# 3 GPP Evolution from 5G to 6G: A 10-Year Retrospective
## 6 3 GPP Release 19: 5G-Advanced Growth
### New Features  
The Release-18 study on AI/ML for NR established a framework, as illustrated in Figure 6. Following this, Release 19 introduces support for one-sided AI/ML models, applicable to either the UE or network side [80]. Signaling and protocol aspects related to lifecycle management are specified, facilitating AI/ML-based beam management and positioning for both UE- and network-sided models, and AI/ML-based CSI prediction for UE-sided models. Besides these normative advancements, Release 19 continues to study CSI compression which relies on two-sided AI/ML models involving a UE-side encoder and a network-side decoder, as well as AI/ML for mobility.  
SBFD introduces simultaneous downlink and uplink coexistence within the TDD carrier, blending TDD and FDD techniques [81]. By configuring an uplink subband during the time allocated for legacy downlink symbols in TDD, SBFD extends uplink duration, improving uplink coverage and latency [82]. Release 19 specifies the time and frequency locations of SBFD subbands and establishes procedures for transmission, reception, measurement, and random access in SBFD symbols. Additionally, Release 19 addresses CLI which arises when one node transmits while another receives on the same frequency band.  
UE power usage can be reduced if the UE wakes up only when it detects an LP-WUS from the gNB. This approach involves equipping the UE with a low-power wake-up receiver (LP-WUR) alongside its main radio [83]. The LP-WUR activates the UE's main radio upon detecting an LP-WUS from the gNB. Release 19 specifies the LP-WUS design and procedures [84]. For UEs in idle/inactive mode, configurations enable LP-WUS to activate the main radio for paging monitoring, relax serving and neighbor cell measurements for RRM, and offload serving cell RRM measurements to the LP-WUR. For UEs in connected mode, procedures are specified to allow LP-WUS to trigger the main radio for monitoring PDCCH.  
Ambient IoT technology represents a significant leap in enabling ultra-low-power and battery-less IoT devices [85]. These devices operate by harvesting energy from ambient sources, such as radio waves, and use backscatter communication to transmit data. This approach reduces complexity and power consumption, making it ideal for applications like asset tracking and environmental monitoring. The Release-19 work item standardizes the ambient IoT framework, focusing on deployment in the licensed FR1 spectrum, with devices consuming as little as 1 \(\upmu\)W of power [86]. It includes specifications for communication protocols, lightweight signaling, and RF requirements tailored to ambient IoT. This new  
Figure 6: An illustration of 3 GPP AI/ML framework for NR.  
feature expands the potential of cellular IoT, complementing existing 3 GPP low-power wide-area technologies like NB-IoT [87] and LTE machine-type communication (LTE-M) [88].  
Release 19 introduces wireless access backhaul (WAB) nodes to enhance 5G RAN topologies to support broader use cases, such as providing connectivity for onboard UEs in aircraft, ships, or vehicles in remote areas. A WAB node includes CU, distributed unit (DU), and mobile termination (MT) functions, unlike IAB nodes, which support only DU and MT functions. Key areas of focus include WAB architecture, protocol enhancements, and resource multiplexing.  
5G femtocell extends the concept of LTE hone eNB, delivering NR access at home and enterprise premises [89]. It provides cost-effective indoor coverage and offloads macro gNB traffic. Release 19 specifies NR femtocell architecture with optional femto-gateway for NG interface and access control in open, hybrid, and closed modes.