An empirical polarization domain channel availability model for cognitive radio

In dynamic spectrum access networks, cognitive radio terminals monitor their spectral environment in order to detect and opportunistically access unoccupied frequency channels. The overall performance of such networks depends on the spectrum occupancy or availability patterns. Accurate knowledge on the channel availability enables optimum performance of such networks in terms of spectrum and energy efficiency. This work proposes a novel probabilistic channel availability model that can describe the channel availability in different polarizations for mobile cognitive radio terminals that are likely to change their orientation during their operation. A Gaussian approximation is used to model the empirical occupancy data that was obtained through a measurement campaign in the cellular frequency bands within a realistic operational scenario.

A statistical framework for channel availability modelling in the polarisation domain

Cognitive radio has been proposed as a means of improving the spectrum utilisation and increasing spectrum efficiency of wireless systems. This can be achieved by allowing cognitive radio terminals to monitor their spectral environment and opportunistically access the unoccupied frequency channels. Due to the opportunistic nature of cognitive radio, the overall performance of such networks depends on the spectrum occupancy or availability patterns. Appropriate knowledge on channel availability can optimise the sensing performance in terms of spectrum and energy efficiency. This work proposes a statistical framework for the channel availability in the polarization domain. A Gaussian Normal approximation is used to model real-world occupancy data obtained through a measurement campaign in the cellular frequency bands within a realistic scenario.

Techno-Economic Feasibility of Selective CO2 Capture Processes from Biogas Streams using Ionic Liquids as Physical Absorbents

Biogas from anaerobic digestion of sewage sludge is a renewable resource with high energy content, which is formed mainly of CH4 (40-75 vol.%) and CO2 (15-60 vol.%) Other components such as water (H2O, 5-10 vol.%) and trace amounts of hydrogen sulfide and siloxanes can also be present. A CH4-rich stream can be produced by removing the CO2 and other impurities so that the upgraded bio-methane can be injected into the natural gas grid or used as a vehicle fuel. The main objective of this paper is to develop a new modeling methodology to assess the technical and economic performance of biogas upgrading processes using ionic liquids which physically absorb CO2. Three different ionic liquids, namely the 1-ethyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide, 1-hexyl-3-methylimidazoliumbis[(trifluoromethyl)sulfonyl]imide and trihexyl(tetradecyl)phosphonium bis[(trifluoromethyl)sulfonyl]imide, are considered for CO2 capture in a pressure-swing regenerative absorption process. The simulation software Aspen Plus and Aspen Process Economic Analyzer is used to account for mass and energy balances as well as equipment cost. In all cases, the biogas upgrading plant consists of a multistage compressor for biogas compression, a packed absorption column for CO2 absorption, a flash evaporator for solvent regeneration, a centrifugal pump for solvent recirculation, a pre-absorber solvent cooler and a gas turbine for electricity recovery. The evaluated processes are compared in terms of energy efficiency, capital investment and bio-methane production costs. The overall plant efficiency ranges from 71-86 % whereas the bio-methane production cost ranges from £6.26-7.76 per GJ (LHV). A sensitivity analysis is also performed to determine how several technical and economic parameters affect the bio-methane production costs. The results of this study show that the simulation methodology developed can predict plant efficiencies and production costs of large scale CO2 capture processes using ionic liquids without having to rely on gas solubility experimental data.