In this work, we study the problem of energy-efficient computation offloading enabled by edge computing. In the considered scenario, multiple users simultaneously compete for limited radio and edge computing resources to get offloaded tasks processed under a delay constraint, with the possibility of exploiting low power sleep modes at all network nodes. The radio resource allocation takes into account inter- and intra-cell interference, and the duty cycles of the radio and computing equipment have to be jointly optimized to minimize the overall energy consumption. To address this issue, we formulate the underlying problem as a dynamic long-term optimization. Then, based on Lyapunov stochastic optimization tools, we decouple the formulated problem into a CPU scheduling problem and a radio resource allocation problem to be solved in a per-slot basis. Whereas the first one can be optimally and efficiently solved using a fast iterative algorithm, the second one is solved using distributed multi-agent reinforcement learning due to its non-convexity and NP-hardness. The resulting framework achieves up to 96.5% performance of the optimal strategy based on exhaustive search, while drastically reducing complexity. The proposed solution also allows to increase the network's energy efficiency compared to a benchmark heuristic approach.

You try resetting your modem. Frustrated, you call the customer service department of your telecommunications company. The average wait time to reach a call center agent for a telecom company is 5 minutes. When the agent finally joins, she helps you troubleshoot what turns out to be a relatively simple and common issue. The total time-to-resolution, though, is close to 7 minutes.

As the realities of climate change take hold across the planet, the risks of natural hazards and disasters are becoming ever more familiar. Meteorologists, aiming to protect increasingly populous countries and communities, are tapping into artificial intelligence (AI) to get them the edge in early detection and disaster relief. This potential was in focus at a recent workshop feeding into the first meeting of the new Focus Group on AI for Natural Disaster Management. The group is open to all interested parties, supported by the International Telecommunication Union (ITU) together with the World Meteorological Organization (WMO) and UN Environment. "AI can help us tackle disasters in development work as well as standardization work. With this new Focus Group, we will explore AI's ability to analyze large datasets, refine datasets and accelerate disaster-management interventions," said Chaesub Lee, Director of the ITU Telecommunication Standardization Bureau, in opening remarks to the workshop.

Heterogeneous Ultra-Dense Network (HUDN) is one of the vital networking architectures due to its ability to enable higher connectivity density and ultra-high data rates. However, efficiently managing the wireless resource of HUDNs to reduce the wireless interference faces challenges. In this paper, we tackle this challenge by jointly optimizing user association and power control. The joint user association and power control problem is a typical non-convex problem that is hard and time-consuming to solve by traditional optimization techniques. This paper proposes a novel idea for resolving this question: the optimal user association and Base Station (BS) transmit power can be represented by some network parameters of interest, such as the channel information, the precoding matrices, etc. Then, we solve this problem by transforming it into an optimal representation function learning problem. We model the HUDNs as a heterogeneous graph and train a Graph Neural Network (GNN) to approach this representation function by using semi-supervised learning (SSL), in which the loss function is composed of the unsupervised part that helps the GNN approach the optimal representation function and the supervised part that utilizes the previous experience to reduce useless exploration in the initial phase. Besides, we use the entropy regularization to guarantee the effectiveness of exploration in the configuration space. To embrace both the generalization of the learning algorithm and higher performance of HUDNs, we separate the learning process into two parts, the generalization-representation learning (GRL) part, and the specialization-representation learning (SRL) part. In the GRL part, the GNN learns a representation with a tremendous generalized ability to suit any scenario with different user distributions, which processes offline. Based on the learned GRL representation, the SRL finely turn the parameters of GNN on-line to further improving the performance for quasi-static user distribution. Simulation results demonstrate that the proposed GRL-based solution has higher computational efficiency than the traditional optimization algorithm. Besides, the results also show that the performance of SRL outperforms the GRL.

Over the last year, Qualcomm's 7-series chips have enabled some of the best value phones. They tend to buckle under more processor-intensive tasks and, outside of the capable hands of Google, they haven't had delivered the best camera experiences. Qualcomm's latest 7-series chip, the Snapdragon 780G looks to address those issues. To start, it features new CPU and GPU components. In practice, Qualcomm says the 780G's Kyro 670 CPU runs 40 percent faster than the processor on its predecessor, the 768G. Any task involving an AI component will also get a boost from the 780G, thanks to its Hexagon 770 processor.

The Arab region, composed of 22 countries spanning Asia and Africa, opens ample room for communications and networking innovations and services and contributes to the critical mass of the global networking innovation. While the Arab world is considered an emerging market for communications and networking services, the rate of adoption is outpacing the global average. In fact, as of 2019, the mobile Internet penetration stands at 67.2% in the Arab world, as opposed to a global average of 56.5%.12 Furthermore, multiple countries in the region are either building new infrastructure or developing existing infrastructure at an unprecedented pace. Examples include, Neom city in Saudi Arabia, the new administrative capital in Egypt, as well as the Smart Dubai 2021 project in the United Arab Emirates (UAE), among others. This provides a unique opportunity to fuse multiple advanced networking technologies as an integral part of the infrastructure design phase and not just as an afterthought.

To improve the system performance towards the Shannon limit, advanced radio resource management mechanisms play a fundamental role. In particular, scheduling should receive much attention, because it allocates radio resources among different users in terms of their channel conditions and QoS requirements. The difficulties of scheduling algorithms are the tradeoffs need to be made among multiple objectives, such as throughput, fairness and packet drop rate. We propose a smart scheduling scheme based on deep reinforcement learning (DRL). We not only verify the performance gain achieved, but also provide implementation-friend designs, i.e., a scalable neural network design for the agent and a virtual environment training framework. With the scalable neural network design, the DRL agent can easily handle the cases when the number of active users is time-varying without the need to redesign and retrain the DRL agent. Training the DRL agent in a virtual environment offline first and using it as the initial version in the practical usage helps to prevent the system from suffering from performance and robustness degradation due to the time-consuming training. Through both simulations and field tests, we show that the DRL-based smart scheduling outperforms the conventional scheduling method and can be adopted in practical systems. The wireless communication industry has been keeping a fast growing and updating speed for several decades. About every ten years, new generations of mobile communication system were standardized with lots of new features and supported scenarios. Thanks to the evolution of wireless communications technologies, we are now enjoying diverse services and applications conveniently. It is well known that the fifth generation (5G) mobile communications system supports three major categories of services, i.e., enhanced mobile broadband (eMBB), ultrareliable and low-latency communications (uRLLC) and massive machine-type communications (mMTC). Meanwhile, new applications and scenarios have never stopped coming up, which sets up new requirements including even higher throughput, more connected devices, faster access with lower latency and higher efficiency for wireless communication systems. With all these requirements in mind, designing a new generation of mobile communications system becomes a quite challenging work.

The next generation of wireless networks, dubbed 5G, will have more capacity, faster speeds, and lower latency than its predecessor 4G. As a result, it's expected to bring technologies like augmented reality, self-driving cars, data-crunching Internet of Things devices, and even smart cities closer to the mainstream than ever before. Deeply entwined with cloud computing, 5G is expected to be the backbone of so many future products and services that it has the potential to power economic growth for decades to come, analysts predict. At the moment, 5G networks are still being rolled out by wireless carriers, and the public has yet to fully realize its benefits. But there are plenty of opportunities for startups and major companies alike, including in partnering with wireless carriers, deploying private and enterprise 5G networks, and developing 5G-enabled applications.

The combination of cloud computing capabilities at the network edge and artificial intelligence promise to turn future mobile networks into service- and radio-aware entities, able to address the requirements of upcoming latency-sensitive applications. In this context, a challenging research goal is to exploit edge intelligence to dynamically and optimally manage the Radio Access Network Slicing (that is a less mature and more complex technology than fifth-generation Network Slicing) and Radio Resource Management, which is a very complex task due to the mostly unpredictably nature of the wireless channel. This paper presents a novel architecture that leverages Deep Reinforcement Learning at the edge of the network in order to address Radio Access Network Slicing and Radio Resource Management optimization supporting latency-sensitive applications. The effectiveness of our proposal against baseline methodologies is investigated through computer simulation, by considering an autonomous-driving use-case.

New funding: Seattle-based startup OctoML raised a $28 million Series B round. The University of Washington spinout aims to help companies deploy machine learning models on various hardware configurations. The technology: OctoML is led by the creators of Apache TVM, an open source "deep learning compiler stack" that started as a research project at the UW's computer science school. The idea is to reduce the amount of cost and time it takes companies to develop and deploy deep learning software for specific hardware such as phones, cars, health devices, etc. -- "using ML to optimize ML," as OctoML CEO Luis Ceze explains. Traction: OctoML is working with Qualcomm, Microsoft, AMD, Bosch, and many others.