Showing posts with label Artificial Intelligence. Show all posts
Showing posts with label Artificial Intelligence. Show all posts

Tuesday, 20 January 2026

Telecom Security Realities from 2025 and Lessons for 2026

Telecom security rarely stands still. Each year brings new technologies, new attack paths, and new operational realities. Yet 2025 was not defined by dramatic new exploits or spectacular network failures. Instead, it became a year that highlighted how persistent, patient and methodical modern telecom attackers have become.

The recent SecurityGen Year-End Telecom Security Webinar offered a detailed look back at what the industry experienced during 2025. The session pulled together research findings, real world incidents and practical lessons from across multiple domains, including legacy signalling, eSIM ecosystems, VoLTE vulnerabilities and the emerging world of satellite-based mobile connectivity.

For anyone working in mobile networks, the message was clear. The threats are evolving, but many of the core problems remain stubbornly familiar.

A Year of Stealth Rather Than Spectacle

One of the most important themes from the webinar was that 2025 did not bring a wave of highly visible disruptive telecom attacks. Instead, it was characterised by quiet, low profile intrusions that often went undetected for long periods.

Operators around the world reported that attackers increasingly favoured living-off-the-land techniques. Rather than deploying noisy malware, intruders looked for ways to gain legitimate access to core systems and remain hidden. Lawful interception platforms, subscriber databases such as HLR and HSS, and internal management platforms were all targeted.

The primary objective in many cases was intelligence collection. Attackers were interested in call data, subscriber information and network topology rather than immediate disruption. This shift in motivation makes detection far more difficult, as there are often few obvious signs of compromise.

At the same time, automation has become a defining feature on both sides of the security battle. Operators are investing heavily in AI and machine learning to identify abnormal behaviour. Attackers are doing exactly the same, using automation to scale phishing campaigns and to accelerate exploit development.

Despite all this technology, basic security discipline continues to be a major challenge. A significant proportion of incidents still originate from human error, poor operational practices or simple failure to apply patches. The industry continues to invest billions in cybersecurity, but much of that effort is consumed by reporting and compliance activities rather than direct threat mitigation.

eSIM Security Comes into Sharp Focus

The transition from physical SIM cards to eSIM and remote provisioning is one of the most significant structural changes in the mobile industry. It offers clear benefits in terms of flexibility and user experience. However, the webinar highlighted that it also introduces entirely new security concerns.

Traditional SIM security models relied heavily on physical control. Fraudsters needed access to large numbers of real SIM cards to operate at scale. With eSIM, many of those physical constraints disappear. Remote provisioning expands the number of parties involved in the connectivity chain, including resellers and intermediaries who may not always operate under strict regulatory oversight.

During 2025 several major SIM farm operations were dismantled by law enforcement. These infrastructures contained tens of thousands of active SIM cards and were used for large scale fraud, smishing campaigns and automated account creation. While such operations existed long before eSIM, the technology has the potential to make them even easier to deploy and manage.

Research discussed in the session pointed to additional concerns. Analysis of travel eSIM services revealed issues such as cross-border routing of management traffic, excessive levels of control granted to resellers, and lifecycle management weaknesses that could potentially be abused by attackers. In some cases, resellers were found to have capabilities similar to full mobile operators, but without equivalent governance or transparency.

The conclusion was not that eSIM is inherently insecure. The technology itself uses strong encryption and robust mechanisms. The problem lies in the wider ecosystem of trust boundaries, partners and processes that surround it. Securing eSIM therefore requires cooperation between operators, vendors, regulators and service providers.

SS7 Remains a Persistent Weak Point

Few topics in telecom security generate as much ongoing concern as SS7. Despite being a technology from a previous era, it remains deeply embedded in global mobile infrastructure. The webinar dedicated significant attention to why SS7 continues to be exploited in 2025 and why it is likely to remain a problem for many years to come.

Throughout the year, media reports and research papers continued to demonstrate practical abuses of SS7 signalling. Attackers probed networks, attempted to bypass signalling firewalls and looked for new ways to manipulate protocol behaviour. Techniques such as parameter manipulation and protocol parsing tricks were highlighted as methods that can sometimes evade existing protections.

One particularly interesting demonstration showed how SS7 messages could be used as a covert channel for data exfiltration. By embedding information inside otherwise legitimate signalling transactions, attackers can potentially move data across networks without triggering traditional security alarms.

Perhaps the most striking point raised was how little progress has been made in eliminating SS7 dependencies. Analysis of global network deployments showed that only a handful of countries operate mobile networks entirely without SS7. Everywhere else, the protocol remains a foundational element of roaming and interconnect.

As a result, even operators that have invested heavily in 4G and 5G security can still be undermined by weaknesses in this legacy layer. The uncomfortable reality is that SS7 vulnerabilities will continue to be exploited well into 2026 and beyond.

VoLTE and Modern Core Network Risks

While legacy protocols remain a problem, modern technologies are not immune. VoLTE infrastructure in particular was identified as an increasingly attractive target.

VoLTE relies on complex interactions between signalling systems, IP multimedia subsystems and subscriber databases. Weaknesses in configuration or interconnection can open the door to call interception, fraud or denial of service. Several real world incidents during 2025 demonstrated that attackers are actively exploring these paths.

The move toward fully virtualised and cloud-native mobile cores also introduces new operational challenges. Telecom networks now resemble large IT environments, complete with the same risks around misconfiguration, insecure APIs and exposed management interfaces.

The Emerging Security Challenge of 5G Satellites

One of the most forward-looking parts of the webinar focused on non-terrestrial networks and direct-to-device satellite connectivity. What was once a concept for the distant future is rapidly becoming a commercial reality.

Satellite integration promises to extend 5G coverage to remote areas, oceans and disaster zones. However, it also changes the security model in fundamental ways. Satellites can act either as simple relay systems or as active components of the mobile radio access network. In both cases, new threat vectors emerge.

Potential issues discussed included the risk of denial of service against shared satellite resources, difficulties in applying traditional radio security controls in space-based equipment, and the possibility of more precise user tracking due to the way satellite systems handle location information.

Experts from the space cybersecurity community explained how vulnerabilities in mission control software and ground segment infrastructure could be exploited. Much of this software was originally designed for isolated environments and is only now being connected to wider networks and the internet.

As telecom networks expand beyond the boundaries of the Earth, security responsibilities extend with them. Operators will need to think not only about terrestrial threats but also about risks originating from space-based components.

The Human Factor and the Skills Gap

Technology was only part of the story. Another recurring theme was the global shortage of skilled telecom cybersecurity professionals.

Studies referenced in the session suggested that millions of additional specialists are needed worldwide, yet only a fraction of that demand can currently be filled. Many security teams are overwhelmed by the sheer volume of alerts and data they must process.

This shortage has real consequences. When teams are stretched thin, patching is delayed, anomalies are missed and complex investigations become difficult to sustain. The panel emphasised that throwing more tools at the problem is not enough. Organisations must focus on training, automation and smarter operational processes.

Automation and AI-driven analysis were presented as essential enablers. Given the scale of modern mobile networks, it is simply not feasible for human analysts to monitor every signalling protocol, every core interface and every emerging technology manually.

Preparing for 2026

Looking ahead, the experts agreed on several broad trends. Attacks on legacy systems such as SS7 will continue. Fraudsters will increasingly target eSIM provisioning processes. VoLTE and 5G core components will face growing scrutiny. Satellite-based connectivity will introduce new and unfamiliar security questions.

Perhaps most importantly, the line between traditional telecom security and general cybersecurity will continue to blur. Mobile networks are now large, distributed IT platforms, and they inherit all the complexities that come with that transformation.

Operators, regulators and vendors must therefore adopt a holistic view. Investment must go beyond compliance reporting and focus on practical defences, real time monitoring and collaborative intelligence sharing.

Final Reflections

The SecurityGen webinar provided a valuable snapshot of an industry at a crossroads. Telecom networks are becoming more advanced and more capable, but also more complex and interconnected than ever before.

2025 demonstrated that attackers do not always need new vulnerabilities. Often they succeed simply by exploiting old weaknesses in smarter ways. The challenge for 2026 is to close those gaps while also preparing for the technologies that are only just beginning to emerge.

For those involved in telecom security, the full discussion is well worth watching. The complete webinar recording can be viewed below:

Related Posts:

Tuesday, 4 November 2025

AIoT and A-IoT

Our industry loves acronyms. In fact, sometimes it feels as if half our job is simply keeping up with them, while the other half is explaining them to everyone else. A recent example I saw referenced D2D for satellites, but expanded it as Device to Device instead of Direct to Device. Today, two similar acronyms are gaining momentum and are likely to become far more mainstream: AIoT and A-IoT.

Artificial Intelligence (AI) and the Internet of Things (IoT) are two of the key technological pillars of the modern digital world. IoT connects billions of devices, from sensors and cameras to industrial machinery, all producing vast amounts of useful data. AI enables these devices and systems to learn from this data, recognise patterns, predict outcomes, and act autonomously.

When these technologies come together, we get the Artificial Intelligence of Things, or AIoT. In simple terms, AIoT allows connected devices to analyse the data they generate and make decisions without always relying on central systems.

The intelligence in AIoT can sit in different places. Cloud based AI offers extensive processing power and the ability to leverage wider datasets. Edge AI processes data closer to where it is generated, enabling faster and more context aware decision making while reducing bandwidth use and protecting data privacy. Increasingly, lightweight machine learning models allow intelligence directly on devices themselves, enabling instant reactions without constant network access. This evolution transforms IoT devices from passive data collectors into proactive decision makers.

The benefits are significant. AIoT increases automation, improves efficiency, enhances reliability, and enables predictive maintenance, energy optimisation, autonomous navigation, and smarter logistics. It also supports sustainability initiatives, for instance by improving energy and water use monitoring or enabling more intelligent control of municipal utilities. In short, AIoT forms a key part of the digital transformation strategies emerging across industries.

To get a better sense of how AIoT could shape our everyday lives, I have embedded a couple of older Ericsson videos below that imagine a future where intelligence is seamlessly built into everything.

For anyone interested in going deeper into this topic, Transforma Insights and Supermicro have good explainers. While 3GPP continues to work on AI, ML and IoT, AIoT as a concept is largely implementation driven rather than a standardised feature in itself.

In contrast, 3GPP is actively defining a different acronym: A-IoT, short for Ambient IoT.

Ambient IoT represents a major shift in connected device design. Instead of relying on batteries or frequent charging, Ambient IoT devices operate using energy harvested from their surroundings. This can include radio signals, light, heat, or motion. The technology supports both passive operation, where devices backscatter incoming RF signals, and active operation, where they harvest enough power to generate and transmit signals independently.

Unlike traditional IoT devices, Ambient IoT units are extremely low power, low cost, and very simple in design. They have a shorter range and lower data throughput than conventional wireless technologies, but they excel in scenarios where massive numbers of tiny, battery-free sensors can be deployed and left to operate with minimal maintenance.

This makes Ambient IoT well suited to applications such as environmental sensing, supply chain tracking, inventory monitoring, smart agriculture, and intelligent labelling. It also opens opportunities in consumer environments, from smart packaging to indoor positioning. With the right network support, these devices can operate indefinitely, enabling sustainable, large-scale sensing networks.

Ambient IoT is already included in 5G Advanced Release 19. For those interested in learning more, 3GPP has a detailed overview, Oppo has produced an excellent white paper, and LG Uplus has published a forward looking document exploring Ambient IoT in the context of 6G.

Both AIoT and Ambient IoT represent the next phase of connected intelligence. AIoT pushes computation and decision making closer to where data originates, while Ambient IoT removes power barriers and enables pervasive, maintenance-free connectivity. Together, they will support systems that are scalable, energy efficient and context aware.

As these technologies mature, we can expect a world where devices are not only always connected, but also constantly learning, adapting, and operating independently with minimal energy demands. The future of connectivity lies in this balance between intelligence and efficiency, and both AIoT and Ambient IoT will play a crucial role in shaping it.

Related Posts

Thursday, 20 March 2025

AI/ML in 3GPP: Progress, Challenges, and the Road to 6G

The ETSI Artificial Intelligence (AI) Conference – Status, Implementation and Way Forward of AI Standardization – took place from 5-7 February 2024 at ETSI, Sophia Antipolis, France. This in-person event provided a valuable platform for experts and peers to exchange insights, explore demos and posters, and discuss AI and Machine Learning (ML) within the Information and Communications Technology (ICT) sector.

The event agenda is available online, and all presentations can be accessed here.

AI/ML Work in 3GPP: Insights from Dr. Juan Montojo

Dr. Juan Montojo, a leading figure in 3GPP TSG Radio Access Networks (RAN) and rapporteur for the work item Artificial Intelligence/Machine Learning for NR air interface (NR_AIML_air), delivered an insightful presentation titled "Overview of AI/ML related work in 3GPP." His talk covered the current status of AI/ML in 3GPP and prospects as 6G priorities begin to take shape.

Further details are available in the 3GPP post and presentation.

Focus Areas in 3GPP AI/ML Work

Dr. Montojo outlined the critical focus areas for AI/ML within 3GPP:

  • Infrastructure and Operator Control: Ensuring that operators maintain control over AI/ML implementations within their networks.
  • Performance Monitoring: Establishing standards for monitoring AI/ML model performance, activation, and deactivation.
  • Air Interface Extensions: Developing extensions to support AI/ML-specific use cases.
  • Data Standards: Defining standardized processes for data collection, AI/ML model transfer, and delivery.
  • Testing and Interoperability: Ensuring consistent device behavior and interoperability in AI/ML deployments.

Principles Guiding AI/ML in 3GPP

The AI/ML work in 3GPP is grounded in principles that echo regulatory frameworks like the European Commission’s AI Act:

  • Data Security and Integrity: Safeguarding data confidentiality and ensuring integrity.
  • Privacy and User Consent: Respecting data privacy and user anonymity, with explicit consent mechanisms.
  • Operator Control: Empowering operators with control over data collection, transfer initiation, termination, and management.
  • Future-Proof Design: Ensuring the system design is extendable to accommodate future advancements.

AI/ML Training Models: Current Practices and Future Directions

  • Off-line Training: Currently, AI/ML models in 3GPP assume off-line training, where models are fully trained before deployment in commercial networks.
  • On-line Training and Federated Learning: Future 6G developments may introduce on-line training. 3GPP WG SA2 is already exploring federated learning to enhance network automation.

Challenges and Opportunities for AI/ML in Cellular Networks

Dr. Montojo emphasized both the strengths and limitations of AI/ML in cellular networks:

  • Strengths: AI/ML excels in tackling complex, non-linear problems that traditional methods struggle with. It enhances localized, data-driven decision-making.
  • Challenges: High energy consumption remains a concern for both network and device sides. Standardization must balance flexibility with technical consistency.

The Path Forward: AI/ML in 6G

AI/ML is expected to become pervasive in 6G, influencing all aspects of system design and operation. Notable expectations include:

  • Rel-21 Specifications: AI/ML will be incorporated from the outset, supporting evolving use cases and dynamic requirements.
  • Flexible Standardization: Future specifications may be less rigid, enabling AI/ML to drive optimization through data-driven parameterization.

Conclusion

AI/ML's integration into 3GPP workstreams is advancing steadily, laying the groundwork for significant contributions to 6G networks. While AI/ML models themselves are not being standardized, the supporting frameworks around data collection, model management, and interoperability are set to shape the future of cellular technology.

For anyone invested in AI/ML's role in telecoms, understanding these foundational steps is essential as we move towards a more automated, intelligent, and adaptable network landscape.

Related Posts

Friday, 15 November 2024

RAN, AI, AI-RAN and Open RAN

The Japanese MNO Softbank is taking an active role in trying to bring AI to RAN. In a research story published recently, they explain that AI-RAN integrates AI into mobile networks to enhance performance and enable low-latency, high-security services via distributed AI data centres. This innovative infrastructure supports applications like real-time urban safety monitoring and optimized network throughput. Through the AI-RAN Alliance, SoftBank collaborates with industry leaders to advance technology and create an ecosystem for AI-driven societal and industrial solutions.

This video provides a nice short explanation of what AI-RAN means:

SoftBank's recent developments in AI-RAN technology further its mission to integrate AI with mobile networks, highlighted by the introduction of "AITRAS." This converged solution leverages NVIDIA's Grace Hopper platform and advanced orchestrators to unify vRAN and AI applications, enabling efficient and scalable networks. By collaborating with partners like Red Hat and Fujitsu, SoftBank aims to commercialize AI-RAN globally, addressing the demands of next-generation connectivity. Together, these initiatives align with SoftBank's vision of transforming telecommunications infrastructure to power AI-driven societies. Details are available on SoftBank's page here.

Last month, theNetworkingChannel hosted a webinar looking at 'AI-RAN and Open RAN: Exploring Convergence of AI-Native Approaches in Future Telecommunication Technologies'. The slides have not been shared and the details of the speakers are available here. The webinar is embedded below:

NVIDIA has a lot more technical details available on their blog post here.

Related Posts

Wednesday, 14 August 2024

3GPP Release 18 Description and Summary of Work Items

The first official release of 3GPP TR 21.918: "Release 18 Description; Summary of Rel-18 Work Items" has been published. It's the first official version of 5G-Advanced. Quoting from the report: 

Release 18 specifies further improvements of the 5G-Avanced system. 

These improvements consist both in enhancements of concepts/Features introduced in the previous Releases and in the introduction of new topics.

Some of the key improvements are:

  • a further integration of the Satellite (NTN) access (introduced in Rel-17) in the 5G System (5GS), 
  • a more efficient support of Internet of Things (IoT), Machine-Type Communication (MTC), including by satellite coverage
  • and also several aspects of proximity communication and location (Sidelink, Proximity, Location and Positioning, better support of the industrial needs (Verticals, Industries, Factories, Northbound API), Multicast and Broadcast Services (MBS), Network Slicing or Uncrewed Aerial Vehicles (UAV).

As for the new topics, some of the key aspects are:

  • Energy Efficiency (EE)
  • Artificial Intelligence (AI)/Machine Learning (ML)
  • eXtended, Augmented and Virtual Reality (XR, AR, VR), immersive communications

The following list is from the v1.0.0 table of contents to make it easier to find the list of topics. If it interests you, download the latest version technical report from the directory here.

5 Satellite / Non-Terrestrial Network (NTN)
5.1 General aspects
5.1.1 User plane: “5G system with satellite backhaul”
5.1.2 Discontinuous coverage: “Satellite access Phase 2”
5.1.3 Radio: "NR NTN enhancements"
5.1.4 Charging and Management aspects of Satelite
5.2 Specific aspects
5.2.1 IoT (Internet of Things) NTN enhancements
5.2.2 Guidelines for Extra-territorial 5G Systems
5.2.3 5G system with satellite access to Support Control and/or Video Surveillance
5.2.4 Introduction of the satellite L-/S-band for NR
5.2.5 Other band-related aspects of satellite

6 Internet of Things (IoT), Machine-Type Communication (MTC)
6.1 Personal IoT and Residential networks
6.2 Enhanced support of Reduced Capability (RedCap) NR devices
6.3 NR RedCap UE with long eDRX for RRC_INACTIVE State
6.4 Application layer support for Personal IoT Network
6.5 5G Timing Resiliency System
6.6 Mobile Terminated-Small Data Transmission (MT-SDT) for NR
6.7 Adding new NR FDD bands for RedCap in Rel-18
6.8 Signal level Enhanced Network Selection
6.9 IoT NTN enhancements

7 Energy Efficiency (EE)
7.1 Enhancements of EE for 5G Phase 2
7.2 Network energy savings for NR
7.3 Smart Energy and Infrastructure

8 Uncrewed Aerial Vehicles (UAV), UAS, UAM
8.1 Architecture for UAV and UAM Phase 2
8.2 Architecture for UAS Applications, Phase 2
8.3 NR support for UAV
8.4 Enhanced LTE Support for UAV

9 Sidelink, Proximity, Location and Positioning
9.1 5GC LoCation Services - Phase 3
9.2 Expanded and improved NR positioning
9.3 NR sidelink evolution
9.4 NR sidelink relay enhancements
9.5 Proximity-based Services in 5GS Phase 2
9.6 Ranging-based Service and sidelink positioning
9.7 Mobile Terminated-Small Data Transmission (MT-SDT) for NR
9.8 5G-enabled fused location service capability exposure

10 Verticals, Industries, Factories, Northbound API
10.1 Low Power High Accuracy Positioning for industrial IoT scenarios
10.2 Application enablement aspects for subscriber-aware northbound API access
10.3 Smart Energy and Infrastructure
10.4 Generic group management, exposure and communication enhancements
10.5 Service Enabler Architecture Layer for Verticals Phase 3
10.6 SEAL data delivery enabler for vertical applications
10.7 Rel-18 Enhancements of 3GPP Northbound and Application Layer interfaces and APIs
10.8 Charging Aspects of B2B
10.9 NRF API enhancements to avoid signalling and storing of redundant data
10.10 GBA_U Based APIs
10.11 Other aspects

11 Artificial Intelligence (AI)/Machine Learning (ML)
11.1 AI/ML model transfer in 5GS
11.2 AI/ML for NG-RAN
11.3 AI/ML management & charging
11.4 NEF Charging enhancement to support AI/ML in 5GS

12 Multicast and Broadcast Services (MBS)
12.1 5G MBS Phase 2
12.2 Enhancements of NR MBS
12.3 UE pre-configuration for 5MBS
12.4 Other MBS aspects

13 Network Slicing
13.1 Network Slicing Phase 3
13.2 Enhancement of NSAC for maximum number of UEs with at least one PDU session/PDN connection
13.3 Enhancement of Network Slicing UICC application for network slice-specific authentication and authorization
13.4 Charging Aspects of Network Slicing Phase 2
13.5 Charging Aspects for NSSAA
13.6 Charging enhancement for Network Slice based wholesale in roaming
13.7 Network Slice Capability Exposure for Application Layer Enablement
13.8 Other slice aspects

14 eXtended, Augmented and Virtual Reality (XR, AR, VR), immersive
14.1 XR (eXtended Reality) enhancements for NR
14.2 Media Capabilities for Augmented Reality
14.3 Real-time Transport Protocol Configurations
14.4 Immersive Audio for Split Rendering Scenarios  (ISAR)
14.5 Immersive Real-time Communication for WebRTC
14.6 IMS-based AR Conversational Services
14.7 Split Rendering Media Service Enabler
14.8 Extended Reality and Media service (XRM)
14.9 Other XR/AR/VR items

15 Mission Critical and emergencies
15.1 Enhanced Mission Critical Push-to-talk architecture phase 4
15.2 Gateway UE function for Mission Critical Communication
15.3 Mission Critical Services over 5MBS
15.4 Mission Critical Services over 5GProSe
15.5 Mission Critical ad hoc group Communications
15.6 Other Mission Critical aspects

16 Transportations (Railways, V2X, aerial)
16.1 MBS support for V2X services
16.2 Air-to-ground network for NR
16.4 Interconnection and Migration Aspects for Railways
16.5 Application layer support for V2X services; Phase 3
16.6 Enhanced NR support for high speed train scenario in frequency range 2 (FR2)

17 User Plane traffic and services
17.1 Enhanced Multiparty RTT
17.2 5G-Advanced media profiles for messaging services
17.3 Charging Aspects of IMS Data Channel
17.4 Evolution of IMS Multimedia Telephony Service
17.5 Access Traffic Steering, Switch and Splitting support in the 5G system architecture; Phase 3
17.6 UPF enhancement for Exposure and SBA
17.7 Tactile and multi-modality communication services
17.8 UE Testing Phase 2
17.9 5G Media Streaming Protocols Phase 2
17.10 EVS Codec Extension for Immersive Voice and Audio Services
17.11 Other User Plane traffic and services items

18 Edge computing
18.1 Edge Computing Phase 2
18.2 Architecture for enabling Edge Applications Phase 2
18.3 Edge Application Standards in 3GPP and alignment with External Organizations

19 Non-Public Networks
19.1 Non-Public Networks Phase 2
19.2 5G Networks Providing Access to Localized Services
19.3 Non-Public Networks Phase 2

20 AM and UE Policy
20.1 5G AM Policy
20.2 Enhancement of 5G UE Policy
20.3 Dynamically Changing AM Policies in the 5GC Phase 2
20.4 Spending Limits for AM and UE Policies in the 5GC
20.5 Rel-18 Enhancements of UE Policy

21 Service-based items
21.1 Enhancements on Service-based support for SMS in 5GC
21.2 Service based management architecture
21.3 Automated certificate management in SBA
21.4 Security Aspects of the 5G Service Based Architecture Phase 2
21.5 Service Based Interface Protocol Improvements Release 18

22 Security-centric aspects
22.1 IETF DTLS protocol profile for AKMA and GBA
22.2 IETF OSCORE protocol profiles for GBA and AKMA
22.3 Home network triggered primary authentication
22.4 AKMA phase 2
22.5 5G Security Assurance Specification (SCAS) for the Policy Control Function (PCF)
22.6 Security aspects on User Consent for 3GPP services Phase 2
22.7 SCAS for split-gNB product classes
22.8 Security Assurance Specification for AKMA Anchor Function Function (AAnF)
22.9 Other security-centric items

23 NR-only items
23.1 Not band-centric
23.1.1 NR network-controlled repeaters
23.1.2 Enhancement of MIMO OTA requirement for NR UEs
23.1.3 NR MIMO evolution for downlink and uplink
23.1.4 Further NR mobility enhancements
23.1.5 In-Device Co-existence (IDC) enhancements for NR and MR-DC
23.1.6 Even Further RRM enhancement for NR and MR-DC
23.1.7 Dual Transmission Reception (TxRx) Multi-SIM for NR
23.1.8 NR support for dedicated spectrum less than 5MHz for FR1
23.1.9 Enhancement of NR Dynamic Spectrum Sharing (DSS)
23.1.10 Multi-carrier enhancements for NR
23.1.11 NR RF requirements enhancement for frequency range 2 (FR2), Phase 3
23.1.12 Requirement for NR frequency range 2 (FR2) multi-Rx chain DL reception
23.1.13 Support of intra-band non-collocated EN-DC/NR-CA deployment
23.1.14 Further enhancements on NR and MR-DC measurement gaps and measurements without gaps
23.1.15 Further RF requirements enhancement for NR and EN-DC in frequency range 1 (FR1)
23.1.16 Other non-band related items
23.2 Band-centric
23.2.1 Enhancements of NR shared spectrum bands
23.2.2 Addition of FDD NR bands using the uplink from n28 and the downlink of n75 and n76
23.2.3 Complete the specification support for BandWidth Part operation without restriction in NR
23.2.4 Other NR band related topics

24 LTE-only items
24.1 High Power UE (Power Class 2) for LTE FDD Band 14
24.2 Other LTE-only items

25 NR and LTE items
25.1 4Rx handheld UE for low NR bands (<1GHz) and/or 3Tx for NR inter-band UL Carrier Aggregation (CA) and EN-DC
25.2 Enhancement of UE TRP and TRS requirements and test methodologies for FR1 (NR SA and EN-DC)
25.3 Other items

26 Network automation
26.1 Enablers for Network Automation for 5G phase 3
26.2 Enhancement of Network Automation Enablers

27 Other aspects
27.1 Support for Wireless and Wireline Convergence Phase 2
27.2 Secondary DN Authentication and authorization in EPC IWK cases
27.3 Mobile IAB (Integrated Access and Backhaul) for NR
27.4 Further NR coverage enhancements
27.5 NR demodulation performance evolution
27.6 NR channel raster enhancement
27.7 BS/UE EMC enhancements for NR and LTE
27.8 Enhancement on NR QoE management and optimizations for diverse services
27.9 Additional NRM features phase 2
27.10 Further enhancement of data collection for SON (Self-Organising Networks)/MDT (Minimization of Drive Tests) in NR and EN-DC
27.11 Self-Configuration of RAN Network Entities
27.12 Enhancement of Shared Data ID and Handling
27.13 Message Service within the 5G system Phase 2
27.14 Security Assurance Specification (SCAS) Phase 2
27.15 Vehicle-Mounted Relays
27.16 SECAM and SCAS for 3GPP virtualized network products
27.17 SECAM and SCAS for 3GPP virtualized network products
27.18 MPS for Supplementary Services
27.19 Rel-18 enhancements of session management policy control
27.20 Seamless UE context recovery
27.21 Extensions to the TSC Framework to support DetNet
27.22 Multiple location report for MT-LR Immediate Location Request for regulatory services
27.23 Enhancement of Application Detection Event Exposure
27.24 General Support of IPv6 Prefix Delegation in 5GS
27.25 5G Timing Resiliency System
27.26 MPS when access to EPC/5GC is WLAN
27.27 Data Integrity in 5GS
27.28 Security Enhancement on RRCResumeRequest Message Protection

28 Administration, Operation, Maintenance and Charging-centric Features
28.1 Introduction
28.2 Intent driven Management Service for Mobile Network phase 2
28.3 Management of cloud-native Virtualized Network Functions
28.4 Management of Trace/MDT phase 2
28.5 Security Assurance Specification for Management Function (MnF)
28.6 5G performance measurements and KPIs phase 3
28.7 Access control for management service
28.8 Management Aspects related to NWDAF
28.9 Management Aspect of 5GLAN
28.10 Charging Aspects of TSN
28.11 CHF Distributed Availability
28.12 Management Data Analytics phase 2
28.12 5G System Enabler for Service Function Chaining
28.13 Other Management-centric items

29 Other Rel-18 Topics

If you find them useful then please get the latest document from here.

Related Posts

Wednesday, 29 November 2023

AI/ML and Other ICT Industry Trends in the coming decades

At the Brooklyn 6G Summit (B6GS) 2023, top tier economist Dr. Jeff Shen from BlackRock, presented a talk from the industry perspective of AI (Artificial Intelligence) and investment. Jeff Shen, PhD, Managing Director, is Co-CIO and Co-Head of Systematic Active Equity (SAE) at BlackRock. He is a member of the BlackRock Global Operating Committee, BlackRock Systematic (BSYS) Management Committee and the BlackRock Asian Middle Eastern & Allies Network (AMP) Executive Committee.

In his talk he covered the history of how and where AI has been traditionally used and how the thinking around AI has changed over the last few decades. He then presented his view on if AI is just a fad or it's more than that. To illustrate the fact, he provided an example of how Generative AI market is expected to grow from $40 Billion in 2022 to $1.3 Trillion in 2032. 

There are many challenges that AI faces that one should be aware of; namely regulation, cyber threats and ethical concerns. In the US, AI touches the entire economy, from legal to healthcare. In their quarterly reporting, firms are now discussing AI and the larger tech companies are not afraid to grow inorganically in order to get more exposure to the trend. 

You can watch the whole of his talk embedded below, courtesy of IEEE Tv.

Related Posts

Wednesday, 4 October 2023

Presentations from 2nd IEEE Open RAN Summit

The second IEEE SA (Standards Association) Open RAN summit, hosted by the Johns Hopkins University Applied Physics Lab, took place on 9-10 Aug 2023. It covered the topics related to the standardization of Open RAN including O-RAN Alliance, 3GPP, IEEE, various deployment scenarios, testing and integration, Open RAN security, RAN slicing, and RAN optimization among others. 

The videos of the presentations can be viewed on the summit page here or though the video playlist here.

The talk from Dr. Chih-Lin I, O-RAN Alliance TSC Co-Chair and CMCC Chief Scientist, Wireless Technologies on 'AI/ML impact, from 5.5G to 6G' is embedded below:

Related Posts

Wednesday, 10 August 2022

AI/ML Enhancements in 5G-Advanced for Intelligent Network Automation

Artificial Intelligence (AI) and Machine Learning (ML) has been touted to automate the network and simplify the identification and debug of issues that will arise with increasing network complexity. For this reason 3GPP has many different features that are already present in Release-17 but are expected to evolve further in Release-18. 

I have already covered some of this topics in earlier posts. Ericsson's recent whitepaper '5G Advanced: Evolution towards 6G' also has a good summary on this topic. Here is an extract from that:

Intelligent network automation

With increasing complexity in network design, for example, many different deployment and usage options, conventional approaches will not be able to provide swift solutions in many cases. It is well understood that manually reconfiguring cellular communications systems could be inefficient and costly.

Artificial intelligence (AI) and machine learning (ML) have the capability to solve complex and unstructured network problems by using a large amount of data collected from wireless networks. Thus, there has been a lot of attention lately on utilizing AI/ML-based solutions to improve network performance and hence providing avenues for inserting intelligence in network operations.

AI model design, optimization, and life-cycle management rely heavily on data. A wireless network can collect a large amount of data as part of its normal operations. This provides a good base for designing intelligent network solutions. 5G Advanced addresses how to optimize the standardized interfaces for data collection while leaving the automation functionality, for example, training and inference up to the proprietary implementation to support full flexibility in the automation of the network.

AI/ML for RAN enhancements

Three use cases have been identified in the Release 17 study item related to RAN performance enhancement by using AI/ML techniques. Selected use cases from the Release 17 technical report will be taken into the normative phase in the next releases. The selected use cases are: 1) network energy saving; 2) load balancing; and 3) mobility optimization.

The selected use cases can be supported by enhancements to current NR interfaces, targeting performance improvements using AI/ML functionality in the RAN while maintaining the 5G NR architecture. One of the goals is to ensure vendor incentives in terms of innovation and competitiveness by keeping the AI model implementation specific. As shown in Fig.2 (on the top) an intent-based management approach can be adopted for use cases involving RAN-OAM interactions. The intent will be received by the RAN. The RAN will need to understand the intent and trigger certain functionalities as a result.

AI/ML for physical layer enhancements

It is generally expected that AI/ML functionality can be used to improve the radio performance and/or reduced the complexity/overhead of the radio interface. 3GPP TSG RAN has selected three use cases to study the potential air interface performance improvements through AI/ML techniques, such as beam management, channel state information feedback enhancement, and positioning accuracy enhancements for different scenarios. The AI/ML-based methods may provide benefits compared to traditional methods in the radio interface. The challenge will be to define a unified AI/ML framework for the air interface by adequate AI/ML model characterization using various levels of collaboration between gNB and UE.

AI/ML in 5G core

5G Advanced will provide further enhancements of the architecture for analytics and on ML model life-cycle management, for example, to improve correctness of the models. The advancements in the architecture for analytics and data collection serve as a good foundation for AI/ML-based use cases within the different network functions (NFs). Additional use cases will be studied where NFs make use of analytics with the target to support in their decision making, for example, network data analytics functions (NWDAF)- assisted generation of UE policy for network slicing.

If you are interested in studying this topic further, check out 3GPP TR 37.817: Study on enhancement for data collection for NR and ENDC. Download the latest version from here.

Related Posts

Tuesday, 15 February 2022

What Is the Role of AI and ML in the Open RAN and 5G Future?

Artificial Intelligence and Machine Learning have moved on from just being buzzwords to bringing much needed optimization and intelligence in devices, networks and infrastructure; whether on site, on the edge or in the cloud.

Qualcomm has been very active in talking about AI/ML in webinars and on their site. A detailed blog post looking at 'What’s the role of artificial intelligence in the future of 5G and beyond?' is available here. It was posted in time for a Light Reading webinar where Gabriel Brown, Principal Analyst – Mobile Networks and 5G, Heavy Reading and Tingfang Ji, Senior Director, Engineering - Wireless R&D, Qualcomm discuss the topic. The video is embedded below and slide deck is available here.

Louis Scialabba, Senior Director of Marketing at Mavenir, looking at AI and Analytics spoke at Layer 123 conference on the topic, 'AI/ML for Next Gen 5G Mobile Networks'. His talk is embedded below and a blog post by him on the topic, 'The RIC Opens a New World of Opportunities for CSPs' is available here.

Related Posts