The effect of COVID-19 restrictions on atmospheric new particle formation in Beijing

During the COVID-19 lockdown, the dramatic reduction of anthropogenic emissions provided a unique opportunity to investigate the effects of reduced anthropogenic activity and primary emissions on atmospheric chemical processes and the consequent formation of secondary pollutants. Here, we utilize comprehensive observations to examine the response of atmospheric new particle formation (NPF) to the changes in the atmospheric chemical cocktail. We find that the main clustering process was unaffected by the drastically reduced traffic emissions, and the formation rate of 1.5 nm particles remained unaltered. However, particle survival probability was enhanced due to an increased particle growth rate (GR) during the lockdown period, explaining the enhanced NPF activity in earlier studies. For GR at 1.5–3 nm, sulfuric acid (SA) was the main contributor at high temperatures, whilst there were unaccounted contributing vapors at low temperatures. For GR at 3–7 and 7–15 nm, oxygenated organic molecules (OOMs) played a major role. Surprisingly, OOM composition and volatility were insensitive to the large change of atmospheric NOx concentration;instead the associated high particle growth rates and high OOM concentration during the lockdown period were mostly caused by the enhanced atmospheric oxidative capacity. Overall, our findings suggest a limited role of traffic emissions in NPF.

FDD LTE MIMO closed-loop vs open-loop performance evaluation in commercial network

With a growing network traffic Mobile Network Operators (MNO) looking for ways to increase network capacity and improve customer experience. One of the ways is to find the best parameters from the set defined by 3GPP. In the study, closed-loop MIMO was compared to open-loop MIMO on the LTE FDD network. Network performance was evaluated in 3 different scenarios: slow and fast-moving UE under different SINR levels and large scale on 2T2R and 4T4R cells. The result shows gains of using closed-loop and it is recommended to use it commercial LTE networks. Article in English. LTE MIMO atvirojo ir uždarojo MIMO ciklo palyginimas komerciniame tinkleAlternate : Didėjant mobiliojo ryšio tinkle perduodamam duomenų srautui, mobiliojo ryšio operatoriai (MNO) ieško būdų, kaip padidinti tinklo pajėgumą ir pagerinti klientų patirtį. Vienas iš būdų yra rasti geriausius parametrus iš 3GPP apibrėžto rinkinio. Tyrimo metu uždarojo ciklo MIMO buvo lyginamas su atvirojo ciklo MIMO LTE FDD tinkle. Tinklo našumas buvo vertinamas pagal 3 skirtingus scenarijus: lėtai ir greitai judantis klientas. Taip pat palyginta pagal skirtingą signalo ir triukšmo santykio vertę. Taip pat atliktas masinis palyginimas 2T2R ir 4T4R tipo ląstelėse. Rezultatas rodo uždarojo ciklo naudojimo pranašumus. Todėl uždarojo ciklo MIMO rekomenduojama naudoti komerciniuose LTE tinkluose.

An Innovative Reinforcement Learning-Based Framework for Quality of Service Provisioning Over Multimedia-Based SDN Environments

Within the current global context, the coronavirus pandemic has led to an unprecedented surge in the Internet traffic, with most of the traffic represented by video. The improved wired and guided network infrastructure along with the emerging 5G networks enables the provisioning of increased bandwidth support while the virtualization introduced by the integration of Software Defined Networks (SDN) enables traffic management and remote orchestration of networking devices. However, the popularity and variety of multimedia-rich applications along with the increased number of users has led to an ever increasing pressure that these multimedia-rich content applications are placing on the underlying networks. Consequently, a simple increase in the system capacity will not be enough and an intelligent traffic management solution is required to enable the Quality of Service (QoS) provisioning. In this context, this paper proposes a Reinforcement Learning (RL)-based framework within a multimedia-based SDN environment, that decides on the most suitable routing algorithm to be applied on the QoS-based traffic flows to improve QoS provisioning. The proposed RL-based solution was implemented and evaluated using an experimental setup under a realistic SDN environment and compared against other state-of-the-art solutions from the literature in terms of throughput, packet loss, latency, peak signal-to-noise ratio (PSNR) and mean opinion score (MOS). The proposed RL-based framework finds the best trade-off between QoS vs. Quality of User Experience (QoE) when compared to other state-of-the-art approaches.