Radio link failure (RLF) prediction system in Radio Access Networks (RANs) is critical for ensuring seamless communication and meeting the stringent requirements of high data rates, low latency, and improved reliability in 5G networks. However, weather conditions such as precipitation, humidity, temperature, and wind impact these communication links. Usually, historical radio link Key Performance Indicators (KPIs) and their surrounding weather station observations are utilized for building learning-based RLF prediction models. However, such models must be capable of learning the spatial weather context in a dynamic RAN and effectively encoding time series KPIs with the weather observation data. Existing works fail to incorporate both of these essential design aspects of the prediction models. This paper fills the gap by proposing GenTrap, a novel RLF prediction framework that introduces a graph neural network (GNN)-based learnable weather effect aggregation module and employs state-of-the-art time series transformer as the temporal feature extractor for radio link failure prediction. The proposed aggregation method of GenTrap can be integrated into any existing prediction model to achieve better performance and generalizability. We evaluate GenTrap on two real-world datasets (rural and urban) with 2.6 million KPI data points and show that GenTrap offers a significantly higher F1-score (0.93 for rural and 0.79 for urban) compared to its counterparts while possessing generalization capability.

It is a common sense that datasets with high-quality data samples play an important role in artificial intelligence (AI), machine learning (ML) and related studies. However, although AI/ML has been introduced in wireless researches long time ago, few datasets are commonly used in the research community. Without a common dataset, AI-based methods proposed for wireless systems are hard to compare with both the traditional baselines and even each other. The existing wireless AI researches usually rely on datasets generated based on statistical models or ray-tracing simulations with limited environments. The statistical data hinder the trained AI models from further fine-tuning for a specific scenario, and ray-tracing data with limited environments lower down the generalization capability of the trained AI models. In this paper, we present the Wireless AI Research Dataset (WAIR-D)1, which consists of two scenarios. Scenario 1 contains 10,000 environments with sparsely dropped user equipments (UEs), and Scenario 2 contains 100 environments with densely dropped UEs. The environments are randomly picked up from more than 40 cities in the real world map. The large volume of the data guarantees that the trained AI models enjoy good generalization capability, while fine-tuning can be easily carried out on a specific chosen environment. Moreover, both the wireless channels and the corresponding environmental information are provided in WAIR-D, so that extra-information-aided communication mechanism can be designed and evaluated. WAIR-D provides the researchers benchmarks to compare their different designs or reproduce results of others. In this paper, we show the detailed construction of this dataset and examples of using it.

We demonstrate a first example for employing deep learning in predicting frame errors for a Collaborative Intelligent Radio Network (CIRN) using a dataset collected during participation in the final scrimmages of the DARPA SC2 challenge. Four scenarios are considered based on randomizing or fixing the strategy for bandwidth and channel allocation, and either training and testing with different links or using a pilot phase for each link to train the deep neural network. Interestingly, we unveil the efficacy of randomization in improving detection accuracy and the generalization capability of certain deep neural network architectures with Bootstrap Aggregating (Bagging).

To counter the threats posed by small drones, the U.S. military may have to rapidly step up its R&D timeframes, according to a new report commissioned by the U.S. Army. Small unmanned aircraft systems (sUASs) have become increasingly affordable and sophisticated. With millions of these drones now available worldwide, "It's become very easy for an adversary to use them in nefarious ways," says Albert Sciarretta, chair of the committee behind the new study and president of CNS Technologies in Springfield, Virginia. The U.S. Army asked for a detailed report from the National Academies of Sciences, Engineering, and Medicine that analyzes potential risks from these devices, especially to dismounted infantry (that is, foot soldiers) and lightly armored vehicles. For example, hobby drones could be fitted with lethal weapons such as explosive, chemical, biological, or radiological payloads--or modified to jam military radio signals, Sciarretta says.

While next-generation 5G cellular will bring faster downloads for consumers, the new networking technology is poised to bring big benefits to business users enabling new uses for cellular networks. At this week's Mobile World Congress in Barcelona, Japan's NTT DoCoMo is demonstrating one such use: remote control of robots via a wireless virtual reality system. In one corner of the company's booth was a simulated factory floor with three robots. The area was surrounded by four depth-sensing 3D cameras that together provide enough video for an immersive, all-around virtual reality image. That 3D video, totaling roughly 700Mbps of data, was sent across a 5G radio link to a receiver where it was processed and fed to a VR headset.