Applications like extended reality (XR) are targeted for support in fifth generation (5G)-Advanced, and upcoming sixth generation (6G) wireless networks. XR often requires high data rates and stringent latency constraints, as well as reliability constraints. This PhD project proposes practical radio resource management (RRM) solutions to improve XR system capacity over 5G-Advanced networks with emphasis on the downlink performance, as well as a few aspects of uplink XR performance. XR services are expected to coexist with the existing traffic, and therefore, the greater part of the research in this thesis focuses on system-level effects for cases with a mixture of XR and enhanced mobile broadband (eMBB) traffic. Selected essential findings from the research have already been contributed to 5G-Advanced, while other findings would be relevant for the upcoming 6G standards.

First, new schemes for improving the link performance of XR to make it more radio resource efficient are developed. Specifically, enhanced link adaptation (LA) algorithms designed for code block group (CBG)-based transmissions are proposed. These algorithms aim to optimize the radio resource utilization while accurately controlling the CBG decoding failures for the XR user. Firstly, two low-complexity enhanced outer loop link adaptation algorithms are introduced to efficiently leverage the benefits of CBG-based transmission. Secondly, an enhanced channel quality indication (eCQI) scheme tailored for XR with CBG-based transmissions is proposed. The XR capacity assessments show that the enhanced LA schemes outperform baseline LA schemes by up to 50%. The improvements are more significant for XR cases with higher data rates and tighter packet delay budgets. Additionally, a computationally efficient method for implementation of the eCQI in the UE is proposed, making it feasible for practical implementation.

Secondly, this study proposes intra-cell user scheduling enhancements. This includes a survey of the XR application awareness (AA) framework that covers various aspects from vendor-specific solutions to 5G and 5G-Advanced standards. Fundamental enablers to acquire and use AA to enhance XR performance are identified. A novel AA scheduling algorithm is proposed that gains from incorporating protocol data unit (PDU)-set information. The proposed AA scheduling scheme is a practical feasible, heuristic, solution that significantly improves XR system capacity by 45% while preserving the eMBB cell throughput performance when compared with recognized baseline schedulers. This underscores the effectiveness of integrating the AA framework into network RRM. It is also found that adding XR users to the network with eMBB users yields a linear loss of eMBB throughput and the loss depends on the XR source data rate. On the other hand, the addition of eMBB into an XR-only network notably reduces the overall XR capacity.

Thirdly, the thesis advances to inter-cell enhancements. In particular, the problem of eMBB-generated inter-cell interference (ICI) on XR performance is studied and RRM enhancements to address this issue are proposed. A novel proactive quality of service (QoS)-aware dynamic inter-cell interference coordination (ICIC) technique is proposed. The proposed ICIC method identifies XR UEs at risk of failing to meet their QoS requirements (due to ICI) by assessing performance metrics such as delay, dominant interference ratio, and signal-to-interference-plus-noise ratio (SINR). The scheme then instructs the potential aggressive cell(s) to stop their transmissions for a certain time window, thereby mitigating the resulting ICI. The developed ICIC solution improves the XR capacity by up to 32% while causing a reasonable decrease in the average eMBB cell throughput of 22%. Finally, this thesis also investigates the optimization of key parameters for the UEs’ transmit power control (PC) to facilitate the coexistence of XR and eMBB users, as well as controlling the uplink ICIC via such PC optimizations.

After the defence there will be a small reception at Fredrik Bajers Vej 7B2-104