Battery-aware probabilistic access scheme with nature and RF energy harvesting for cognitive radio networks

A short-range unlicensed network is intended to coexist with a strong licensed link. It is assumed that nodes' density of the primary network is much lower than that of the secondary one. Each node implements an energy-based probabilistic access scheme powered with nature and/or RF energy harvesting. The goal is to decide a criterion for each nodes' access based on a Markov chain under some constraints on the battery finite capacity and the maximum admissible interference on the primary network link. The proposed setup will be simulated in Matlab in order to evaluate and opIn this work, a short-range unlicensed link intends to coexist with a directional-nodes licensed network, deployed as a 2D Homogeneous Poisson Point Process. The unlicensed transmitter uses two energy harvesting mechanisms and implements a probabilistic scheme in order to access the channel according to the available energy in its battery. The aim of this work is to optimize the throughput of the proposed cognitive radio link, employing linear programming methods, provided that the licensed network QoS constraint is satisfied. It is demonstrated the effectiveness of the mixed energy harvesting scheme for energy-restricted scenarios, and it is tested as well in which scenarios the energy detection would be useful, considering different packet length cases. It is also considered non-energy restricted scenarios in which is demonstrated that a Vehicle-To-Vehicle link can coexist with a wireless network.En este trabajo, un enlace de corta distancia sin licencia intenta coexistir con una red de nodos direccionales licenciada, desplegada como una red de Poisson homogénea 2D. El transmisor sin licencia utiliza dos técnicas de recolección de energía e implementa un esquema probabilístico de acceso al canal de acuerdo con la energía disponible en su batería. El objetivo de este trabajo es optimizar el throughput del enlace cognitivo, a través de métodos de programación lineal, cumpliendo con la restricción de QoS impuesta por la red licenciada. Se demuestra la efectividad del esquema mixto de recolección de energía para escenarios limitados por la energía de la batería y se prueba también en que escenarios la detección de energía tiene sentido, considerando servicios con paquetes de diferente longitud. Además, se consideran escenarios no limitados por la energía de la batería, donde se demuestra que un enlace V2V puede coexistir con una red inalámbrica.En aquest treball, un enllaç de curta distancia sense llicència intenta coexistir amb una xarxa de nodes direccionals llicenciada, desplegada com una xarxa de Poisson homogènia 2D. El transmissor sense llicència utilitza dues tècniques de recol·lecció d’energia i implementa un esquema probabilístic d’accés al canal d’acord amb l’energia disponible a la seva bateria. L’objectiu d’aquest treball és optimitzar el throughput de l’enllaç cognitiu, a través de mètodes de programació lineal, garantint que la restricció de QoS imposada per la xarxa llicenciada sigui satisfeta. Es demostra l’efectivitat de l’esquema mixt de recol·lecció d’energia per a escenaris limitats per l’energia de la bateria i es prova també en quins escenaris la detecció d’energia té sentit, considerant serveis amb paquets de diferents longituds. A més a més, es consideren escenaris no limitats per l’energia de la bateria, on es demostra que un enllaç V2V pot coexistir amb una xarxa sense cables

A Self-Learning MAC Protocol for Energy Harvesting and Spectrum Access in Cognitive Radio Sensor Networks

The fusion of Wireless Sensor Networks (WSNs) and Cognitive Radio Networks (CRNs) into Cognitive Radio Sensor Networks (CRSNs) is quite an attractive proposal, because it allows a distributed set of low-powered sensor nodes to opportunistically access spectrum bands that are underutilized by their licensed owners (called primary users (PUs)). In addition, when the PUs are actively transmitting in their own bands, sensor nodes can switch to energy harvesting mode to obtain their energy needs (for free), to achieve almost perpetual life. In this work, we present a novel and fully distributed MAC protocol, called S-LEARN, that allows sensor nodes in a CRSN to entwine their RF energy harvesting and data transmission activities, while intelligently addressing the issue of disproportionate difference between the high power necessary for the node to transmit data packets and the small amount of power it can harvest wirelessly from the environment. The presented MAC protocol can improve both the network throughput and total harvested energy, while being robust to changes in the network configuration. Moreover, S-LEARN can keep the cost of the system low, and it avoids the pitfalls from which centralized systems suffer