Dynamic and Collaborative Spectrum Sharing: The SCATTER Approach

Giannoulis, Spilios; Mehari, Michael; Shahid, Adnan; Stojadinovic, Dragoslav; Claeys, Maxim; Mahfoudhi, Farouk; Liu, Wei; Seskar, Ivan; Latré, Steven; Moerman, Ingrid; Donato, Carlos; Mennes, Ruben; Figueiredo, Felipe Augusto Pereira de; Jabandzic, Irfan; Bock, Yorick De; Camelo, Miguel; Struye, Jakob; Maddala, Prasanthi

doi:10.1109/dyspan.2019.8935774

Cited by 17 publications

(18 citation statements)

References 6 publications

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“…If a new slot needs to be selected, the sender sends a request to the receiver for slot allocation. The sender proposes a number of slots, while the receiver selects one of them, based on the value of their local prediction [5]. Other nodes can overhear this communication, as long as there is no interference.…”

Section: B Darpa Sc2 Competition Experimental Setupmentioning

confidence: 99%

“…Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2020.2995456, IEEE Access As mentioned previously, the algorithm was also implemented in the SCATTER radio [5] . To compare our al- gorithm in the entire system, we compared against an Exponentially Weighted Moving Average (EWMA) collision avoidance slot selection algorithm.…”

Section: ) Csma Ap Topologymentioning

confidence: 99%

“…We run simulations to compare our algorithm with traditional slot selection algorithms. Also, this algorithm was implemented in the SCATTER radio [5], a wireless radio developed by the two times prize winner SCATTER team that reached 6th place in the DARPA Spectrum Collaboration Challenge (SC2) 1 . In this competition, teams were challenged to build a collaborative wireless radio to improve the Quality of Service (QoS) of all the networks in the same collision domain [6], [7].…”

Section: Introductionmentioning

confidence: 99%

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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: B Darpa Sc2 Competition Experimental Setupmentioning

confidence: 99%

Section: ) Csma Ap Topologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2020

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“…As shown in Figure 3 and described in [30], the SCATTER radio system exists out of five independent layers. These layers communicate via a message bus, using ZeroMQ, 1 through well-defined messages, using Google's Protobuf.…”

Section: A Overall Architecturementioning

confidence: 99%

Collaborative Flow Control in the DARPA Spectrum Collaboration Challenge

Mennes

Struye

Donato

et al. 2020

IEEE Trans. Netw. Serv. Manage.

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Wireless network technologies are becoming more and more popular. Because of this, important parts of the wireless spectrum become overloaded. Static spectrum allocation, which has been the norm for decades, is not suitable anymore. To maintain the high demand for spectrum and the continuous development of new wireless technologies, there is a need for an intelligent, dynamic spectrum allocation mechanism, where different network technologies collaboratively optimize the spectrum usage. New wireless network paradigms, such as Neutral Host Networks (NHNs) and private 5G, require a smart, spectrumfootprint-aware flow control algorithm to overcome the spectrum scarcity in collaborative way. This article presents a strategy, vision and flow control mechanism to implement collaboration in a Quality of Service (QoS)-driven way. The solution in this article is based on policies which may activate depending on its current and neighbor's network states. Through a flow ordering and selection strategy, these policies optimize the spectrum footprint, based on the performance and QoS-requirements of the own and surrounding networks. The proposed algorithm is tested extensively and validated on a large scale during the DARPA Spectrum Collaboration Challenge (SC2) competition. The results of the SC2 final event and intermediate scrimmages showed that the proposed approach increased the score, indicating increased inter-network collaboration was achieved. Index Terms-Quality of service, wireless flow control, wireless networks and cellular networks, wireless spectrum collaboration. I. INTRODUCTION C OLLABORATION in the wireless spectrum is key to further exploration of the capacity of today's wireless communication. Driven by the increasing need for wireless capacity [1], spectrum scarcity is a familiar phenomenon,

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“…The SCATTER team has designed and built a multi-layered system, with physical, Medium Access Control (MAC), Artificial Intelligence (AI), and other layers, which were developed and implemented as completely autonomous systems, but interconnected through a publish-subscribe messaging system known as ZeroMQ [3,4]. ZeroMQ (also known as 0MQ or ZMQ) is a high-performance asynchronous messaging library, aimed for use in concurrent or distributed applications [5].…”

Section: Introductionmentioning

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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Dynamic and Collaborative Spectrum Sharing: The SCATTER Approach

Cited by 17 publications

References 6 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

Collaborative Flow Control in the DARPA Spectrum Collaboration Challenge

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

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