With the introduction of Reduced Capability (RedCap) and enhanced Reduced Capability (eRedCap) user equipment, 3GPP Release 17 and 18 define new device categories aimed at bridging the gap between traditional 5G New Radio (NR) devices and IoT-oriented solutions such as NB-IoT and eMTC. These categories target mid-tier applications, such as process / asset monitoring and electricity distribution automation, which demand reduced device complexity, high energy efficiency, and moderate data rates. In this paper, we present a comprehensive simulation-based performance analysis of 5G RedCap and eRedCap using an extended ns-3 5G-LENA implementation. Our simulation framework integrates an energy consumption model and Bandwidth Part-aware scheduling, calibrated with empirical energy measurements from commercial RedCap devices, ensuring realistic and reproducible simulation results. While eRedCap shows improved energy efficiency due to its reduced 5MHz bandwidth, its overall battery lifetime and latency tend to be inferior in practical scenarios. Our analysis proposes optimized eDRX power consumption and Release Assistance Indication (RAI) for (e)RedCap to reflect future energy-saving potential. Our results highlight both the limitations and opportunities of (e)RedCap in expanding the 5G ecosystem towards 6G.

Traditional cellular Radio Access Networks (RANs) are associated with significant costs and low agility due to proprietary hard- and software resulting in vendor lock-ins. Open RAN promises to change this by harnessing open source and Commercial of-the-Shelf (COTS) solutions. Hence, the Open Radio Access Network (O-RAN) Alliance, a consortium of partners from industry and research, aims to identify and close gaps in 3rd Generation Partnership Project (3GPP) specifications. It defines a software-centric RAN architecture with open interfaces to increase interoperability, strengthen innovation and lower market entry barriers towards future 6G infrastructures. A core concept in this context is the near-Real-Time RAN Intelligent Controller (RIC), a virtual platform for hosting so-called xApps. These software-based network functions provide functionalities such as monitoring or network slicing. Using proactive resource management, slicing has the potential of enabling concurrent service profiles such as Ultra-Reliable Low Latency Communication (URLLC) and Enhanced Mobile Broadband (eMBB), while rising spectral efficiency and lowering latency. Thus, this work introduces an O-RAN-based framework for predictive uplink slicing. An xApp is presented, harnessing deep learning to dynamically reconfigure RAN scheduling via the RIC's E2 interface. The evaluation is performed on the challenging example of URLLC traffic from the Smart Grid domain via an experimental laboratory setup. O-RAN introduces additional interfaces, yet the framework performs about on par with proprietary solutions with latencies down to 5 ms.

Mission critical applications in domains such as Industry 4.0, autonomous vehicles or Smart Grids are increasingly dependent on flexible, yet highly reliable communication systems. In this context, Fifth Generation of mobile Communication Networks (5G) promises to support mixed-criticality applications on a single unified physical communication network. This is achieved by a novel approach known as network slicing, that promises to fulfil diverging requirements while providing strict separation between network tenants. We focus in this work on hard performance guarantees by formalizing an analytical method for bounding response times in mixed-criticality 5G network slicing. We reduce pessimism considering models on workload variations.

The 5th generation of mobile communication networks (5G) introduced the concept of network slicing for enabling multiple, diverging service types by providing virtually independent communications within one physical network. While Ultra-Reliable Low Latency Communication (URLLC) aims to provide latency guarantees below 5 ms for mission-critical applications such as Smart Grid as well as Industry 4.0, Enhanced Mobile Broadband (eMBB) focuses mainly on high data rates. Thus, since different Key Performance Indicators (KPIs) such as latency, data rate or time-criticality need to be considered, the allocation of resources between corresponding slices is challenging. This work, therefore, aims to reduce uplink latency for URLLC transmissions by deploying Proactive Grants to minimize the impact of time-consuming scheduling requests and any negative influence on other slices. Resources are allocated proactively by base stations utilizing Machine Learning (ML) models trained on real-world measurements. An experimental evaluation via an Software-Defined Radio (SDR)-based physical testbed demonstrates delay reductions towards the mission-critical threshold while simultaneously increasing spectral efficiency. Compared to Round Robin (RR)-based slicing, latency decreases by 49 %, while maintaining a high throughput of 98 % in the eMBB slice.

Mission critical applications in domains such as Industry 4.0, autonomous vehicles or smart grids are increasingly dependent on flexible, yet highly reliable communication systems. The Fifth Generation of mobile Communication Networks (5G) promises to support critical communications on a single unified physical communication network through a novel approach known as network slicing. We focus in this work on context-based hard performance guarantees by formalizing an analytical method for bounding response times in critical systems. This approach allows to consider different contexts based on models of degradation of channel quality, and avoids a global highly pessimistic worst-case bound computed for worst possible channel conditions. We demonstrate that the proposed method for computing context-based response times guarantees successfully bounds results obtained in realistic mobility scenarios using a machine-learning based 5G simulation framework.

Multiple service types such as Ultra-Reliable Low Latency Communication (uRLLC) and Enhanced Mobile Broadband (eMBB) are envisioned to be incorporated into the next generation mobile communication standard 5G based on a single physical communication network. To unite these services with partly contradicting Quality of Service (QoS) requirements, Network Slicing is considered a key technology. uRLLC slices in particular are highly demanding, requiring extremely high reliability and low latency in the single-digit milliseconds range. Consequentially, the latency impact of radio resource management on the end-to-end latency is optimized in this work by using so-called Configured Grants (CGs), which aim to minimize latency-intensive scheduling requests by pre-allocating radio resources. As predicting future traffic demands and channel conditions are required to use CGs, a data-driven machine learning-based radio resource scheduler prototype is introduced and evaluated in this work based on a specifically developed 5G radio resource simulator. The results show promising latency optimizations and possible trade-offs in uRLLC and eMBB coexistence.