With a wide deployment of Multi-Access Edge Computing (MEC) in the Fifth Generation (5G) mobile networks, virtual network functions (VNF) can be flexibly migrated between difference locations, and therewith significantly enhances the network resilience to counter the degradation in quality of service (QoS) due to network function outages. A balance has to be taken carefully, between the loss reduced by VNF migration and the operations cost generated thereby. To achieve this in practical scenarios with realistic user behavior, it calls for models of both cost and user mobility. This paper proposes a novel cost model and a AI-empowered approach for a rational migration of stateful VNFs, which minimizes the sum of operations cost and potential loss caused by outages, and is capable to deal with the complex realistic user mobility patterns.

Network slicing manages network resources as virtual resource blocks (RBs) for the 5G Radio Access Network (RAN). Each communication request comes with quality of experience (QoE) requirements such as throughput and latency/deadline, which can be met by assigning RBs, communication power, and processing power to the request. For a completed request, the achieved reward is measured by the weight (priority) of this request. Then, the reward is maximized over time by allocating resources, e.g., with reinforcement learning (RL). In this paper, we introduce a novel flooding attack on 5G network slicing, where an adversary generates fake network slicing requests to consume the 5G RAN resources that would be otherwise available to real requests. The adversary observes the spectrum and builds a surrogate model on the network slicing algorithm through RL that decides on how to craft fake requests to minimize the reward of real requests over time. We show that the portion of the reward achieved by real requests may be much less than the reward that would be achieved when there was no attack. We also show that this flooding attack is more effective than other benchmark attacks such as random fake requests and fake requests with the minimum resource requirement (lowest QoE requirement). Fake requests may be detected due to their fixed weight. As an attack enhancement, we present schemes to randomize weights of fake requests and show that it is still possible to reduce the reward of real requests while maintaining the balance on weight distributions.

To meet the ever increasing mobile traffic demand in 5G era, base stations (BSs) have been densely deployed in radio access networks (RANs) to increase the network coverage and capacity. However, as the high density of BSs is designed to accommodate peak traffic, it would consume an unnecessarily large amount of energy if BSs are on during off-peak time. To save the energy consumption of cellular networks, an effective way is to deactivate some idle base stations that do not serve any traffic demand. In this paper, we develop a traffic-aware dynamic BS sleep control framework, named DeepBSC, which presents a novel data-driven learning approach to determine the BS active/sleep modes while meeting lower energy consumption and satisfactory Quality of Service (QoS) requirements. Specifically, the traffic demands are predicted by the proposed GS-STN model, which leverages the geographical and semantic spatial-temporal correlations of mobile traffic. With accurate mobile traffic forecasting, the BS sleep control problem is cast as a Markov Decision Process that is solved by Actor-Critic reinforcement learning methods. To reduce the variance of cost estimation in the dynamic environment, we propose a benchmark transformation method that provides robust performance indicator for policy update. To expedite the training process, we adopt a Deep Deterministic Policy Gradient (DDPG) approach, together with an explorer network, which can strengthen the exploration further. Extensive experiments with a real-world dataset corroborate that our proposed framework significantly outperforms the existing methods.

The edge of a network, as you may know, is the furthest extent of its reach. A cloud platform is a kind of network overlay that makes multiple network locations part of a single network domain. It should therefore stand to reason that an edge cloud is a single addressable, logical network at the furthest extent of a physical network. And an edge cloud on a global scale should be a way to make multiple, remote data centers accessible as a single pool of resources -- of processors, storage, and bandwidth. The combination of 5G and edge computing will unleash new capabilities from real-time analytics to automation to self-driving cars and trucks.

Edge computing-enhanced Internet of Vehicles (EC-IoV) enables ubiquitous data processing and content sharing among vehicles and terrestrial edge computing (TEC) infrastructures (e.g., 5G base stations and roadside units) with little or no human intervention, plays a key role in the intelligent transportation systems. However, EC-IoV is heavily dependent on the connections and interactions between vehicles and TEC infrastructures, thus will break down in some remote areas where TEC infrastructures are unavailable (e.g., desert, isolated islands and disaster-stricken areas). Driven by the ubiquitous connections and global-area coverage, space-air-ground integrated networks (SAGINs) efficiently support seamless coverage and efficient resource management, represent the next frontier for edge computing. In light of this, we first review the state-of-the-art edge computing research for SAGINs in this article. After discussing several existing orbital and aerial edge computing architectures, we propose a framework of edge computing-enabled space-air-ground integrated networks (EC-SAGINs) to support various IoV services for the vehicles in remote areas. The main objective of the framework is to minimize the task completion time and satellite resource usage. To this end, a pre-classification scheme is presented to reduce the size of action space, and a deep imitation learning (DIL) driven offloading and caching algorithm is proposed to achieve real-time decision making. Simulation results show the effectiveness of our proposed scheme. At last, we also discuss some technology challenges and future directions.

O2 has rolled out its 5G network in 53 new towns and cities across the UK, pulling ahead of its rival EE to become the nation's biggest provider of ultra-fast mobile internet. The new locations include Birmingham, Durham and Portsmouth, bringing O2's total number of locations with 5G to 150. The network also allows for larger amounts of data to be transferred at once, which could one day help power technologies such as fully autonomous cars. O2 has rolled out its 5G network in 53 new towns and cities across the UK, taking it ahead of its rival EE to become the nation's biggest provider of the ultra-fast internet The network also allow for larger amounts of data to be transferred at once, which could one day help power technologies such as fully autonomous cars. For most consumers, 5G will allow you to carry out tasks on your smartphone more quickly and efficiently.

Job hunters may now need to impress not just prospective bosses but artificial intelligence algorithms too--as employers screen candidates by having them answer interview questions on a video that is then assessed by a machine. HireVue, a leading provider of software for vetting job candidates based on an algorithmic assessment, said Tuesday it is killing off a controversial feature of its software: analyzing a person's facial expressions in a video to discern certain characteristics. Job seekers screened by HireVue sit in front of a webcam and answer questions. Their behavior, intonation, and speech is fed to an algorithm that assigns certain traits and qualities. HireVue says that an "algorithmic audit" of its software conducted last year shows it does not harbor bias.

As 5G connectivity rolls out across the country in fits and starts, we're still asking whether the upgrades will make for a noticeable change in our wireless connectivity. During a CES keynote, Verizon CEO Hans Vestberg (Verizon owns Engadget's parent company) tried to make the case that 5G is "the platform that makes other innovations possible." Verizon announced a deal between its subsidiary, Skyward and UPS Flight Forward to team up on delivery drones that use 4G LTE at first, and include testing with 5G connections later this year. In a statement, UPS CEO Carol B. Tomé said 5G will be necessary to do these kinds of deliveries at scale. Other deals include Live Nation and the NFL.

In frequency-division duplexing systems, the downlink channel state information (CSI) acquisition scheme leads to high training and feedback overheads. In this paper, we propose an uplink-aided downlink channel acquisition framework using deep learning to reduce these overheads. Unlike most existing works that focus only on channel estimation or feedback modules, to the best of our knowledge, this is the first study that considers the entire downlink CSI acquisition process, including downlink pilot design, channel estimation, and feedback. First, we propose an adaptive pilot design module by exploiting the correlation in magnitude among bidirectional channels in the angular domain to improve channel estimation. Next, to avoid the bit allocation problem during the feedback module, we concatenate the complex channel and embed the uplink channel magnitude to the channel reconstruction at the base station. Lastly, we combine the above two modules and compare two popular downlink channel acquisition frameworks. The former framework estimates and feeds back the channel at the user equipment subsequently. The user equipment in the latter one directly feeds back the received pilot signals to the base station. Our results reveal that, with the help of uplink, directly feeding back the pilot signals can save approximately 20% of feedback bits, which provides a guideline for future research. J. Guo and S. Jin are with the National Mobile Communications Research Laboratory, Southeast University, Nanjing, 210096, P. R. C.-K. Wen is with the Institute of Communications Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan (email: chaokai.wen@mail.nsysu.edu.tw). Since the standardization of the fifth generation (5G) communication system has gradually been solidified, researchers in the communication community are beginning to turn their attention to 5G evolution and 6G [1]. Further advancement, such as massive multiple-input and multipleoutput (MIMO) with increased antennas, distributed antenna arrangement combined with new network topology, and increased layers for spatial multiplexing, is expected [2]. A massive MIMO architecture is integral to 5G networks, especially as a key technology to utilize millimeter waves effectively [3], [4]. In massive MIMO systems, base station (BSs) are equipped with a large number of antennas to improve spectral and energy efficiencies through relatively simple (linear) processing.

The hot topics expected to dominate the 2021 CES show include the biggest names in electronics, telecommunications and software.