3GPP continued to evolve 5G-Advanced in Release 19, enhancing a range of business-driven features and introducing a series of innovations, further strengthening 5G capabilities. Through forward-looking research on channel modeling, it serves as a bridge to 6G.
1. MIMO, a cornerstone of 5G technology, was introduced in Release 19 with the fifth stage of its evolution, designed to improve beam management accuracy and efficiency. Release 19 supports user equipment-initiated beam reporting, allowing user equipment to trigger reports without relying on base station (gNB) requests. Another key enhancement in Release 19 is the expansion of the number of CSI reporting ports from 32 to 128, enabling better support for larger antenna arrays. This is crucial for scaling MIMO systems in high-capacity scenarios. Coherent joint transmission capabilities have been enhanced to address challenges in non-ideal synchronization and backhaul scenarios (such as inter-site coherent joint transmission). Release 19 also introduced new measurement and reporting mechanisms to address time misalignment and frequency/phase offset between Transmitter Relays (TRPs). To further improve uplink throughput, Release 19 enhances the non-coherent uplink codebook for UEs equipped with three transmit antennas. Furthermore, asymmetric configurations are supported, where a UE receives downlink transmissions from a macro base station while simultaneously sending data to multiple micro TRPs in the uplink. These configurations include enhanced power control mechanisms and path loss adjustments to optimize performance in heterogeneous network environments.
2. Mobility management is another key focus in Release 19. Specifically, extended LTM, originally introduced in Release 18 for intra-CU (Central Unit) mobility, expands support for inter-CU mobility, enabling smoother transitions between cells associated with different CUs. To further optimize mobility, Release 19 introduces conditional LTM, combining the advantages of LTM's reduced outage time with the reliability of CHO. Furthermore, event-triggered Layer 1 measurement reporting reduces signaling overhead compared to periodic reporting. Combining CSI reference signal (CSI-RS) measurements with SSB measurements enhances mobility performance.
3. The evolution of NR NTN continues in Release 19, with 3GPP defining new reference satellite payload parameters to account for the reduced equivalent isotropically radiated power (EIRP) density per satellite beam compared to previous releases. To accommodate the reduced EIRP, this release explores downlink coverage improvements. Given the expected large number of user equipment (UE) within satellite coverage, Release 19 also aims to increase uplink capacity by incorporating orthogonal cover codes into the DFT-s-OFDM-based PUSCH. To support MBS within NTNs, 3GPP enhances MBS by defining a signaling mechanism for specifying target service areas. Another major advancement in Release 19 is the introduction of a regenerative payload feature, enabling 5G system functions to be implemented directly on the satellite platform. Unlike the transparent payload supported in previous releases, regenerative payloads allow for more flexible and efficient NTN deployments. Furthermore, NR NTN is evolving to support RedCap user equipment (UE).
4. 5G-Advanced is optimized to better accommodate XR applications, including enabling transmission and reception during gaps or restrictions caused by RRM measurements and RLC acknowledgment modes. Furthermore, Release 19 explores improvements to PDCP and uplink scheduling mechanisms, with a particular focus on integrating latency information. 3GPP is also researching technologies to more efficiently support XR applications, ensuring they meet the diverse and stringent QoS requirements associated with multimodal XR use cases.
5. AI/ML: At the NG-RAN architecture level, 3GPP is leveraging AI/ML to address more use cases in Release 19. One new use case is AI/ML-based network slicing, where AI/ML is used to dynamically optimize resource allocation across different network slices. Another area of focus is coverage and capacity optimization, leveraging AI/ML to dynamically adjust cell and beam coverage, a technique commonly known as cell shaping.
6. Functional Enhancements include:
