PREDICCIÓN ESPECTRAL EN REDES INALÁMBRICAS DE RADIO COGNITIVA

Uno de los grandes desafíos de la radio cognitiva es establecer la forma  adecuada para realizar predicciones futuras en las diferentes fases que componen un radio cognitivo. A partir de las estimaciones se puede determinar el estado y características de los canales, la actividad de los usuarios primarios y secundarios, la movilidad espectral; todo ello con el fin de que los nodos no licenciados puedan aprovechar adecuadamente y de manera oportunista las bandas subutilizadas. En este artículo se presenta una revisión de algunas de las técnicas más relevantes que han sido aplicadas en la predicción espectral en la Radio Cognitiva

A cognitive QoS management framework for WLANs

Due to the precipitous growth of wireless networks and the paucity of spectrum, more interference is imposed to the wireless terminals which constraints their performance. In order to preserve such performance degradation, this paper proposes a framework which uses cognitive radio techniques for quality of service (QoS) management of wireless local area networks (LANs). The framework incorporates radio environment maps as input to a cognitive decision engine that steers the network to optimize its QoS parameters such as throughput. A novel experimentally verified heuristic physical model is developed to predict and optimize the throughput of wireless terminals. The framework was applied to realistic stationary and time-variant interference scenarios where an average throughput gain of 344% was achieved in the stationary interference scenario and 70% to 183% was gained in the time-variant interference scenario

Contract-Based Cooperative Spectrum Sharing

Providing proper economic incentives is essential for the success of dynamic spectrum sharing. Cooperative spectrum sharing is one effective way to achieve this goal. In cooperative spectrum sharing, secondary users (SUs) relay traffics for primary users (PUs), in exchange for dedicated transmission time for the SUs' own communication needs. In this paper, we study the cooperative spectrum sharing under incomplete information, where SUs' types (capturing their heterogeneity in relay channel gains and evaluations of power consumptions) are private information and not known by PUs. Inspired by the contract theory, we model the network as a labor market. The single PU is the employer who offers a contract to the SUs. The contract consists of a set of contract items representing combinations of spectrum accessing time (i.e., reward) and relaying power (i.e., contribution). The SUs are employees, and each of them selects the best contract item to maximize his payoff. We study the optimal contract design for both weak and strong incomplete information scenarios. First, we provide necessary and sufficient conditions for feasible contracts in both scenarios. In the weak incomplete information scenario, we further derive the optimal contract that achieves the same maximum PU's utility as in the complete information benchmark. In the strong incomplete information scenario, we propose a Decompose-and-Compare algorithm that achieves a close-to-optimal contract. We future show that the PU's average utility loss due to the suboptimal algorithm and the strong incomplete information are both relatively small (less than 2% and 1:3%, respectively, in our numerical results with two SU types).Comment: Part of this paper has appeared in IEEE DySPAN 2011, and this version has been submitted to IEEE J-SA

Towards a more efficient spectrum usage: spectrum sensing and cognitive radio techniques

The traditional approach of dealing with spectrum management in wireless communications has been through the definition on a license user granted exclusive exploitation rights for a specific frequency.Peer ReviewedPostprint (published version

Geostatistical Techniques for Practical Wireless Network Coverage Mapping

The problem of mapping the extent of “usable” coverage of an existing wireless network is important in a large number of applications, including communicating the abilities of the network to users, identifying coverage gaps and planning expansion, discovering opportunities for spectrum reuse, and determining possible sources of interference with other networks. This thesis addresses fundamental but unsolved problems of measurement-based wireless coverage mapping: where should measurements be made, how many are necessary, and what can be said about the coverage at points that have not been measured. To address these problems, this thesis advocates a geostatistical approach using optimized spatial sampling and ordinary Kriging. A complete system for coverage mapping is developed that systematically addresses measurement, sampling, spatial modeling, interpolation, and visualization. This geostatistical method is able to produce more accurate and robust coverage maps than the current state of the art methods, and is able to discover coverage holes as effectively as dedicated heuristic methods using a small number of measurements. Several important practical extensions are investigated: applying these methods to drive-test measurements which have been resampled to alleviate effects from sampling bias, and crowd-sourced coverage mapping applications where volunteer-collected measurements may be sparse or infrequent. The resulting maps can then be refined iteratively, and updated systematically over time using an optimized iterative sampling scheme. An extensive validation is performed using measurements of production WiFi, WiMax, GSM, and LTE networks in representative urban and suburban outdoor environments