EEG-based Transceiver Design with Data Decomposition for Healthcare IoT Applications

The emergence of Internet of Things (IoT) applications and rapid advances in wireless communication technologies have motivated a paradigm shift in the development of viable applications such as mobile-health (m-health). These applications boost the opportunity for ubiquitous real-time monitoring using different data types such as electroencephalography (EEG), electrocardiography (ECG), etc. However, many remote monitoring applications require continuous sensing for different signals and vital signs, which result in generating large volumes of real time data that requires to be processed, recorded, and transmitted. Thus, designing efficient transceivers is crucial to reduce transmission delay and energy through leveraging data reduction techniques. In this context, we propose an efficient data-specific transceiver design that leverages the inherent characteristics of the generated data at the physical layer to reduce transmitted data size without significant overheads. The goal is to adaptively reduce the amount of data that needs to be transmitted in order to efficiently communicate and possibly store information, while maintaining the required application quality-of-service (QoS) requirements. Our results show the excellent performance of the proposed design in terms of data reduction gain, signal distortion, low complexity, and the advantages that it exhibits with respect to state-of-the-art techniques since we could obtain about 50% compression ratio at 0% distortion and sample error rate

Energy-Efficient Proactive Scheduling Policies for Finite-Buffer Regular Service Guarantees

In this work, we study the energy saving merits of proactive scheduling for downlink multimedia streaming towards a finite-buffer receiver under Rayleigh fading. Three different threshold-based proactive scheduling policies are proposed, each with a different threshold structure on the queue/channel state space. The first two policies consider a single channel gain threshold per queue state, either with a fixed or variable cache amount, whereas the last policy imposes a set of thresholds on the channel gain, fixed on all queue states. We consider a time-causal system in which only the current and previous environment states are known. Required transmit power/signal-to-noise ratio (SNR) for the proposed policies is analytically derived, and numerically benchmarked against a non-proactive (reactive) transmission upper bound, as well as to a non-causal genie-aided lower bound with fully-observable future channel values. Numerical results show that proactive scheduling could save more than 50% of the transmission energy on average. 2022 IEEE.This work has been made possible by partial support from the Center for Energy Systems Research at Tennessee Technological University.Scopu

Green mobile networks for 5G and beyond

International audienceThe heated 5G network deployment race has already begun with the rapid progress in standardization efforts, backed by the current market availability of 5G-enabled network equipment, ongoing 5G spectrum auctions, early launching of non-standalone 5G network services in a few countries, among others. In this paper, we study current and future wireless networks from the viewpoint of energy efficiency (EE) and sustainability to meet the planned network and service evolution toward, along, and beyond 5G, as also inspired by the findings of the EU Celtic-Plus SooGREEN Project. We highlight the opportunities seized by the project efforts to enable and enrich this green nature of the network as compared to existing technologies. In specific, we present innovative means proposed in SooGREEN to monitor and evaluate EE in 5G networks and beyond. Further solutions are presented to reduce energy consumption and carbon footprint in the different network segments. The latter spans proposed virtualized/cloud architectures, efficient polar coding for fronthauling, mobile network powering via renewable energy and smart grid integration, passive cooling, smart sleeping modes in indoor systems, among others. Finally, we shed light on the open opportunities yet to be investigated and leveraged in future developments