RF-Powered Cognitive Radio Networks: Technical Challenges and Limitations

The increasing demand for spectral and energy efficient communication networks has spurred a great interest in energy harvesting (EH) cognitive radio networks (CRNs). Such a revolutionary technology represents a paradigm shift in the development of wireless networks, as it can simultaneously enable the efficient use of the available spectrum and the exploitation of radio frequency (RF) energy in order to reduce the reliance on traditional energy sources. This is mainly triggered by the recent advancements in microelectronics that puts forward RF energy harvesting as a plausible technique in the near future. On the other hand, it is suggested that the operation of a network relying on harvested energy needs to be redesigned to allow the network to reliably function in the long term. To this end, the aim of this survey paper is to provide a comprehensive overview of the recent development and the challenges regarding the operation of CRNs powered by RF energy. In addition, the potential open issues that might be considered for the future research are also discussed in this paper.Comment: 8 pages, 2 figures, 1 table, Accepted in IEEE Communications Magazin

Per-user service model for opportunistic scheduling scheme over fading channels

In this paper, we propose a ¯nite-state Markov model for per-user service of an oppor- tunistic scheduling scheme over Rayleigh fading channels, where a single base station serves an arbitrary number of users. By approximating the power gain of Rayleigh fading chan- nels as ¯nite-state Markov processes, we develop an algorithm to obtain dynamic stochastic model of the transmission service, received by an individual user for a saturated scenario, where user data queues are highly loaded. The proposed analytical model is a ¯nite-state Markov process. We provide a comprehensive comparison between the predicted results by the proposed analytical model and the simulation results, which demonstrate a high degree of match between the two sets

London's Housing Crisis

One does not need to be an expert in housing to be concerned about the severity of the current housing crisis in London. An overwhelming majority of the city’s population face the predicament of deteriorating housing affordability. Median house prices are now more than ten times median incomes, additions to the stock are insufficient, and prices are volatile. In the search for remedies, well-intended solutions emerging from fragmented analyses of the problem inevitably lead to unintended consequences. The incapacity of the human mind to correctly infer the behaviour of complex systems presents a case for System Dynamics. This paper, the result of the first year of a PhD, describes the underlying socio-economic structure responsible for the Housing crisis in London. We have built a causal loop diagram of London’s housing situation which demonstrates how the interlocking of numerous reinforcing feedback loops have contributed to the house price inflation, and how the potential for a future crash is essentially built into the system. We contend that this type of conceptualisation can help prevent the typical ‘blame game’ going on in various circles, and focus resources on overhauling the broken system via designing and implementing a concerted package of transformative policies

A participatory process for modelling green infrastructure implementation in London

Cities face the interlinked challenges of transforming environmental quality, sustainability, population health and health equity. There is increasing interest in green infrastructure in connection with these challenges. In order to go beyond an understanding of the effects of green infrastructure and develop an understanding of how policymakers should think about it, we need to capture potential interactions and be aware of possible unintended consequences. In our research, we applied a systems-thinking approach: integrating participatory engagements, qualitative system dynamics modelling, and an assessment framework in order to address the challenge. This allowed us to see multiple dynamics between the prioritisation of policymakers, different types of green infrastructure, and environmental and health outcomes. It also made us ask different and more integrated questions, and suggested a methodology for addressing the challenge of transforming cities

Applications of different machine learning approaches in prediction of breast cancer diagnosis delay

Background: The increasing rate of breast cancer (BC) incidence and mortality in Iran has turned this disease into a challenge. A delay in diagnosis leads to more advanced stages of BC and a lower chance of survival, which makes this cancer even more fatal. Objectives: The present study was aimed at identifying the predicting factors for delayed BC diagnosis in women in Iran.Methods: In this study, four machine learning methods, including extreme gradient boosting (XGBoost), random forest (RF), neural networks (NNs), and logistic regression (LR), were applied to analyze the data of 630 women with confirmed BC. Also, different statistical methods, including chi-square, p-value, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC), were utilized in different steps of the survey.Results: Thirty percent of patients had a delayed BC diagnosis. Of all the patients with delayed diagnoses, 88.5% were married, 72.1% had an urban residency, and 84.8% had health insurance. The top three important factors in the RF model were urban residency (12.04), breast disease history (11.58), and other comorbidities (10.72). In the XGBoost, urban residency (17.54), having other comorbidities (17.14), and age at first childbirth (>30) (13.13) were the top factors; in the LR model, having other comorbidities (49.41), older age at first childbirth (82.57), and being nulliparous (44.19) were the top factors. Finally, in the NN, it was found that being married (50.05), having a marriage age above 30 (18.03), and having other breast disease history (15.83) were the main predicting factors for a delayed BC diagnosis.Conclusion: Machine learning techniques suggest that women with an urban residency who got married or had their first child at an age older than 30 and those without children are at a higher risk of diagnosis delay. It is necessary to educate them about BC risk factors, symptoms, and self-breast examination to shorten the delay in diagnosis

Complex Urban Systems for Sustainability and Health: A structured approach to support the development and implementation of city policies for population and planetary health

Context: The multi-disciplinary and multi-partner CUSSH project (Complex Urban Systems for Sustainability and Health) seeks to support cities to take transformative action towards population and planetary health goals. Rationale: As all cities are complex systems with unique contexts and priorities, our approach is to engage with partner cities in a participatory process to build a shared understanding of relevant systems that will inform the development and implementation of new city policies. Description: Six partner cities were selected to represent larger and smaller cities from across the global spectrum of income: London (UK) and Rennes (France) in Europe, Nairobi and Kisumu in Kenya, and Beijing and Ningbo in China. In each setting we are engaging stakeholders in a broadly similar structured approach that integrates evidence gathering and modelling, participatory engagement framework, and ongoing tracking and evaluation. In addition, we are developing a working theory of change in each setting to explain how and why the chosen policies may work. Achievements: Our city engagement to date has focused on indoor air pollution (Nairobi), green infrastructure (London) and GHG emissions (Rennes), where findings highlighted not only multiple pathways by which policy interventions could affect health, but also potential counter-intuitive influences and tensions, and synergistic opportunities for solving both sustainability and health problems

Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps

The increasing complexity of communication systems, following the advent of heterogeneous technologies, services and use cases with diverse technical requirements, provide a strong case for the use of artificial intelligence (AI) and data-driven machine learning (ML) techniques in studying, designing and operating emerging communication networks. At the same time, the access and ability to process large volumes of network data can unleash the full potential of a network orchestrated by AI/ML to optimise the usage of available resources while keeping both CapEx and OpEx low. Driven by these new opportunities, the ongoing standardisation activities indicate strong interest to reap the benefits of incorporating AI and ML techniques in communication networks. For instance, 3GPP has introduced the network data analytics function (NWDAF) at the 5G core network for the control and management of network slices, and for providing predictive analytics, or statistics, about past events to other network functions, leveraging AI/ML and big data analytics. Likewise, at the radio access network (RAN), the O-RAN Alliance has already defined an architecture to infuse intelligence into the RAN, where closed-loop control models are classified based on their operational timescale, i.e., real-time, near real-time, and non-real-time RAN intelligent control (RIC). Different from the existing related surveys, in this review article, we group the major research studies in the design of model-aided ML-based transceivers following the breakdown suggested by the O-RAN Alliance. At the core and the edge networks, we review the ongoing standardisation activities in intelligent networking and the existing works cognisant of the architecture recommended by 3GPP and ETSI. We also review the existing trends in ML algorithms running on low-power micro-controller units, known as TinyML. We conclude with a summary of recent and currently funded projects on intelligent communications and networking. This review reveals that the telecommunication industry and standardisation bodies have been mostly focused on non-real-time RIC, data analytics at the core and the edge, AI-based network slicing, and vendor inter-operability issues, whereas most recent academic research has focused on real-time RIC. In addition, intelligent radio resource management and aspects of intelligent control of the propagation channel using reflecting intelligent surfaces have captured the attention of ongoing research projects

A system dynamics-based scenario analysis of residential solid waste management in Kisumu, Kenya

The problem of solid waste management presents an issue of increasing importance in many low-income settings, including the progressively urbanised context of Kenya. Kisumu County is one such setting with an estimated 500 t of waste generated per day and with less than half of it regularly collected. The open burning and natural decay of solid waste is an important source of greenhouse gas (GHG) emissions and atmospheric pollutants with adverse health consequences. In this paper, we use system dynamics modelling to investigate the expected impact on GHG and PM_{2.5} emissions of (i) a waste-to-biogas initiative and (ii) a regulatory ban on the open burning of waste in landfill. We use life tables to estimate the impact on mortality of the reduction in PM_{2.5} exposure. Our results indicate that combining these two interventions can generate over 1.1 million tonnes of cumulative savings in GHG emissions by 2035, of which the largest contribution (42%) results from the biogas produced replacing unclean fuels in household cooking. Combining the two interventions is expected to reduce PM_{2.5} emissions from the waste and residential sectors by over 30% compared to our baseline scenario by 2035, resulting in at least around 1150 cumulative life years saved over 2021–2035. The contribution and novelty of this study lies in the quantification of a potential waste-to-biogas scenario and its environmental and health impact in Kisumu for the first time