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A novel wideband common-mode (CM) suppression filter is proposed for high-speed transmission. The filter is embedded in 3 10 mm × 10 mm layers of a printed circuit board (PCB) that combines a mushroom structure and a defected corrugated reference plane structure (MDCRP). Using the novel MDCRP structure generates more resonance frequencies in the CM current return path. This generates a wider CM noise suppression performance. We used a simulation method to obtain the best geometric parameters for the MDCRP structure. The experimental results proved that the full-wave simulation results were consistent with the actual measurement results. This novel filter shows good signal integrity according to actual measurements, and the insertion loss can be kept to less than −2.306 dB from DC to 21 GHz in differential mode (DM). The CM noise can be suppressed by over −10 dB from 5.09 GHz to 20.62 GHz. The fractional bandwidth is 120.8%, and the CM noise improves by 64.5%. An eye diagram proves that the filter can support a 20 Gb/s transmission rate with complete differential signal transmission capability. The MDCRP structure filter can support HDMI 2.0, PCI Express 4.0, USB 3.2, and SATA Express. Therefore, the filter meets current computer and server system products’ design needs.

The resource management in wireless networks with massive Internet of Things (IoT) users is one of the most crucial issues for the advancement of fifth-generation networks. The main objective of this study is to optimize the usage of resources for IoT networks. Firstly, the unmanned aerial vehicle is considered to be a base station for air-to-ground communications. Secondly, according to the distribution and fluctuation of signals; the IoT devices are categorized into urban and suburban clusters. This clustering helps to manage the environment easily. Thirdly, real data collection and preprocessing tasks are carried out. Fourthly, the deep reinforcement learning approach is proposed as a main system development scheme for resource management. Fifthly, K-means and round-robin scheduling algorithms are applied for clustering and managing the users’ resource requests, respectively. Then, the TensorFlow (python) programming tool is used to test the overall capability of the proposed method. Finally, this paper evaluates the proposed approach with related works based on different scenarios. According to the experimental findings, our proposed scheme shows promising outcomes. Moreover, on the evaluation tasks, the outcomes show rapid convergence, suitable for heterogeneous IoT networks, and low complexity.

In the Internet of Things (IoT) era, indoor localization plays a vital role in academia and industry. Wi-Fi is a promising scheme for indoor localization as it is easy and free of charge, even for private networks. However, Wi-Fi has signal fluctuation problems because of dynamic changes of environments and shadowing effects. In this paper, we propose to use a deep neural network (DNN) to achieve accurate localization in Wi-Fi environments. In the localization process, we primarily construct a database having all reachable received signal strengths (RSSs), and basic service set identifiers (BSSIDs). Secondly, we fill the missed RSS values using regression, and then apply linear discriminant analysis (LDA) to reduce features. Thirdly, the 5-BSSIDs having the strongest RSS values are appended with reduced RSS vector. Finally, a DNN is applied for localizing Wi-Fi users. The proposed system is evaluated in the classification and regression schemes using the python programming language. The results show that 99.15% of the localization accuracy is correctly classified. Moreover, the coordinate-based localization provides 50%, 75%, and 93.10% accuracies for errors less than 0.50 m, 0.75 m, and 0.90 m respectively. The proposed method is compared with other algorithms, and our method provides motivated results. The simulation results also show that the proposed method can robustly localize Wi-Fi users in hierarchical and complex wireless environments.