ChatGPT is now a smartphone app, which could be good news for people who like to use the artificial intelligence chatbot and bad news for all the clone apps that have tried to profit off the technology. The free app started to become available on iPhones in the United States on Thursday and will later be coming to Android phones. Unlike the web version, you can also ask it questions using your voice. The company that makes it, OpenAI, said it will remain ad-free but "syncs your history across devices". "We're starting our rollout in the US and will expand to additional countries in the coming weeks," said a blog post announcing the new app, which is described in the App Store as the "official app" by OpenAI.

Self-awareness is the key capability of autonomous systems, e.g., autonomous driving network, which relies on highly efficient time series forecasting algorithm to enable the system to reason about the future state of the environment, as well as its effect on the system behavior as time progresses. Recently, a large number of forecasting algorithms using either convolutional neural networks or graph neural networks have been developed to exploit the complex temporal and spatial dependencies present in the time series. While these solutions have shown significant advantages over statistical approaches, one open question is to effectively incorporate the global information which represents the seasonality patterns via the time component of time series into the forecasting models to improve their accuracy. This paper presents a general approach to integrating the time component into forecasting models. The main idea is to employ conditional neural fields to represent the auxiliary features extracted from the time component to obtain the global information, which will be effectively combined with the local information extracted from autoregressive neural networks through a layer-wise gated fusion module. Extensive experiments on road traffic and cellular network traffic datasets prove the effectiveness of the proposed approach.

By enabling vehicles to communicate with each other and with the traffic environment using cellular networks, we redefine transportation, improving road safety and transportation services, increasing efficiency of traffic flows, and reducing environmental impact. This paper proposes a decentralized approach for provisioning Cellular Vehicular-to-Network (C-V2N) services, addressing the coupled problems of service task placement and scaling of edge resources. We formalize the joint problem and prove its complexity. We propose an approach to tackle it, linking the two problems, employing decentralized decision-making using (i) a greedy approach for task placement and (ii) a Deep Deterministic Policy Gradient (DDPG) based approach for scaling. We benchmark the performance of our approach, focusing on the scaling agent, against several State-of-the-Art (SoA) scaling approaches via simulations using a real C-V2N traffic data set. The results show that DDPG-based solutions outperform SoA solutions, keeping the latency experienced by the C-V2N service below the target delay while optimizing the use of computing resources. By conducting a complexity analysis, we prove that DDPG-based solutions achieve runtimes in the range of sub-milliseconds, meeting the strict latency requirements of C-V2N services. Index Terms--cellular vehicle to network, task placement, edge resource scaling, deep deterministic policy gradient.

The effectiveness and efficiency of 5G software stack vulnerability and unintended behavior detection are essential for 5G assurance, especially for its applications in critical infrastructures. Scalability and automation are the main challenges in testing approaches and cybersecurity research. In this paper, we propose an innovative approach for automatically detecting vulnerabilities, unintended emergent behaviors, and performance degradation in 5G stacks via run-time profiling documents corresponding to fuzz testing in code repositories. Piloting on srsRAN, we map the run-time profiling via Logging Information (LogInfo) generated by fuzzing test to a high dimensional metric space first and then construct feature spaces based on their timestamp information. Lastly, we further leverage machine learning-based classification algorithms, including Logistic Regression, K-Nearest Neighbors, and Random Forest to categorize the impacts on performance and security attributes. The performance of the proposed approach has high accuracy, ranging from $ 93.4 \% $ to $ 95.9 \% $, in detecting the fuzzing impacts. In addition, the proof of concept could identify and prioritize real-time vulnerabilities on 5G infrastructures and critical applications in various verticals.

Recently, deep neural networks have emerged as a solution to solve NP-hard wireless resource allocation problems in real-time. However, multi-layer perceptron (MLP) and convolutional neural network (CNN) structures, which are inherited from image processing tasks, are not optimized for wireless network problems. As network size increases, these methods get harder to train and generalize. User pairing is one such essential NP-hard optimization problem in wireless communication systems that entails selecting users to be scheduled together while minimizing interference and maximizing throughput. In this paper, we propose an unsupervised graph neural network (GNN) approach to efficiently solve the user pairing problem. Our proposed method utilizes the Erdos goes neural pipeline to significantly outperform other scheduling methods such as k-means and semi-orthogonal user scheduling (SUS). At 20 dB SNR, our proposed approach achieves a 49% better sum rate than k-means and a staggering 95% better sum rate than SUS while consuming minimal time and resources. The scalability of the proposed method is also explored as our model can handle dynamic changes in network size without experiencing a substantial decrease in performance. Moreover, our model can accomplish this without being explicitly trained for larger or smaller networks facilitating a dynamic functionality that cannot be achieved using CNNs or MLPs.

Multicast communication technology is widely applied in wireless environments with a high device density. Traditional wireless network architectures have difficulty flexibly obtaining and maintaining global network state information and cannot quickly respond to network state changes, thus affecting the throughput, delay, and other QoS requirements of existing multicasting solutions. Therefore, this paper proposes a new multicast routing method based on multiagent deep reinforcement learning (MADRL-MR) in a software-defined wireless networking (SDWN) environment. First, SDWN technology is adopted to flexibly configure the network and obtain network state information in the form of traffic matrices representing global network links information, such as link bandwidth, delay, and packet loss rate. Second, the multicast routing problem is divided into multiple subproblems, which are solved through multiagent cooperation. To enable each agent to accurately understand the current network state and the status of multicast tree construction, the state space of each agent is designed based on the traffic and multicast tree status matrices, and the set of AP nodes in the network is used as the action space. A novel single-hop action strategy is designed, along with a reward function based on the four states that may occur during tree construction: progress, invalid, loop, and termination. Finally, a decentralized training approach is combined with transfer learning to enable each agent to quickly adapt to dynamic network changes and accelerate convergence. Simulation experiments show that MADRL-MR outperforms existing algorithms in terms of throughput, delay, packet loss rate, etc., and can establish more intelligent multicast routes.

We investigate the problem of wireless routing in integrated access backhaul (IAB) networks consisting of fiber-connected and wireless base stations and multiple users. The physical constraints of these networks prevent the use of a central controller, and base stations have limited access to real-time network conditions. We aim to maximize packet arrival ratio while minimizing their latency, for this purpose, we formulate the problem as a multi-agent partially observed Markov decision process (POMDP). To solve this problem, we develop a Relational Advantage Actor Critic (Relational A2C) algorithm that uses Multi-Agent Reinforcement Learning (MARL) and information about similar destinations to derive a joint routing policy on a distributed basis. We present three training paradigms for this algorithm and demonstrate its ability to achieve near-centralized performance. Our results show that Relational A2C outperforms other reinforcement learning algorithms, leading to increased network efficiency and reduced selfish agent behavior. To the best of our knowledge, this work is the first to optimize routing strategy for IAB networks.

Due to the highly dynamic changes in wireless network topologies, efficiently obtaining network status information and flexibly forwarding data to improve communication quality of service are important challenges. This article introduces an intelligent routing algorithm (DRL-PPONSA) based on proximal policy optimization deep reinforcement learning with network situational awareness under a software-defined wireless networking architecture. First, a specific data plane is designed for network topology construction and data forwarding. The control plane collects network traffic information, sends flow tables, and uses a GCN-GRU prediction mechanism to perceive future traffic change trends to achieve network situational awareness. Second, a DRL-based data forwarding mechanism is designed in the knowledge plane. The predicted network traffic matrix and topology information matrix are treated as the environment for DRL agents, while next-hop adjacent nodes are treated as executable actions. Accordingly, action selection strategies are designed for different network conditions to achieve more intelligent, flexible, and efficient routing control. The reward function is designed using network link information and various reward and penalty mechanisms. Additionally, importance sampling and gradient clipping techniques are employed during gradient updating to enhance convergence speed and stability. Experimental results show that DRL-PPONSA outperforms traditional routing methods in network throughput, delay, packet loss rate, and wireless node distance. Compared to value-function-based Dueling DQN routing, the convergence speed is significantly improved, and the convergence effect is more stable. Simultaneously, its consumption of hardware storage space is reduced, and efficient routing decisions can be made in real-time using the current network state information.

TOKYO--Japanese technology investor SoftBank Group said it was ready to go on the offensive again with its eye on artificial-intelligence companies after cashing in almost its entire stake in Chinese e-commerce company Alibaba Group. In the fiscal year ended March 31, SoftBank cut its investments to about $3 billion, less than a 10th of its investments the previous fiscal year. It had said it was playing defense after many of its investments in startups went sour during the recent tech downturn.

Modern cellular networks are multi-cell and use universal frequency reuse to maximize spectral efficiency. This results in high inter-cell interference. This problem is growing as cellular networks become three-dimensional with the adoption of unmanned aerial vehicles (UAVs). This is because the strength and number of interference links rapidly increase due to the line-of-sight channels in UAV communications. Existing interference management solutions need each transmitter to know the channel information of interfering signals, rendering them impractical due to excessive signaling overhead. In this paper, we propose leveraging deep reinforcement learning for interference management to tackle this shortcoming. In particular, we show that interference can still be effectively mitigated even without knowing its channel information. We then discuss novel approaches to scale the algorithms with linear/sublinear complexity and decentralize them using multi-agent reinforcement learning. By harnessing interference, the proposed solutions enable the continued growth of civilian UAVs.