5G and beyond technologies are designed to convey multiple QoS requirements coming from highly heterogeneous services. In this regard, 5G extends the QoS definition of previous technologies. In order to fulfill the requirements of new services, it is necessary to develop resource management solutions that effectively and efficiently translate them to usage of resources. Therefore, MAC schedulers are to be designed to accommodate the demands of emerging applications with stringent QoS requirements, such as XR. In this work, we introduce a Lyapunov-based MAC scheduler designed to appropriately tackle coexisting heterogeneous QoS flows. The proposed approach allows the implementation of policies that explicitly consider the QoS requirements, as well as an efficient use of allocated resources. The results obtained through extensive experiments over the ns-3 5G-LENA simulator demonstrate that our scheduler guarantees the required time-average bit rate for every QoS flow at every time window, while ensuring the stability of traffic queues for all users.

XR applications place increasing pressure on mobile networks to meet their stringent Quality of Service (QoS) demands. In particular, XR services require high throughput and low latency, with traffic patterns that differ greatly from traditional applications. To address these challenges, we propose a novel Medium Access Control (MAC) scheduling solution that takes into account the segmentation of XR media units into Packet Data Units (PDUs), referred to as PDU sets in 5G-Advanced networks. These PDU sets correspond to the core traffic unit for XR services and must be delivered according to their specific QoS requirements, which are dynamically established in real-time by the XR application. The proposed scheduling solution leverages Lyapunov drift-plus-penalty optimization to jointly optimize resource allocation and stabilize traffic queues while ensuring the timely delivery of PDU sets. Our solution is implemented within the ns-3 5G-LENA framework, and the paper features a comparative analysis through extensive simulations, evaluating the performance of our proposal against existing MAC scheduling algorithms. Results evince that the proposed scheduler enhances resource utilization, ensures QoS compliance, and improves network performance.

Resource scheduling plays a critical role in managing congestion in modern networks. This problem is inherently complex, as it requires timely decision-making where previous allocations influence future resource availability. Traditional scheduling policies aim to guarantee Quality-of-Service (QoS) while ensuring fair resource distribution. More recently, Machine Learning (ML)-based schedulers have been proposed to enhance scheduling efficiency. However, existing solutions fail to explicitly integrate Guaranteed Flow Bit Rate (GFBR) constraints into Reinforcement Learning (RL)-based scheduling decisions. In this paper, we propose Delay-Responsive Intelligent Learning for Latency-sensitive QoS (DRILL-Q), a novel RL based, QoS-aware Medium Access Control (MAC) scheduler designed to manage multi-flow traffic while explicitly ensuring GFBR compliance. DRILL-Q leverages a reward function that incorporates Packet Delay Budget (PDB), Head-of-Line (HOL) delay, and priority levels to optimize scheduling decisions. Simulation results demonstrate that DRILL-Q successfully prioritizes traffic based on its QoS requirements, while significantly improving Jain’s fairness among flows with the same 5G QoS Identifier (5QI) values. Compared to traditional and deterministic scheduling algorithms, DRILL-Q achieves superior resource allocation, reducing delay and jitter while maintaining higher throughput under congested network conditions.

In 5th Generation (5G) virtualized Radio Access Networks (vRAN), where the protocol stack is split across three different elements (Radio Unit (RU), Distributed Unit (DU), and Centralized Unit (CU)), and data is transmitted across the transport network (fronthaul (FH) and midhaul) between these units, the capacity of the fronthaul pipe can become a limiting factor for packet transmission. To address this challenge, state-of-the-art approaches have introduced several fronthaul control methods, designed to limit the sending rate, to ensure that it does not exceed the available capacity. However, it is important to avoid potential conflicts between the decisions of such fronthaul control methods and those of the Medium Access Control (MAC) scheduling schemes, as they might impact one another. In this context, we provide a thorough analysis of the trade-off between the fronthaul and the air interface limitations and we propose a new FH Control method to limit packet transmissions without altering the decisions made by the MAC scheduler. An extensive simulation campaign in a 3GPP defined scenario with Virtual Reality (VR) and Cloud Gaming (CG) services is conducted, for air-limited and air/fronthaul capacity-limited conditions. The results demonstrate that the proposed fronthaul control method efficiently coordinates the MAC scheduler decisions with the constraints imposed by the fronthaul capacity.

5G networks are designed to support a variety of services with highly demanding Quality-of-Service (QoS) requirements. This opened the door for novel extended reality (XR) media applications to emerge with 5G. However, recent 5G field tests and system-level simulation studies show that further XR enhancements are required to support a massive adoption of XR services in 5G networks. Such enhancements are expected to come into play with 5G-Advanced. In this line, we propose and study an XR loopback mechanism that adapts the XR traffic to the instantaneous 5G network conditions by exploiting an XR application feedback. We propose various XR loopback algorithms, strategies, and parameters’ configurations and study their impact on the 5G end-to-end performance. We conduct extensive simulation campaigns by building realistic end-to-end 5G network scenarios with 3GPP mixed XR traffic setups. Results show that the proposed XR loopback mechanism can boost XR performance in 5G networks by adapting to 5G network conditions, while keeping the XR QoS requirements under control. We provide various insights and practical directions on XR loopback design that allow us to take full advantage of the 5G network capabilities and progress toward 5G-Advanced network design.

When users exchange data with Unmanned Aerial Vehicles - (UAVs) over Air-to-Ground - (A2G) wireless communication networks, they expose the link to attacks that could increase packet loss and might disrupt connectivity. For example, in emergency deliveries, losing control information (i.e., data related to the UAV control communication) might result in accidents that cause UAV destruction and damage to buildings or other elements. To prevent these problems, these issues must be addressed in 5G and 6G scenarios. This research offers a Deep Learning (DL) approach for detecting attacks on UAVs equipped with Orthogonal Frequency Division Multiplexing - (OFDM) receivers on Clustered Delay Line (CDL) channels in highly complex scenarios involving authenticated terrestrial users, as well as attackers in unknown locations. We use the two observable parameters available in 5G UAV connections: the Received Signal Strength Indicator (RSSI) and the Signal to Interference plus Noise Ratio (SINR). The developed algorithm is generalizable regarding attack identification, which does not occur during training. Further, it can identify all the attackers in the environment with 20 terrestrial users. A deeper investigation into the timing requirements for recognizing attacks shows that after training, the minimum time necessary after the attack begins is 100 ms, and the minimum attack power is 2 dBm, which is the same power that the authenticated UAV uses. The developed algorithm also detects moving attackers from a distance of 500 m.

Network slicing is a fundamental feature of 5G systems to partition a single network into a number of segregated logical networks, each optimized for a particular type of service or dedicated to a particular customer or application. The realization of network slicing is particularly challenging in the Radio Access Network (RAN) part, where multiple slices can be multiplexed over the same radio channel and Radio Resource Management (RRM) functions shall be used to split the cell radio resources and achieve the expected behaviour per slice. In this context, this paper describes the key design and implementation aspects of a Software-Defined RAN (SD-RAN) experimental testbed with slicing support. The testbed has been designed consistently with the slicing capabilities and related management framework established by 3GPP in Release 15. The testbed is used to demonstrate the provisioning of RAN slices (e.g., preparation, commissioning, and activation phases) and the operation of the implemented RRM functionality for slice-aware admission control and scheduling.

Radio Access Network (RAN) slicing is a key technology, based on Software Defined Networks (SDN) and Network Function Virtualization (NFV), which aims at providing a more efficient utilization of the available network resources and the reduction of the operational costs. On that respect, in this demo a Wireless LAN hypervisor is presented that is based on a time variant scheduling mechanism and that is able to follow the dynamicity of the traffic variations seen by the different tenants in the network Access Points (APs). The work builds upon the 5G-EmPOWER tool kit, which is provided with SDN and NFV capabilities. During this demo it will be shown that the proposed hypervisor is able to dynamically assign, in every AP of the network, the appropriate resources per tenant according to their traffic requirements.

Heterogeneous Networks (HetNets) have been a crucial point in the evolution of mobile networks that boost the network performance with the addition of small cells. The use of Enhanced ICIC (eICIC) techniques such as Almost Blank Subframes (ABS) has been introduced in HetNets to protect the small cells from high interferences. However this is done at the expense of lower macrocell user capacities. In this respect, in this work we propose a solution that starts from the principles of ABS and exploits jointly the dimensions of frequency, power and time in order to balance the trade-off between interference mitigation on the small cells and capacity degradation in the macrocells. Comparisons against the classical ABS and other existing solutions show the efficiency of the proposed scheme with improvements in the user capacity that reach up to 26% in the considered scenarios. This is achieved without significant degradation of the performance observed by small cell users.

User demand for capacity and high data rate applications imposes the need for new technologies able to cope with these challenges. Fourth Generation cellular networks have set the initiative for a technology evolution that will surpass the constraints and provide better quality of service and improved performance. In this context, Heterogeneous Networks (HetNets) deployments, combining a variety of different cell sizes, are considered in the literature in order to enhance the coverage and capacity of cellular systems. However, they require enhanced techniques especially for the user-to-cell association, resource allocation and interference management processes. On that respect, in this work we present a novel scheme that exploits jointly the frequency, power and time dimensions for interference mitigation in order to balance the trade-off between interference reduction to small cell users and throughput degradation for macrocell users. Simulation results have shown that the proposed solution utilizes more efficiently the available resources compared to a conventional scheme and boosts the capacity up to 45%.