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A Spectrum Sharing Framework for Intelligent Next Generation Wireless Networks

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TLDR
This paper presents an open source software-defined radio-based framework that can be employed to devise disruptive techniques to optimize the sub-optimal use of radio spectrum that exists today and describes three use cases where the framework can beEmployed along with intelligent algorithms to achieve improved spectrum utilization.
Abstract
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 competing for the same spectrum band instead of harmoniously sharing it. 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 software-defined radio-based framework that can be employed to devise disruptive techniques to optimize the sub-optimal use of radio spectrum that exists today. We describe three use cases where the framework can be employed along with intelligent algorithms to achieve improved spectrum utilization. In addition, we provide several experimental results showing the performance and effectiveness of the proposed framework.

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Citations
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Journal ArticleDOI

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

TL;DR: A Deep Neural Network approach is presented that can predict spectrum occupation of unknown neighbouring networks in the near future by using online supervised learning in a multi-agent setting and can be employed by existing network schedulers to avoid collisions with surrounding networks or other electromagnetic sources.
Journal ArticleDOI

Multi-Agent Reinforcement Learning-Based Distributed Channel Access for Next Generation Wireless Networks

TL;DR: A new MAC protocol, QMIX-advanced Listen-Before-Talk (QLBT), based on the cutting-edge multi-agent reinforcement learning (MARL) algorithm is proposed, which outperforms CSMA/CA and even its theoretical performance bound in various scenarios including saturated traffic, unsaturated traffic and delay-sensitive traffic.
Proceedings ArticleDOI

Intelligent Tracking of Network Dynamics for Cross-Technology Coexistence Over Unlicensed Bands

TL;DR: This paper introduces the concept of Sensing Fingerprint (SF) profile to characterize the state of coexisting networks and track their dynamics over unlicensed bands, and conducts extensive experiments to show the effectiveness of SF profile in tracking key network dynamics.
Journal ArticleDOI

A Baseband Wireless Spectrum Hypervisor for Multiplexing Concurrent OFDM Signals.

TL;DR: A wireless spectrum hypervisor architecture that abstracts a radio frequency front-end into a configurable number of virtual RF front ends that multiplexes the signals of several different and concurrent multi-carrier-based radio access technologies with numerologies that are multiple integers of one another.
Proceedings ArticleDOI

Scatter Phy: A Physical Layer for the DARPA Spectrum Collaboration Challenge

TL;DR: The design and main features of SCATTER PHY are described and the experiments performed to verify the achieved benefits are showcased.
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