Deep Learning-Based Spectrum Prediction Collision Avoidance for Hybrid Wireless Environments

Mennes, Ruben; Claeys, Maxim; Figueiredo, Felipe Augusto Pereira de; Jabandzic, Irfan; Moerman, Ingrid; Latré, Steven

doi:10.1109/access.2019.2909398

Cited by 35 publications

(19 citation statements)

References 22 publications

(28 reference statements)

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“…Note that there are clear differences with our previous work described in [29]. In [29], the observation O n t,c was defined as the energy detected at node n at time t at channel c produced by the INC.…”

Section: Problem Formulationmentioning

confidence: 98%

Multi-Agent Deep Learning for Multi-Channel Access in Slotted Wireless Networks

2020

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As the number of devices connected to the internet and the amount of data they generate increases, the wireless spectrum is becoming an essential and scarce resource. Most connected devices use wireless technologies that use the industrial, scientific, and medical (ISM) radio bands. As a result, different technologies are interfering with each other. Today's existing collision avoidance techniques either apply a random back-off when a signal collision is detected or assume that knowledge about other nodes' spectrum occupation is known. These approaches are competent approaches to optimise inter-network spectrum usage, but fail to optimise overall channel capacity and throughput of all neighbouring wireless networks. In this paper, we present a Deep Neural Network (DNN) approach that can predict spectrum occupation of unknown neighbouring networks in the near future by using online supervised learning in a multi-agent setting. This prediction can be employed by existing network schedulers to avoid collisions with surrounding networks or other electromagnetic sources. The DNN is trained in an online way, as the problem is a partially observable stochastic game with continuous action space. Our findings show a reduction in the number of collisions between the own network and neighbouring networks of 30%, and an increase in overall throughput of 10% in a medium-sized network with an unknown set of neighbouring networks.

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Section: Problem Formulationmentioning

confidence: 98%

Multi-Agent Deep Learning for Multi-Channel Access in Slotted Wireless Networks

2020

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“…This use case is similar to the one described by Figure 6. Mainly aligned with this use case, 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 smarter/more intelligent coexistence and collaboration techniques of heterogeneous networks in the same wireless spectrum bands [31,32]. One of the examples they have been advocating for is the adoption of such spectrum-sharing technologies in the CBRS band [33,34].…”

Section: A/b B/a C/a A/d B/dmentioning

confidence: 99%

A Baseband Wireless Spectrum Hypervisor for Multiplexing Concurrent OFDM Signals

Figueiredo

Mennes

Jabandzic

et al. 2020

Sensors

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The next generation of wireless and mobile networks will have to handle a significant increase in traffic load compared to the current ones. This situation calls for novel ways to increase the spectral efficiency. Therefore, in this paper, we propose a wireless spectrum hypervisor architecture that abstracts a radio frequency (RF) front-end into a configurable number of virtual RF front ends. The proposed architecture has the ability to enable flexible spectrum access in existing wireless and mobile networks, which is a challenging task due to the limited spectrum programmability, i.e., the capability a system has to change the spectral properties of a given signal to fit an arbitrary frequency allocation. The proposed architecture is a non-intrusive and highly optimized wireless hypervisor that multiplexes the signals of several different and concurrent multi-carrier-based radio access technologies with numerologies that are multiple integers of one another, which are also referred in our work as radio access technologies with correlated numerology. For example, the proposed architecture can multiplex the signals of several Wi-Fi access points, several LTE base stations, several WiMAX base stations, etc. As it able to multiplex the signals of radio access technologies with correlated numerology, it can, for instance, multiplex the signals of LTE, 5G-NR and NB-IoT base stations. It abstracts a radio frequency front-end into a configurable number of virtual RF front ends, making it possible for such different technologies to share the same RF front-end and consequently reduce the costs and increasing the spectral efficiency by employing densification, once several networks share the same infrastructure or by dynamically accessing free chunks of spectrum. Therefore, the main goal of the proposed approach is to improve spectral efficiency by efficiently using vacant gaps in congested spectrum bandwidths or adopting network densification through infrastructure sharing. We demonstrate mathematically how our proposed approach works and present several simulation results proving its functionality and efficiency. Additionally, we designed and implemented an open-source and free proof of concept prototype of the proposed architecture, which can be used by researchers and developers to run experiments or extend the concept to other applications. We present several experimental results used to validate the proposed prototype. We demonstrate that the prototype can easily handle up to 12 concurrent physical layers.

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“…This use case is similar to the one described by Figure 6. Mainly aligned with this use case, 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 smarter/more intelligent coexistence and collaboration techniques of heterogeneous networks in the same wireless spectrum bands [54,55]. One of the examples they have been advocating for is the adoption of such spectrum-sharing technologies in the CBRS band [56].…”

Section: A Dynamic Spectrum Accessmentioning

confidence: 99%

A Base-Band Wireless Spectrum Hypervisor for Multiplexing Concurrent OFDM signals

Figueiredo¹,

Mennes²,

Jabandzic³

et al. 2019

Preprint

Self Cite

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The next generation of wireless and mobile networks will have to handle a significant increase in traffic load compared to the actual one. This situation calls for novel ways to increase spectral efficiency. Therefore in this paper, we propose a wireless spectrum hypervisor architecture that abstracts a radio frequency (RF) front-end into a configurable number of virtual RF front-ends. The proposed architecture has the ability to enable flexible spectrum access in existing wireless and mobile networks, which is a challenging task due to the limited spectrum programmability, $i.e.$, the capability a system has to change the spectral properties of a given signal to fit an arbitrary frequency allocation. The main goal of the proposed approach is to improve spectral efficiency by efficiently using vacant gaps in congested spectrum-bandwidths or adopting network densification through infrastructure sharing. We demonstrate mathematically how our proposed approach works and present several simulation results proving its functionality and efficiency. Additionally, we designed and implemented an open-source and free proof of concept prototype of the proposed architecture, which can be used by researchers and developers to run experiments or extend the concept to other applications. We present several experimental results used to validate the proposed prototype. We demonstrate that the prototype can easily handle up to 12 concurrent physical layers.

show abstract

Deep Learning-Based Spectrum Prediction Collision Avoidance for Hybrid Wireless Environments

Cited by 35 publications

References 22 publications

Multi-Agent Deep Learning for Multi-Channel Access in Slotted Wireless Networks

Multi-Agent Deep Learning for Multi-Channel Access in Slotted Wireless Networks

A Baseband Wireless Spectrum Hypervisor for Multiplexing Concurrent OFDM Signals

A Base-Band Wireless Spectrum Hypervisor for Multiplexing Concurrent OFDM signals

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