Aeu-International Journal of Electronics and Communications最新文献

A circuit design methodology for space applications is presented with an 8-bit resistive digital-to-analog converter (DAC) with XY addressing mode and BiCMOS buffer designed in IHP’s 130 nm SiGe BiCMOS technology (SG13S). The radiation tolerance of the implemented DAC is evaluated by circuit-level simulations particularly analyzing the radiation sensitivity of the DAC to analog single-event transients (ASETs). Radiation mitigation techniques are addressed. The total current consumption with a 3.3 V supply is 0.54 mA at a 1 MHz sampling frequency.

Despite the inherent transmission-range limitations of 60 GHz band, it has applications in a wide variety of fields, notably in rear occupant alert (ROA) systems for monitoring vehicle occupants in rear seats. In-vehicle monitoring radar systems commonly utilize microstrip patch antennas for transmitting and receiving electromagnetic waves, which generally suffer from limited bandwidth. Although various techniques such as stub matching and single and multi-section quarter-wave transformers can be employed to widen the bandwidth of a microstrip patch antenna, they lead to an overall increase in the antenna size. This study proposes a compact 3 × 1 series-fed patch array antenna designed for operation in 60 GHz band. The proposed compact antenna achieves impedance matching by employing a symmetrical inset structure on both the top and bottom of the patches, with different patch sizes and interelement spacings. The overall antenna dimensions, using the RO4830 substrate (ε_r = 3.23, tan δ = 0.0033), are 1.41 mm × 7.04 mm × 0.125 mm. The impedance bandwidth ranges from 60.95 to 66.73 GHz, and the maximum realized gain is 10.24 dBi at 62.5 GHz. The half-power beam-widths (HPBWs) are 33.96 degrees in the E-plane and 76.19 degrees in the H-plane at 62.5 GHz.

This paper investigates a cooperative non-orthogonal multiple access (NOMA) system designed for multiple users. It integrates simultaneous wireless information and power transfer (SWIPT) while accounting for the imperfections in successive interference cancellation (SIC). In this scenario, a nearby user functions as a relay for a more distant user. Through the utilization of a power splitting (PS) protocol, the nearby user adeptly manages both energy harvesting and decoding signals from users. A user pairing strategy is employed, aiming to guarantee a significant contrast in channel gains between users within each pair. Subsequently, the power allocation coefficients and PS factor for each cluster are derived to optimize the performance of nearby users and amplify the overall system throughput. This optimization takes into account the individual target rates of each user while accommodating the presence of imperfect SIC. Numerical results affirm the enhanced performance of the nearby user, showcasing greater system throughput and minimal outage probability compared to existing schemes, even in the presence of imperfect SIC.

In the rapidly evolving landscape of the metaverse, efficiently managing and processing vast amounts of data is a critical challenge. This paper introduces a novel data management framework designed specifically for the metaverse, addressing the complexities of handling extensive data in its increasingly intricate and expansive digital environments. Leveraging advancements in wireless communication technologies, our framework optimizes resource allocation while managing massive data loads. We propose an innovative approach that integrates change grading and semantic encoding techniques, utilizing edge computing to process data locally and reduce server load. By processing data locally and minimizing server load, our approach stores static scenes on local devices and detects significant changes by comparing them with newly captured data. This selective transmission of only new or altered information based on change grades dramatically reduces bandwidth requirements and boosts network efficiency. This paper details the technical aspects of our methodology, showcases experimental results that demonstrate its effectiveness, and discusses its implications for the future scalability and sustainability of metaverse architectures. Our framework aims to refine the real-time digital representation of the metaverse, ensuring its operational efficiency and continuous accessibility as it evolves.

The fifth generation (5G) mobile networks have introduced new features compared to the previous generation that resulted in increased overall throughput and decreased latency. At the same time, the complexity of 5G-based standards and deployment scenarios is increasing. This further drives the development of software for testing new ideas and different 5G and Beyond-5G use cases in academia and industry. In this paper we propose a flexible end-to-end (E2E) standalone 5G system platform based on the Open Air Interface software, which is fully reprogrammable and customizable. We created a set of Linux shell scripts, which improves the ease of use of the software, speeding up the process of implementing different connectivity scenarios. We present the main capabilities of the platform and the achievable throughput and latency for different bandwidth parts. Setting the DL/UL Transmission periodicity to 2 ms, we measured a latency of 6.78 ms for the mean RTT value, which is acceptable for many applications requiring low latency.

This paper presents a novel Doherty power amplifier (DPA) structure that employs two equal-cell transistors with an extended high-efficiency range. It is illustrated that the DPA high-efficiency range can be extended by employing a novel coupler-based network through introducing different impedance at the isolation port, which can improve the back-off range. The corresponding theoretical architecture of the proposed coupled DPA is constructed with design parameters. The load modulation effect with different circuit parameters are also analyzed, and DPA performance in different back-off range can be obtained. To validate the proposed architecture, a symmetrical coupled DPA with a high-efficiency range of around 9 dB is designed at 2 GHz. Under the continuous-wave(CW) signal, the fabricated DPA achieves saturated output power of 44.2 dBm and saturated drain efficiency (DE) of 65%. At 8.5 dB back-off, DE of 51.2% can be obtained with a gain of 14 dB. When driven by a 20 MHz Long Term Evolution (LTE) signal with 8 dB peak to average power ratio (PAPR), the measured adjacent channel power ratio (ACPR) keeps better than −20 dBc without linearization. The measurement results well conform to the theoretical analysis and simulation results.

This paper proposes a generalized design methodology for extending the bandwidth and output back-off range of an equal-cell Doherty power amplifier (EC-DPA). The parameters of the EC-DPA combiner are derived based on pre-defined asymmetrical voltage and nonlinear current profiles. It is illustrated that the design parameters of the EC-DPA can be expressed as a function of the combining load. Then, the combining load can be optimized to extend the bandwidth of the proposed EC-DPA. As a validation, an 1.4–2.1 GHz EC-DPA with extended output back-off range is implemented in this paper. Under a continuous-wave (CW) signal excitation, the fabricated EC-DPA achieves a maximum output power of 42.6–43.8 dBm, a saturation drain efficiency (DE) of 50.2%–70.2% and a 10 dB back-off DE of 45.2%–53.7% over 1.4–2.1 GHz. Moreover, under the excitation of a 20 MHz modulated signal with a peak-to-average power ratio (PAPR) of 8.0 dB, the measured adjacent channel leakage ratios (ACLRs) of the fabricated EC-DPA changes from −36.2 dBc to −25.5 dBc at the lower band and from −34.6 to −23.8 dBc at the upper band over 1.4–2.1 GHz when the average output power is 35.0 dBm.

There are numerous studies on Hopfield neural networks with electromagnetic induction using memristors in either autaptic or synaptic connections. In this study, we explore a novel scenario where all connections are influenced by electromagnetic induction. We investigate and compare the network’s dynamics with one memristive autapse, two memristive autapses, and a memristive synapse. The results indicate that having two memristive autapses instead of one increases the dynamical range, leading to chaotic dynamics in unequal autaptic strengths. In contrast, in the presence of the memristive synapse, chaos can emerge only in very strong synaptic strength. Using fractional-order derivatives can transform the periodic attractor of the integer-order model into a chaotic one in some parameters. Furthermore, incorporating more memristors leads to chaos at lower fractional orders.

This paper is the first demonstration of a dual-functional metasurface (DFM) based on all-dielectric materials for biosensing applications. The proposed biosensor possesses a unique capability to switch between polarization conversion and absorber functionalities owing to the phase changing properties of VO₂. Accordingly, the sensor works in two modes: a highly sensitive reflective cross-polarization converter mode (4.38 - 5.08 THz) and an absorber mode (4.36 - 5.96 THz), both of which hold significant promise for the detection of hemoglobin and cancer cells, respectively. When VO₂ is in the insulating state, the reflective cross-polarization converter function of this biosensor exhibits exceptional sensitivity of 3.08 THz/RIU for hemoglobin detection. This is the highest sensitivity among the existing THz based biosensors. Likewise, the same biosensor transforms into an absorber when VO₂ is in the conducting state, offering an impressive sensitivity of 2.93 THz/RIU for detection of cancerous cells. The all-dielectric based DFM biosensor also provides a high degree of angular stability, providing stable response for incident angles of up to 60° for both its polarization converter and absorber functions.

A hybrid radio frequency (RF) and light fidelity (LiFi) network combines the strengths of RF and LiFi technologies. RF offers broad coverage, while LiFi provides high data rates. As these technologies operate on non-interfering spectra, they can co-exist without interfering with each other. This setup not only boosts data rate but also makes the network more reliable, especially when physical obstacles might block signals. However, resource management in hybrid RF/LiFi networks is challenging because of the dynamic environment and the different characteristics of the two technologies. Efficient resource allocation maximizes the data rate in these networks. In this paper, we introduce a model-free deep reinforcement learning (DRL) approach to solve the resource allocation problem in hybrid RF/LiFi networks. Our DRL model is designed to handle real-world conditions, considering factors like blockages and user mobility. Unlike traditional methods that need extensive modeling and assumptions, our approach learns directly from interacting with the environment, making it highly adaptable and robust. Through simulations, it is observed that our method improves resource utilization and overall network performance, achieving a 62.8% increase in sum rate and a 42.8% improvement in optimal transmit power compared to conventional methods.