The emergence and spread of COVID-19 since December, 2019, has brought great challenges to global public health. As of April 23, 2020, more than 2•5 million confirmed cases and more than 175 000 deaths had been reported globally. 1 Respiratory tract manifestations such as fever and cough are the most commonly reported symptoms in patients with COVID-19. 2 Evidence of digestive system involvement in patients with COVID-19 was first reported by a group in China. 3 Emerging data showed that the gastrointestinal tract and liver might also represent target organs of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on the basis of the findings that angiotensin-converting enzyme 2 (ACE2), the major receptor of SARS-CoV-2, is expressed in the gastro intestinal tract as well as liver cells. 4 The detection of SARS-CoV-2 viral RNA in patients' stool and the potential for faecal-oral transmission has raised

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We consider a cognitive heterogeneous network (HetNet), in which multiple pairs of secondary users adopt sensing-based approaches to coexist with a pair of primary users on a certain spectrum band. Due to imperfect spectrum sensing, secondary transmitters (STs) may cause interference to the primary receiver (PR) and make it difficult for the PR to select a proper modulation and/or coding scheme (MCS). To deal with this issue, we exploit deep reinforcement learning (DRL) and propose an intelligent MCS selection algorithm for the primary transmission. To reduce the system overhead caused by MCS switchings, we further introduce a switching cost factor in the proposed algorithm. Simulation results show that the primary transmission rate of the proposed algorithm without the switching cost factor is 90% ∼ 100% of the optimal MCS selection scheme, which assumes that the interference from the STs is perfectly known at the PR as prior information, and is 30% ∼ 100% higher than those of the benchmark algorithms. Meanwhile, the proposed algorithm with the switching cost factor can achieve a better balance between the primary transmission rate and system overheads than both the optimal algorithm and benchmark algorithms.

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