A neural-network-based MF-TDMA MAC scheduler for collaborative wireless networks

Mennes, Ruben; Camelo, Miguel; Claeys, Maxim; Latré, Steven

doi:10.1109/wcnc.2018.8377044

Cited by 28 publications

(22 citation statements)

References 14 publications

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“…Simulation results show that the proposed Q-Learning based approach improves the overall system capacity performance by 19 % and Wi-Fi capacity performance by 77% when compared to a scenario with fixed duty cycles where highest aggregate capacity is achieved. The results show that the approach In [37] the authors demonstrate that a Neural Network (NN) can accurately predict slots in a Multiple Frequencies Time Division Multiple Access (MF-TDMA) network. Through spectrum observation, the proposed Neural Network models are able to do online learning and predict the behavior of spectrum usage a second in advance.…”

Section: Related Workmentioning

confidence: 99%

“…It allows upper layers to access spectrum sensing measurements, which can be used to train ML and AI modules to better understand the environment, optimize the spectrum usage/sharing and cooperatively work with other networks without any previous knowledge on the other network's operation and implementation, i.e., without any co-design [61]. For instance, this module allows the implementation of adaptive carrier selection algorithms such as CSAT [62] and can also [37].…”

Section: Rf Monitormentioning

confidence: 99%

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A Spectrum Sharing Framework for Intelligent Next Generation Wireless Networks

Figueiredo¹,

Mennes²,

Jiao³

et al. 2018

Preprint

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The explosive emergence of wireless technologies and standards, covering licensed and unlicensed spectrum bands has triggered the appearance of a huge amount of wireless technologies, with many of them coexisting in the same band. Unfortunately, the wireless spectrum is a scarce resource, and the available frequency bands will not scale with the foreseen demand for new capacity. Certain parts of the spectrum, in particular the license-free ISM bands, are overcrowded, while other parts, mostly licensed bands, may be significantly underutilized. As such, there is a need to introduce more advanced techniques to access and share the wireless medium, either to improve the coordination within a given band, or to explore the possibilities of intelligently using unused spectrum in underutilized (e.g., licensed) bands. Therefore, in this paper, we present an open source SDR-based framework that can be employed to devise disruptive techniques to optimize the sub-optimal use of radio spectrum that exists today. Additionally, we describe three use cases where the proposed framework can be employed along with intelligent algorithms to achieve improved spectrum utilization.

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Section: Related Workmentioning

confidence: 99%

Section: Rf Monitormentioning

confidence: 99%

A Spectrum Sharing Framework for Intelligent Next Generation Wireless Networks

Figueiredo¹,

Mennes²,

Jiao³

et al. 2018

Preprint

Self Cite

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“…Deep learning can be a good alternative for interference management, spectrum management, multi-path usage, link adaptation, multi-channel access, and traffic congestion. For instance, the authors of [23] proposed an AI scheduler to infer the free slots in a multiple frequencies time division multiple access to avoid congestion and high packet loss. Four last frames state are fed to a neural network, which consists of two fully connected hidden layers.…”

Section: Intelligent Radio Resource and Network Managementmentioning

confidence: 99%

Artificial Intelligence for 5G Wireless Systems: Opportunities, Challenges, and Future Research Direction

Arjoune

Faruque

2020

2020 10th Annual Computing and Communication Workshop and Conference (CCWC)

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The advent of the wireless communications systems augurs new cutting-edge technologies, including self-driving vehicles, unmanned aerial systems, autonomous robots, the Internet of-Things, and virtual reality. These technologies require high data rates, ultra-low latency, and high reliability, all of which are promised by the fifth generation of wireless communication systems (5G). Many research groups state that 5G cannot meet its demands without artificial intelligence (AI) integration as 5G wireless networks are expected to generate unprecedented traffic giving wireless research designers access to big data that can help in predicting the demands and adjust cell designs to meet the users' requirements. Subsequently, many researchers applied AI in many aspects of 5G wireless communication design including radio resource allocation, network management, and cyber-security. In this paper, we provide an in-depth review of AI for 5G wireless communication systems. In this respect, the aim of this paper is to survey AI in 5G wireless communication systems by discussing many case studies and the associated challenges, and shedding new light on future research directions for leveraging AI in 5G wireless communications.

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“…The authors of [9] employed a Deep Learning Neural Network (DLNN) for the slot prediction task. They demonstrated through simulations that their trained DLNN did online learning and could accurately predict, through spectrum monitoring, the behavior of spectrum usage (i.e., slot transmissions) one second in advance in the context of Multiple Frequency Time Division Multiple Access (MF-TDMA) networks.…”

Section: Related Workmentioning

confidence: 99%

“…A novel frame structure with a focus on higher throughput (with 32 Modulation Code Scheme values (MCS), achieving more than 84 megabits per second (Mbps) for a configured PHY bandwidth (BW) of 9 MHz and MCS 31) and with control signals being sent by the robust and more reliable M-sequences instead of LTE-like control channels.…”

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confidence: 99%

SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge

et al. 2019

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DARPA, the Defense Advanced Research Projects Agency from the United States, has started the Spectrum Collaboration Challenge with the aim to encourage research and development of coexistence and collaboration techniques of heterogeneous networks in the same wireless spectrum bands. Team SCATTER has been participating in the challenge since its beginning, back in 2016. SCATTER’s open-source software defined physical layer (SCATTER PHY) has been developed as a standalone application, with the ability to communicate with higher layers through a set of well defined messages (created with Google’s Protocol buffers) and that exchanged over a ZeroMQ bus. This approach allows upper layers to access it remotely or locally and change all parameters in real time through the control messages. SCATTER PHY runs on top of USRP based software defined radio devices (i.e., devices from Ettus or National Instruments) to send and receive wireless signals. It is a highly optimized and real-time configurable SDR based PHY layer that can be used for the research and development of novel intelligent spectrum sharing schemes and algorithms. The main objective of making SCATTER PHY available to the research and development community is to provide a solution that can be used out of the box to devise disruptive algorithms and techniques to optimize the sub-optimal use of the radio spectrum that exists today. This way, researchers and developers can mainly focus their attention on the development of smarter (i.e., intelligent algorithms and techniques) spectrum sharing approaches. Therefore, in this paper, we describe the design and main features of SCATTER PHY and showcase several experiments performed to assess the effectiveness and performance of the proposed PHY layer.

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A neural-network-based MF-TDMA MAC scheduler for collaborative wireless networks

Cited by 28 publications

References 14 publications

A Spectrum Sharing Framework for Intelligent Next Generation Wireless Networks

A Spectrum Sharing Framework for Intelligent Next Generation Wireless Networks

Artificial Intelligence for 5G Wireless Systems: Opportunities, Challenges, and Future Research Direction

SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge

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