Ultra-reliable low-latency communication (URLLC) is the cornerstone for a broad range of emerging services in next-generation wireless networks. URLLC fundamentally relies on the network's ability to proactively determine whether sufficient resources are available to support the URLLC traffic, and thus, prevent so-called cell overloads. Nonetheless, achieving accurate quality-of-service (QoS) predictions for URLLC user equipment (UEs) and preventing cell overloads are very challenging tasks. This is due to dependency of the QoS metrics (latency and reliability) on traffic and channel statistics, users' mobility, and interdependent performance across UEs. In this paper, a new QoS-aware UE admission control approach is developed to proactively estimate QoS for URLLC UEs, prior to associating them with a cell, and accordingly, admit only a subset of UEs that do not lead to a cell overload. To this end, an optimization problem is formulated to find an efficient UE admission control policy, cognizant of UEs' QoS requirements and cell-level load dynamics. To solve this problem, a new machine learning based method is proposed that builds on (deep) neural contextual bandits, a suitable framework for dealing with nonlinear bandit problems. In fact, the UE admission controller is treated as a bandit agent that observes a set of network measurements (context) and makes admission control decisions based on context-dependent QoS (reward) predictions. The simulation results show that the proposed scheme can achieve near-optimal performance and yield substantial gains in terms of cell-level service reliability and efficient resource utilization.

Packet buffers in datacenter switches are shared across all the switch ports in order to improve the overall throughput. The trend of shrinking buffer sizes in datacenter switches makes buffer sharing extremely challenging and a critical performance issue. Literature suggests that push-out buffer sharing algorithms have significantly better performance guarantees compared to drop-tail algorithms. Unfortunately, switches are unable to benefit from these algorithms due to lack of support for push-out operations in hardware. Our key observation is that drop-tail buffers can emulate push-out buffers if the future packet arrivals are known ahead of time. This suggests that augmenting drop-tail algorithms with predictions about the future arrivals has the potential to significantly improve performance. This paper is the first research attempt in this direction. We propose Credence, a drop-tail buffer sharing algorithm augmented with machine-learned predictions. Credence can unlock the performance only attainable by push-out algorithms so far. Its performance hinges on the accuracy of predictions. Specifically, Credence achieves near-optimal performance of the best known push-out algorithm LQD (Longest Queue Drop) with perfect predictions, but gracefully degrades to the performance of the simplest drop-tail algorithm Complete Sharing when the prediction error gets arbitrarily worse. Our evaluations show that Credence improves throughput by $1.5$x compared to traditional approaches. In terms of flow completion times, we show that Credence improves upon the state-of-the-art approaches by up to $95\%$ using off-the-shelf machine learning techniques that are also practical in today's hardware. We believe this work opens several interesting future work opportunities both in systems and theory that we discuss at the end of this paper.

We consider the problem of predicting cellular network performance (signal maps) from measurements collected by several mobile devices. We formulate the problem within the online federated learning framework: (i) federated learning (FL) enables users to collaboratively train a model, while keeping their training data on their devices; (ii) measurements are collected as users move around over time and are used for local training in an online fashion. We consider an honest-but-curious server, who observes the updates from target users participating in FL and infers their location using a deep leakage from gradients (DLG) type of attack, originally developed to reconstruct training data of DNN image classifiers. We make the key observation that a DLG attack, applied to our setting, infers the average location of a batch of local data, and can thus be used to reconstruct the target users' trajectory at a coarse granularity. We build on this observation to protect location privacy, in our setting, by revisiting and designing mechanisms within the federated learning framework including: tuning the FL parameters for averaging, curating local batches so as to mislead the DLG attacker, and aggregating across multiple users with different trajectories. We evaluate the performance of our algorithms through both analysis and simulation based on real-world mobile datasets, and we show that they achieve a good privacy-utility tradeoff.

Radio frequency (RF) signal mapping, which is the process of analyzing and predicting the RF signal strength and distribution across specific areas, is crucial for cellular network planning and deployment. Traditional approaches to RF signal mapping rely on statistical models constructed based on measurement data, which offer low complexity but often lack accuracy, or ray tracing tools, which provide enhanced precision for the target area but suffer from increased computational complexity. Recently, machine learning (ML) has emerged as a data-driven method for modeling RF signal propagation, which leverages models trained on synthetic datasets to perform RF signal mapping in "unseen" areas. In this paper, we present Geo2SigMap, an ML-based framework for efficient and high-fidelity RF signal mapping using geographic databases. First, we develop an automated framework that seamlessly integrates three open-source tools: OpenStreetMap (geographic databases), Blender (computer graphics), and Sionna (ray tracing), enabling the efficient generation of large-scale 3D building maps and ray tracing models. Second, we propose a cascaded U-Net model, which is pre-trained on synthetic datasets and employed to generate detailed RF signal maps, leveraging environmental information and sparse measurement data. Finally, we evaluate the performance of Geo2SigMap via a real-world measurement campaign, where three types of user equipment (UE) collect over 45,000 data points related to cellular information from six LTE cells operating in the citizens broadband radio service (CBRS) band. Our results show that Geo2SigMap achieves an average root-mean-square-error (RMSE) of 6.04 dB for predicting the reference signal received power (RSRP) at the UE, representing an average RMSE improvement of 3.59 dB compared to existing methods.

Millimeter wave (mmWave) has been recognized as one of key technologies for 5G and beyond networks due to its potential to enhance channel bandwidth and network capacity. The use of mmWave for various applications including vehicular communications has been extensively discussed. However, applying mmWave to vehicular communications faces challenges of high mobility nodes and narrow coverage along the mmWave beams. Due to high mobility in dense networks, overlapping beams can cause strong interference which leads to performance degradation. As a remedy, beam switching capability in mmWave can be utilized. Then, frequent beam switching and cell change become inevitable to manage interference, which increase computational and signalling complexity. In order to deal with the complexity in interference control, we develop a new strategy called Multi-Agent Context Learning (MACOL), which utilizes Contextual Bandit to manage interference while allocating mmWave beams to serve vehicles in the network. Our approach demonstrates that by leveraging knowledge of neighbouring beam status, the machine learning agent can identify and avoid potential interfering transmissions to other ongoing transmissions. Furthermore, we show that even under heavy traffic loads, our proposed MACOL strategy is able to maintain low interference levels at around 10%.

Decentralized optimization methods often entail information exchange between neighbors. Transmission failures can happen due to network congestion, hardware/software issues, communication outage, and other factors. In this paper, we investigate the random link failure problem in decentralized multi-task online convex optimization, where agents have individual decisions that are coupled with each other via pairwise constraints. Although widely used in constrained optimization, conventional saddle-point algorithms are not directly applicable here because of random packet dropping. To address this issue, we develop a robust decentralized saddle-point algorithm against random link failures with heterogeneous probabilities by replacing the missing decisions of neighbors with their latest received values. Then, by judiciously bounding the accumulated deviation stemming from this replacement, we first establish that our algorithm achieves $\mathcal{O}(\sqrt{T})$ regret and $\mathcal{O}(T^\frac{3}{4})$ constraint violations for the full information scenario, where the complete information on the local cost function is revealed to each agent at the end of each time slot. These two bounds match, in order sense, the performance bounds of algorithms with perfect communications. Further, we extend our algorithm and analysis to the two-point bandit feedback scenario, where only the values of the local cost function at two random points are disclosed to each agent sequentially. Performance bounds of the same orders as the full information case are derived. Finally, we corroborate the efficacy of the proposed algorithms and the analytical results through numerical simulations.

Hypergraphs are a representation of complex systems involving interactions among more than two entities and allow to investigation of higher-order structure and dynamics in real-world complex systems. Community structure is a common property observed in empirical networks in various domains. Stochastic block models have been employed to investigate community structure in networks. Node attribute data, often accompanying network data, has been found to potentially enhance the learning of community structure in dyadic networks. In this study, we develop a statistical framework that incorporates node attribute data into the learning of community structure in a hypergraph, employing a stochastic block model. We demonstrate that our model, which we refer to as HyperNEO, enhances the learning of community structure in synthetic and empirical hypergraphs when node attributes are sufficiently associated with the communities. Furthermore, we found that applying a dimensionality reduction method, UMAP, to the learned representations obtained using stochastic block models, including our model, maps nodes into a two-dimensional vector space while largely preserving community structure in empirical hypergraphs. We expect that our framework will broaden the investigation and understanding of higher-order community structure in real-world complex systems.

This paper introduces Auto-modeling of Formal Verification with Real-world Prompting for 5G and NextG protocols (AVRE), a novel system designed for the formal verification of Next Generation (NextG) communication protocols, addressing the increasing complexity and scalability challenges in network protocol design and verification. Utilizing Large Language Models (LLMs), AVRE transforms protocol descriptions into dependency graphs and formal models, efficiently resolving ambiguities and capturing design intent. The system integrates a transformer model with LLMs to autonomously establish quantifiable dependency relationships through cross- and self-attention mechanisms. Enhanced by iterative feedback from the HyFuzz experimental platform, AVRE significantly advances the accuracy and relevance of formal verification in complex communication protocols, offering a groundbreaking approach to validating sophisticated communication systems. We compare CAL's performance with state-of-the-art LLM-based models and traditional time sequence models, demonstrating its superiority in accuracy and robustness, achieving an accuracy of 95.94\% and an AUC of 0.98. This NLP-based approach enables, for the first time, the creation of exploits directly from design documents, making remarkable progress in scalable system verification and validation.

Traffic from distributed training of machine learning (ML) models makes up a large and growing fraction of the traffic mix in enterprise data centers. While work on distributed ML abounds, the network traffic generated by distributed ML has received little attention. Using measurements on a testbed network, we investigate the traffic characteristics generated by the training of the ResNet-50 neural network with an emphasis on studying its shortterm burstiness. For the latter we propose metrics that quantify traffic burstiness at different time scales. Our analysis reveals that distributed ML traffic exhibits a very high degree of burstiness on short time scales, exceeding a 60:1 peak-to-mean ratio on time intervals as long as 5 ms. We observe that training software orchestrates transmissions in such a way that burst transmissions from different sources within the same application do not result in congestion and packet losses. An extrapolation of the measurement data to multiple applications underscores the challenges of distributed ML traffic for congestion and flow control algorithms. This paper studies and analyzes the burstiness of traffic from training deep neural network (DNN) models as a root cause for short-lived surges of traffic, known as microbursts, that cause periods of high packet delay and loss in a data center network (DCN) even at a low utilization. Since microbursts occur at a time scale of less than a millisecond [1], traditional traffic control methods are not effective with avoiding packet losses in such scenarios. Research on microbursts in DCNs has suggested a range of potential root causes, including the inherent burstiness of application traffic, confluence of traffic flows to a common destination (fan-in, incast), offloading of protocol processing at hosts, and traffic control algorithms, such as packet scheduling and flow control [1]-[10]. While training of neural networks makes up a large fraction of the workload in data centers [11], to the best of our knowledge, there does not exist a detailed analysis of distributed ML traffic and its potential impact on the creation of microbursts. The vast majority of network traffic from training DNN models is due to the exchange of gradients of model parameters. As modern DNN models involve millions, and, in the case of large language models such as GPT, billions of parameters [12], the transmission of gradients creates huge data bursts. The measurement experiments are performed in a testbed network with a single switch with 100 Gbps line rates. We evaluate a server-based and a serverless mode of training. In server-based training, the nodes involved in the training, referred to as workers, exchange gradients with a dedicated server. Here, the transmissions to the server create a bottleneck.

Despite technological advancements, the significance of interdisciplinary subjects like complex networks has grown. Exploring communication within these networks is crucial, with traffic becoming a key concern due to the expanding population and increased need for connections. Congestion tends to originate in specific network areas but quickly proliferates throughout. Consequently, understanding the transition from a flow-free state to a congested state is vital. Numerous studies have delved into comprehending the emergence and control of congestion in complex networks, falling into three general categories: soft strategies, hard strategies, and resource allocation strategies. This article introduces a routing algorithm leveraging reinforcement learning to address two primary objectives: congestion control and optimizing path length based on the shortest path algorithm, ultimately enhancing network throughput compared to previous methods. Notably, the proposed method proves effective not only in Barab\'asi-Albert scale-free networks but also in other network models such as Watts-Strogatz (small-world) and Erd\"os-R\'enyi (random network). Simulation experiment results demonstrate that, across various traffic scenarios and network topologies, the proposed method can enhance efficiency criteria by up to 30% while reducing maximum node congestion by five times.