In emerging Industrial Cyber-Physical Systems (ICPSs), the joint design of communication and control sub-systems is essential, as these sub-systems are interconnected. In this paper, we study the joint design problem of an event-triggered control and an energy-efficient resource allocation in a fifth generation (5G) wireless network. We formally state the problem as a multi-objective optimization one, aiming to minimize the number of updates on the actuators' input and the power consumption in the downlink transmission. To address the problem, we propose a model-free hierarchical reinforcement learning approach \textcolor{blue}{with uniformly ultimate boundedness stability guarantee} that learns four policies simultaneously. These policies contain an update time policy on the actuators' input, a control policy, and energy-efficient sub-carrier and power allocation policies. Our simulation results show that the proposed approach can properly control a simulated ICPS and significantly decrease the number of updates on the actuators' input as well as the downlink power consumption.

Customer churn is a key business concept that determines the number of customers that stop doing business with a specific company. The churn rate is then defined as the rate by which a company loses customers in a given time frame. For example, a churn rate of 15%/year means that a company loses 15% of its total customer base every year. Customer churn takes special importance in the telecommunication sector, given the increasing competition and appearance of new telecommunication companies. For this reason, the telecom industry expects high churn rates every year.

Fast and reliable connectivity is essential to enhancing situational awareness and operational efficiency for public safety mission-critical (MC) users. In emergency or disaster circumstances, where existing cellular network coverage and capacity may not be available to meet MC communication demands, deployable-network-based solutions such as cells-on-wheels/wings can be utilized swiftly to ensure reliable connection for MC users. In this paper, we consider a scenario where a macro base station (BS) is destroyed due to a natural disaster and an unmanned aerial vehicle carrying BS (UAV-BS) is set up to provide temporary coverage for users in the disaster area. The UAV-BS is integrated into the mobile network using the 5G integrated access and backhaul (IAB) technology. We propose a framework and signalling procedure for applying machine learning to this use case. A deep reinforcement learning algorithm is designed to jointly optimize the access and backhaul antenna tilt as well as the three-dimensional location of the UAV-BS in order to best serve the on-ground MC users while maintaining a good backhaul connection. Our result shows that the proposed algorithm can autonomously navigate and configure the UAV-BS to improve the throughput and reduce the drop rate of MC users.

According to research by mobile security firm Certo Software and confirmed by MIT Technology Review, Google Search queries related to tracking partners such as a wife or girlfriend commonly return ads for software and services that explicitly offer to spy on other individuals. Stalkerware, also referred to as spyware, is software designed to secretly monitor another person, tracking their location, phone calls, private messages, web searches, and keystrokes. Although Google banned ads promoting stalkerware in August 2020, stalkerware companies are still able to buy ads containing phrases including "app to see spouse's text messages," "see who your girlfriend is texting," and "it's like having their device" against search results such as "read wife's texts app." "We understand that this is not a war between Ukraine and Russia. This is a war of the pure and the light that exists on this earth, and darkness." The problem is that no one can agree how to save it.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Qulacomm said it is launching new platform support for advanced robotics products, 5G wireless networking and edge-AI applications. The company unveiled the Qualcomm Robotics solutions at its annual Qualcomm 5G Summit event as it recognizes the ways in which 5G is proliferating beyond smartphones. The San Diego, California-based company took the wraps off its Qualcomm Robotics RB6 Platform and the Qualcomm RB5AMR reference design, which can be used to build advanced edge-AI and robotics products using Qualcomm's chips. The solutions will help unlock new commercial markets for autonomous mobile robots, delivery robots, highly automated manufacturing robots, collaborative robots, unmanned aircraft, industrial drone infrastructure, autonomous defense solutions and beyond.

In smartphones, "AI" is often used to enhance the look and quality of your photos. Qualcomm said that it's going even further, using AI to improve the cellular performance and coverage of your 5G smartphone as well. Specifically, Qualcomm claims that it's building AI capabilities into its modems to improve their signal coverage, further refining the range of all the radios connected to the device, including 4G and 5G. The AI technology will be included in the Snapdragon X70 modem that should ship this year and appear in smartphones in 2023. Qualcomm used the occasion of its 5G Summit to make the announcements, many of which are designed to improve the performance of the short-range, high-speed millimeter-wave (mmWave) technology.

Samsung Electronics today released the second edition of its private 5G networks whitepaper, highlighting the architectures, features and benefits of private 5G networks for industrial scenarios--such as smart factories, smart hospitals, smart logistics and transportation, among others. With the growing interest in private networks, Samsung explores how enterprises can successfully deploy private 5G networks to meet business goals and service demands. The whitepaper outlines various architectural options for building private networks that enable 5G services -- such as Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC) and Massive Machine Type Communications (mMTC) -- which can bring new innovation to a range of sectors rapidly transitioning to Industry 4.0. The paper spotlights Samsung's complete set of private 5G network solutions, which enable enterprises to simplify network deployment and operation. With a portfolio and capability to build highly reliable private 5G networks, Samsung offers solutions for small, medium to large-scale enterprises.

Qualcomm is on a roll. They just keep hitting the ball out of the park, quarter after quarter, with strong and expanding growth. A few short years ago they were bogged down with Apple and U.S. government battles, but now that things have settled down, they are looking stronger than ever going forward. Let's take a closer look at what we can expect. First, Qualcomm is a leader in the very attractive wireless industry which keeps expanding, decade after decade.

The wireless industry has been one of the fastest growing spaces for several decades. That does not mean, however, that it is always on fire. Every growth wave has ebbs and flows. It all depends on the period of time in which you are focused. The good news is the wireless industry has entered the next growth wave with 5G, AI, IoT, AR, VR, cloud and more.

Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this paper, we offer a novel and straightforward data cleansing algorithm for WLAN fingerprinting radio maps. This algorithm is based on the correlation among fingerprints using the Received Signal Strength (RSS) values and the Access Points (APs)'s identifier. We use those to compute the correlation among all samples in the dataset and remove fingerprints with low level of correlation from the dataset. We evaluated the proposed method on 14 independent publicly-available datasets. As a result, an average of 14% of fingerprints were removed from the datasets. The 2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with a slight increase in the floor hit rate by 1.2% on average. Consequently, the average speed of position prediction was also increased by 14%.