This paper introduces an improved filtering operator based on the first-order derivative Gaussian filtering operator. As the defocus distance decreases, the edges of an image transition from being smooth to sharp. Using the operator proposed in this study, a parameter can be obtained to quantify the degree of edge sharpness, where a smaller value indicates sharper edges. By using the characteristic, we can judge whether the object is near the focal plane. Combining this information, we proposed two focus method. Both of them can avoid the problem of the hill climbing algorithm locating at local extremum points and one of them can locate the watershed between “coarse search” and “fine search” before the sharpness value crosses the peak value, so as to realize the transition from “coarse search” to “fine search” before crossing the peak value and improve focusing efficiency. This is different from previous passive focusing strategies. The final experiment shows that the proposed solutions in this paper is effective. However, the methods proposed in this study are only applicable to incoherent optical microscopy imaging systems and are not suitable for coherent light source microscopy imaging systems. Besides, the prerequisite for the successful implementation of the two proposed focusing strategies in this study is that the target exhibits sharp edges when it is within the focal plane.

With the ever-increasing demand for data services, the spectrum becomes precious resource. The cooperative transmission mechanism, which allows broadcast and unicast to share the same spectrum, can effectively alleviate this situation. The success of LDM in ATSC 3.0 has inspired many scholars to superimpose broadcast and unicast services with a fixed power injection ratio. To further improve radio resource utilization, A flexible LDM scheme with a variable power injection ratio is proposed. The scheduling policy in Medium Access Control layer is formulated as a sequential decision problem, and it proved to have Markov property. A DRL-based algorithm is proposed to obtain the optimal scheduling scheme. Considering the requirements of broadcasting service and unicast services, the Lyapunov optimization method is used to convert the problem to the Lyapunov penalty form that is more conducive for the agent to learn the optimal strategy. Simulation results show that the proposed scheme can achieve much improved performance in system throughput when guaranteeing the quality of the broadcast service.

Angular reciprocity based downlink (DL) channel covariance matrix (CCM) reconstruction has the significant superiority of requiring no uplink feedback in frequency division duplex multi-antenna systems. In this realm, maximal angle of arrival estimation-based schemes show superior performance due to its noise-robust merit for environment with a dominant scattering cluster. To extend the scheme to multiple scattering clusters environments, we propose to decompose the DL CCM into the covariance matrix of each scattering cluster and then reconstruct each DL sub-covariance matrix utilizing the estimated angular reciprocal parameters. Simulation results verify the validity of the proposed scheme in a 3GPP standard based multiple scattering clusters channel model.

In this paper, we investigate the packet-level throughput and energy efficiency of millimeter-wave unmanned aerial vehicle (UAV)-assisted integrated access and backhaul (IAB) heterogeneous cellular networks with spatiotemporal traffic. Specifically, we develop a theoretical framework to analyze the mean packet throughput and energy efficiency of the network based on stochastic geometry and queueing theory, whereby the spatial randomness of network deployment and the temporal randomness of network traffic can be appropriately characterized. Different from the traditional network performance metrics emphasizing transmission and resource consumption, the packet-level performance helps to better understand the impact of not only packet transmission but also packet waiting time in a multihop network. Simulation results demonstrate that the assistance of IAB-based UAVs can efficiently relieve the load of terrestrial macro- and small-cell networks, and the appropriate network deployment parameters play pivotal roles in improving both the packet-level throughput and energy efficiency performance. By jointly optimizing the key network parameters, the packet energy efficiency can be significantly improved while ensuring the required mean packet throughput of the network.