Fine Grained vs Coarse Grained Channel Quality Prediction: A 5G-RedCap Perspective for Industrial IoT Networks

The article evaluates the effectiveness of coarsegrained channel quality prediction (CQP) for 5G-RedCap/5G NR-Light devices within industrial IoT (IIoT) networks. Finegrained predictions refine real-time communication, enhancing throughput and reducing resource utilization (RU), albeit with increased computation complexity. In contrast, coarse-grained CQP offers low computational overhead while optimizing longterm network characteristics, such as redundancy planning. Our study investigates the potential applications of coarsegrained CQP in real-time communication within the IIoT context, aiming to enhance the efficiency of simple devices (5G-RedCap) without adding the computational overhead. The varying traffic profiles and quality of service levels across diverse 5G use cases, including massive Machine Type Communication (mMTC), enhanced Mobile Broadband (eMBB), and Ultra-Reliable and Low Latency Communication (URLLC), present different challenges. For mMTC devices, coarse-grained CQP demonstrates comparable RU gains to fine-grained CQP (with up to a 50% reduction in RU), showcasing its effectiveness without added complexity. However, in the eMBB scenario, where throughput is paramount, it yields only marginal improvements. Similarly, RU gains for URLLC devices are negligible due to their stricter QoS requirements. The effectiveness of coarse-grained CQP is intricately linked to the variability in experienced channel quality across different scenarios within an indoor IIoT network. This research underscores the potential of AI applications for enhancing the performance of simple 5G-RedCap/5G NR-Light devices without compromising device complexity