Eigenvalue-based Cyclostationary Spectrum Sensing Using Multiple Antennas

In this paper, we propose a signal-selective spectrum sensing method for cognitive radio networks and specifically targeted for receivers with multiple-antenna capability. This method is used for detecting the presence or absence of primary users based on the eigenvalues of the cyclic covariance matrix of received signals. In particular, the cyclic correlation significance test is used to detect a specific signal-of-interest by exploiting knowledge of its cyclic frequencies. The analytical threshold for achieving constant false alarm rate using this detection method is presented, verified through simulations, and shown to be independent of both the number of samples used and the noise variance, effectively eliminating the dependence on accurate noise estimation. The proposed method is also shown, through numerical simulations, to outperform existing multiple-antenna cyclostationary-based spectrum sensing algorithms under a quasi-static Rayleigh fading channel, in both spatially correlated and uncorrelated noise environments. The algorithm also has significantly lower computational complexity than these other approaches.Comment: 6 pages, 6 figures, accepted to IEEE GLOBECOM 201

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Advanced Spectrum Sensing for Multiple Transmitter Identification

The exponential growth in demand for mobile data has led to significant research efforts aimed at more efficient methods of utilizing the scarce RF spectrum resource. One potential solution to this scarcity problem is Cognitive Radio (CR) which involves dynamic spectrum access in which a set of unlicensed users occupy spectrum holes without causing significant degradation of performance to the incumbent users. A key enabling technology for CR networks is accurate spectrum sensing which aims to learn the radio environment in order to adapt the CR transmission. Traditional spectrum sensing techniques have mainly focused on determining only the presence or absence of a licensed user. Recent work in the past few years have shown however that more detailed knowledge pertaining to radio-scene analysis can be used to improve the performance of CR networks. The more the secondary user knows about the active licensed users, the better it can adapt its transmission strategies. In this work, we put forward the concept of advance spectrum sensing which takes a multi-dimensional approach to radio-scene analysis that estimates various parameters of the active transmitters through sensing, localization and tracking, modulation classification, PHY parameter estimation, and MAC-layer classification. In this work we investigate the elements of such an advance spectrum sensing system. Firstly, we will look at the problem of conventional spectrum sensing, or detecting the presence or absence of transmitting Primary Users. In particular, we study how detection performance could be improved through the use of cyclostationary feature detection and how it could be made robust to fading, noise uncertainty, and co-channel interferers through the optimal use of multiple sensors. Second, we attack the problem of modulation classification which we argue is a critical piece of information for future cognitive radio systems. We present a new type of pattern classification algorithm based on the concept of sampled distribution distance which offers a low computational complexity alternative to maximum-likelihood based classification. Through our extensive analysis, we have derived the optimal form of this type of classifier and applied it to the modulation classification problem. Finally, we propose a system of MAC-layer classification based on 4th-order cumulants which distinguishes between TDMA, OFDMA, CDMA and contention-based schemes. In addition, it is also able to jointly perform modulation classification with channel access method. The analysis of the statistics of the 4th-order cumulant used in our work also offers large potential for applications in different areas including modulation classification, channel estimation, and estimation of number of users