Is Blockchain for Internet of Medical Things a Panacea for COVID-19 Pandemic?

The outbreak of the COVID-19 pandemic has deeply influenced the lifestyle of the general public and the healthcare system of the society. As a promising approach to address the emerging challenges caused by the epidemic of infectious diseases like COVID-19, Internet of Medical Things (IoMT) deployed in hospitals, clinics, and healthcare centers can save the diagnosis time and improve the efficiency of medical resources though privacy and security concerns of IoMT stall the wide adoption. In order to tackle the privacy, security, and interoperability issues of IoMT, we propose a framework of blockchain-enabled IoMT by introducing blockchain to incumbent IoMT systems. In this paper, we review the benefits of this architecture and illustrate the opportunities brought by blockchain-enabled IoMT. We also provide use cases of blockchain-enabled IoMT on fighting against the COVID-19 pandemic, including the prevention of infectious diseases, location sharing and contact tracing, and the supply chain of injectable medicines. We also outline future work in this area.Comment: 15 pages, 8 figure

A promising non-orthogonal multiple access based networking architecture: Motivation, conception, and evolution

Since it can offer higher spectral efficiency by granting the served data to share the same spectrum resource synchronously, NOMA is considered as a bright multiple access technique for future wireless networks. Accommodating a large number of users with NOMA mode may easily cause inappropriate power assignment problem and result in performance degradation. As a result, existing NOMA operation unit mainly focuses on the two-user scenario, where a centre user is only paired with an edge user. However, such a user pairing strategy will also introduce an unbalance to their data rate fairness. Hence, it is of great importance to develop an effective structure that facilitates low decoding complexity, high system performance, and reasonable user fairness with NOMA. To this end, this article proposes a novel NOMA operation unit, in which successive interference cancellation can be performed to accommodate an arbitrary number of users by integrating the NOMA technique with the orthogonal resource allocation strategy. The proposed NOMA operation unit can further improve the edge user's achievable rate by incorporating the index modulation technique according to the data service demanded by center users. 2019 IEEE.AcknowledgMent This work was supported in part by the National Natural Science Foundation of China under Grants 61871190, 61571020, and 61622101; in part by the Natural Science Foundation of Guangdong Province under Grant 2018B030306005; and in part by the Pearl River Nova Program of Guangzhou under Grant 201806010171. The work has also received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 815178.Scopus2-s2.0-8507070419

Robust Preamble-Based Timing Synchronization for OFDM Systems

This study presents a novel preamble-based timing offset estimation method for orthogonal frequency division multiplexing (OFDM) systems. The proposed method is robust, immune to the carrier frequency offset (CFO), and independent of the structure of the preamble. The performance of the new method is demonstrated in terms of mean square error (MSE) obtained by simulation in multipath fading channels. The results indicate that the new method significantly improves timing performance in comparison with existing methods

Digital twins based intelligent state prediction method for maneuvering-target tracking

Maneuvering-target tracking has always been an important and challenge work because the unknown and changeable motion-models can easily lead to the failure of model-driven target tracking. Recently, many neural network methods are proposed to improve the tracking accuracy by constructing direct mapping relationships from noisy observations to target states. However, limited by the coverage of training data, those data-driven methods suffer other problems, such as weak generalization abilities and unstable tracking effects. In this paper, a digital twin system for maneuvering-target tracking is built, and all kinds of simulated data are created with different motion-models. Based on those data, the features of noisy observations and their relationship to target states are found by two specially designed neural networks: one eliminates the observation noises and the other one predicts the target states according to the noise-limited observations. Combining the above two networks, the state prediction method is proposed to intelligently predict targets by understanding the information of motion-model hidden in noisy observations. Simulation results show that, in comparison with the state-of-the-art model-driven and data-driven methods, the proposed method can correctly and timely predict the motion-models, increase the tracking generalization ability and reduce the tracking root-mean-squared-error by over 50% in most of maneuvering-target tracking scenes