As the fifth generation (5G) of mobile communication systems are becoming a commercial reality, this paper attempts to investigate the reliability and propagation latency performance of UAV-assisted cellular systems that caters to a scalable, flexible and on-demand solution for differentiated 5G services. The reliability performance of the considered system is evaluated in terms of block error rates (BLER) along with the propagation latency for three classes of services: Ultra Reliable Low Latency Communications (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine Type Communications (mMTC). Considering the impact of fading and shadowing, the closed-form approximate expressions for BLER and propagation latency are evaluated over Rician shadowed fading with various shadowing scenarios. The numerical results reveal important insights related to the achievable reliability performance for three different services under given fading and shadowing severity. Specifically, The tightness of the ap-proximation presented is validated through the Monte-Carlo simulations.

Unmanned Aerial Vehicles (UAVs) are critical in modern emergency response due to their high mobility. However, limited computing resources and energy supplies necessitate the use of UAV networks for collaborative inference. UAV intelligent tasks are often Deep Neural Networks (DNN)-based, with DNN partitioning enabling collaborative inference. However, executing DNN partitioning in a highly dynamic UAV network faces two challenges that have not been addressed in existing research: the time gap between the state sampling and the execution of the corresponding action based on that state, and the unknown trajectories in advance. The time gap requires predictive action decision-making. To address this, we model DNN partitioning and edge offloading with hybrid action decisions in dynamic, energy-harvesting UAV networks as a Predictive Markov Decision Process (P-MDP). The rapidly changing and previously unknown network topology significantly impacts channel and data transmission energy consumption, affecting DNN partitioning decisions. To better solve the action prediction problem, we use the Transformer module to extract motion features from recent time slots in the proposed Transformer-enhanced Multi-Agent Hybrid Action Proximal Policy Optimization (TE-MHAPPO) framework. Simulation results show that TE-MHAPPO reduces the reward which comprehensively considers task delay and energy consumption, by at least 12.1% compared to the state-of-theart MHAPPO. Additionally, its reward performance degradation with the increase in prediction time is at most 55.2% of that observed in the baseline.

Unmanned Aerial Vehicle (UAV) as a cellular Base Station (BS), referred to as UAV-BS, has become a vital asset for emergency response due to their ability to offer an on-demand, adaptable, and expeditious communication solution. The current literature indicates a notable absence of a unified robotics and communication architecture for prototyping UAV-BS, which utilizes open source software and hardware. Further, current work on positioning algorithms of UAV- BS also fails to adequately address the adaptability of UAV-BS in effectively managing spatio-temporal skewness in traffic demands from UEs. In this backdrop, we present Sky5G, a real-world prototype platform for UAV-BS comprised entirely of open-source soft-ware and hardware components. Sky5G platform relies on a middleware to unify the radio and flight responsibilities of UAV-BS. Through several open-sky experiments of Sky5G, we assess various optimal UAV-BS positioning algorithms in response to ground user distribution and spatio-temporal traffic demands.

The ambitious goals of Fifth Generation (5G) mobile networks for higher system capacity, massive number of devices and flexibility in operations demand the network architecture to be much more flexible, efficient and autonomous. The design of Radio Access Network (RAN) is undergoing architectural transformations to increase the flexibility of deployment and programmability by leveraging Network Functions Virtualization (NFV), Software Defined Networking (SDN), and Cloud Computing. Hence, efficient resource management strategies with enhanced service quality play a vital role in realizing true benefits of 5G RAN. In this work, we propose a novel and dynamic resource management framework called "KORA" for 5G Cloud Radio Access network (C-RAN) considering spatio-temporal traffic heterogeneity exhibited at Remote Radio Units (RRUs). To minimize net energy consumption in the cloud data center and to maximize the service quality to end users, we formulate an Integer Linear Programming model (ILP) for KORA that performs efficient consolidation and relocation of Control Units (CUs) in 5G C-RAN. To alleviate the computational heaviness of the ILP optimization model, we propose a light-weight heuristic algorithm that is scalable and applicable to real-world dense deployments spanning a large set of CUs. By simulations, we compare and contrast between various distinctive features of 5G C-RAN architecture under study as well as evaluate the efficacy of our proposed KORA framework. The heuristic algorithm can save 27% of relocations and 33% of GBR flows from disruption, at increased energy consumption of 6.6% in data center as compared to KORA.

Due to spatio-temporal variation of mobile subscriber's data traffic requirements, traffic load experienced by base stations present at different cell sites exhibit highly dynamic behavior in traditional cellular systems. This non-uniform and dynamic traffic load leads to under utilization of the base station computing resources at cell sites. Cloud Radio Access Network (C-RAN) is an innovative architecture which addresses this issue and keeps the Total Cost of Ownership (TCO) under safe limit for cellular operators. In C-RAN, the baseband processing units (BBUs) are segregated from cell sites and are pooled in a central cloud data center thereby facilitating shared access for a set of Remote Radio Heads (RRHs) present at cell sites. In order to truly exploit the benefits of C-RAN, the BBU pool deployed in the cloud has to efficiently serve clusters of RRHs (i.e., many-to-one mapping between RRHs and BBUs in the BBU pool) and thereby minimizing the required number of active BBUs. In this work, potential benefits of C-RAN are studied by considering realistic traffic loads of base stations deployed in urban areas by using statistical models. We propose a lightweight and load-aware algorithm, Dynamic RRH Assignment (DRA), which achieves BBU pooling gain close to that of a well known First-Fit Decreasing (FFD) bin packing algorithm. Using extensive simulations, we show that DRA consumes only 25% of time on average compared to FFD for the case of urban cellular deployment of 1000 RRHs. DRA slightly overestimates the required number of active BBUs as compared to FFD by 1.7% and 1.4% for weekdays and weekends, respectively.