A Novel Spectrum Sharing Scheme Using Dynamic Long Short-Term Memory With CP-OFDMA in 5G Networks

Jacob, Sunil; Menon, Varun G.; Joseph, Saira; Vinoj, P G; Jolfaei, Alireza; Lukose, Jibin; Raja, Gunasekaran

doi:10.1109/tccn.2020.2970697

Cited by 41 publications

(18 citation statements)

References 17 publications

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“…Recently, LSTM networks [30] [31] have been used to model the temporal behavior of vehicles and humans. Ma [32] developed an LSTM network for highway vehicle speed prediction in which samples of historical speed data ending at time t are input and an estimate of the speed over the interval ∆t is output.…”

Section: Related Workmentioning

confidence: 99%

A Data-Driven Approach for Collision Risk Early Warning in Vessel Encounter Situations Using Attention-BiLSTM

Jia

Yang

et al. 2020

IEEE Access

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Collision risk early warning is critical to sailing safety in vessel encounter situations because it provides ship officers with sufficient time to react to emergencies and take evasive actions in advance. In this study, we take spatiotemporal motion behaviors of encountering vessels into account since vessel motion behaviors have great influences on the occurrence of a dangerous situation. For this purpose, a datadriven approach is proposed to associate the motion behaviors with the future risk and early prediction of risk is achieved through classifying the behaviors into corresponding risk level. Specifically, we first derive a sequence of relative motion features between encountering vessels to characterize the spatial interactions that vary over time. Then a novel deep learning architecture, which combines bidirectional long short-term memory (BiLSTM) and attention mechanism, is developed to capture the spatial-temporal dependences of behaviors as well as their impacts on future risk. In particular, the BiLSTM is able to discover correlations among behaviors and the attention mechanism can emphasize the key information relevant to the risk prediction task. Exploiting the advantages of these two mechanisms makes the risk prediction more reasonable and reliable. Extensive experiments using ship trace data from the Yangtze River Estuary demonstrate that the proposed Attention-BiLSTM approach outperforms conventional LSTM in terms of accuracy and stability. Moreover, the real-time capability of the approach gives it a significant potential for use in predicting collisions at the early stages.

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Section: Related Workmentioning

confidence: 99%

A Data-Driven Approach for Collision Risk Early Warning in Vessel Encounter Situations Using Attention-BiLSTM

Jia

Yang

et al. 2020

IEEE Access

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“…In general, one of the key parts of the IoT design focuses on the wireless communication system that acts as a data-bridge to offload the computation, storage, and access requirements from a large number of diversified applications and control messages. However, although evolving at the 5G era, the existing cellular technologies still lack the ability to support a large amount of data exchange from many battery-powered devices spread over a wide area [5], [6]. Even with the ongoing narrow-band IoT (NB-IoT) technology, which claims to support a deployment density of 200 000 NB-IoT devices within a cell, the network performance is still not satisfied after several field tests due to the overwhelming computations, complex signal fading, and differentiated Quality-of-Service (QoS) requirements, especially diversified data or service models.…”

Section: Introductionmentioning

confidence: 99%

Contention Resolution in Wi-Fi 6-Enabled Internet of Things Based on Deep Learning

Chen

Balasubramaniam

et al. 2021

IEEE Internet Things J.

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Internet of Things (IoT) is expected to vastly increase the number of connected devices. As a result, a multitude of IoT devices transmit various information through wireless communication technology, such as the Wi-Fi technology, cellular mobile communication technology, low-power wide-area network (LPWAN) technology. However, even the latest Wi-Fi technology is still ready to accommodate these large amounts of data. Accurately setting the contention window (CW) value significantly affects the efficiency of the Wi-Fi network. Unfortunately, the standard collision resolution used by IEEE 802.11ax networks is nonscalable; thus, it cannot maintain stable throughput for an increasing number of stations, even when Wi-Fi 6 has been designed to improve performance in dense scenarios. To this end, we propose a CW control strategy for Wi-Fi 6 systems. This strategy leverages deep learning to search for optimal configuration of CW under different network conditions. Our deep neural network is trained by data generated from a Wi-Fi 6 simulation system with some varying key parameters, e.g., the number of nodes, short interframe space (SIFS), distributed interframe space (DIFS), and data transmission rate. Numerical results demonstrated that our deep learning scheme could always find the optimal CW adjustment multiple by adaptively perceiving the channel competition status. The finalized performance of our model has been significantly improved in terms of system throughput, average transmission delay, and packet retransmission rate. This makes Wi-Fi 6 better adapted to the access of a large number of IoT devices.

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“…Due to the continued development of 5G and the gradual proliferation of 6G, the wireless data traffic is estimated to reach the unprecedented quantity. Most of the future dataintensive applications handling a massive number of connected IoT will demand high data rates with low latency in some scenarios (such as vehicle network, smart city, and remote health) [1,2]. MIMO, millimeter wave (mmWave), nonorthogonal multiple access (NOMA), and heterogeneous cellular network (HetNet) have been broadly investigated from all aspects of academia, research, and industry at home and abroad [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Power Allocation in Massive MIMO-HWSN Based on the Water-Filling Algorithm

Verma

Jin³

2021

Wireless Communications and Mobile Computing

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Pilot power allocation for Internet of Things (IoT) devices in massive multi-input multioutput heterogeneous wireless sensor networks (MIMO-HWSN) is studied in this paper. The interference caused by fractional pilot reusing in adjacent cells had a negative effect on the MIMO-HWSN system performance. Reasonable power allocation for users can effectively weaken the interference. Motivated by the water-filling algorithm, we proposed a suboptimal pilot transmission power method to improve the system capacity. Simulation results show that the proposed method can significantly improve the uplink capacity of the system and explain the influence of different pilot transmission power on the performance of the system, but the complexity of the system almost does not increase.

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A Novel Spectrum Sharing Scheme Using Dynamic Long Short-Term Memory With CP-OFDMA in 5G Networks

Cited by 41 publications

References 17 publications

A Data-Driven Approach for Collision Risk Early Warning in Vessel Encounter Situations Using Attention-BiLSTM

A Data-Driven Approach for Collision Risk Early Warning in Vessel Encounter Situations Using Attention-BiLSTM

Contention Resolution in Wi-Fi 6-Enabled Internet of Things Based on Deep Learning

Power Allocation in Massive MIMO-HWSN Based on the Water-Filling Algorithm

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