Complexity-Effective Sequential Detection of Synchronization Signal for Cellular Narrowband IoT Communication Systems

You, Young-Hwan; Jung, Yongju; Choi, Sung-Chan; Hwang, Intae

doi:10.1109/jiot.2020.3021101

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…According to (10), we obtain the superimposed data and pilot s ZF . Subsequently, we remove the superimposed interference from the pilot signal, obtaining the coarse data s d , which is expressed as…”

Section: Equalization Feature Extractionmentioning

confidence: 99%

“…Moreover, energy consumption is a critical concern in IoT systems. For instance, [10] aims to extend the battery lifetime of user equipment (UE) up to ten years. The time-division mode for transmitting pilots and data separately significantly increases energy consumption and makes it challenging to achieve the desired system targets.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, superimposed pilot-based method can effectively alleviate the problem of channel changes frequently and refine the estimation performance. As far as we know, with prolonged battery life of UE and enhanced spectral efficiency of the IoT system, the issue of improving the accuracy of the CE has not been well addressed in [10,22]. Therefore, it is beneficial to study the superimposed pilot-based CE for RIS-assisted systems.…”

Section: Introductionmentioning

confidence: 99%

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Superimposed Pilot-based Channel Estimation for RIS-assisted IoT Systems Using Lightweight Network

Qing,

Hu,

Wang

et al. 2024

Wireless Pers Commun

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Channel estimation (CE) in Internet of Things (IoT) systems faces challenges of low spectral efficiency, high energy consumption, and blocked propagation paths. To address these issues, this paper proposes a superimposed pilot-based CE scheme with reconfigurable intelligent surface (RIS) assistance. The pilot signal is superimposed on the uplink user data to improve spectral efficiency and reduce user equipment (UE) energy consumption. RIS is introduced to enhance communication robustness in the complex propagation environments. At the base station (BS), dedicated lightweight neural networks (NNs) are developed for CE and symbol detection (SD) to alleviate computational complexity and processing delay. The limited learning ability of these lightweight NNs is addressed by employing conventional CE and SD methods for initial feature extraction. This enables the NNs to learn along with the extracted features, reducing the required training set size. The proposed scheme improves spectral efficiency, reduces energy consumption, computational complexity, and processing delay, while enhancing the performance of both normalized mean square error (NMSE) of CE and the bit error rate (BER) of SD. Simulation results demonstrate the robustness of the proposed method against various parameter settings.

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Section: Equalization Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Superimposed Pilot-based Channel Estimation for RIS-assisted IoT Systems Using Lightweight Network

Qing,

Hu,

Wang

et al. 2024

Wireless Pers Commun

View full text Add to dashboard Cite

show abstract

“…One of the key study areas is the identification and analysis of nonstationary time series signals, which has been applied in a variety of industries and fields, including streaming media, meteorology, medicine, and business [7][8][9][10][11][12][13][14][15][16][17]. Researchers both domestically and internationally have been using deep learning techniques with significant self-learning capabilities to detect time series signals in recent years, but the signal recognition rate is still suboptimal [18][19][20][21][22][23]. With change point monitoring as a representative application scenario, the extraction of time series signal stationarity indicators and the development of deep learning techniques to enhance signal recognition accuracy represent an unavoidable trend and have significant practical implications.…”

Section: Introductionmentioning

confidence: 99%

Identification and Analysis of Non-Stationary Time Series Signals Based on Data Preprocessing and Deep Learning

Li¹,

Cai²,

Liu³

et al. 2022

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Deep learning is not the most accurate way for recognizing time series signals, and it is unable to identify non-stationary time series signals with numerous chaotic classes. Moreover, the signal detection benefits from data preprocessing have gone unnoticed. Therefore, this paper investigates the detection and analysis of non-stationary time series signals using deep learning and data preprocessing. The fitting model of the historical stationarity index is built based on the Gaussian mixture model of single Gaussian models, and the change point of the non-stationary time series signal is detected. To further increase the signal's recognition rate, the non-stationary time series signal is preprocessed using the truncated migration algorithm. The main classification task and the auxiliary classification tasks are constructed to identify non-stationary time series signals characterized by huge chaotic classes through multi-task learning. The efficiency of the suggested method and model is validated by experimental data.

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Joint resource allocation and beamforming design for RIS-aided symbiotic radio networks: A DRL approach

Li,

Liu,

Xie

2024

Digital Communications and Networks

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Complexity-Effective Sequential Detection of Synchronization Signal for Cellular Narrowband IoT Communication Systems

Cited by 7 publications

References 17 publications

Superimposed Pilot-based Channel Estimation for RIS-assisted IoT Systems Using Lightweight Network

Superimposed Pilot-based Channel Estimation for RIS-assisted IoT Systems Using Lightweight Network

Identification and Analysis of Non-Stationary Time Series Signals Based on Data Preprocessing and Deep Learning

Joint resource allocation and beamforming design for RIS-aided symbiotic radio networks: A DRL approach

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