Efficient radio resource management (RRM) in 5G networks is increasingly challenged by the diverse quality of service (QoS) requirements of emerging applications and the growing uplink (UL) traffic from resource-constrained devices. Existing scheduling approaches often lack user and service-specific context, limiting their ability to guarantee timely and energy-efficient data transmission, particularly critical for the internet of things (IoT) and mission-critical services. In this work, we introduceQUEST, a QoS-aware UL scheduling framework that exploits the 5G QoS model alongside network and device context to efficiently allocate radio resources. Designed and evaluated in an indoor factory environment,QUESTsupports users with various heterogeneous 5QI services under dynamic multi-user conditions. Evaluation results, validated through both real-world measurements and 3GPP-compliant simulations, show thatQUESTconsistently outperforms traditional channel- and QoS-aware schedulers. It improves QoS compliance, reduces packet drops and serving time, and enhances energy efficiency. For users with stringent QoS demands, measurements show a 13% increase in successfully transmitted packets and a 6.2% reduction in delay for 50% of transmissions, compared to the best-performing baseline. Benchmarking against an optimal scheduler shows thatQUESTachieves the closest performance among baselines, while maintaining low complexity, making it a practical and scalable solution for 5G and beyond UL RRM.

Mobile users frequently face significant interruptions in transmission and reception during handovers from one Base Station (BS) to another, resulting in latencies that are incompatible with the stringent requirements of Ultra-Reliable Low Latency Communications (URLLC). To address this, 3GPP introduced a novel handover procedure, called Layer 1/Layer 2 Triggered Mobility (LTM), in Release 18. LTM uses lower level signaling to respond quicker to mobility events, bypassing the reconfiguration of higher layers while keeping modifications to the lower layers at a minimal level. This drastically reduces service interruptions during handovers, making them practically negligible. However, since LTM uses more frequent L1 measurements, it has a higher handover and ping-pong handover rates, as well as signaling overhead. In this work, we propose to incorporate future channel predictions in LTM to perform cell preparations and handover decisions with the goal of reducing signaling overhead and resource reservation. We focus on a controlled indoor scenario, where future user channel predictions are possible with a high accuracy. Our proactive algorithm reduces the cell preparation rate by 76 % and the handover rate by 72 %, without compromising the network sum throughput. Moreover, the resource reservation time at the target BS is reduced to nearly 0 ms.

Providing a high-quality real-time video streaming experience to mobile users is one of the biggest challenges in cellular networks. This is due to the need of these services for high rates with low variability, i.e., stable throughput, which is not easily accomplished given the competition among (an ever-increasing number of) users for limited network resources and the high variability of their channel conditions. A way to improve the user experience is by exploiting users’ buffers and the ability to provide a constant data rate to everyone, as one of the initially envisioned features of 5G networks. However, it was already shown that the latter is not very efficient, neither in terms of the achievable data rates nor in terms of the amount of resources left unused. In this paper, we provide a theoretical-analysis framework for resource allocation in 5G networks that leads to an improved user experience when watching live video while providing a constant video resolution at almost all times. We do this by solving four problems, in which the objectives are to provide the highest achievable video resolution to all single-class and multi-class users, and to maximize the number of users that experience a given video resolution. The analysis is validated by simulations that are run on publicly-available traces. We also compare the performance of our approach against other techniques for different Quality of Experience metrics. Results show that performance can be improved by at least 15% with our approach compared to state of the art.

Teleoperated robotic systems have become instrumental in advancing remote healthcare services, especially in tasks that require precision and expert oversight. The advent of cutting-edge telecommunication infrastructures, such as 5G, has amplified interest in these systems, although their full potential remains untapped. This study delves into the effectiveness of teleoperated robotic systems for medicine dispensing, comparing the performance of Wi-Fi and 5G networks in a transnational setup between two cities - Prague and Munich. We focus on the robot's ability to accurately dispense a predefined volume of a syrup-like substance, simulating a delicate healthcare operation, under the guidance of a distant operator. Our research examines the system's holistic performance in real-world implementation across diverse scenarios, encompassing varying network states and feedback methods. Two primary feedback scenarios are considered: one incorporating real-time video streaming and another offering explicit quantitative data on the dispensed volume. Using a blend of quantitative and qualitative methods, we aim to determine the influence of network type and feedback on task efficacy and user satisfaction. This study provides insights into the potential and hurdles of deploying teleoperated robotic systems in crucial healthcare contexts, guiding future advancements in this domain, especially in scenarios, where precision and dependability are crucial.

Advanced wireless communication networks provide lower latency and a higher transmission rate. Although this is an enabler for many new teleoperation applications, the risk of network instability or packet drop is still unavoidable. Real-time manipulator teleoperation requires data transmission with no discontinuity. Shared autonomy (SA) is a standard method to mitigate this issue. In this way, if the data from the remote side is unavailable, the controller can continue based on the previously observed models. However, due to the spatial gap between human and robot trajectories, indisputable fluctuations occur, which cause issues in teleoperation applications. This motivates us to propose a new skill refinement strategy to modify the previously trained skill and mitigate the sudden unwanted motions within the control takeover phase. To this end, our approach comprises applying the Hidden Semi-Markov Model (HSMM) and Linear Quadratic Tracker (LQT) in combination to learn and predict the user's intentions and then exploiting Coherent Point Drift (CPD) to refine the executable trajectory. We test our method both in simulation and in the real world for 2D English letter drawing and 3D robot-assisted feeding scenarios. Our experimental results using the Kinova® Movo platform show that the proposed refinement approach generates a stable trajectory and mitigates the control switching inconsistency. All comprehensive experiments and source code is available at: http://cxdcxd.github.io/SkillCPD.

In the next-generation wireless networks, energy efficiency (EE) is a fundamental requirement due to the limited battery power and the deployment of various devices in hardly accessible areas. While a plethora of approaches have been proposed to increase users’ EE, there are still many unresolved issues stemming mainly from the limited wireless resources. In this paper, we investigate the energy-efficient resource allocation, taking into account users’ radio resource control (RRC) state. We aim to achieve max-min fairness among users in an uplink orthogonal frequency-division multiple access (OFDMA) system while fulfilling data rate requirements and transmit power constraints. In particular, we avoid waste of the energy through unnecessary state transitions when no network resources are available. We study the impact of the RRC Resume procedure on users’ EE and propose allocating resources while users are in their current RRC Connected or RRC Inactive state. The solution is obtained from a constrained optimization problem, whose output is max-min fair and energy-efficient. To that end, we use generalized fractional programming and the Lagrangian dual decomposition approach to allocate the radio resources and transmission power iteratively. Using extensive realistic simulations with input parameters from measurement data, we compare the results of our approach against benchmark models and show the performance improvements RRC state awareness brings. Specifically, using our approach, the users’ EE increases by at least 10% on average.

5G radio access network (RAN) slicing envisions a solution to flexibly deploy heterogeneous services as slices sharing the same infrastructure. However, this level of flexibility renders slice isolation challenging, mainly due to the stochastic nature of wireless resources. In the state-of-the-art, RAN slicing algorithm’s efficiency with respect to slice isolation is related to the ability of meeting individual slice requirements. However, mostly an aggregated slice performance guarantee is considered instead of per user guarantees. Hence, state-of-the-art approaches might not always provide the satisfaction of all users within a slice. Indeed, our results demonstrate that if user requirements within a slice are not included in the RAN slicing algorithm, the per user quality-of-service (QoS) may not be fulfilled. In this paper, we investigate the definition of slice isolation as the ability to satisfy individual users’ throughput within slices, in a frequency selective, multi-cell wireless scenario with focus on maximizing slices’ throughput. Our problem is tackled with a Lyapunov optimization approach, which proves to always achieve slice isolation. Our results show that our solution does not only achieve 100% user QoS guarantees compared to 50% achieved in the state-of-the-art, but also doubles the throughput with increasing number of BSs.

The upcoming industrial revolution requires deployment of critical wireless sensor networks for automation and monitoring purposes. However, the reliability of the wireless communication is rendered unpredictable by mobile elements in the communication environment such as humans or mobile robots which lead to dynamically changing radio environments. Changes in the wireless channel can be monitored with frequent pilot transmission. However, that would stress the battery life of sensors. In this work a new wireless channel estimation technique, Veni Vidi Dixi, VVD, is proposed. VVD leverages the redundant information in depth images obtained from the surveillance camera(s) in the communication environment and utilizes Convolutional Neural Networks (CNNs) to map the depth images of the communication environment to complex wireless channel estimations. VVD increases the wireless communication reliability without the need for frequent pilot transmission and with no additional complexity on the receiver. The proposed method is tested by conducting measurements in an indoor environment with a single mobile human. Up to authors' best knowledge our work is the first to obtain complex wireless channel estimation from only depth images without any pilot transmission. The collected wireless trace, depth images and codes are publicly available.