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Today’s wired networks have become highly flexible, thanks to the fact that an increasing number of functionalities are realized by software rather than dedicated hardware. This trend is still in its early stages for wireless networks, but it has the potential to improve the network’s flexibility and resource utilization regarding both the abundant computational resources and the scarce radio spectrum resources. In this work we provide an overview of the enabling technologies for network reconfiguration, such as Network Function Virtualization, Software Defined Networking, and Software Defined Radio. We review frequently used terminology such as softwarization, virtualization, and orchestration, and how these concepts apply to wireless networks. We introduce the concept of Virtual Radio Function, and illustrate how softwarized/virtualized radio functions can be placed and initialized at runtime, allowing radio access technologies and spectrum allocation schemes to be formed dynamically. Finally we focus on embedded Software-Defined Radio as an end device, and illustrate how to realize the placement, initialization and configuration of virtual radio functions on such kind of devices.
Wei Liu; Joao F. Santos; Jonathan Van De Belt; Xianjun Jiao; Ingrid Moerman; Johann Marquez-Barja; Luiz DaSilva; Sofie Pollin. Enabling Virtual Radio Functions on Software Defined Radio for Future Wireless Networks. Wireless Personal Communications 2020, 113, 1579 -1595.
AMA StyleWei Liu, Joao F. Santos, Jonathan Van De Belt, Xianjun Jiao, Ingrid Moerman, Johann Marquez-Barja, Luiz DaSilva, Sofie Pollin. Enabling Virtual Radio Functions on Software Defined Radio for Future Wireless Networks. Wireless Personal Communications. 2020; 113 (3):1579-1595.
Chicago/Turabian StyleWei Liu; Joao F. Santos; Jonathan Van De Belt; Xianjun Jiao; Ingrid Moerman; Johann Marquez-Barja; Luiz DaSilva; Sofie Pollin. 2020. "Enabling Virtual Radio Functions on Software Defined Radio for Future Wireless Networks." Wireless Personal Communications 113, no. 3: 1579-1595.
This work leverages recent advances in machine learning for radio environment monitoring with context awareness, and uses the obtained information for creating radio slices that can optimally coexist with ongoing traffic in a given spectrum band. We instantiate radio slices as virtualised radios built on a software-defined radio platform. Then, we describe a proof-of-concept experiment that validates and demonstrates our proposed solution.
Wei Liu; Joao F. Santos; Xianjun Jiao; Francisco Paisana; Luiz A. DaSilva; Ingrid Moerman. Using Deep Learning and Radio Virtualisation for Efficient Spectrum Sharing Among Coexisting Networks. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019, 165 -174.
AMA StyleWei Liu, Joao F. Santos, Xianjun Jiao, Francisco Paisana, Luiz A. DaSilva, Ingrid Moerman. Using Deep Learning and Radio Virtualisation for Efficient Spectrum Sharing Among Coexisting Networks. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2019; ():165-174.
Chicago/Turabian StyleWei Liu; Joao F. Santos; Xianjun Jiao; Francisco Paisana; Luiz A. DaSilva; Ingrid Moerman. 2019. "Using Deep Learning and Radio Virtualisation for Efficient Spectrum Sharing Among Coexisting Networks." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 165-174.
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.
Felipe A. P. De Figueiredo; Ruben Mennes; Xianjun Jiao; Wei Liu; Ingrid Moerman. A Spectrum Sharing Framework for Intelligent Next Generation Wireless Networks. 2018, 1 .
AMA StyleFelipe A. P. De Figueiredo, Ruben Mennes, Xianjun Jiao, Wei Liu, Ingrid Moerman. A Spectrum Sharing Framework for Intelligent Next Generation Wireless Networks. . 2018; ():1.
Chicago/Turabian StyleFelipe A. P. De Figueiredo; Ruben Mennes; Xianjun Jiao; Wei Liu; Ingrid Moerman. 2018. "A Spectrum Sharing Framework for Intelligent Next Generation Wireless Networks." , no. : 1.
Driven by the fast growth of wireless communication, the trend of sharing spectrum among heterogeneous technologies becomes increasingly dominant. Identifying concurrent technologies is an important step towards efficient spectrum sharing. However, due to the complexity of recognition algorithms and the strict condition of sampling speed, communication systems capable of recognizing signals other than their own type are extremely rare. This work proves that multi-model distribution of the received signal strength indicator (RSSI) is related to the signals’ modulation schemes and medium access mechanisms, and RSSI from different technologies may exhibit highly distinctive features. A distinction is made between technologies with a streaming or a non-streaming property, and appropriate feature spaces can be established either by deriving parameters such as packet duration from RSSI or directly using RSSI’s probability distribution. An experimental study shows that even RSSI acquired at a sub-Nyquist sampling rate is able to provide sufficient features to differentiate technologies such as Wi-Fi, Long Term Evolution (LTE), Digital Video Broadcasting-Terrestrial (DVB-T) and Bluetooth. The usage of the RSSI distribution-based feature space is illustrated via a sample algorithm. Experimental evaluation indicates that more than 92% accuracy is achieved with the appropriate configuration. As the analysis of RSSI distribution is straightforward and less demanding in terms of system requirements, we believe it is highly valuable for recognition of wideband technologies on constrained devices in the context of dynamic spectrum access.
Wei Liu; Merima Kulin; Tarik Kazaz; Adnan Shahid; Ingrid Moerman; Eli De Poorter. Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices. Sensors 2017, 17, 2081 .
AMA StyleWei Liu, Merima Kulin, Tarik Kazaz, Adnan Shahid, Ingrid Moerman, Eli De Poorter. Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices. Sensors. 2017; 17 (9):2081.
Chicago/Turabian StyleWei Liu; Merima Kulin; Tarik Kazaz; Adnan Shahid; Ingrid Moerman; Eli De Poorter. 2017. "Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices." Sensors 17, no. 9: 2081.