AI in 5G Networks: Deployment Aspects, Risks and Telecom LLM

Navigate Gartner's hype cycle for AI and 5G, tracing hype, disillusionment, slope of enlightenment, and the plateau of productivity toward a mature 5G-AI era.

Explore historical ideas of algorithmic network optimization and how automated operating support systems monitor performance, identify problems, propose corrective actions, and automatically tune mobile networks.

Explore AI application areas in telecom across network levels, from improved channel modeling and adaptive modulation to self-optimization, predictive analytics, and low-latency codecs.

Explore how generative AI deployments are expanding in telecom across regions, with trials and live use, and assess risks in network domain and adoption in products and customer care.

Assess the adoption challenges of generative AI in telecom, including differentiation, skills gaps, return on investment, data privacy, and regulatory uncertainty shaping multi-phase deployments.

Build an AI centre of excellence to centralize AI/ML expertise and accelerate adoption and maturity of AI in telecom, a practice linked to a 2024 SDL Partners survey.

Accelerate telecom ai adoption by embracing open approaches and multi-lens platforms. Develop in-house models with open source foundations to reduce vendor lock-in and improve data privacy.

Embrace open and open source AI to accelerate telecom adoption, identifying the right framework, owning your AI with open source LMS, and implementing MLOps for efficient customization and operation.

Survey the ai index 2024 visuals, highlight global leadership by the United States and China, compare human cognition benchmarks, and examine model costs, compute needs, and open vs closed governance.

Learn how large language models use natural language inputs, predict the next word from text corpora, and enable content creation, processing, and AI agent automation in telecom to avoid hallucinations.

Examine the market landscape for large language models, detailing competition among Meta, OpenAI, Apple, Nvidia, and Anthropic's rise with Google's growth, per the Menlo Ventures 2024 chart.

Explore the cost landscape of foundation models in telecom, comparing building from scratch, enhancing, or fine-tuning, and highlight drivers, risks, and ROI considerations.

Fine-tune large language models on three gb specifications to answer 5g protocol questions and explore a telecom-domain framework for adapting models like bert and robota.

Explore two scenarios—telecom for LM and LM for telecom—using 3GPP/IEEE data and prompts to twin telecommunication and AI, enabling multi-modal networks, AI agents, and self-design potential.

Explore how AI-native hardware, edge computing, and distributed AI ML agents enable efficient LLMs across devices and the network, from base stations to the cloud.

Analyze infrastructure challenges for AI apps at the edge, including data locality, distributed learning, interconnection, power and cooling demands, and regulatory implications for mobile edge computing and 5G standalone networks.

Explore collective intelligence in future networks by coordinating generative agents and multiple language models that communicate wirelessly to decompose tasks, exchange semantics, and translate human intent into network configurations.

Identify key telecom LM KPIs across on-device LM, MEC and cloud offloading, including latency, cache hit rate, task handoff, energy usage, memory, and load balancing.

Apple intelligence places a 3 billion-parameter model in iOS 18 for cloud use across devices, while Google Gemini Nano offers a compact on-device model for text, code, audio, and images.

Open source models close the gap with proprietary giants like GPT-4, Claude, and Gemini, enabling smaller, phone-friendly LLMs and task-dependent intelligence across text, image, and programming.

Compare proprietary and open source llms, examining monetization and cost pressures in the ai era. Explore deployment options, including on-device and hybrid cloud edge approaches for 5g networks.

Explore the inference limits of on-device LLMs for smartphones, from 1 billion to 22 billion parameters. Understand trade-offs between device resources and cloud or hybrid processing for privacy and latency.

on-device lm optimization showcases a 56 billion parameter model on a regular Android smartphone, enabled by caching and swap techniques, signaling efficient mobile inference and future 10 billion models.

Examine semantic communication using on-device large language model transformers that convert 4k video into latent representations, enabling up to 20x data reduction for future 6g networks.

Explore how AI-powered apps, semantic communication, and traffic compression will reshape mobile traffic over the next decade, with on-device LLMs and edge deployments redefining networks, devices, and apps.

Explore the evolution of network automation, from manual to level five autonomous networks, and how AI agents and large language models enable zero-touch operations for cost savings.

Trace the AI and ML timeline in 3GPP, from self-optimizing networks and drive-test minimization to release 15 data collection and 18 AI feats like beam management and channel prediction.

NWDAF is a centralized platform in the 5g core that collects raw data and pre-computed analytics from multiple network functions to provide insights for network automation, optimization, and user experience.

Explore NWDAF integration with 5G functions, including distributed deployments and cross-instance data transfer, analytics generation via MTLF and ANEFL, and report delivery by subscription-based mechanisms or direct requests.

Explore NWDAF solutions from Amdocs, Nokia, Ericsson, Huawei, and Oracle and learn how this standardized, multi-vendor framework enables iterative ML deployment, API-driven analytics, and trade-offs for predictive maintenance, network slicing.

Explore the management data analytics function (MDAF) in 5G for automation in service and management, and compare WDF and MDF as real-time core-network analytics versus cross-domain, long-term operation analytics.

Explore MDA functionality as a management service that collects current and historical data, alarms, and configuration data to enable analytics-driven optimization and automation for 5G networks and network slicing.

NWDAF insights go unused because policy control functions are designed for deterministic thresholds rather than probabilistic analytics from WDF outputs.

Explore semantic drift and data inconsistencies in multi-vendor 5G cores, and how a hybrid network and data science team ensures NWDAF data normalization, ownership, and lifecycle management.

Relay UPF data through SMF, avoiding extra processing, latency, and reduced visibility; expose UPF events directly to WDF via the event exposure service for near real-time analytics.

Assess NWDAF from an operator perspective, detailing tco overhead of analytics pipelines, and propose a phased deployment with high-value use cases, closed-loop automation, and ML components.

Explore ai native radio interface as a solution to air interface limits, where cascading errors from channel estimation, hardware nonlinearities, and massive mimo overhead motivate ai/ml-driven adaptive waveform design.

Develop an ai-native radio interface through three phases: phase one low-risk legacy block replacement, phase two neural receiver, and phase three end-to-end joint optimization of transmitter and receiver.

Analyze how ML architectures enable AI native 5G networks, using convolutional neural networks for channel estimation, autoencoders for anomaly detection, GANs for synthetic data, and transformers for signaling protocols.

Phase one applies targeted neural network replacement in the legacy receiver, swapping heavy blocks for neural networks like a neural demodulator to handle non-linearities, with channel estimation and time-frequency transforms.

Move into phase two by adopting an integrated deep receiver that aggregates functions into a model, using post-DFT grid to output log-likelihood ratios for all bits and eliminate interface losses.

Phase three introduces an ai designer that uses channel data and hardware specifics to craft a custom communication scheme, with transmitter and receiver learning to optimize waveform for environment-specific conditions.

Explore ai designed physical layer enabling pilotless transmission with learned constellations, reducing pilots, and boosting throughput while maintaining bit error rate through data-driven channel inference.

Extend generative AI to the RC layer, treating RRC messages as a domain-specific language. Train a lightweight transformer to handle connection setup and handovers, addressing inference latency toward sub-100 ms.

Explore how neural receivers disrupt legacy auto loop link adaptation, altering signal to noise ratio and block error rate curves, and propose AI aware link adaptation with calibrated uncertainty reporting.

Explore computational and power challenges deploying large AI models in 5G networks, and how heterogeneous SoC architectures with NPUs or FPGAs accelerate compute, addressing data movement and near memory computing.

3GPP shifts standardization from model-centric AI to a framework that manages lifecycle and communication for AI models, with release 18 initiating AI ML for the read interface.

Transition to AI native air interface demands statistical performance envelopes, thousands of data points, and high-fidelity digital twins for data-driven validation and compliant behavior.

Analyze conventional CSI feedback limitations in 5G advanced, including codebook quantization, overhead, and channel aging, and how AI encoder-decoder can compress and predict channel states.

This lecture explains how AIML enables spatial-frequency CSI compression and time-domain CSI prediction to reduce feedback overhead and combat channel aging in 5G networks.

Explore a two-sided compression model for channel state information feedback, where an ai encoder compresses the channel into a latent vector and a base station decoder reconstructs a precoding matrix.

Overcome channel aging with time-domain CSI predictions, using historical data and RNNs or transformers to forecast future channel states and maintain high mobility beams in dense urban networks.

Assess system-level and intermediate KPIs, including CSI accuracy, to show how AI-based channel reconstruction boosts user throughput and cuts feedback overhead, achieving up to 1.5x gains in high mobility.

Explore inter-vendor operability and model generalization in 5G AI deployments, outlining interoperability approaches, mixed-data training for robustness, and scalable transformer-based solutions for multi-vendor environments.

Examine ai/ml-based beam management to overcome 5G NR limitations in FR2 networks. Apply spatial and temporal beam predictions from historical data to reduce overhead and latency, improving access and mobility.

AI uses a subset of beams to predict the best options and cross-frequency performance for spatial-domain prediction, and history-based timing enables proactive beam tracking and switching.

Assess ai/ml model placements for 5g beam management, balancing us side edge inference with local measurements against node side centralized inference at the base station, using rsrp and snr data.

Evaluate beam management with a simulation-based framework and KPIs, measuring AI model accuracy, beam switching, overhead reduction, and user throughput, using small datasets for robust predictions.

Examine how generalized AI models support robust beam management across outdoor and indoor 5G scenarios, incorporating site-specific optimization, signaling, standardized reporting, and lifecycle readiness for AI-based deployments.

Explore classical 5G nr positioning methods—time difference of arrival, angle, uplink angle of arrival, and multi-cell round-trip time—and how AIML enhancements address accuracy in urban indoor environments for 6G.

Explore direct positioning via fingerprinting, where deep neural networks output 2D/3D coordinates from raw channel data, and AI-assisted positioning that enhances traditional methods through pre-processing and denoising.

Explore where AI/ML positioning functions run—on device, at gNB, or in the location management function of the core network—balancing privacy, latency, and accuracy with radio measurements and channel state information.

Explore large language models as super-compressors and AI aided codecs to transmit prompts instead of raw media, enabling semantic reconstruction and dramatically boosting bandwidth and spectral efficiency in 5G.

Explore post-Shannon semantic communication, highlighting level B/C encoder–decoder designs that extract meaning to reduce data, lower latency, and bandwidth needs in 5G networks, with privacy and mean opinion score metrics.

Explore how AI-aided codecs mitigate radio propagation and application latency in 5G and 6G networks, enabling low-latency, connected experiences for VR, AR, and haptic applications worldwide.

Leverage ai-based media processing to adapt video quality in real time, shifting from fixed pipelines to intelligent edge and device engines that ingest telemetry and upscale, denoise, and SDR-to-HDR convert.

Decoupling media streams from ai models enables split inference between device and edge server, with data cleaning on phones and context analysis on the edge.

Explore using large language models as large action models to enable an app-free, intent-driven smartphone interface that connects to edge agents for various tasks.