Analysis of Radio Spectrum Market Evolution Possibilities

A tremendous growth in wireless traffic volumes and a shortage of feasible radio spectrum has led to a situation where the old and rigid spectrum regime is not a viable option for spectrum management and a shift towards a more market driven approach has begun. Great uncertainty still exists over how such a radio spectrum market will come about and what kind of shape it would take. This paper studies some long term macro level evolution possibilities for how this radio spectrum market could emerge and what would be the corresponding value chain configurations. The scenario planning and system dynamics methods are utilized to build four alternative future spectrum market scenarios.Spectrum Markets, Spectrum Policy, Flexible Spectrum Usage, Cognitive Radio, Value Networks, Scenario Planning, System Dynamics.

TVWS policies to enable efficient spectrum sharing

The transition from analogue to the Digital Terrestrial Television (DTV) in Europe is planned to be completed by the end of the year 2012. The DTV spectrum allocation is such that there are a number of TV channels which cannot be used for additional high power broadcast transmitters due to mutual interference and hence are left unused within a given geographical location, i.e. the TV channels are geographically interleaved. The use of geographically interleaved spectrum provides for the so-called TV white spaces (TVWS) an opportunity for deploying new wireless services. The main objective of this paper is to present the spectrum policies that are suitable for TVWS at European level, identified within the COGEU project. The COGEU project aims the efficient exploitation of the geographical interleaved spectrum (TVWS). COGEU is an ICT collaborative project supported by the European Commission within the 7th Framework Programme. Nine partners from seven EU countries representing academia, research institutes and industry are involved in the project. The COGEU project is a composite of technical, business, and regulatory/policy domains, with the objective of taking advantage of the TV digital switchover by developing cognitive radio systems that leverage the favorable propagation characteristics of the UHF broadcast spectrum through the introduction and promotion of real-time secondary spectrum trading and the creation of new spectrum commons regimes. COGEU will also define new methodologies for compliance testing and certification of TVWS equipment to ensure non-interference coexistence with the DVB-T European standard. The innovation brought by COGEU is the combination of cognitive access to TV white spaces with secondary spectrum trading mechanisms.telecommunications,spectrum management,secondary spectrum market,regulation,TV white spaces,cognitive radio

Reinforcement learning-based trust and reputation model for spectrum leasing in cognitive radio networks

Cognitive Radio (CR), which is the next generation wireless communication system, enables unlicensed users or Secondary Users (SUs) to exploit underutilized spectrum (called white spaces) owned by the licensed users or Primary Users(PUs) so that bandwidth availability improves at the SUs, which helps to improve the overall spectrum utilization. Collaboration, which has been adopted in various schemes such distributed channel sensing and channel access, is an intrinsic characteristic of CR to improve network performance. However, the requirement to collaborate has inevitably open doors to various forms of attacks by malicious SUs, and this can be addressed using Trust and Reputation Management (TRM). Generally speaking, TRM detects malicious SUs including honest SUs that turn malicious. To achieve a more efficient detection, we advocate the use of Reinforcement Learning (RL), which is known to be flexible and adaptable to the changes in operating environment in order to achieve optimal network performance. Its ability to learn and re-learn throughout the duration of its existence provides intelligence to the proposed TRM model, and so the focus on RL-based TRM model in this paper. Our preliminary results show that the detection performance of RLbased TRM model has an improvement of 15% over the traditional TRM in a centralized cognitive radio network. The investigation in the paper serves as an important foundation for future work in this research field