30,222 research outputs found
An experimental assessment of channel selection in cognitive radio networks
The management of future networks is expected to fully exploit cognitive capabilities that embrace knowledge and intelligence, increasing the degree of automation, making the network more self-autonomous and enabling a personalized user experience. In this context, this paper presents the use of knowledge-based capabilities through a specific lab experiment focused on the Channel Selection functionality for Cognitive Radio Networks (CRN). The selection is based on a supervised classification that allows estimating the number of interfering sources existing in a given frequency channel. Four different classifiers are considered, namely decision tree, neural net-work, naive Bayes and Support Vector Machine (SVM). Additionally, a comparison against other channel selection strategies using Q-learning and game theory has also been performed. Results obtained in an illustrative and realistic test scenario have revealed that all the strategies allow identifying an optimum solution. However, the time to converge to this solution can be up to 27 times higher according to the algorithm selected.Peer ReviewedPostprint (author's final draft
Survey of Spectrum Sharing for Inter-Technology Coexistence
Increasing capacity demands in emerging wireless technologies are expected to
be met by network densification and spectrum bands open to multiple
technologies. These will, in turn, increase the level of interference and also
result in more complex inter-technology interactions, which will need to be
managed through spectrum sharing mechanisms. Consequently, novel spectrum
sharing mechanisms should be designed to allow spectrum access for multiple
technologies, while efficiently utilizing the spectrum resources overall.
Importantly, it is not trivial to design such efficient mechanisms, not only
due to technical aspects, but also due to regulatory and business model
constraints. In this survey we address spectrum sharing mechanisms for wireless
inter-technology coexistence by means of a technology circle that incorporates
in a unified, system-level view the technical and non-technical aspects. We
thus systematically explore the spectrum sharing design space consisting of
parameters at different layers. Using this framework, we present a literature
review on inter-technology coexistence with a focus on wireless technologies
with equal spectrum access rights, i.e. (i) primary/primary, (ii)
secondary/secondary, and (iii) technologies operating in a spectrum commons.
Moreover, we reflect on our literature review to identify possible spectrum
sharing design solutions and performance evaluation approaches useful for
future coexistence cases. Finally, we discuss spectrum sharing design
challenges and suggest future research directions
Exploiting programmable architectures for WiFi/ZigBee inter-technology cooperation
The increasing complexity of wireless standards has shown that protocols cannot be designed once for all possible deployments, especially when unpredictable and mutating interference situations are present due to the coexistence of heterogeneous technologies. As such, flexibility and (re)programmability of wireless devices is crucial in the emerging scenarios of technology proliferation and unpredictable interference conditions.
In this paper, we focus on the possibility to improve coexistence performance of WiFi and ZigBee networks by exploiting novel programmable architectures of wireless devices able to support run-time modifications of medium access operations. Differently from software-defined radio (SDR) platforms, in which every function is programmed from scratch, our programmable architectures are based on a clear decoupling between elementary commands (hard-coded into the devices) and programmable protocol logic (injected into the devices) according to which the commands execution is scheduled.
Our contribution is two-fold: first, we designed and implemented a cross-technology time division multiple access (TDMA) scheme devised to provide a global synchronization signal and allocate alternating channel intervals to WiFi and ZigBee programmable nodes; second, we used the OMF control framework to define an interference detection and adaptation strategy that in principle could work in independent and autonomous networks. Experimental results prove the benefits of the envisioned solution
Physical-Layer Security with Multiuser Scheduling in Cognitive Radio Networks
In this paper, we consider a cognitive radio network that consists of one
cognitive base station (CBS) and multiple cognitive users (CUs) in the presence
of multiple eavesdroppers, where CUs transmit their data packets to CBS under a
primary user's quality of service (QoS) constraint while the eavesdroppers
attempt to intercept the cognitive transmissions from CUs to CBS. We
investigate the physical-layer security against eavesdropping attacks in the
cognitive radio network and propose the user scheduling scheme to achieve
multiuser diversity for improving the security level of cognitive transmissions
with a primary QoS constraint. Specifically, a cognitive user (CU) that
satisfies the primary QoS requirement and maximizes the achievable secrecy rate
of cognitive transmissions is scheduled to transmit its data packet. For the
comparison purpose, we also examine the traditional multiuser scheduling and
the artificial noise schemes. We analyze the achievable secrecy rate and
intercept probability of the traditional and proposed multiuser scheduling
schemes as well as the artificial noise scheme in Rayleigh fading environments.
Numerical results show that given a primary QoS constraint, the proposed
multiuser scheduling scheme generally outperforms the traditional multiuser
scheduling and the artificial noise schemes in terms of the achievable secrecy
rate and intercept probability. In addition, we derive the diversity order of
the proposed multiuser scheduling scheme through an asymptotic intercept
probability analysis and prove that the full diversity is obtained by using the
proposed multiuser scheduling.Comment: 12 pages. IEEE Transactions on Communications, 201
Coordinated Dynamic Spectrum Management of LTE-U and Wi-Fi Networks
This paper investigates the co-existence of Wi-Fi and LTE in emerging
unlicensed frequency bands which are intended to accommodate multiple radio
access technologies. Wi-Fi and LTE are the two most prominent access
technologies being deployed today, motivating further study of the inter-system
interference arising in such shared spectrum scenarios as well as possible
techniques for enabling improved co-existence. An analytical model for
evaluating the baseline performance of co-existing Wi-Fi and LTE is developed
and used to obtain baseline performance measures. The results show that both
Wi-Fi and LTE networks cause significant interference to each other and that
the degradation is dependent on a number of factors such as power levels and
physical topology. The model-based results are partially validated via
experimental evaluations using USRP based SDR platforms on the ORBIT testbed.
Further, inter-network coordination with logically centralized radio resource
management across Wi-Fi and LTE systems is proposed as a possible solution for
improved co-existence. Numerical results are presented showing significant
gains in both Wi-Fi and LTE performance with the proposed inter-network
coordination approach.Comment: Accepted paper at IEEE DySPAN 201
Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
IEEE Access
Volume 3, 2015, Article number 7217798, Pages 1512-1530
Open Access
Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article)
Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc
a Department of Information Engineering, University of Padua, Padua, Italy
b Department of General Psychology, University of Padua, Padua, Italy
c IRCCS San Camillo Foundation, Venice-Lido, Italy
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Abstract
In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network
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