ICT Express最新文献

Open Radio Access Network (RAN) is an important architecture design shift for 5G and next generation telecommunications networks. With open RAN, mobile network operators would be able to mix and match multi-vendor RAN solutions as long as the solutions comply with open standards. O-RAN Alliance is the global leader in standardizing the open RAN architecture, where RAN Intelligent Controller (RIC) is positioned centrally as the brain of a RAN to manage and optimize the RAN operations. Open-source projects play a key role in accelerating the adoption of open RAN architecture, especially the use of RIC. However, the fast pace of development and the lack of documentation in open-source projects create steep learning curve for beginners. In this paper, we first provide an overview of widely used open-source RIC projects and discuss their pros and cons. We then share our first-hand experience to use RIC in our campus 5G network that consists of commercial-grade RAN solutions. In particular, we developed a suite of three RAN control applications (i.e., energy efficiency, interference management, and predictive maintenance) on an open-source RIC, and we deploy and evaluate them on a commercial-grade 5G network in a university campus. For these RIC applications, we design and evaluate different ML models based on real-world data collected from our 5G network, which we publish together with this paper. Our experimental results show that AI-based RIC applications can achieve more than 90% of accuracy in inferring the situation of the RAN for each given task. Our energy-saving RIC application can reduce 65% of energy consumption of the RAN over a simulated period of one year. Our project also validates the feasibility to interfacing an open-source RIC with existing commercial-grade 5G solutions.

By considering the limited energy of Internet of Things (IoT) devices. We take the resource allocation to guarantee the stringent Quality of Service (QoS) depending on the joint optimization of power control and finite blocklength of channel. To achieve large volumes of arrival rates, we propose Adversarial Training based Generative Adversarial Networks (AT-GANs), which utilize a significant number of extreme events to provide high reliability and adjust real data in real-time. Simulation results show that Deep-Reinforcement Learning (Deep-RL) for AT-GAN could eliminate the transient training time. As a result, the AT-GAN keeps the reliability higher than 99.9999%.

In this paper, we propose a novel federated learning framework named FedHM that aims to address the challenge of training models on heterogeneous devices with varying architectures. Our approach enables the collaborative training of diverse local models by sharing a fully convolutional network (FCN) architecture that effectively extracts the local-to-global representations. By leveraging the weights with respect to this abstraction as common information across different DNN architectures, FedHM achieves efficient federated learning with minimal computational and communication overhead. We compare FedHM with three federated learning frameworks using two datasets for image classification tasks. Our results show that FedHM achieves high accuracy with considerably lower computational and communication costs compared to the other frameworks.

Aerial Computing, utilizing unmanned aerial vehicles (UAVs), has emerged as a promising solution to enhance mobile cloud computing (MCC) infrastructure for the Internet of Things (IoT). The continuous generation of vast amounts of data by IoT devices requires efficient processing and monitoring for timely decision-making. However, wireless connections between IoT devices and remote servers can be unreliable, resulting in data loss. UAVs, with their increasing processing power and autonomy, can act as bridges between IoT devices and remote servers such as edge or cloud computing. In that context, this paper proposes a continuous time Markov chain (CTMC) models for an aerial computing system to evaluate system dependability metrics including availability and reliability. Sensitivity analysis is conducted to provide extended CTMC models with improved system availability. The proposed advanced model reduces downtime by 62 h compared to the baseline model, showcasing the potential of UAVs in enhancing the availability and reliability of MCC infrastructures. The use of UAVs and MCC in aerial computing is believed to be a win–win solution for cost-effective and energy-saving communication and computation services in various environments.

In this paper, we study covert communications strategies in a compress-and-forward (CF) relay system. Along with a public message to a destination node via a CF relay, a source node attempts to transmit a covert message while evading the surveillance of the CF relay. We identify the optimal power distribution between the public and covert messages and the optimal amount of compression that maximize the covert rate subject to the minimum detection error probability requirement. Our provided solutions also reveal that both the power distribution and the achievable covert rate are identical to that of an equivalent amplify-and-forward (AF) relay system if an adequate quantization codebook with the optimal compression is employed. The numerical results verify the effectiveness of the optimal solutions and confirm our analyses.

In this study, we explore the potential of leveraging machine learning techniques, specifically auto-encoders (AE), for the decoding of linear block codes. Our findings suggest that this approach can outperform the conventional ordered statistics decoding (OSD) method, especially in a Rayleigh fading channel environment. We have rigorously trained the AE under both additive white Gaussian noise and Rayleigh fading channel conditions to ensure robustness in its performance. The output of the AE is combined with the received vector in a suitable manner to perform OSD. Through our experiments, we demonstrate that this proposed decoding approach yields better results than the conventional OSD method in Rayleigh fading channel when we used (23,12) Golay code.

The metaverse promises to revolutionize the internet by introducing a new era of three-dimensional objects. The advancements in artificial intelligence and immersive technologies have made the metaverse a trending topic in various industries, including manufacturing. This study aims to provide a comprehensive understanding of the metaverse, its underlying technologies, and the current trends shaping its development. By delving into the concept and potential applications of the metaverse in manufacturing, we seek to uncover its transformative benefits for the industry. Through an exploration of existing use cases, we aim to highlight the practical implications of the metaverse. Recognizing the need for further research, this study also addresses the limitations, privacy concerns, and security implications associated with the metaverse. By examining these aspects, we hope to motivate further investigation and contribute to the ongoing discourse surrounding this emerging paradigm.

A novel channel estimation method based on deep learning algorithm is proposed for large-scale IoT networks. We consider asymmetric backscatter communication system to maintain low-power at sensor nodes. In order to obtain channel data, we design denoising autoencoder which consists of encoder with Feedforward Neural Network (FNN) and decoder with Convolutional Neural Network (CNN). Finally, the channel estimation error is minimized, while the pilots are optimized. Especially, we adopt beamforming technique that relies only on cascaded channel data to reduce complexity in multi-sensor system. It is shown that the accuracy is slightly degraded while the complexity is greatly reduced.