Performance analysis of spectrum sensing techniques for cognitive radio

Spectrum sensing is a key element for cognitive radio and is process of obtaining awareness about the radio spectrum in order to detect the presence of other users. In this paper we study the performance of different spectrum sensing techniques in terms of detection performance and required SNR, based on theoretical expressions. Keywords- cognitive radio; spectrum sensing; energy detection; matced filter detection; cyclostationary feature detectio

Dynamic co-movements of stock market returns, implied volatility and policy uncertainty

We examine time-varying correlations among stock market returns, implied volatility and policy uncertainty. Our findings suggest that correlations are indeed time-varying and sensitive to oil demand shocks and US recessions. Highlights: We examine dynamic correlations of stock market returns, implied volatility and policy uncertainty. Dynamic correlations reveal heterogeneous patterns during US recessions. Aggregate demand oil price shocks and US recessions affect dynamic correlations. A rise in the volatility of policy uncertainty dampens stock market returns and increases uncertainty. Increases in stock market volatility reduce stock market returns and increase uncertainty

Dynamic spillovers of oil price shocks and economic policy uncertainty

This study examines the dynamic relationship between changes in oil prices and the economic policy uncertainty index for a sample of both net oil-exporting and net oil-importing countries over the period 1997:01–2013:06. To achieve that, an extension of the Diebold and Yilmaz (2009, 2012) dynamic spillover index based on structural decomposition is employed. The results reveal that economic policy uncertainty (oil price shocks) responds negatively to aggregate demand oil price shocks (economic policy uncertainty shocks). Furthermore, during the Great Recession of 2007–2009, total spillovers increase considerably, reaching unprecedented heights. Moreover, in net terms, economic policy uncertainty becomes the dominant transmitter of shocks between 1997 and 2009, while in the post-2009 period there is a significant role for supply-side and oil specific demand shocks, as net transmitters of spillover effects. These results are important for policy makers, as well as, investors interested in the oil market

The new Syriza government must prioritise economic growth and job creation to get Greece back on its feet

Syriza emerged as the largest party from the Greek parliamentary election held on 20 September. Ioannis Chatziantoniou writes on what the new government should do to attempt to revive the country’s economy. He argues that the key priority should be to establish long-term prospects for its workforce instead of simply short-run opportunities of employment

Spectrum sensing and occupancy prediction for cognitive machine-to-machine wireless networks

A thesis submitted to the University of Bedfordshire, in partial fulfil ment of the requirements for the degree of Doctor of Philosophy (PhD)The rapid growth of the Internet of Things (IoT) introduces an additional challenge to the existing spectrum under-utilisation problem as large scale deployments of thousands devices are expected to require wireless connectivity. Dynamic Spectrum Access (DSA) has been proposed as a means of improving the spectrum utilisation of wireless systems. Based on the Cognitive Radio (CR) paradigm, DSA enables unlicensed spectrum users to sense their spectral environment and adapt their operational parameters to opportunistically access any temporally unoccupied bands without causing interference to the primary spectrum users. In the same context, CR inspired Machine-to-Machine (M2M) communications have recently been proposed as a potential solution to the spectrum utilisation problem, which has been driven by the ever increasing number of interconnected devices. M2M communications introduce new challenges for CR in terms of operational environments and design requirements. With spectrum sensing being the key function for CR, this thesis investigates the performance of spectrum sensing and proposes novel sensing approaches and models to address the sensing problem for cognitive M2M deployments. In this thesis, the behaviour of Energy Detection (ED) spectrum sensing for cognitive M2M nodes is modelled using the two-wave with dffi use power fading model. This channel model can describe a variety of realistic fading conditions including worse than Rayleigh scenarios that are expected to occur within the operational environments of cognitive M2M communication systems. The results suggest that ED based spectrum sensing fails to meet the sensing requirements over worse than Rayleigh conditions and consequently requires the signal-to-noise ratio (SNR) to be increased by up to 137%. However, by employing appropriate diversity and node cooperation techniques, the sensing performance can be improved by up to 11.5dB in terms of the required SNR. These results are particularly useful in analysing the eff ects of severe fading in cognitive M2M systems and thus they can be used to design effi cient CR transceivers and to quantify the trade-o s between detection performance and energy e fficiency. A novel predictive spectrum sensing scheme that exploits historical data of past sensing events to predict channel occupancy is proposed and analysed. This approach allows CR terminals to sense only the channels that are predicted to be unoccupied rather than the whole band of interest. Based on this approach, a spectrum occupancy predictor is developed and experimentally validated. The proposed scheme achieves a prediction accuracy of up to 93% which in turn can lead to up to 84% reduction of the spectrum sensing cost. Furthermore, a novel probabilistic model for describing the channel availability in both the vertical and horizontal polarisations is developed. The proposed model is validated based on a measurement campaign for operational scenarios where CR terminals may change their polarisation during their operation. A Gaussian approximation is used to model the empirical channel availability data with more than 95% confi dence bounds. The proposed model can be used as a means of improving spectrum sensing performance by using statistical knowledge on the primary users occupancy pattern

Energy Consumption, CO2 Emissions, and Economic Growth: A Moral Dilemma

In this study we examine the dynamic interrelationship in the output-energy-environment nexus by applying panel vector autoregression (PVAR) and impulse response function analyses to data on energy consumption (and its subcomponents), carbon dioxide emissions and real GDP in 106 countries classified by different income groups over the period 1971-2011. Our results reveal that the effects of the various types of energy consumption on economic growth and emissions are heterogeneous on the various groups of countries. Moreover, causality between total economic growth and energy consumption is bidirectional, thus making a case for the feedback hypothesis. However, we cannot report any statistically significant evidence that renewable energy consumption, in particular, is conducive to economic growth, a fact that weakens the argument that renewable energy consumption is able to promote growth in a more efficient and environmentally sustainable way. Finally, in analysing the case for an inverted U-shaped EKC, we find that the continued process of growth aggravates the greenhouse gas emissions phenomenon. In this regard, we cannot provide any evidence that developed countries may actually grow-out of environmental pollution. In the light of these findings, the efficacy of recent government policies in various countries to promote renewable energy consumption as a means for sustainable growth is questioned. Put differently, there seems to be a moral dilemma, between high economic growth rates and unsustainable environment and low or zero economic growth and environmental sustainability

Periostin and Discoidin Domain Receptor 1: New Biomarkers or Targets for Therapy of Renal Disease

Chronic kidney disease (CKD) can be a life-threatening condition, which eventually requires renal replacement therapy through dialysis or transplantation. A lot of effort and resources have been invested the last years in the identification of novel markers of progression and targets for therapy, in order to achieve a more efficient prognosis, diagnosis, and treatment of renal diseases. Using experimental models of renal disease, we identified and studied two promising candidates: periostin, a matricellular protein with high expression in bone and dental tissues, and discoidin domain receptor 1 (DDR1), a transmembrane collagen receptor of the tyrosine kinase family. Both proteins are inactive in physiological conditions, while they are highly upregulated during development of renal disease and are primarily expressed at the sites of injury. Further studies demonstrated that both periostin and DDR1 are involved in the regulation of inflammation and fibrosis, two major processes implicated in the development of renal disease. Targeting of either protein by genetic deletion or pharmacogenetic inhibition via antisense oligonucleotides highly attenuates renal damage and preserves renal structure and function in several animal models. The scope of this review is to summarize the existing evidence supporting the role of periostin and DDR1 as novel biomarkers and therapeutic targets in CKD

Oil shocks and stock markets: Dynamic connectedness under the prism of recent geopolitical and economic unrest.

In this study we examine the dynamic structural relationship between oil price shocks and stock market returns or volatility for a sample of both net oil–exporting and net oil–importing countries between 1995:09 and 2013:07. We accomplish that, by extending the Diebold and Yilmaz (2014) dynamic connectedness measure using structural forecast error variance de- composition. The results for both stock market returns and volatility suggest that connect- edness varies across different time periods, and that this time–varying character is aligned with certain developments that take place in the global economy. In particular, aggregate demand shocks appear to act as the main transmitters of shocks to stock markets during periods characterised by economic–driven events, while supply–side and oil–specific demand shocks during periods of geopolitical unrest. Furthermore, differences regarding the direc- tions and the strength of connectedness can be reported both between and within the net oil–importing and net oil–exporting countries. These results are of particular importance to investors and portfolio managers, given the recent financialisation of the oil market