A Monitoring Network for Spectrum Governance

Dynamic Spectrum Access (DSA) is an exciting new technology, which has introduced a paradigm shift in spectrum access. As a result it also changes the role of the regulator. On one hand the scarce radio spectrum should be used in an optimal way, so that society is best served. On the other hand interference between users and between networks should be avoided. For that reason rules have to be defined for spectrum use. This topic is called spectrum governance. For evaluation and to check whether devices obey the rules, a monitoring system is needed. In this paper, we propose to use a fleet of mobile monitoring vehicles for this purpose.\u

Spectrum occupancy measurements and lessons learned in the context of cognitive radio

Various measurement campaigns have shown that numerous spectrum bands are vacant even though licenses have been issued by the regulatory agencies. Dynamic spectrum access (DSA) based on Cognitive Radio (CR) has been regarded as a prospective solution to improve spectrum utilization for wireless communications. Empirical measurement of the radio environment to promote understanding of the current spectrum usage of the different wireless services is the first step towards deployment of future CR networks. In this paper we present our spectrum measurement setup and discuss lessons learned during our measurement activities. The main contribution of the paper is to introduce global spectrum occupancy measurements and address the major drawbacks of previous spectrum occupancy studies by providing a unifying methodological framework for future spectrum measurement campaigns

Applications of Cognitive Radio Networks

The term cognitive radio (CR), originally coined in the late 1990s, envisaged a radio that is aware of its operational environment so that it can dynamically and autonomously adjust its radio-operating parameters to accordingly adapt to the different situations. Cognition is achieved through the so-called cognitive cycle, consisting of the observation of the environment, the orientation and planning that leads to making appropriate decisions in accordance with specific operation goals, and finally, the execution of these decisions (e.g., access to the appropriate channel). Decisions can be reinforced by learning procedures based on the past observations and the corresponding results of prior actuations

RF-Powered Cognitive Radio Networks: Technical Challenges and Limitations

The increasing demand for spectral and energy efficient communication networks has spurred a great interest in energy harvesting (EH) cognitive radio networks (CRNs). Such a revolutionary technology represents a paradigm shift in the development of wireless networks, as it can simultaneously enable the efficient use of the available spectrum and the exploitation of radio frequency (RF) energy in order to reduce the reliance on traditional energy sources. This is mainly triggered by the recent advancements in microelectronics that puts forward RF energy harvesting as a plausible technique in the near future. On the other hand, it is suggested that the operation of a network relying on harvested energy needs to be redesigned to allow the network to reliably function in the long term. To this end, the aim of this survey paper is to provide a comprehensive overview of the recent development and the challenges regarding the operation of CRNs powered by RF energy. In addition, the potential open issues that might be considered for the future research are also discussed in this paper.Comment: 8 pages, 2 figures, 1 table, Accepted in IEEE Communications Magazin

Cooperative Wideband Spectrum Sensing Based on Joint Sparsity

COOPERATIVE WIDEBAND SPECTRUM SENSING BASED ON JOINT SPARSITY By Ghazaleh Jowkar, Master of Science A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University Virginia Commonwealth University 2017 Major Director: Dr. Ruixin Niu, Associate Professor of Department of Electrical and Computer Engineering In this thesis, the problem of wideband spectrum sensing in cognitive radio (CR) networks using sub-Nyquist sampling and sparse signal processing techniques is investigated. To mitigate multi-path fading, it is assumed that a group of spatially dispersed SUs collaborate for wideband spectrum sensing, to determine whether or not a channel is occupied by a primary user (PU). Due to the underutilization of the spectrum by the PUs, the spectrum matrix has only a small number of non-zero rows. In existing state-of-the-art approaches, the spectrum sensing problem was solved using the low-rank matrix completion technique involving matrix nuclear-norm minimization. Motivated by the fact that the spectrum matrix is not only low-rank, but also sparse, a spectrum sensing approach is proposed based on minimizing a mixed-norm of the spectrum matrix instead of low-rank matrix completion to promote the joint sparsity among the column vectors of the spectrum matrix. Simulation results are obtained, which demonstrate that the proposed mixed-norm minimization approach outperforms the low-rank matrix completion based approach, in terms of the PU detection performance. Further we used mixed-norm minimization model in multi time frame detection. Simulation results shows that increasing the number of time frames will increase the detection performance, however, by increasing the number of time frames after a number of times the performance decrease dramatically