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.

Efficient and reliable data transmission over networks is crucial for businesses, governments, and individuals in today's interconnected world, particularly for time-sensitive applications requiring reliable and timely data transmission. In modern networks, Quality of Service (QoS) switches are used to manage traffic flow and ensure that critical traffic is prioritized over non-critical traffic. The Buffer Management in QoS Switches problem aims to develop effective algorithms to ensure critical traffic is not lost while minimizing the impact on non-critical traffic. The goal of the algorithm is to control which packets are discarded and which are transmitted at each step. Online algorithms are often evaluated based on competitive analysis; (i.e., the ratio of the optimal offline cost to the cost achieved by online algorithms over worst-case instances).

Over the past few decades, Industrial Control Systems (ICSs) have been targeted by cyberattacks and are becoming increasingly vulnerable as more ICSs are connected to the internet. Using Machine Learning (ML) for Intrusion Detection Systems (IDS) is a promising approach for ICS cyber protection, but the lack of suitable datasets for evaluating ML algorithms is a challenge. Although there are a few commonly used datasets, they may not reflect realistic ICS network data, lack necessary features for effective anomaly detection, or be outdated. This paper presents the 'ICS-Flow' dataset, which offers network data and process state variables logs for supervised and unsupervised ML-based IDS assessment. The network data includes normal and anomalous network packets and flows captured from simulated ICS components and emulated networks. The anomalies were injected into the system through various attack techniques commonly used by hackers to modify network traffic and compromise ICSs. We also proposed open-source tools, `ICSFlowGenerator' for generating network flow parameters from Raw network packets. The final dataset comprises over 25,000,000 raw network packets, network flow records, and process variable logs. The paper describes the methodology used to collect and label the dataset and provides a detailed data analysis. Finally, we implement several ML models, including the decision tree, random forest, and artificial neural network to detect anomalies and attacks, demonstrating that our dataset can be used effectively for training intrusion detection ML models.

The steady evolution of the Open RAN concept sheds light on xApps and their potential use cases in O-RANcompliant deployments. There are several areas where xApps can be used that are being widely investigated, but the issue of mitigating conflicts between xApp decisions requires further in-depth investigation. This article defines a conflict mitigation framework (CMF) built into the existing O-RAN architecture; it enables the Conflict Mitigation component in O-RAN's Near- Real-Time RAN Intelligent Controller (Near-RT RIC) to detect and resolve all conflict types defined in the O-RAN Alliance's technical specifications. Methods for detecting each type of conflict are defined, including message flows between Near-RT RIC components. The suitability of the proposed CMF is proven with a simulation of an O-RAN network. Results of the simulation show that enabling the CMF allows balancing the network control capabilities of conflicting xApps to significantly improve network performance, with a small negative impact on its reliability. It is concluded that defining a unified CMF in Near-RT RIC is the first step towards providing a standardized method of conflict detection and resolution in O-RAN environments.

However, due to the limited recording capabilities Learning causal structure among event types from and storage capacities, retaining event's occurred times discrete-time event sequences is a particularly important with high-resolution is expensive or practically impossible in but challenging task. Existing methods, such many real-world applications, and we usually only can access as the multivariate Hawkes processes based methods, the corresponding discrete-time event sequences. For example, mostly boil down to learning the so-called in large wireless networks, the event sequences are usually Granger causality which assumes that the cause logged at a certain frequency by different devices whose event happens strictly prior to its effect event. Such time might not be accurately synchronized. As a result, lowresolution an assumption is often untenable beyond applications, discrete-time event sequences are obtained and the especially when dealing with discrete-time temporal precedence assumption will be frequently violated event sequences in low-resolution; and typical discrete in discrete-time event sequences, which raises a serious identifiability Hawkes processes mainly suffer from identifiability issue of causal discovery. For example, as shown issues raised by the instantaneous effect, in Figure 1, there are three event sequences produced by three i.e., the causal relationship that occurred simultaneously event types v

Federated Learning (FL) is a widely embraced paradigm for distilling artificial intelligence from distributed mobile data. However, the deployment of FL in mobile networks can be compromised by exposure to interference from neighboring cells or jammers. Existing interference mitigation techniques require multi-cell cooperation or at least interference channel state information, which is expensive in practice. On the other hand, power control that treats interference as noise may not be effective due to limited power budgets, and also that this mechanism can trigger countermeasures by interference sources. As a practical approach for protecting FL against interference, we propose Spectrum Breathing, which cascades stochastic-gradient pruning and spread spectrum to suppress interference without bandwidth expansion. The cost is higher learning latency by exploiting the graceful degradation of learning speed due to pruning. We synchronize the two operations such that their levels are controlled by the same parameter, Breathing Depth. To optimally control the parameter, we develop a martingale-based approach to convergence analysis of Over-the-Air FL with spectrum breathing, termed AirBreathing FL. We show a performance tradeoff between gradient-pruning and interference-induced error as regulated by the breathing depth. Given receive SIR and model size, the optimization of the tradeoff yields two schemes for controlling the breathing depth that can be either fixed or adaptive to channels and the learning process. As shown by experiments, in scenarios where traditional Over-the-Air FL fails to converge in the presence of strong interference, AirBreahing FL with either fixed or adaptive breathing depth can ensure convergence where the adaptive scheme achieves close-to-ideal performance.

Cloud native technology has revolutionized 5G beyond and 6G communication networks, offering unprecedented levels of operational automation, flexibility, and adaptability. However, the vast array of cloud native services and applications presents a new challenge in resource allocation for dynamic cloud computing environments. To tackle this challenge, we investigate a cloud native wireless architecture that employs container-based virtualization to enable flexible service deployment. We then study two representative use cases: network slicing and Multi-Access Edge Computing. To optimize resource allocation in these scenarios, we leverage deep reinforcement learning techniques and introduce two model-free algorithms capable of monitoring the network state and dynamically training allocation policies. We validate the effectiveness of our algorithms in a testbed developed using Free5gc. Our findings demonstrate significant improvements in network efficiency, underscoring the potential of our proposed techniques in unlocking the full potential of cloud native wireless networks.

Gaps, dropouts and short clips of corrupted audio are a common problem and particularly annoying when they occur in speech. This paper uses machine learning to regenerate gaps of up to 320ms in an audio speech signal. Audio regeneration is translated into image regeneration by transforming audio into a Mel-spectrogram and using image in-painting to regenerate the gaps. The full Mel-spectrogram is then transferred back to audio using the Parallel-WaveGAN vocoder and integrated into the audio stream. Using a sample of 1300 spoken audio clips of between 1 and 10 seconds taken from the publicly-available LJSpeech dataset our results show regeneration of audio gaps in close to real time using GANs with a GPU equipped system. As expected, the smaller the gap in the audio, the better the quality of the filled gaps. On a gap of 240ms the average mean opinion score (MOS) for the best performing models was 3.737, on a scale of 1 (worst) to 5 (best) which is sufficient for a human to perceive as close to uninterrupted human speech.