Predicting Channel Quality Indicators for 5G Downlink Scheduling in a Deep Learning Approach

Yin, Hao; Guo, Xiaojun; Liu, Pengyu; Hei, Xiaojun; Gao, Yayu

doi:10.48550/arxiv.2008.01000

Cited by 4 publications

(6 citation statements)

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“…The objective is to optimise the main utility function that is responsible to select the proper RAT that can minimize delay for safety user-cases. Furthermore, the study [51] proposed an online LSTM-based channel prediction approach and compared it with traditional Feedforward Neural Networks (FNN). The authors propose, this approach could be used in the future for RAT selection purposes and traffic scheduling.…”

Section: Multi-rats Selectionmentioning

confidence: 99%

“…Various deep learning methods are proposed in the literature to address the prediction of channel quality estimation in realtime. The works in [51], [52], and [55] have demonstrated that LSTM is an effective prediction technique for wireless channel quality assessment in different mobility patterns. The LSTM is a modified version of the common time-series deep learning Recurrent Neural Network (RNN) algorithm, but LSTM can effectively overcome the vanishing problem of RNN by implementing a forget gate.…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

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Service-Oriented LSTM Multi-Criteria RAT Selection Scheme for Vehicle-to-Infrastructure Communication

2022

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The evolution towards future Intelligent Transportation Systems (ITS) is significantly influenced by enabling telematics and safety services for vehicular use-cases, especially for autonomous driving. These vehicular use-cases have their own network requirements, with a variety of instantaneous requirements and dynamic characterizations that make delivering data via a single Radio Access Technology (RAT) severely challenging. By utilizing current and future network context and vehicular use-case requirements, the RAT selection scheme must select the optimum network that guarantees the continuity and high throughput data transfer. Moreover, the consideration of on-time stochastic queue backlog of all use-cases in each RATs, while maximizing throughput is important for a reliable vehicular use-case deployment. In this paper, we developed a Service-Oriented Joint LSTM Multi-Criteria (SOLMC) RAT selection scheme for Vehicle-to-Infrastructure (V2I) networks. The objective of this scheme is to maximize the overall network throughput while reducing the current stochastic queue backlog in deployed RATs. LSTM prediction technique is used as a network filter process that rejects the worst channel quality before the initialization RAT selection scheme. Transmission capacity, connectivity time, data delivery cost, and queue backlog are the decision criteria in our proposed SOLMC RAT scheme. In particular, to characterize the importance of these criteria factors, we construct Analytical Hierarchy Process (AHP) for each use-case. Extensive simulations are carried out under different network context conditions. The results demonstrate that the SOLMC scheme can significantly achieve up to 47.5% network throughput maximization and 20.42% packet delivery ratio improvement, using lower queue length compared to nearest-RAT-based selection. In addition, implementing LSTM in SOLMC improves the total average throughput by up to 3.1% and 6% packet delivery ratio.

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Section: Multi-rats Selectionmentioning

confidence: 99%

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Service-Oriented LSTM Multi-Criteria RAT Selection Scheme for Vehicle-to-Infrastructure Communication

2022

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“…In wireless communication systems, the quality of the channel plays a significant role in determining the overall communication quality. The channel quality indicator (CQI) is a critical parameter that determines the maximum achievable data rate of the system [ 1 , 2 ]. Advances in wireless communication technology have been focused on improving channel estimation performance and enhancing modulation schemes.…”

Section: Introductionmentioning

confidence: 99%

Multi-Parameter Estimation Method and Closed-Form Solution Study for k-µ Channel Model

Tian

Fan

et al. 2023

Sensors

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This paper proposes a novel multi-parameter estimation algorithm for the k-µ fading channel model to analyze wireless transmission performance in complex time-varying and non-line-of-sight communication scenarios involving moving targets. The proposed estimator offers a mathematically tractable theoretical framework for the application of the k-µ fading channel model in realistic scenarios. Specifically, the algorithm obtains expressions for the moment-generating function of the k-µ fading distribution and eliminates the gamma function using the even-order moment value comparison method. It then obtains two sets of solution models for the moment-generating function at different orders, which enable the estimation of the k and µ parameters using three sets of closed-form solutions. The k and µ parameters are estimated based on received channel data samples generated using the Monte Carlo method to restore the distribution envelope of the received signal. Simulation results show strong agreement between theoretical and estimated values for the closed-form estimated solutions. Additionally, the differences in complexity, accuracy exhibited under different parameter settings, and robustness under decreasing SNR may make the estimators suitable for different practical application scenarios.

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“…As a result, every device must notify the BS of its CQI regularly or periodically. The BS uses the CQI provided by the IoT device to select the modulation and coding scheme (MCS)[96,97]. According to Yin et al[96], an online training module enhances the downlink scheduling of the 5G New Radio (NR) system to lessen the detrimental effects of obsolete CQI due to communication degradation.…”

mentioning

confidence: 99%

“…The BS uses the CQI provided by the IoT device to select the modulation and coding scheme (MCS)[96,97]. According to Yin et al[96], an online training module enhances the downlink scheduling of the 5G New Radio (NR) system to lessen the detrimental effects of obsolete CQI due to communication degradation. CQI serves as a solution for underwater networks due to its ability to assess and communicate the quality of the communication channel.…”

mentioning

confidence: 99%

Internet of Underwater Things: A Survey on Simulation Tools and 5G-Based Underwater Networks

Nkenyereye,

Ndibanje

2024

Electronics

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The term “Internet of Underwater Things (IoUT)” refers to a network of intelligent interconnected underwater devices designed to monitor various underwater activities. The IoUT allows for a network of autonomous underwater vehicles (AUVs) to communicate with each other, sense their surroundings, collect data, and transmit them to control centers on the surface at typical Internet speeds. These data serve as a valuable resource for various tasks, including conducting crash surveys, discovering shipwrecks, detecting early signs of tsunamis, monitoring animal health, obtaining real-time aquatic information, and conducting archaeological expeditions. This paper introduces an additional set of alternative simulation tools for underwater networks. We categorize these tools into open-source and licensed simulator options and recommend that students consider using open-source simulators for monitoring underwater networks. There has not been widespread deployment or extensive research on underwater 5G-based networks. However, simulation tools provide some general insights into the challenges and potential issues associated with evaluating such networks, based on the characteristics of underwater communication and 5G, by surveying 5G-based underwater networks and 5G key aspects addressed by the research community in underwater network systems. Through an extensive review of the literature, we discuss the architecture of both Internet of Underwater application-assisted AUVs and Internet of Underwater Things communications in the 5G-based system.

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Predicting Channel Quality Indicators for 5G Downlink Scheduling in a Deep Learning Approach

Cited by 4 publications

References 6 publications

Service-Oriented LSTM Multi-Criteria RAT Selection Scheme for Vehicle-to-Infrastructure Communication

Service-Oriented LSTM Multi-Criteria RAT Selection Scheme for Vehicle-to-Infrastructure Communication

Multi-Parameter Estimation Method and Closed-Form Solution Study for k-µ Channel Model

Internet of Underwater Things: A Survey on Simulation Tools and 5G-Based Underwater Networks

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