT) use cases [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Rate-Convergence Tradeoff of Federated Learning over Wireless Channel

Salari¹,

Shirvanimoghaddam²,

Vucetic³

et al. 2022

Preprint

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In this paper, we consider a federated learning problem over wireless channel that takes into account the coding rate and packet transmission errors. Communication channels are modelled as packet erasure channels (PEC), where the erasure probability is determined by the block length, code rate, and signal-to-noise ratio (SNR). To lessen the effect of packet erasure on the FL performance, we propose two schemes in which the central node (CN) reuses either the past local updates or the previous global parameters in case of packet erasure. We investigate the impact of coding rate on the convergence of federated learning (FL) for both short packet and long packet communications considering erroneous transmissions. Our simulation results shows that even one unit of memory has considerable impact on the performance of FL in erroneous communication.

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“…The predefined rules and one-to-one mappings reduce the non-orthogonal interference, but also limit the preamble set size to some extent. Besides, preambles formed by the sum of randomly selected orthogonal sequences have also been proposed, where sequences are transmitted at different power levels [22], [23]. The power levels expand the preamble pool but reduce the accuracy of the AUD.…”

mentioning

confidence: 99%

Composite Preambles Based on Differential Phase Rotations for Grant-Free Random Access Systems

Wang

Juntti

et al. 2023

IEEE Internet Things J.

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With the advantages of low signaling overhead and latency, grant-free random access (GFRA) becomes a promising technology for supporting massive machine-type communications (mMTC), but poses new challenges for active user detection (AUD) and channel estimation (CE), whose performance mainly depends on the preamble detection. In this paper, we design the composite preamble based on differential phase rotations by aggregating orthogonal Zadoff-Chu (ZC) sequences and multiple root ZC sequences with differential phase rotations to reduce the probability of preamble collisions, thereby improving the performance of AUD and CE. In particular, differential phase rotations extend the preamble set size so that users colliding in orthogonal sequences can be distinguished by phase rotations. In addition, it also reduces non-orthogonal interference and thus reduces CE errors. The preamble detection algorithm and CE scheme are proposed, along with the theoretical analysis of AUD and CE performance to verify the effectiveness of the designed preamble. In addition, the proposed preamble is extended to combine phase rotations with cyclic shifts to further enlarge the preamble set size with low non-orthogonality. Simulation results show that the proposed composite preamble outperforms existing preambles in terms of the probability of detection and CE accuracy.

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Aggregate Preamble Sequence Design and Detection for Massive IoT With Deep Learning

Cited by 11 publications

References 33 publications

IIFNet: A Fusion based Intelligent Service for Noisy Preamble Detection in 6G

IIFNet: A Fusion based Intelligent Service for Noisy Preamble Detection in 6G

Rate-Convergence Tradeoff of Federated Learning over Wireless Channel

Composite Preambles Based on Differential Phase Rotations for Grant-Free Random Access Systems

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