This paper presents a wideband circularly polarized (CP) stacked patch antenna for 5G Sub-6 GHz applications. A major challenge in wideband dual-feed antennas is the fabrication limit caused by standard chemical etching and mechanical milling processes for extremely narrow high-impedance microstrip lines in two-stage branch-line couplers. To overcome this bottleneck, a Defected Ground Structure (DGS) is utilized. By etching the ground plane, the distributed inductance is increased, allowing the highly sensitive narrow traces to be physically widened while strictly maintaining the 50 Ω impedance. The antenna features an aperture-coupled mechanism via an H-shaped slot to excite the driven and parasitic patches, which are separated by an air gap to maximize bandwidth. The fabricated prototype demonstrates an impedance bandwidth (|S11|<−10 dB) of 41.6% (2.92–4.37 GHz) and a simulated 3 dB axial ratio bandwidth of 23.14% (3.26–4.07 GHz) with a peak realized gain of 7.54 dBi. Excellent agreement between simulated and measured results validates the robustness of the proposed DGS technique against fabrication tolerances.
Massive Multiple-Input Multiple-Output (MIMO) is a key enabling technology for fifth-generation (5G) and beyond wireless communication systems because of its ability to greatly enhance both spectral efficiency (SE) and energy efficiency (EE). However, maximizing these two performance metrics simultaneously remains a challenging multi-objective optimization problem due to the conflicting effects of the transmit power, antenna deployment, and circuit power consumption. This paper investigates the EE–SE trade-off in a downlink Massive MIMO system with Minimum Mean Square Error (MMSE) channel estimation (CE) and different linear combining and precoding techniques. A power optimization framework based on transmit power allocation and antenna configuration is analyzed to identify operating points that maximize EE while maintaining high SE. Performance analysis of the number of base station (BS) antennas in MIMO systems, user equipment density, transmit power, and inter-cell interference on system performance is evaluated through numerical simulations. The results demonstrate that appropriately selecting the number of transmit antennas and optimizing the transmit power significantly improve the EE–SE trade-off. Furthermore, although increasing the number of antennas enhances SE, EE exhibits a non-monotonic behavior because of the additional circuit power required by the radio-frequency hardware. The findings confirm that MMSE-based CE provides higher spectral efficiency than the MR, ZF, RZF, and S-MMSE schemes, albeit at increased computational complexity, offering useful design guidelines for energy-efficient Massive MIMO networks.
Battery-powered IoT sensor nodes require energy-efficient Medium Access Control (MAC) protocols to extend network lifetime while supporting priority-sensitive traffic. This paper proposes a Priority-Aware Packet Aggregation/Piggybacking-Based (Priority + Packet Aggregation) Hybrid Energy-Efficient MAC (P2A-HMAC) protocol that integrates bit-mapped priority signaling with lightweight packet aggregation/piggybacking to reduce control overhead. The protocol classifies traffic into emergency, buffer-overflow, priority, and normal categories, providing guaranteed access to delay-sensitive data while minimizing radio activity, idle listening, and contention. A mathematical energy model is developed for both priority- and contention-based operations. MATLAB-based evaluation demonstrates that P2A-HMAC consistently achieves lower energy consumption than TDMA, EA-TDMA, ASHMAC, E-BMA, and P2A-HMAC across varying network sizes, packet sizes, and event-generation probabilities. The proposed protocol provides substantial energy savings, particularly in large-scale and moderate-traffic networks, while maintaining priority-aware communication and QoS, making it suitable for low-power IoT and wireless sensor network applications.
In this work, a plasma-free atomic layer deposition (ALD)-based H2O post-treatment method is proposed to precisely modulate hydrogen-related (H-related) traps in indium gallium zinc oxide (IGZO) thin-film transistors (TFTs) by the number of H2O treatment cycles. Under the optimized condition, the scaled device with a channel length of 70 nm exhibits a near-ideal subthreshold swing of 62.9 mV/dec, a low threshold voltage (VTH) of 0.18 V, an acceptable static leakage current, and a high drive current of 3.29 μA/μm at an overdrive voltage and drain voltage of 1 V. In addition, the treated device shows only a 13 mV of VTH shift after 1000 s positive bias stress (PBS), corresponding to a 94% improvement compared with the pristine device. These improvements are attributed to the introduction of two different polarities of hydrogen-related traps after H2O treatment. Furthermore, the influence of H-related traps on bias stability and the mechanisms responsible for VTH shift are systematically clarified. These results establish that an optimized hydrogen incorporation window that maximizes the beneficial effects while balancing severe hydrogen-induced degradation caused by excessive hydrogen incorporation. Consequently, scaled IGZO TFTs with fast switching, low-power operation, high performance, and high reliability can be achieved, providing strong potential for back-end-of-line (BEOL)-compatible electronics and monolithic three-dimensional integrated applications.
This paper proposes a sensorless current estimation method for piezoelectric energy harvesting (PEH) systems using a first-order Takagi–Sugeno fuzzy system. Unlike invasive current sensing, the proposed estimator uses only non-invasive measurements: output voltage VO, its derivative V˙O, and load resistance RL. The fuzzy rules are initialized directly from the physical equivalent circuit parameters and trained via the ANFIS on a large-scale dataset (78 million samples). The proposed model achieves a mean coefficient of determination R2=0.9999 (95% CI: [0.99989, 0.99991]), root mean square error RMSE=3.12×10−8 A, mean absolute percentage error MAPE = 2.51% (95% CI: [1.98, 3.04]%), and fitness FIT = 98.98%—outperforming multiple linear regression (R2=0.9738 and MAPE = 116.25%) and a shallow neural network with 211 parameters (R2=0.9991 and MAPE = 13.85%) despite having only 170 trainable parameters. Unlike black-box neural networks, the fuzzy model provides interpretable rules whose consequent parameters map directly to physical quantities (effective capacitance Cp(eff) and leakage conductance 1/Rp(eff)). The low computational footprint (170 parameters, <5 μs inference, and ≈1.4 kB of memory) makes it suitable for real-time deployment on low-power microcontrollers. These results demonstrate the viability of the proposed approach under controlled laboratory conditions for the single, series, and parallel PEH configurations considered. This work establishes that physically informed fuzzy modeling is a viable, interpretable, and efficient alternative to deep learning for sensorless monitoring in low-power energy harvesting systems.
