Wideband Self-Adaptive RF Cancellation Circuit for Full-Duplex Radio: Operating Principle and Measurements

This paper presents a novel RF circuit architecture for self-interference cancellation in inband full-duplex radio transceivers. The developed canceller is able to provide wideband cancellation with waveform bandwidths in the order of 100 MHz or beyond and contains also self-adaptive or self-healing features enabling automatic tracking of time-varying self-interference channel characteristics. In addition to architecture and operating principle descriptions, we also provide actual RF measurements at 2.4 GHz ISM band demonstrating the achievable cancellation levels with different bandwidths and when operating in different antenna configurations and under low-cost highly nonlinear power amplifier. In a very challenging example with a 100 MHz waveform bandwidth, around 41 dB total cancellation is obtained while the corresponding cancellation figure is close to 60 dB with the more conventional 20 MHz carrier bandwidth. Also, efficient tracking in time-varying reflection scenarios is demonstrated.Comment: 7 pages, to be presented in 2015 IEEE 81st Vehicular Technology Conferenc

Reliability-Optimized User Admission Control for URLLC Traffic: A Neural Contextual Bandit Approach

Ultra-reliable low-latency communication (URLLC) is the cornerstone for a broad range of emerging services in next-generation wireless networks. URLLC fundamentally relies on the network's ability to proactively determine whether sufficient resources are available to support the URLLC traffic, and thus, prevent so-called cell overloads. Nonetheless, achieving accurate quality-of-service (QoS) predictions for URLLC user equipment (UEs) and preventing cell overloads are very challenging tasks. This is due to dependency of the QoS metrics (latency and reliability) on traffic and channel statistics, users' mobility, and interdependent performance across UEs. In this paper, a new QoS-aware UE admission control approach is developed to proactively estimate QoS for URLLC UEs, prior to associating them with a cell, and accordingly, admit only a subset of UEs that do not lead to a cell overload. To this end, an optimization problem is formulated to find an efficient UE admission control policy, cognizant of UEs' QoS requirements and cell-level load dynamics. To solve this problem, a new machine learning based method is proposed that builds on (deep) neural contextual bandits, a suitable framework for dealing with nonlinear bandit problems. In fact, the UE admission controller is treated as a bandit agent that observes a set of network measurements (context) and makes admission control decisions based on context-dependent QoS (reward) predictions. The simulation results show that the proposed scheme can achieve near-optimal performance and yield substantial gains in terms of cell-level service reliability and efficient resource utilization.Comment: To be published in the proceedings of the 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN

Digital transformation of healthcare sector. What is impeding adoption and continued usage of technology-driven innovations by end-users?

The digital transformation of businesses is no longer debatable, and the effects are visible in all sectors. What is arguable, however, is why the transformation has not been seamless—particularly given the multiple benefits of digitalization. We seek to address this question for the healthcare sector, where various reports have acknowl- edged end-users’ resistance to the adoption and continued usage of technology-driven innovations (e-health innovations). These accounts, though, are largely anecdotal, and the volume of academic research in the area has remained rather confined. To address this paucity of insights, particularly after the onset of the pandemic, which has brought the healthcare sector to the fore, we conducted a qualitative study among healthcare providers (doctors, nurses, and other clinical staff). The key objective of our study was to identify the perceived barriers and other inhibiting factors that impede individuals’ adoption and continued usage of e-health innovations. We conducted our study in the United Kingdom and analyzed the data using the classic approach of manual content analysis. Through these efforts, we identified barriers from the perspectives of healthcare providers (task-related, patient-care, and system barriers), healthcare organizations (threat perception and infrastructural barriers), patients (usability and resource barriers), and end-users in general (self-efficacy, tradition, and image barriers). Our study makes a noteworthy theoretical contribution by proposing a conceptual framework for resistance to e- health innovations that is grounded in innovation resistance theory (IRT). We also make some useful suggestions for practice that have the potential to accelerate the diffusion of e-health innovations