This letter investigates user fairness in wireless powered communication networks (WPCNs) with non-orthogonal multiple access (NOMA) with the aim of maximizing the equal individual data rates. Initially, it is proposed the bisection method to obtain the optimal power allocation coefficients. Then, Lagrange dual-decomposition is considered for evaluating the optimal time-splitting ratio. Next, Newton’s method combined with the sub-gradient method are employed to achieve the optimal solution. In addition, two sub-optimal solutions that provide a good balance between performance and complexity are proposed. Lastly, simulation results are presented to evaluate the performance of the proposed solutions.

This letter studies the resource allocation problem of sum rate maximization in wireless powered communication networks (WPCNs) with non-orthogonal multiple access (NOMA) that satisfy minimum data rate requirements per user. Firstly, we derive closed-form solutions for the time-splitting ratio and power allocation coefficients that maximize the approximated sum rate NOMA expression. Then, we use the solution of the approximated problem as the starting point for solving the original problem through Newton’s method. Simulation results show the effectiveness of the proposed approximation as well as the fast convergence of the optimal solution.

The continued performance scaling of AI gigafactories requires the development of energy-efficient devices to meet the rapidly growing global demand for AI services. Emerging materials offer promising opportunities to reduce energy consumption in such systems. In this work, we propose an electro-optic microring modulator that exploits a graphene (Gr) and transition-metal dichalcogenide (TMD) interface for phase modulation of data-bit signals. The interface is configured as a capacitor composed of a top Gr layer and a bottom WSe₂ layer, separated by a dielectric Al₂O₃ film. This multilayer stack is integrated onto a silicon (Si) waveguide such that the microring is partially covered, with coverage ratios varying from 10% to 100%. In the design with the lowest power consumption, the device operates at 26.3 GHz and requires an energy of 5.8 fJ/bit under 10% Gr-TMD coverage while occupying an area of only 20 μm². Moreover, a modulation efficiency of V_πL = 0.203 V·cm and an insertion loss of 6.7 dB are reported for the 10% coverage. The Gr-TMD-based microring modulator can be manufactured with standard fabrication techniques. This work introduces a compact microring modulator designed for dense system integration, supporting high-speed, energy-efficient data modulation and positioning it as a promising solution for sustainable AI gigafactories.

The continued performance scaling of AI gigafactories requires the development of energy-efficient devices to meet the rapidly growing global demand for AI services. Emerging materials offer promising opportunities to reduce energy consumption in such systems. In this work, we propose a novel electro-optic microring modulator that exploits a graphene (Gr) and transition-metal dichalcogenide (TMD) interface for phase modulation of data-bit signals. The interface is configured as a capacitor composed of a top graphene layer and a bottom WSe2 layer, separated by a dielectric Al2O3 film. This multilayer stack is integrated onto a silicon (Si) waveguide such that the microring is partially covered, with coverage ratios varying from 10% to 100%. The proposed device design and its key figures of merit, including energy efficiency, are analyzed. Simulation results indicate that the microring modulator achieves low energy consumption and high-speed operation while maintaining a compact footprint. In particular, the device operates at 39.1 GHz with an energy consumption of 8.3 fJ/bit under 25% Gr–TMD coverage, whilst occupying an area of only 20 μm2. Furthermore, a modulation efficiency of VπL= 0.082 V·cm and an insertion loss of 6.8 dB are obtained for the 25% coverage. The proposed Gr–TMD-based microring modulator demonstrates significant potential for high-speed, energy-efficient data modulation, contributing to the development of more sustainable AI gigafactories.