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  3GPP Release 18 is the first 5G-Advanced release, focusing on AI/ML integration, ultimate performance in XR/Industrial IoT, mobile IAB, enhanced positioning, and spectrum efficiency up to 71GHz. RAN1 further promotes AI/ML enhancements in RAN optimization and artificial intelligence (PHY/AI) through physical layer evolution.   I. Key Features of RAN1 (Physical Layer and AI/Machine Learning Innovations)   1.1 MIMO Evolution: Multi-panel uplink (Level 8), MU-MIMO with up to 24 DMRS ports, multi-TRP TCI framework.   Operating Principle: Extends Type I/II CSI reporting through a unified TCI framework across multiple TRP panels. The gNB schedules up to 24 DMRS ports for MU-MIMO (12 in Rel-17), enabling each UE to use Level 8 UL links; DCI indicates joint TCI status; UE applies phase/precoding across panels. Progress: The lack of unified signaling in Rel-17 multi-TRP resulted in a 20-30% loss of spectral efficiency in dense deployments; level restrictions limited the UL throughput of each UE to layers 4-6, thereby achieving a 40% increase in uplink (UL) capacity for stadiums/music festivals.   1.2 AI/ML Applications to CSI Feedback Compression, Beam Management, and Positioning.   Working Principle: The neural network uses an offline-trained codebook to compress Type II CSI (32 ports → 8 coefficients). The gNB deploys the model via RRC; the UE reports the compressed feedback. Beam prediction uses the L1-RSRP mode to pre-position beams before handover. Project Progress: CSI overhead consumed 15-20% of DL resources; in high-mobility scenarios (e.g., highways), beam management failure rates reached as high as 25%. Improvement Results: Channel State Information (CSI) overhead reduced by 50%, handover success rate improved by 30%. 1.3 Enhanced Coverage (Uplink full-power transmission, low-power wake-up signal).   Operating Principle: The gNB sends a signal to the UE, enabling it to apply full power output across all uplink layers (without tiered power backoff). An independent low-power wake-up receiver (duty cycle controlled, sensitivity -110dBm) receives the wake-up signal (WUS) before the main receive cycle. The WUS carries 1 bit of indication information (monitoring PDCCH or sleep). Project Progress: Rel-17 uplink coverage is limited by tiered power backoff (4th order MIMO loss of 3dB); the main receiver consumes 50% of the UE's power during DRX monitoring. Improvements: Uplink coverage extended by 3dB; IoT/video streaming applications saved 40% of power. 1.4 ITS Band Sidelink Carrier Aggregation (CA) and Dynamic Spectrum Sharing (DSS) with LTE CRS.   Operating Principle: Sidelink supports CA across the n47 (5.9GHz ITS) + FR1 bands; supports autonomous resource selection for Type 2c coordination among UEs. Due to a round-trip time (RTT) greater than 500 milliseconds, NTN IoT disables HARQ (only supports open-loop repetition); pre-compensation is implemented for the Doppler effect in DMRS. Project Progress: Rel-17 Sidelink only supports single-carrier (50% throughput loss); NTN IoT HARQ timeouts result in 30% packet loss. Improvements: V2X formation sidelink throughput is increased by 2x, and NTN IoT reliability reaches 95%. 1.5 Extended Reality (XR)/Multi-sensor Communication (High Reliability, Low Latency Support).   Operating Principle: New QoS procedure, latency budget less than 1 millisecond, supports multi-sensor packet tagging (video + haptic + audio stream). gNB prioritizes data through a preemption mechanism. UE reports attitude/motion data for predictive scheduling. Project Progress: Rel-17 XR support only supports unicast; haptic feedback latency exceeds 20 milliseconds (unusable for remote operation). Improvements: End-to-end latency of AR/VR + haptic in industrial remote control is less than 5 milliseconds.   1.6 NTN Functionality Enhancement (Smartphone Uplink Coverage, Disabling HARQ for IoT Devices).   How it Works: Rel-18 improves the uplink coverage of smartphones in non-terrestrial networks (NTNs) by optimizing physical layer transmission, allowing for higher transmit power and better link budget management to accommodate satellite channels. For IoT devices on NTNs, traditional HARQ feedback is inefficient due to long satellite round-trip times (RTTs), therefore HARQ feedback is disabled, and an open-loop repetition scheme is adopted instead. Project Progress: Previously, due to insufficient power control and link margin, the uplink coverage of smartphones on NTNs was limited, resulting in poor connectivity. HARQ feedback caused throughput reduction and latency issues for IoT devices due to satellite latency. Disabling HARQ eliminates feedback latency and improves the reliability of constrained IoT devices. This enables robust global connectivity for IoT and smartphones beyond terrestrial networks. II. RAN1 Project Applications Dense Urban XR (Multi-TRP MIMO technology reduces AR/VR latency to below 1 millisecond); Industrial Automation (AI/ML beam prediction reduces handover failure rate by 30%); V2X/High Mobility (Sidelink CA improves reliability).   III. RAN1 Project Implementation gNB PHY (Base Station Physical Layer): Integrates an AI model for CSI compression (e.g., neural networks predict Type II CSI based on Type I CSI, reducing overhead by 50%). Deploys Multi-TRP TCI via RRC/DCI and uses 2 TAs for uplink timing. Terminal Equipment (UE): Supports low-power wake-up receivers (independent of the main RF link) for DRX alignment signaling.

  RAN3 Release 17 focuses on major evolutions in 5G (NR), bringing enhancements to key architectures such as native multi-access edge computing (MEC) support, the introduction of reduced-capacity RedCap for IoT, enhanced sidechains, positioning and MIMO, and increased support for new frequency bands (up to 71 GHz) and non-terrestrial NTN. All of these improvements are built upon core network function evolution to enhance spectrum efficiency and device power saving, enabling broader 5G applications.   I. Key Features of RAN3 in Release-17 IAB Function Enhancements—Improved resource reuse, topology robustness, and routing options between IAB parent and child links. NTN (Non-Terrestrial Network) Architecture—System architecture supports integration of satellite/HAP with terrestrial 5G (NR). NPN (Non-Public Network) Enhancements and Edge Computing Integration Support. II. Key Technical Details and System Integration of RAN3   2.1 Enhanced IAB (Integrated Access and Backhaul) Technology Resource Reuse: Rel-17 defines additional mechanisms that enable IAB nodes to allocate resources more flexibly between access (to UE) and backhaul (to child IAB nodes) based on existing scheduling. Specifically: Updating F1/Xn internal signaling between the parent node and the IAB-DU/MT. Achieving robust path management and rerouting—the IAB control plane (IAB-CU) must be able to reallocate provider relationships in the event of link failure. Topology and Routing: Support for semi-static routing table updates and enhanced bearer mapping; vendors need to test congestion/priority rules for backhaul and access traffic. 2.2 NTN Architecture   GW and NG-RAN Integration: Rel-17 defines NTN Stage 2/Stage 3 architectural changes to support satellite link features end-to-end. Implementers must coordinate with the CN (SA/CT) to support PDU sessions and mobility differences (such as longer handover times due to GEO/LEO satellite movement).   Timing and Synchronization: NTN nodes typically require GNSS/time distribution (or alternative time synchronization) and specific handling of timing advance and HARQ timers within the RAN architecture is necessary.

  RAN2's 5G work focuses on consolidating and enhancing the concepts and functions introduced in R16, while adding new system features; improving vertical industry applications including positioning and dedicated networks; advancing short-range (direct) communication between terminal devices in the field of autonomous driving (V2X) for Internet of Things (IoT) support; improving support for multiple media (codecs, streaming media, broadcast) related to the entertainment industry; and improving support for mission-critical communications. Furthermore, it improves several network functions (such as network slicing, flow control, and edge computing). The specific key points regarding the radio interface architecture and protocols (such as MAC, RLC, PDCP, SDAP), radio resource control protocol specifications, and radio resource management processes under the responsibility of 3GPP RAN2 are as follows:   I. Key Features of RAN2 Rel-17: Sidelink Enhancements (Relay, Multicast, V2X Functionality Extensions). RedCap Protocol Support (Lightweight RRC Status, Energy Saving, Feature Set Reduction). QoE/slice control enhancements and mobility handling (slice improvements and ATSSS interaction). Location enhancement procedures (new measurement methods and reference signal usage). II. Rel-17 Implementation Impact and Details   2.1 Sidelink Enhancements (Relay, Multicast, V2X Functionality Extensions) RRC message and MAC/PHY multiplexing changes; new Sidelink relay (L2/L3) multicast and group management procedures. In application: Extended sidelink control channel processing and HARQ management for relay nodes, RC upgrade to support Sidelink configuration lists, group identifiers, and security context distribution. Resource allocation enhancements support scheduling and autonomous resource selection and add an RRC TLV field for authorization timing and reservation windows. 2.2 RedCap and RRC Reduced RRC complexity: RedCap devices may support fewer RRC states and optional functions (e.g., limited measurements). RAN2 specifies capability signaling and fewer RRC IEs; implementers must ensure that the gNodeB's RRC can handle capability-limited UEs without affecting normal UE processing. Energy-saving timers and RRC inactive: Tight integration with MAC and DRX to optimize power consumption; the scheduler supports longer DRX cycles and fewer grant allocations. 2.3 Location and Measurement Rel-17 introduces new measurement types and reporting formats to improve the application of PRS/CSI-RS in location. Implementation requires changes to UE measurement reports (RRC measurement objects and reports) and the LPP/NRPPa interface of the location server. ​