<p class="0abstract">The novel coronavirus (COVID-19) has become widespread around the world. It started in Wuhan, China, and has since spread rapidly among people living in other countries. Hence, the World Health Organization has considered COVID-19 as a pandemic that threatens millions of people’s lives. Due to the high number of infected people, many hospitals have been facing critical issues in providing the required medical services. For instance, some clinical centers have been unable to provide one of the most important medical services, namely blood tests to determine whether an individual is infected with COVID-19. Therefore, it is important to present an alternative diagnosis option to prevent the further spread of COVID-19. In this paper, a proposed intelligent detection communication system (IDCS) is configured for distributed mobile clinical centers to control the pandemic. In addition, the intelligent system is integrated with the Zigbee communication protocol to build a mobile COVID-19 detection system. The proposed system was trained on X-ray COVID-19 lung images used to identify infected people. The Zigbee protocol and decision tree algorithm were used to design the IDCS. The results of the proposed system show high accuracy 94.69% and accept results according to the performance measurements.</p>

Bit error rate in mobile communication is of parameters which effect on performance of network then effect on service quality of network. There are many technique used to improve this performance one of these techniques are equalization. As we know there are many type of equalization here in our article we will use zero forcing equalizer (ZF) and zero forcing with successive interference cancellation(ZF-SIC) which technique used to improving zero forcing equalization in another meaning used this technique to combat Inter-symbol interference (ISI) in network as our goal to improve quality of service (Qos).