5G-Advanced represents a transformative leap in wireless technology, building on the foundation of 5G to deliver unprecedented capabilities in network intelligence, smart connectivity, industrial automation, and advanced slicing.
This article explores the technical foundations of 5G-Advanced, its real-world applications across industries, and its role as a bridge to the 6G era.
1. The Evolutionary Path to 5G-Advanced
1.1 From 5G NSA to Standalone: building the foundation
3GPP Release 15 (2018) introduced 5G Standalone (SA), which previously established 5G Non-Standalone (NSA) architecture leveraging existing 4G LTE infrastructure. While early deployments focused on enhanced mobile broadband (eMBB) with peak speeds up to 20 Gbps for downlink, limitations in latency (30-50 ms) and reliability restricted industrial applications.
Etisalat by e& commercially launched 5G SA for Fixed Wireless Access (FWA) and smartphones. This deployment leverages the full potential of 5G SA to provide high-speed connectivity, which is crucial for eMBB applications like high-definition video streaming and virtual reality experiences. Additionally, Etisalat by e& demonstrated a remarkable 5G SA speed of 13.2 Gbps at GITEX Global 2023, showcasing the advanced capabilities of 5G SA networks for eMBB use cases.
3GPP Release 16 (2020) marked a pivotal shift by introducing Ultra-Reliable Low Latency Communication (URLLC) with 1 ms latency and 99.9999% reliability, Time-Sensitive Networking (TSN) for industrial automation, and advanced Network Slicing capabilities
South Korea's deployment of cloud gaming services, such as those by SK Telecom, demonstrated the potential of these enhancements by achieving low latency, which is crucial for real-time gaming applications. South Korea has been at the forefront of leveraging URLLC for low-latency applications.
3GPP Release 17 (2022) expanded support for Non-Terrestrial Networks (NTN), enabling satellite-based 5G connectivity. This development is crucial for extending 5G services to remote and underserved areas and supporting IoT deployments globally.
STC in Saudi Arabia has partnered with Omnispace to develop satellite-to-phone communications using space-based 5G connectivity. This initiative aims to provide cost-effective broadband connectivity beyond traditional land-based networks, benefiting sectors like agriculture and finance. Additionally, MediaTek, Eutelsat Group, and Airbus Defence and Space successfully trialed 5G-Advanced NR NTN technology over OneWeb LEO satellites, paving the way for future satellite and terrestrial interoperability.
1.2 The 5G-Advanced Milestone
This next evolutionary phase – standardized through 3GPP Release 18 (2024) and Release 19 (2025) – introduces significant new technical specifications including improvements in satellite access, IoT, energy efficiency, AI/ML, and XR technologies that fundamentally reshape how enterprises operate, and consumers interact with technology.
𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲:
XR Pro enhancements enable truly immersive environments through:
Split Rendering Architecture: Offloading GPU processing to edge nodes, enabling lightweight AR glasses (sub-100g) with 8-hour battery life.
Haptic Codecs: Nokia's Immersive Voice and Audio Services (IVAS) standard delivers 3D spatial audio with 16-bit/48 kHz quality at 32 kbps bitrates.
Volumetric Video Streaming: 6DoF (6 Degrees of Freedom) streaming at 200 Mbps enables holographic telepresence for remote collaboration.
𝗘𝘅𝗽𝗮𝗻𝘀𝗶𝗼𝗻:
Positioning and Timing revolution enables:
Smart Grid Synchronization: μs timing accuracy across power distribution networks, preventing cascading failures through phasor measurement.
Automated Warehouse Navigation: AGVs (Automated Guided Vehicles) with 5 cm positional accuracy achieve 99.9% inventory tracking accuracy.
NTN enhancements bridge the digital divide through:
Hybrid Satellite-Terrestrial Networks: GEO satellites backhaul rural base stations, delivering 50 Mbps broadband to 95% of Earth's surface
Energy-Harvesting IoT: Backscatter communication enables battery-free sensors using ambient RF energy from 5G signals. Reduced Capability (RedCap) devices with 75% lower power consumption.
Disaster Recovery: Portable NTN gNBs restore connectivity within minutes of infrastructure damage through drone-deployed temporary cells.
𝗘𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲:
Operational Excellence introduces:
RAN Intelligent Controller (RIC): Real-time AI optimization of Beam management (30% coverage improvement), Traffic prediction (85% accuracy for load balancing), and Energy savings (40% reduction during low usage).
Network Digital Twin: Physics-based modeling simulates 5G-Advanced deployments with 94% prediction accuracy for coverage/capacity/predictive maintenance.
Automated Slicing: Dynamic resource allocation adjusts slice parameters every 50 ms based on application demands across multi-vendor environments.
These capabilities tend to position 5G-Advanced as the first cellular standard explicitly designed for vertical industry transformation rather than just consumer connectivity.
2 The Technical Foundation of 5G-Advanced
2.1 Architectural Evolution From 5G to 5G-Advanced
The transition from conventional 5G to 5G-Advanced marks a paradigm shift from connectivity-focused networks to intelligent systems integration.
5G-Advanced embeds Artificial Intelligence directly into the Radio Access Network (RAN) and core architecture through several critical advancements:
𝗔𝗜-𝗡𝗮𝘁𝗶𝘃𝗲 𝗔𝗶𝗿 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲
Signal-to-Noise ratio improvement through convolutional neural networks (CNNs)
Modulation and Coding Scheme (MCS) optimization through reinforcement learning algorithms
Optimal beamforming configurations prediction through graph neural networks
𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗦𝗲𝗻𝘀𝗶𝗻𝗴 𝗖𝗼𝗻𝘃𝗲𝗿𝗴𝗲𝗻𝗰𝗲:
Signal Design and Optimization through AI algorithms for Integrated Sensing and Communication (ISAC) enabling dual radar/connectivity functions using existing mMIMO arrays
Advanced Signal Processing through ML models to extract information from received signals for both sensing and communication. ML models can improve the accuracy of target detection and tracking by analyzing complex signal patterns.
Sub-10 cm 3D positioning accuracy through AI algorithms used to fuse data from different sources (e.g., GPS, cellular signals) and apply advanced filtering techniques to improve positioning accuracy. ML models predict and correct errors in phase measurements
Predictive Maintenance and Anomaly Detection through AI applications by analyzing time-series data from microsecond-level synchronized measurements. AI models can identify potential faults and optimize grid operations for smart grid phasor measurement and high-frequency trading applications.
𝗘𝗻𝗲𝗿𝗴𝘆-𝗔𝘄𝗮𝗿𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻:
RAN energy consumption reduction through AI algorithms used to analyze network traffic patterns and predict periods of low demand. This information is then used to dynamically switch RAN components into sleep modes, reducing energy consumption without compromising network performance. AI-driven solutions can optimize energy efficiency by up to 12% annually by identifying optimal times to put cells into sleep mode based on real-time demand.
AI-Driven Predictive Modeling: Network digital twins utilize AI to simulate and predict energy consumption patterns with high accuracy. This involves using real-time data from the physical network to model its behavior and optimize energy usage. AI algorithms help in identifying inefficiencies and proposing modifications to improve sustainability and reduce emissions.
𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗦𝗹𝗶𝗰𝗶𝗻𝗴 2.0:
Real-Time Resource Allocation: AI algorithms are used to analyze network conditions and SLA requirements in real-time. This information is then used to dynamically adjust slice resources every 50 ms, ensuring that network performance meets the required service level agreements (SLAs). AI-driven predictive models help in anticipating changes in network demand, allowing for proactive resource reallocation to maintain optimal performance.
Unified Orchestration: AI is integral in managing cross-domain slicing by orchestrating resources across RAN, transport, and cloud domains. AI algorithms help in optimizing resource allocation and ensuring seamless integration of different network segments to meet specific service requirements.
Predictive Analytics: AI-powered predictive analytics are used to detect potential SLA violations in advance. By analyzing historical data and real-time network conditions, AI models can predict when SLA thresholds might be breached, providing early warnings (e.g., 15 minutes) to allow for proactive adjustments
Anomaly Detection: AI-driven anomaly detection systems monitor network performance in real-time, identifying unusual patterns that could lead to SLA violations. This allows for timely interventions to prevent service disruptions
Spectrum Fragmentation: Inconsistent allocation policies across regions complicate multi-country deployments
Device Compatibility: Only 35% of existing 5G devices support 5G-A features, necessitating accelerated refresh cycles
4.2 Cybersecurity Risks
The expansion of attack surfaces via AI-driven networks and IoT devices has prompted initiatives like the UAE’s National Frequency Plan, which mandates stringent encryption for 6 GHz band usage.
5 The Road to 6G: 5G-Advanced as a Catalyst
While 6G research accelerates, 5G-Advanced provides critical steppingstones:
5.1 THz Spectrum Utilization:
World Radiocommunication Conference 2023 (WRC-23) allocated the 6.425–7.125 GHz band for IMT, ensuring spectrum availability for 5G-A and future 6G deployments
140 GHz band prototypes achieve 10 Gbps/mm² area capacity
Reconfigurable Intelligent Surfaces (RIS) extend coverage to 1 km
5.2 Quantum-Safe Security:
Lattice-based cryptography prototypes in SIM authentication
Post-quantum key exchange mechanisms for network slicing
5.3 Native AI Architectures:
Distributed learning across RAN/core/edge nodes
Intent-based networking translating business goals to technical parameters
Nokia Bell Labs' 6G research builds directly on 5G-Advanced innovations, targeting:
1 Tbps peak rates
0.1 ms latency
99.9999999% reliability
Integrated communication and sensing with 1 mm resolution.
6 Conclusion: The 5G-Advanced Imperative
With approximately 80% of Fortune 500 companies planning 5G-Advanced pilots by 2026, the technology represents not just an incremental upgrade, but a fundamental re-architecture of enterprise connectivity paradigms.
5G-Advanced represents both the maturation of 5G and the foundation for 6G.
Operators leveraging strategic investments and partnerships in AI-native networks, enterprise digitization, and subsea infrastructure are driving global innovation. However, sustained success requires addressing CapEx burdens, harmonizing spectrum policies, and accelerating device ecosystems.
As 3GPP Release 21 approaches, the industry must prioritize interoperability testing and cybersecurity frameworks to realize the full potential of 6G.
