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Symmetry is a key factor for Branch-Line Couplers (BLCs) in RF and microwave systems. This balanced approach evenly distributes power between two output ports, aiding impedance matching and reducing unwanted coupling and crosstalk, while increasing inputoutput isolation. Furthermore, the symmetrical design of BLCs ensures favorable return loss and phase balance, which are essential for phase-sensitive detectors and beamforming. This symmetry also guarantees consistent performance over a wide frequency range, making it suitable for broadband or multi-frequency applications. We present a compact BLC operating in two frequency bands, ideal for 5G sub-6 GHz applications. It uses Tshaped lines with folded lines and stubs in a Minkowski fractal shape, resulting in a size reduction of 90%. The design and simulation were performed using the CST Microwave Studio at 0.7 GHz and 3.5 GHz, achieving a new high frequency band ratio of 5. A prototype on Rogers RT5880 substrate (εr = 2.2, h = 0.787 mm) was tested to validate the design's effectiveness, offering potential for modern wireless applications requiring versatile frequency band operation. [ABSTRACT FROM AUTHOR]

The rapid development of 5G communication networks has ushered in a new era of high-speed, low-latency wireless connectivity, as well as the enabling of transformative technologies. However, a crucial aspect of ensuring reliable communication is the accurate modeling of path loss, as it directly impacts signal coverage, interference, and overall network efficiency. This review paper critically assesses the performance of path loss models in mid-band and high-band frequencies and examines their effectiveness in addressing the challenges of 5G deployment. In this paper, we first present the summary of the background, highlighting the increasing demand for high-quality wireless connectivity and the unique characteristics of mid-band (1–6 GHz) and high-band (>6 GHz) frequencies in the 5G spectrum. The methodology comprehensively reviews some of the existing path loss models, considering both empirical and machine learning approaches. We analyze the strengths and weaknesses of these models, considering factors such as urban and suburban environments and indoor scenarios. The results highlight the significant advancements in path loss modeling for mid-band and high-band 5G channels. In terms of prediction accuracy and computing effectiveness, machine learning models performed better than empirical models in both mid-band and high-band frequency spectra. As a result, they might be suggested as an alternative yet promising approach to predicting path loss in these bands. We consider the results of this review to be promising, as they provide network operators and researchers with valuable insights into the state-of-the-art path loss models for mid-band and high-band 5G channels. Future work suggests tuning an ensemble machine learning model to enhance a stable empirical model with multiple parameters to develop a hybrid path loss model for the mid-band frequency spectrum. [ABSTRACT FROM AUTHOR]

As an intrinsic power loss of switched-capacitor circuits (SCCs), capacitor charge-sharing loss reduces the system efficiency. In this article, the approach based on q-u curves of capacitors is proposed to calculate capacitor charge-sharing loss precisely. By considering both capacitor charge-sharing loss and conduction loss, the output impedance of an SCC with a finite output capacitance can be modeled accurately in medium switching frequency. Compared with the previous modeling techniques, the proposed modeling method called RLSL not only counts the output capacitor effect but also applies to a wide range of well-posed switched capacitor circuits. Several high-order SCCs are taken as examples to validate the accuracy of RLSL via simulations and experiments and it could be corroborated that RLSL can predict the output impedance precisely with the change of switching frequency, output capacitor, and duty cycle in the mid-band. [ABSTRACT FROM AUTHOR]

The rapid development of 5G communication networks has ushered in a new era of high-speed, low-latency wireless connectivity, as well as the enabling of transformative technologies. However, a crucial aspect of ensuring reliable communication is the accurate modeling of path loss, as it directly impacts signal coverage, interference, and overall network efficiency. This review paper critically assesses the performance of path loss models in mid-band and high-band frequencies and examines their effectiveness in addressing the challenges of 5G deployment. In this paper, we first present the summary of the background, highlighting the increasing demand for high-quality wireless connectivity and the unique characteristics of mid-band (1–6 GHz) and high-band (>6 GHz) frequencies in the 5G spectrum. The methodology comprehensively reviews some of the existing path loss models, considering both empirical and machine learning approaches. We analyze the strengths and weaknesses of these models, considering factors such as urban and suburban environments and indoor scenarios. The results highlight the significant advancements in path loss modeling for mid-band and high-band 5G channels. In terms of prediction accuracy and computing effectiveness, machine learning models performed better than empirical models in both mid-band and high-band frequency spectra. As a result, they might be suggested as an alternative yet promising approach to predicting path loss in these bands. We consider the results of this review to be promising, as they provide network operators and researchers with valuable insights into the state-of-the-art path loss models for mid-band and high-band 5G channels. Future work suggests tuning an ensemble machine learning model to enhance a stable empirical model with multiple parameters to develop a hybrid path loss model for the mid-band frequency spectrum.