We consider a wireless communication system, where a transmitter sends signals to a receiver with different modulation types while the receiver classifies the modulation types of the received signals using its deep learning-based classifier. Concurrently, an adversary transmits adversarial perturbations using its multiple antennas to fool the classifier into misclassifying the received signals. From the adversarial machine learning perspective, we show how to utilize multiple antennas at the adversary to improve the adversarial (evasion) attack performance. Two main points are considered while exploiting the multiple antennas at the adversary, namely the power allocation among antennas and the utilization of channel diversity. First, we show that multiple independent adversaries, each with a single antenna cannot improve the attack performance compared to a single adversary with multiple antennas using the same total power. Then, we consider various ways to allocate power among multiple antennas at a single adversary such as allocating power to only one antenna, and proportional or inversely proportional to the channel gain. By utilizing channel diversity, we introduce an attack to transmit the adversarial perturbation through the channel with the largest channel gain at the symbol level. We show that this attack reduces the classifier accuracy significantly compared to other attacks under different channel conditions in terms of channel variance and channel correlation across antennas. Also, we show that the attack success improves significantly as the number of antennas increases at the adversary that can better utilize channel diversity to craft adversarial attacks.

Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks. Even though commercial 5G has just been widely deployed in some countries, there have been initial efforts from academia and industrial communities for 6G systems. In such a network, a very large number of devices and applications are emerged, along with heterogeneity of technologies, architectures, mobile data, etc., and optimizing such a network is of utmost importance. Besides convex optimization and game theory, swarm intelligence (SI) has recently appeared as a promising optimization tool for wireless networks. As a new subdivision of artificial intelligence, SI is inspired by the collective behaviors of societies of biological species. In SI, simple agents with limited capabilities would achieve intelligent strategies for high-dimensional and challenging problems, so it has recently found many applications in next-generation wireless networks (NGN). However, researchers may not be completely aware of the full potential of SI techniques. In this work, our primary focus will be the integration of these two domains: NGN and SI. Firstly, we provide an overview of SI techniques from fundamental concepts to well-known optimizers. Secondly, we review the applications of SI to settle emerging issues in NGN, including spectrum management and resource allocation, wireless caching and edge computing, network security, and several other miscellaneous issues. Finally, we highlight open challenges and issues in the literature, and introduce some interesting directions for future research.

Forecasting of multivariate time-series is an important problem that has applications in many domains, including traffic management, cellular network configuration, and quantitative finance. In recent years, researchers have demonstrated the value of applying deep learning architectures for these problems. A special case of the problem arises when there is a graph available that captures the relationships between the time-series. In this paper we propose a novel learning architecture that achieves performance competitive with or better than the best existing algorithms, without requiring knowledge of the graph. The key elements of our proposed architecture are (i) jointly performing backcasting and forecasting with a deep fully-connected architecture; (ii) stacking multiple prediction modules that target successive residuals; and (iii) learning a separate causal relationship graph for each layer of the stack. We can view each layer as predicting a component of the time-series; the differing nature of the causal graphs at different layers can be interpreted as indicating that the multivariate predictive relationships differ for different components. Experimental results for two public traffic network datasets illustrate the value of our approach, and ablation studies confirm the importance of each element of the architecture.

Huawei has launched a South African-based Cloud and Artificial Intelligence (AI) Innovation Centre to drive innovation, knowledge transfer and economic growth through app development in the AI industry. The announcement was made by Ray Rui, president of Huawei Cloud Africa region, during the Huawei Cloud Summit Africa 2020, an online event to unpack the opportunities of cloud computing for African business under the theme "Building an Intelligent Africa". "AI will be critical to social evolution and industrial growth in future," said Rui. "We also believe that when you grow economic opportunities, everyone benefits. For this reason, we are opening the Huawei Cloud & AI Innovation Centre to application developers across all economic sectors." The new centre will be based at Huawei's South African headquarters in Woodmead, Johannesburg, but developers across Africa will be able to access the centre remotely. It will teach AI application best practice, link developers to markets, support AI supply chains, develop talent and support application innovation.

This lightweight framework is ready to give competition to Google's TensorFlow, and Facebook's PyTorch, and it can scale across devices, cloud, and edge environments. One of the key competitive advantages with'Mindspore' is that it uses 20% fewer codes that its competitors for a function like NLP (Natural language processing). Apart from codes, it can also support parallel training to save training time across hardware. Huawei developed this framework with support from partners like the University of Edinburgh, Peking University, Imperial College London, and robotics startup Milvus. Mindspore maintains and preserves sensitive data by not processing any data itself but ingests only the gradient and model information that has already been processed.

French telecoms giant Orange has partnered with Google to research and deploy technologies such as edge computing. Both companies are heavyweights within their respective industries. Orange brings expertise in telecommunications, while Google brings its experience in fields like AI to the partnership. Orange believes that the partnership will help the company to achieve its Engage 2025 plan which aims to drive transformation through widespread use of AI and data to improve operational efficiency and the customer experience. Orange says that the partnership will bring powerful cloud capabilities to the edge of the network, such as in retail, gaming, and other industries.

While most of China was quarantined and Mount Everest was closed to climbers due to COVID-19, a herd of nearly 50 yaks made their way up the snowy north slopes of the world's highest mountain in temperatures that dipped below zero degrees Fahrenheit. On their backs were loads of equipment--metal beams, cables, and solar panels strapped down with cord--that would be used to build 5G antennas on rocky moraines scattered across the mountainside. Chinese tech giant Huawei and state-owned network provider China Mobile teamed up for this project to bring the latest in wireless data to Everest, which previously had very little cell coverage above base camp. Now, data speeds in the "death zone" on Everest, where the altitude is too high and the air is too thin to support life, are faster than in most American neighborhoods. In a press release, Huawei stated that the new super-fast data speeds on Everest will be used for "smart tourism"--with high-definition video streaming and virtual reality experiences for digital tourists to "visit" Everest from anywhere in the world.

Smartphones could soon be able to fully recharge in under 15 minutes after a new fast-charging standard was introduced. Qualcomm's Quick Charge 5 will also allow phones to charge from 0 to 50 per cent in just five minutes, as well as introduce new safety features to prevent overheating. The "world's fastest commercial charging solution" will be up to four-times faster than current charging technologies, according to Qualcomm, and will find its way into commercial devices before the end of the year. It will be compatible with more than 250 smartphones, though it is not a feature that Apple supports. This means that only Androids, not iPhones, will benefit from the technology.

HONG KONG, CHINA / ACCESSWIRE / July 21, 2020 / The arrival of 5G will bring new explosive points for market development. Undeniably, the promotion of 4G promoted the increase of users, and the operators made a lot of investment and construction of data centers to meet the needs of users, which led to a wave of high tide of server procurement. Compared with 3G and 4G, 5G has improved its speed by about 10 times, which has achieved a qualitative leap in the development of server market. In the future, 5G rate is expected to increase by tens of times, which will undoubtedly inject more vitality into the market. For example, industries that were previously limited by data processing speed are expected to break through bottlenecks and achieve substantial growth.

Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.