Time-critical control applications typically pose stringent connectivity requirements for communication networks. The imperfections associated with the wireless medium such as packet losses, synchronization errors, and varying delays have a detrimental effect on performance of real-time control, often with safety implications. This paper introduces multi-service edge-intelligence as a new paradigm for realizing time-critical control over wireless. It presents the concept of multi-service edge-intelligence which revolves around tight integration of wireless access, edge-computing and machine learning techniques, in order to provide stability guarantees under wireless imperfections. The paper articulates some of the key system design aspects of multi-service edge-intelligence. It also presents a temporal-adaptive prediction technique to cope with dynamically changing wireless environments. It provides performance results in a robotic teleoperation scenario. Finally, it discusses some open research and design challenges for multi-service edge-intelligence.

We consider resource management problems in multi-user wireless networks, which can be cast as optimizing a network-wide utility function, subject to constraints on the long-term average performance of users across the network. We propose a state-augmented algorithm for solving the aforementioned radio resource management (RRM) problems, where, alongside the instantaneous network state, the RRM policy takes as input the set of dual variables corresponding to the constraints, which evolve depending on how much the constraints are violated during execution. We theoretically show that the proposed state-augmented algorithm leads to feasible and near-optimal RRM decisions. Moreover, focusing on the problem of wireless power control using graph neural network (GNN) parameterizations, we demonstrate the superiority of the proposed RRM algorithm over baseline methods across a suite of numerical experiments.

Take a look at the new Tesla phone, and find out why it's been called revolutionary. Learn about its features and benefits and why it's set to change the smartphone industry forever. Before Elon Musk or Tesla management unveils a product, it is usually enveloped in rumors, speculation, and increased hype just like their Model S plaid, and just before they unveiled a humanoid robot, Elon Musk was not slowing down as his mind was filled with the craziest ideas. Today we will tell you all about another product from Tesla that has been gathering serious momentum: the Model P. Let's find out why it's the genius body. So what is the model pi? It's a rumored smartphone from Tesla that has been causing chaos on the internet, especially on YouTube, with major phone manufacturers like Apple, Samsung, and Huawei making very similar phones year in and year out.

The developments of satellite communication in network systems require strong and effective security plans. Attacks such as denial of service (DoS) can be detected through the use of machine learning techniques, especially under normal operational conditions. This work aims to provide an interruption detection strategy for Low Earth Orbit (\textsf{LEO}) satellite networks using deep learning algorithms. Both the training, and the testing of the proposed models are carried out with our own communication datasets, created by utilizing a satellite traffic (benign and malicious) that was generated using satellite networks simulation platforms, Omnet++ and Inet. We test different deep learning algorithms including Multi Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Units (GRU), and Long Short-term Memory (LSTM). Followed by a full analysis and investigation of detection rate in both binary classification, and multi-classes classification that includes different interruption categories such as Distributed DoS (DDoS), Network Jamming, and meteorological disturbances. Simulation results for both classification types surpassed 99.33% in terms of detection rate in scenarios of full network surveillance. However, in more realistic scenarios, the best-recorded performance was 96.12% for the detection of binary traffic and 94.35% for the detection of multi-class traffic with a false positive rate of 3.72%, using a hybrid model that combines MLP and GRU. This Deep Learning approach efficiency calls for the necessity of using machine learning methods to improve security and to give more awareness to search for solutions that facilitate data collection in LEO satellite networks.

With 194,000 employees and operating in more than 170 countries and regions, Huawei is a leading global creator and provider of information and communications technology (ICT) infrastructure and smart devices. Integrated solutions span across four key domains – telecom networks, IT, smart devices, and cloud services. Huawei is committed to bringing digital to every person, home and organization for a fully connected, intelligent world. Huawei Canada focuses on fundamental research and development aimed at solving complex technical problems in emerging technologies like 5G, AI, Human Computer Interaction and Autonomous Driving. With ongoing research initiatives with 10 Universities across Canada and strategic collaboration agreements with several Universities, we support Canada's rich research community.

Global food security is an issue that is fast becoming a critical matter in the world today. Global warming, climate change and a range of other impacts caused by humans, such as carbon emissions, sociopolitical and economical challenges (e.g. war), traditional workforce/labour decline and population growth are straining global food security. The need for high-speed and reliable wireless communication in agriculture is becoming more of a necessity rather than a technological demonstration or showing superiority in the field. Governments and industries around the world are seeing more urgency in establishing communication infrastructure to scale up agricultural activities and improve sustainability, by employing autonomous agri-robotics and agri-technologies. The work presented here evaluates the physical performance of 5G in an agri-robotics application, and the results are compared against 4G and WiFi6 (a newly emerging wireless communication standard), which are typically used in agricultural environments. In addition, a series of simulation experiments were performed to assess the ``real-time'' operational delay in critical tasks that may require a human-in-the-loop to support decision making. The results lead to the conclusion that 4G cannot be used in the agricultural domain for applications that require high throughput and reliable communication between robot and user. Moreover, a single wireless solution does not exist for the agricultural domain, but instead multiple solutions can be combined to meet the necessary telecommunications requirements. Finally, the results show that 5G greatly outperforms 4G in all performance metrics, and on average only 18.2ms slower than WiFi6 making it very reliable.

The mixture of Expert (MoE) parallelism is a recent advancement that scales up the model size with constant computational cost. MoE selects different sets of parameters (i.e., experts) for each incoming token, resulting in a sparsely-activated model. Despite several successful applications of MoE, its training efficiency degrades significantly as the number of experts increases. The routing stage in MoE relies on the efficiency of the All2All communication collective, which suffers from network congestion and has poor scalability. To mitigate these issues, we introduce SMILE, which exploits heterogeneous network bandwidth and splits a single-step routing into bi-level routing. Our experimental results show that the proposed method obtains a 2.5x speedup over Switch Transformer in terms of pretraining throughput on the Colossal Clean Crawled Corpus without losing any convergence speed.

Since 3G, the Third Generation Partnership Project (3GPP) has standardized features and specifications for wireless cellular networks around the world. "5G-Advanced enhancements will unleash a diversity of new capabilities for fixed/mobile broadband as well as vertical industries powered by artificial intelligence, machine learning, and full duplex technologies based on a single platform. Chris Pearson, President of 5G Americas said, "The work done by 3GPP in Release 17 improved 5G New Radio (NR) by adding support for new services, reduced-capability user equipment, non-terrestrial networks, frequency bands beyond 52GHz, and the multicast and broadcast service (MBS). He further added, "The next few years will see initial work on Release18, the 5G-Advanced standard, which will offer a boost to network performance and create even more opportunities." This 5G Americas paper provides a detailed background on 3GPP's work leading into 2025, focusing on the organization's processes, a review of major enhancements and new vertical applications, an assessment of global market trends and use cases, and a developmental timeline for currently active 3GPP releases. It also covers radio access technologies, highlighting 22 new and enhanced RAN features and capabilities.

The arise of cutting-edge technologies and services such as XR promise to change the concepts of how day-to-day things are done. At the same time, the appearance of modern and decentralized architectures approaches has given birth to a new generation of mobile networks such as 5G, as well as outlining the roadmap for B5G and posterior. These networks are expected to be the enablers for bringing to life the Metaverse and other futuristic approaches. In this sense, this work presents an ML-based (Machine Learning) framework that allows the estimation of service Key Quality Indicators (KQIs). For this, only information reachable to operators is required, such as statistics and configuration parameters from these networks. This strategy prevents operators from avoiding intrusion into the user data and guaranteeing privacy. To test this proposal, 360-Video has been selected as a use case of Virtual Reality (VR), from which specific KQIs are estimated such as video resolution, frame rate, initial startup time, throughput, and latency, among others. To select the best model for each KQI, a search grid with a cross-validation strategy has been used to determine the best hyperparameter tuning. To boost the creation of each KQI model, feature engineering techniques together with cross-validation strategies have been used. The performance is assessed using MAE (Mean Average Error) and the prediction time. The outcomes point out that KNR (K-Near Neighbors) and RF (Random Forest) are the best algorithms in combination with Feature Selection techniques. Likewise, this work will help as a baseline for E2E-Quality-of-Experience-based network management working in conjunction with network slicing, virtualization, and MEC, among other enabler technologies.

The fifth generation of the Radio Access Network (RAN) has brought new services, technologies, and paradigms with the corresponding societal benefits. However, the energy consumption of 5G networks is today a concern. In recent years, the design of new methods for decreasing the RAN power consumption has attracted interest from both the research community and standardization bodies, and many energy savings solutions have been proposed. However, there is still a need to understand the power consumption behavior of state-ofthe-art base station architectures, such as multi-carrier active antenna units (AAUs), as well as the impact of different network parameters. In this paper, we present a power consumption model for 5G AAUs based on artificial neural networks. We demonstrate that this model achieves good estimation performance, and it is able to capture the benefits of energy saving when dealing with the complexity of multi-carrier base stations architectures. Importantly, multiple experiments are carried out to show the advantage of designing a general model able to capture the power consumption behaviors of different types of AAUs. Finally, we provide an analysis of the model scalability and the training data requirements.