FedNET: Federated Learning for Proactive Traffic Management and Network Capacity Planning

We propose FedNET, a proactive and privacy-preserving framework for early identification of high-risk links in large-scale communication networks, that leverages a distributed multi-step traffic forecasting method. FedNET employs Federated Learning (FL) to model the temporal evolution of node-level traffic in a distributed manner, enabling accurate multi-step-ahead predictions (e.g., several hours to days) without exposing sensitive network data. Using these node-level forecasts and known routing information, FedNET estimates the future link-level utilization by aggregating traffic contributions across all source-destination pairs. The links are then ranked according to the predicted load intensity and temporal variability, providing an early warning signal for potential high-risk links. We compare the federated traffic prediction of FedNET against a centralized multi-step learning baseline and then systematically analyze the impact of history and prediction window sizes on forecast accuracy using the $R^2$ score. Results indicate that FL achieves accuracy close to centralized training, with shorter prediction horizons consistently yielding the highest accuracy ($R^2 >0.92$), while longer horizons providing meaningful forecasts ($R^2 \approx 0.45\text{--}0.55$). We further validate the efficacy of the FedNET framework in predicting network utilization on a realistic network topology and demonstrate that it consistently identifies high-risk links well in advance (i.e., three days ahead) of the critical stress states emerging, making it a practical tool for anticipatory traffic engineering and capacity planning.

Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications

Accurate and adaptive network throughput prediction is essential for latency-sensitive and bandwidth-intensive applications in 5G and emerging 6G networks. However, most existing methods rely on centralized training with uniformly collected data, limiting their applicability in heterogeneous mobile environments with non-IID data distributions. This paper presents the first comprehensive benchmarking of federated learning (FL) strategies for throughput prediction in realistic 5G edge scenarios. We evaluate three aggregation algorithms - FedAvg, FedProx, and FedBN - across four time-series architectures: LSTM, CNN, CNN+LSTM, and Transformer, using five diverse real-world datasets. We systematically analyze the effects of client heterogeneity, cohort size, and history window length on prediction performance. Our results reveal key trade-offs among model complexities, convergence rates, and generalization. It is found that FedBN consistently delivers robust performance under non-IID conditions. On the other hand, LSTM and Transformer models outperform CNN-based baselines by up to 80% in R2 scores. Moreover, although Transformers converge in half the rounds of LSTM, they require longer history windows to achieve a high R2, indicating higher context dependence. LSTM is, therefore, found to achieve a favorable balance between accuracy, rounds, and temporal footprint. To validate the end-to-end applicability of the framework, we have integrated our FL-based predictors into a live adaptive streaming pipeline. It is seen that FedBN-based LSTM and Transformer models improve mean QoE scores by 11.7% and 11.4%, respectively, over FedAvg, while also reducing the variance. These findings offer actionable insights for building scalable, privacy-preserving, and edge-aware throughput prediction systems in next-generation wireless networks.

A Lightweight QoS-Aware Resource Allocation Method for NR-V2X Networks

Vehicle-to-Everything (V2X) communication, which includes Vehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle (V2V), and Vehicle-to-Pedestrian (V2P) networks, is gaining significant attention due to the rise of connected and autonomous vehicles. V2X systems require diverse Quality of Service (QoS) provisions, with V2V communication demanding stricter latency and reliability compared to V2I. The 5G New Radio-V2X (NR-V2X) standard addresses these needs using multi-numerology Orthogonal Frequency Division Multiple Access (OFDMA), which allows for flexible allocation of radio resources. However, V2I and V2V users sharing the same radio resources leads to interference, necessitating efficient power and resource allocation. In this work, we propose a novel resource allocation and sharing algorithm for 5G-based V2X systems. Our approach first groups Resource Blocks (RBs) into Resource Chunks (RCs) and allocates them to V2I users using the Gale-Shapley stable matching algorithm. Power is then allocated to RCs to facilitate efficient resource sharing between V2I and V2V users through a bisection search method. Finally, the Gale-Shapley algorithm is used to pair V2I and V2V users, maintaining low computational complexity while ensuring high performance. Simulation results demonstrate that our proposed Gale-Shapley Resource Allocation with Gale-Shapley Sharing (GSRAGS) achieves competitive performance with lower complexity compared to existing works while effectively meeting the QoS demands of V2X communication systems.

Performance Analysis of Resource Allocation Algorithms for Vehicle Platoons over 5G eV2X Communication

Vehicle platooning is a cooperative driving technology that can be supported by 5G enhanced Vehicle-to-Everything (eV2X) communication to improve road safety, traffic efficiency, and reduce fuel consumption. eV2X communication among the platoon vehicles involves the periodic exchange of Cooperative Awareness Messages (CAMs) containing vehicle information under strict latency and reliability requirements. These requirements can be maintained by administering the assignment of resources, in terms of time slots and frequency bands, for CAM exchanges in a platoon, with the help of a resource allocation mechanism. State-of-the-art on control and communication design for vehicle platoons either consider a simplified platoon model with a detailed communication architecture or consider a simplified communication delay model with a detailed platoon control system. Departing from existing works, we have developed a comprehensive vehicle platoon communication and control framework using OMNET++, the benchmarking network simulation tool. We have carried out an inclusive and comparative study of three different platoon Information Flow Topologies (IFTs), namely Car-to-Server, Multi-Hop, and One-Hop over 5G using the Predecessor-leader following platoon control law to arrive at the best-suited IFT for platooning. Secondly, for the best-suited 5G eV2X platooning IFT selected, we have analyzed the performance of three different resource allocation algorithms, namely Maximum of Carrier to Interference Ratio (MaxC/I), Proportional Fair (PF), and Deficit Round Robin (DRR). Exhaustive system-level simulations show that the One-Hop information flow strategy along with the MaxC/I resource allocation yields the best Quality of Service (QoS) performance, in terms of latency, reliability, Age of Information (AoI), and throughput.

A QoS-Aware Joint Uplink Spectrum and Power Allocation with Link Adaptation for Vehicular Communications in 5G networks

In this work, we have proposed link adaptation based joint spectrum and power allocation algorithms for uplink cellular vehicle-to-everything (C-V2X) communication for OFDMA based 5G systems. In C-V2X, vehicle-to-vehicle (V2V) users share radio resources with vehicle-to-infrastructure (V2I) users. Existing works primarily focus on the optimal pairing of V2V and V2I users under the assumption that each V2I user needs a single resource block (RB); minimizing any interference through power allocation. In contrast, in this work, we have considered that the number of RBs needed by the users is a function of their channel condition and Quality of Service (QoS) - a method called link adaptation. It is effective in compensating for the incessant channel quality fluctuations at the high frequencies of operation of 5G. The first proposed resource allocation scheme of this work greedily allocates RBs to V2I users using link adaptation and then uses Hungarian algorithm to pair V2V with V2I users, while minimizing interference through power allocation. To reduce the complexity, the second scheme groups RBs into resource chunks (RCs); then uses Hungarian algorithm twice - first to allocate RCs to V2I users, and then to pair V2I users with V2V users. Extensive simulations reveal that link adaptation increases the number of satisfied V2I users as well as their sum-rate, while also improving QoS of individual users, thereby making it indispensable for C-V2X system. We have also considered the presence of best-effort users, and the diverse QoS has been provided through the multi-numerology frame structure of 5G.