Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN

In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications. This calls for fast and accurate estimation on the distribution of the radio resources, which is usually represented by the spatial signal power strength over the geographical environment, known as a radio map. In this paper, we present a cooperative radio map estimation (CRME) approach enabled by the generative adversarial network (GAN), called as GAN-CRME, which features fast and accurate radio map estimation without the transmitters' information. The radio map is inferred by exploiting the interaction between distributed received signal strength (RSS) measurements at mobile users and the geographical map using a deep neural network estimator, resulting in low data-acquisition cost and computational complexity. Moreover, a GAN-based learning algorithm is proposed to boost the inference capability of the deep neural network estimator by exploiting the power of generative AI. Simulation results showcase that the proposed GAN-CRME is even capable of coarse error-correction when the geographical map information is inaccurate

Pushing AI to Wireless Network Edge: An Overview on Integrated Sensing, Communication, and Computation towards 6G

Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the huge amount of data scattered at wireless network edge. In general, realizing edge intelligence corresponds to the process of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the sensing, communication, and computation separately in a task-agnostic manner, which encounters difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications such as auto-driving. This thus prompts a new design paradigm of seamless integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in the downstream AI applications. In view of its growing interest, this article provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead

Preparation of TiO 2

Photocatalysts comprising nanosized TiO2 particles on activated carbon (AC) were prepared by a sol-gel method. The TiO2/AC composites were characterized by X-ray diffraction (XRD), thermogravimetric (TG) analysis, nitrogen adsorption, scanning electron microscope (SEM), transmission electron microscope (TEM), and energy dispersive X-ray (EDX). Their photocatalytic activities were studied through the degradation of Rhodamine B (RhB) in photocatalytic reactor at room temperature under ultraviolet (UV) light irradiation and the effect of loading cycles of TiO2 on the structural properties and photocatalytic activity of TiO2/AC composites was also investigated. The results indicate that the anatase TiO2 particles with a crystal size of 10–20 nm can be deposited homogeneously on the AC surface under calcination at 500°C. The loading cycle plays an important role in controlling the loading amount of TiO2 and morphological structure and photocatalytic activity of TiO2/AC composites. The porosity parameters of these composite photocatalysts such as specific surface area and total pore volume decrease whereas the loading amount of TiO2 increases. The TiO2/AC composite synthesized at 2 loading cycles exhibits a high photocatalytic activity in terms of the loading amount of TiO2 and as high as 93.2% removal rate for RhB from the 400 mL solution at initial concentration of 2 × 10−5 mol/L under UV light irradiation

Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving

While federated learning (FL) improves the generalization of end-to-end autonomous driving by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence rate due to long-range communications among vehicles and cloud server. Hierarchical federated learning (HFL) overcomes such drawbacks via introduction of mid-point edge servers. However, the orchestration between constrained communication resources and HFL performance becomes an urgent problem. This paper proposes an optimization-based Communication Resource Constrained Hierarchical Federated Learning (CRCHFL) framework to minimize the generalization error of the autonomous driving model using hybrid data and model aggregation. The effectiveness of the proposed CRCHFL is evaluated in the Car Learning to Act (CARLA) simulation platform. Results show that the proposed CRCHFL both accelerates the convergence rate and enhances the generalization of federated learning autonomous driving model. Moreover, under the same communication resource budget, it outperforms the HFL by 10.33% and the SFL by 12.44%

Supported monodisperse Pt nanoparticles from [Pt-3(CO)(3)(mu(2)-CO)(3)](5)(2-) clusters for investigating support-Pt interface effect in catalysis

MOST of China [2011CB932403]; NSFC [21131005, 21021061, 20925103, 20923004]; Fok Ying Tung Education Foundation [121011]Here we present a surfactant-free strategy to prepare supported monodisperse Pt nanoparticles from molecular [Pt-3(CO)(3)(mu(2)-CO)(3)](5)(2-) clusters. The strategy allows facile deposition of same-sized Pt nanoparticles on various oxide supports to unambiguously study the interface effect between noble metal and metal oxide in catalysis. In this study, Fe2O3 is demonstrated to be a superior support over TiO2, CeO2 and SiO2 to prepare highly active supported Pt nanoparticles for CO oxidation, which indicates that the interfaces between Pt and iron oxide are the active sites for O-2 activation and CO oxidation