6G Resource Allocation for Carrier Aggregation

Sammanfattning

Future 6G networks must flexibly combine spectrum fragments spread across low-, mid- and centimetre-wave bands to satisfy extreme capacity and latency demands. Carrier Aggregation is one of the key enablers, yet its practical gain is constrained by uplink-downlink coupling, secondary cell setup delay, user-equipment power limits and tight real-time scheduling budgets. This thesis develops a time-dynamic, discrete-event system-level simulation framework to quantify how carrier aggregation and multi-band coordination improve user throughput performance, and how non-ideal factors listed above shape user throughput in a realistic 3-band urban-macro multi-cell deployment. Heuristic scheduling schemes are proposed including traffic steering by bitrate-proportional traffic split, load-balancing by redistribution, Resource Blocks allocation by Longest-Wait-First and Largest-Deficit-First schedulers, and Opportunistic Cross-Band Filling as complement. Results show that Carrier Aggregation and multi-band coordination can effectively raise user throughput compared with non-Carrier Aggregation baseline, as traffic split and redistribution perform load-balancing while lifting spectrum efficiency. Carrier Aggregation also mitigates uplink-downlink coupling penalties compared with non-Carrier Aggregation mode by steering traffic from adverse primary-cell to secondary-cells. Nevertheless, secondary cell setup delay still diminishes the performance gain performed by reducing the fraction of traffic served by Carrier Aggregation. In addition, serving-node centric scheduling allows more flexible multi-band coordination but whether or not it can benefit the user throughput is dependent on the scheduling algorithms used. The thesis concludes that Carrier Aggregation effectively improve user throughput even considering non-ideal factors when it is paired with adaptive traffic steering and fairness-conscious multi-band scheduling. The proposed framework offers a benchmark for designing intelligent radio-resource-management algorithms in future 6G mobile systems.