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 Adhoc Networks (MANETs) provide a flexible and infrastructure-free communication framework, making them suitable for smart indoor environments. They enable direct, dynamic, and efficient communication between devices. Nevertheless, achieving optimal routing in such environments remains a critical challenge. Routing plays a critical role in MANETs, as it ensures efficient packet delivery between mobile and dynamically connected nodes. In this paper, we propose and evaluate a novel routing protocol for MANETs, specifically designed for Smart Home networks. Our approach is tailored to address the unique challenges of smart indoor environments, such as energy efficiency, latency, and adaptability to topology changes. Through extensive simulations, we demonstrate that our protocol significantly outperforms state-of-the-art routing protocols, decreasing power consumption by$\text{5 0. 4 6 \%}$, control overhead by 60.85 %, while maintaining end-to-end delay, making it a promising solution for Smart Home networks.

The rising number of connected Internet of Things (IoT) devices in 5G networks and the standardization of the 3GPP reduced capability (RedCap) devices, turn the IoT energy efficiency into a topic of paramount importance for 5G. The design goals and use cases of RedCap devices highlight the need for long device battery life due to the infeasibility of replacing batteries. With the focus emerging on sustainable networks, battery lifetime prediction becomes essential. Therefore, in this paper, we propose and evaluate a Markov Chain based energy consumption model suitable for IoT devices in 5G networks, especially RedCap devices. We design a realistic model consistent with the procedures described in 3GPP standardization, mainly focused on the uplink transmission procedures. The proposed model is validated through extensive analysis with varying interarrival times (IAT) of the uplink traffic. For short IAT, the analytical results show a decrease of 33% in energy consumption and 89% in transmission latency. This demonstrates that our model can be applied to evaluate battery life for a broad range of IoT devices.

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.