Mobile cellular users traveling in city buses are experiencing poor quality of signals due to the interference and the large number of mobile devices. To enhance the Quality-of-Service (QoS), deployment of small cell networks in city buses is a promising solution. The deployment of small cells in vehicular environment makes the resource allocation more challenging because of the dynamic interference relationships experienced by them. Therefore, resource allocation in vehicular environment within moving small cells (MSCs) needs to be handled carefully. In this study, we investigate the problem of resource allocation in city bus transit system with multiple routes. Then, we propose a Percentage Threshold Interference Graph (PTIG) based allocation of resources to MSCs in a network. City buses of multiple routes travel with variable speed and may share some of the same road segments which make it difficult to extract the exact interference patterns between them. Therefore, Long Short Term Memory (LSTM) neural networks are used to predict the city buses locations. The predicted locations of city buses are then used to generate PTIG by finding the dynamic interference relationship between MSCs. Graph coloring algorithm is used to allocate the resources to PTIG. Numerical results are presented to show the comparison of resource allocation using PTIG and Time Interval based Interference Graph (TIIG) in terms of resource block utilization and time complexity.

This work proposes a position-dependent deep learning (DL)-based algorithm that enables interference free resource allocation (RA) among mobile small cells (mScs). The proposed algorithm considers a vehicular environment comprising of city buses that generates historic data about the city buses positions. The position information of the moving buses is exploited to form interference free resource block (RB) allocation as data labels to the respective historic data. The long short-term memory (LSTM) algorithm is used for RA in mSc network based on position-dependent historic data. The numerical results obtained under non-dense and dense mSc network scenarios reveal that the proposed algorithm outperforms other machine learning (ML) and DL-based RA mechanisms. Moreover, the proposed RA algorithm shows improved results when compared to RA using Global Positioning System Dependent Interference Graph (GPS-DIG), but provides less data rates as compared to existing Time Interval Dependent Interference Graph (TIDIG)-based, and Threshold Percentage Dependent Interference Graph (TPDIG)-based RA while fulfilling the users’ demands. The proposed scheme is computationally less expensive in comparison with TIDIG and TPDIG-based algorithms.

With the evolution of 5G edge computing networks, privacy-aware applications are gaining significant attention due to their decentralised processing capabilities. However, these networks face substantial challenges to ensure privacy and security, specifically in a Federated Learning (FL) setup, where adversarial attacks can potentially influence the model integrity. Conventional privacy-preserving FL mechanisms are often susceptible to such attacks, leading to degraded model performance and severe security vulnerabilities. To address this issue, we propose FL with adversarial optimisation framework to improve adversarial robustness in 5G edge computing networks while ensuring privacy preservation. The proposed framework considers two models; a classifier model and an adversary model, where the classifier model is integrated with the adversary model, trained jointly considering Fast Gradient Sign Method (FGSM) for generation of adversarial perturbations. This adversarial optimisation enhances classifier’s resilience to attacks, thereby improving both privacy preservation and model accuracy. Experimental analysis reveals that the proposed model achieves up to $99.44\%$ accuracy on adversarial test data, while improving robustness and sustaining high precision and recall across varying client scenarios. The experimental results further ensure the effectiveness of the proposed model in terms of communication efficiency and computational efficiency while reducing inference time and FLOPs making it ideal for secure 5G edge computing applications.