Towards AI-Driven RANs for 6G and Beyond: Architectural Advancements and Future Horizons

Amandeep Kaur; Hazer Inaltekin; H. Vincent Poor; Mathushaharan Rathakrishnan; Rohit Singh; Samiru Gayan; Sampath Edirisinghe

REVIEW 4 major objections 6 minor 2 cited by

Towards AI-Driven RANs for 6G and Beyond: Architectural Advancements and Future Horizons

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read AI orchestration lifts radio network efficiency by up to 20 percent

desk verdict A competent 6G AI-RAN survey with a plausible but unverified OrchestRAN architecture; the headline spectral efficiency claims rest on an under-specified single simulation. read the letter →

arxiv 2506.16070 v1 pith:54WQGICE submitted 2025-06-19 eess.SP

Mathushaharan Rathakrishnan , Samiru Gayan , Rohit Singh , Amandeep Kaur , Hazer Inaltekin , Sampath Edirisinghe , H. Vincent Poor This is my paper · ORCID

classification eess.SP

keywords 6GAI-drivenradioaccessnetworksnetworkarchitecturereinforcementlearningspectralefficiencyorchestrationdigitaltwinintegratedsensingandcommunication

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that 6G radio access networks should be built around an AI orchestration layer rather than fixed scheduling rules, and it backs that argument with a proof-of-concept architecture and simulation. The proposed framework, OrchestRAN, collects operator requests, selects pre-trained AI models from a catalog, and dispatches them as containerized applications across RIC, CU, DU, and RU nodes. In a simulated 28 GHz urban deployment with multi-agent reinforcement learning for beamforming and scheduling, the framework is reported to cut latency while improving spectral efficiency by up to 20% over Round Robin and by 10-15% over Proportional Fair and Max-Min Fairness. If these results hold, they support the broader claim that AI-native RANs can deliver the flexibility and automation 6G services require.

What carries the argument

The load-bearing mechanism is OrchestRAN, an orchestration layer that separates request collection, model selection, and infrastructure abstraction. A Request Collector takes operator requests for slicing, scheduling, and beamforming along with location and time constraints; the Orchestration Engine matches each request to a pre-trained model in the ML/AI catalog; and an Infrastructure Abstraction module exposes five logical node groups: non-RT RICs, near-RT RICs, CUs, DUs, and RUs. Decisions are converted into executable O-RAN applications and deployed as containers, with E2, A1, and O1 interfaces connecting the controllers. In the simulation, the engine uses multi-agent reinforcement learning to make beamforming and scheduling decisions, and this RL-driven allocation is what produces the reported efficiency and latency gains.

What would settle it

Re-run the same scenario (100 requests per slot, 5 near-RT RICs, 3 CUs, 8 DUs, 25 RUs, 28 GHz, 400 MHz, urban-macro path loss) with several RL hyperparameter sets, reward weights, and independent random seeds; if the spectral-efficiency margin over Round Robin drops below a few percent, or overlaps with the baselines within confidence intervals, the central claim is not supported.

Watch

Extended reading notes

Core claim

The paper's central claim is that an AI-driven orchestration framework called OrchestRAN can outperform conventional RAN scheduling in both latency and spectral efficiency. OrchestRAN's orchestration engine selects suitable models—RL for resource allocation, FL for distributed intelligence, GNNs for topology-aware management, transformers for traffic forecasting—from an ML/AI catalog based on each request's service requirements, and deploys them as O-RAN apps through E2, A1, and O1 interfaces. Simulating 100 operator requests per time slot across 2 non-RT RICs, 5 near-RT RICs, 3 CUs, 8 DUs, and 25 RUs at 28 GHz with 400 MHz bandwidth, the paper reports that multi-agent RL scheduling and beamforming achieves up to 20% spectral-efficiency gain over Round Robin, 10-15% over Proportional Fair and Max-Min Fairness, and lower latency, attributing the gains to adaptive resource allocation and RL-based optimization.

Load-bearing premise

The reported 10-20% spectral-efficiency advantage rests on the assumption that the simulated reinforcement-learning training, reward design, and the three baseline schedulers are all configured fairly enough that the gap reflects the OrchestRAN architecture rather than tuning choices.

Editorial extensions

If this is right

The reported 10-20% spectral-efficiency gains and lower latency suggest that the same architecture can serve stringent 6G service classes without over-provisioning spectrum.
Because the architecture emits standard O-RAN interfaces, the orchestration layer could be deployed gradually on existing vRAN and O-RAN hardware rather than requiring a new physical RAN.
The framework's model-selection step implies that new AI capabilities such as transformer-based traffic forecasting or GNN topology management can be dropped into the catalog and dispatched to the appropriate RIC, CU, or DU level.
If the latency gains persist under load, OrchestRAN-type control is a candidate for near-real-time loops such as beam tracking and intelligent handover, not just scheduling.
The demonstrated gains, if reproducible, would give operators a concrete incentive to move from static scheduling rules to model-driven orchestration in 6G rollouts.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The same orchestration loop could be applied to energy-efficiency objectives: since the engine already reallocates resources based on network state, an energy-cost-aware reward could steer the RL agent without architectural changes, though the paper does not test this.
The simulation compares against three classical schedulers; a natural next benchmark is against other learning-based schedulers or carefully tuned variants of the same baselines, which would isolate the architecture's contribution from the RL algorithm's inherent strength.
The model-catalog design suggests a real-world rollout path where operators start with one AI model per node type and expand; the paper leaves the catalog-to-request matching policy unspecified, so that matching rule becomes a key design choice.
The paper's framing of RAN as a multi-agent system, where each node is an AI agent, points toward a testable scaling behavior: as the number of RICs grows, coordination overhead should be measured, since the simulation fixes the node counts.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short take: this is a magazine-style survey of AI-RAN enablers, and it is a decent one, plus a modest architectural proposal called OrchestRAN. The genuinely new material is the packaging: an orchestration framework that maps pre-trained AI models onto O-RAN components, a RAN-LAM sketch for large action models, and a speculative DAO-based orchestration idea. Those are reasonable vision pieces for a standards magazine, not new theory or reproducible measurement.

What the paper does well: the survey is organized and readable. The AI-readiness framing of D-RAN to C-RAN to vRAN to O-RAN is clean, the coverage of digital twins, GenAI/LAMs, blockchain, IRSs, and federated learning is accurate at the level of a survey, and the reference list includes the right anchors (3GPP TR 33.898, xLAM, O-RAN literature). The challenges section is honest about explainability, regulatory friction, and energy costs. There is no circular derivation anywhere: no equation is fitted to the simulation. The architecture is a plausible aggregation of known O-RAN building blocks, and the authors do not claim it is more than a proof of concept, aside from one overstatement.

The soft spot is real and load-bearing. The Section IV simulation claims up to 20% spectral efficiency gain over Round Robin and 10-15% over Proportional Fair and Max-Min Fairness, with lower latency, yet the text gives only topology counts, a channel model, and a traffic load. Missing: the multi-agent RL state/action/reward definitions, training algorithm, hyperparameters, baseline implementations, request arrival model, and latency accounting. No error bars, confidence intervals, or seed variation appear. The word "validate" in the text goes beyond what one unbenchmarked scenario can support. The stress-test note lands on this exactly. This is not a fatal flaw for a vision paper, but it is a serious mismatch between the strength of the claim and the evidence shown.

Also minor: the RAN-LAM framework is a figure plus a bullet list, not an implementation, and the DAO idea is speculative. That is fine for future-horizons discussion, but the paper should label these as sketches rather than contributions with demonstrated performance.

Who is this for? Readers wanting a compact, current survey of AI-RAN enablers and a glimpse of one orchestration idea. It deserves referee time, but the referee should push hard on the simulation. I would recommend: send to peer review; require either released code/data or a sharp downgrade of the validation language to "illustrative simulation." As submitted, I would not cite the performance numbers, and I would not bring it to reading group as a research contribution.

Referee Report

4 major / 6 minor

Summary. This magazine-style manuscript argues that 6G and beyond require AI-driven RAN architectures and reviews the evolution from D-RAN through C-RAN, vRAN, and O-RAN. It identifies digital twins, large GenAI models, blockchain, intelligent reflecting surfaces, and federated learning as key enablers, and proposes an 'OrchestRAN' framework together with a RAN-LAM component for intelligent orchestration and autonomous decision-making. The paper's quantitative contribution is a Python simulation of a distributed AI-RAN using multi-agent RL, which it claims achieves lower latency and spectral efficiency improvements of up to 20% over Round Robin and 10-15% over Proportional Fair and Max-Min Fairness. The final sections survey technical and regulatory challenges and outline future directions including RAN-MAS, ISAC, energy-efficient AI, and DAO-based orchestration.

Significance. If the Section IV results were reproducible, the paper would offer a useful architectural synthesis and a concrete proof-of-concept direction for AI-native RAN orchestration, and the list of future research directions is timely. The review portion is generally well referenced and clearly organized, and the proposed architecture is easy to follow in Fig. 4. However, the central quantitative claim currently rests on an underspecified single simulation: there is no code, no hyperparameter disclosure, no statistical analysis, and no external validation. The significance is therefore conditional on the authors either supplying the missing experimental detail or explicitly reframing the results as illustrative. The paper contains no machine-checked proofs or parameter-free derivations; its value lies in the synthesis and the proof-of-concept proposal, not in a demonstrated quantitative result as it stands.

major comments (4)

[Section IV] The simulation is described only by network topology counts (2 non-RT RICs, 5 near-RT RICs, 3 CUs, 8 DUs, 25 RUs), 100 requests per time slot, 28 GHz, 400 MHz, and the 3GPP TR 38.901 UMa path loss model, but the multi-agent RL formulation is not given. The text does not state the state space, action space, reward function, training algorithm, number of episodes, learning rate, exploration strategy, or how the three baselines (Round Robin, Proportional Fair, Max-Min Fairness) are implemented and tuned. This omission is load-bearing because the claimed 10-20% spectral efficiency gains and the latency reductions in Fig. 5 could be artifacts of untuned baselines or of an RL reward that directly maximizes the reported metric. The authors should provide a complete experimental specification, including code or detailed pseudocode, so that the comparison can be independently reproduced and assessed for fairness.
[Section IV, Fig. 5] Figures 5a and 5b are presented without error bars, confidence intervals, or any indication of the number of independent runs and random seeds used. The text states that OrchestRAN 'consistently outperforms' traditional scheduling, but a single favorable run would be consistent with the data shown. Please report means and variances over multiple seeds, and ideally a statistical test or, failing that, a clear statement that the curves are representative examples. Without such information, the quantitative claim of consistent improvement is not supported by the evidence presented.
[Section IV, Key Takeaways] The statement that these results 'validate the effectiveness of the AI-RAN framework' overstates what one internal simulation can show. The evaluation is entirely internal: the authors' own simulator tests their own architecture against baselines that are not externally calibrated, and there is no ablation or comparison with published results on O-RAN orchestration. The scenario is also a single operating point (one traffic load, one topology, one channel model), so the assertion that the framework performs well 'under varying network loads' is not demonstrated. Please either add experiments across loads and configurations or soften the validation language to 'preliminary illustration.'
[Section III.B and Fig. 3a] The RAN-LAM framework is introduced as a key enabler for AI-RAN and is illustrated with a detailed action list, but the simulation in Section IV does not appear to exercise RAN-LAM; the described scenario uses multi-agent RL for beamforming and scheduling. The relationship between RAN-LAM and OrchestRAN, and whether the simulation includes any component of RAN-LAM, should be clarified. If RAN-LAM is not part of the proof-of-concept, the paper should state this explicitly so that readers do not infer that the simulation validates the large action model.

minor comments (6)

[Section II-A] The description of D-RAN contains a redundant clause ('Each cell site processes the radio signals on the distributed RRU to directly process the radio signals on site'); consider rewriting for clarity.
[Section II-B] 'Commom Public Radio Interface' is a typo for 'Common Public Radio Interface'.
[Table I] 'UA Vs' should be 'UAVs'.
[Abstract] 'including, digital twin (DTs)' should be 'including digital twins (DTs)' for grammatical consistency.
[References] References [2], [5], and [7] list volume and pages as 'TBD'; please update with final publication data before submission.
[Section IV, Fig. 5] The captions of Fig. 5a and 5b give no axes labels or units; the text should describe what is plotted (e.g., average latency in ms versus time, or spectral efficiency in bits/s/Hz versus number of requests).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: Section IV claims are empirical simulation comparisons, not derivations from their own inputs.

full rationale

The paper contains no equation-level derivation chain in which a predicted quantity is defined in terms of the fitted input. Section IV reports a multi-agent RL-based OrchestRAN architecture and compares it against Round Robin, Proportional Fair, and Max-Min Fairness in terms of latency and spectral efficiency. The text attributes the gains to RL-based optimization, but it nowhere specifies the reward function, training procedure, or baseline objective functions, so one cannot exhibit a specific reduction such as 'the reported spectral efficiency is the RL reward, hence the comparison is forced by construction.' The central results are presented as simulation outcomes rather than as consequences of an analytically imposed relationship. The authors' self-citations (e.g., Refs. [1] and [12]) appear only as background citations in the review sections and are not load-bearing for the OrchestRAN simulation claims. No uniqueness theorem, imported ansatz, or pre-fitted parameter is used to force the reported improvements. The absence of simulation code, hyperparameters, seeds, and confidence intervals is a genuine reproducibility and verification concern, but under the stated rubric that is a correctness and evidence issue, not circularity. Therefore the honest finding is no significant circularity.

Assumptions & free parameters 4 free parameters · 4 assumptions · 3 invented entities

The paper's central performance claim rests on an unreleased simulation with many hand-chosen scenario parameters and unstated training details. The enabler discussions introduce illustrative frameworks such as OrchestRAN and RAN-LAM with no external validation.

free parameters (4)

Simulation topology counts (2 non-RT RICs, 5 near-RT RICs, 3 CUs, 8 DUs, 25 RUs) = chosen by hand
Affects load distribution and may favor the AI scheduler; no sensitivity analysis is reported in Section IV.
Traffic load (100 operator requests per time slot) = 100 requests per time slot
Arbitrary load point; results may not generalize to other loads despite the claim of 'varying network loads'.
Bandwidth and carrier frequency = 28 GHz, 400 MHz
Scenario inputs from Section IV; no variation is shown.
Multi-agent RL hyperparameters and reward weights = unreported
Training procedure is not described; the reported gains could depend on reward shaping and baseline implementations.

assumptions (4)

domain assumption 3GPP TR 38.901 Urban Macro path loss and ray-tracing-based fading are representative of the target 6G deployment.
Invoked in Section IV; the simulation's realism rests on this model, but no calibration or sensitivity analysis is given.
domain assumption The orchestration overhead (container dispatch, AI inference, data collection) is negligible or included in the reported latency.
Latency gains are attributed to intelligent decision making, yet no overhead model is described in Section IV.
domain assumption Multi-agent RL training converges and the agents generalize under the simulated scenario.
The paper reports performance without describing training length, convergence, or evaluation protocol.
ad hoc to paper O-RAN interfaces (E2, A1, O1) can implement the proposed orchestration policies without performance loss.
Section IV asserts policies are 'converted into executable O-RAN applications' but no interface modeling or validation is provided.

invented entities (3)

OrchestRAN orchestration framework
purpose: Coordinates AI model selection, containerized app dispatch, and resource policies across O-RAN nodes.
Proposed in Section IV and evaluated only in the authors' own simulation; no independent implementation or external benchmark exists.
RAN-LAM (large action model for RAN)
purpose: Converts network logs, incident reports, and configuration files into actions such as frequency and handover adjustments.
Introduced in Fig. 3a and Section III as an illustrative framework; no implementation or evaluation is provided.
DAO-based RAN orchestration
purpose: Autonomous management of AI model deployment and policy enforcement through decentralized autonomous organizations.
Described in Section V.C.4 as a future prospect without implementation.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Towards AI-Driven RANs for 6G and Beyond: Architectural Advancements and Future Horizons." pith.science (2026). https://pith.science/paper/54WQGICE

@misc{pith2026250616070,
  author       = {Pith},
  title        = {Pith review of: Towards AI-Driven RANs for 6G and Beyond: Architectural Advancements and Future Horizons},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/54WQGICE}},
  note         = {Machine review of arXiv:2506.16070}
}

read the original abstract

It is envisioned that 6G networks will be supported by key architectural principles, including intelligence, decentralization, interoperability, and digitalization. With the advances in artificial intelligence (AI) and machine learning (ML), embedding intelligence into the foundation of wireless communication systems is recognized as essential for 6G and beyond. Existing radio access network (RAN) architectures struggle to meet the ever growing demands for flexibility, automation, and adaptability required to build self-evolving and autonomous wireless networks. In this context, this paper explores the transition towards AI-driven RAN (AI-RAN) by developing a novel AI-RAN framework whose performance is evaluated through a practical scenario focused on intelligent orchestration and resource optimization. Besides, the paper reviews the evolution of RAN architectures and sheds light on key enablers of AI-RAN including digital twins (DTs), intelligent reflecting surfaces (IRSs), large generative AI (GenAI) models, and blockchain (BC). Furthermore, it discusses the deployment challenges of AI-RAN, including technical and regulatory perspectives, and outlines future research directions incorporating technologies such as integrated sensing and communication (ISAC) and agentic AI.

Figures

Figures reproduced from arXiv: 2506.16070 by the authors.

**Figure 2.** Architectural transition from D-RAN to O-RAN. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Illustration of the RAN-LAM framework and blockchain workflow/use cases for AI-RAN. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Proposed AI-RAN architecture for 6G. latency variance, whereas Proportional Fair and Max-Min Fairness provide moderate latency improvements. The reduced latency in OrchestRAN can be attributed to its intelligent decision-making process with AI models based on service r…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

AI-RAN on NPUs: Baseband Processing Without Baseband Chips
eess.SP 2026-07 accept novelty 7.0 of 10

A complete OFDM transceiver runs end-to-end over the air on a commercial edge NPU by remapping baseband operators onto dense matrix and vector engines.
When Agentic AI Meets Integrated Sensing and Communication
cs.AI 2026-08 conditional novelty 6.0 of 10

A survey proposing the AISAC six-stage loop and five maturity levels, and finding that reviewed ISAC systems rarely report agentic evaluation metrics.

Reference graph

Works this paper leans on

16 extracted references · 12 canonical work pages · cited by 2 Pith papers

[1]

Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions,

T. Gong, P. Gavriilidis, R. Ji, C. Huang, G. C. Alexandropoulos, L. Wei, Z. Zhang, M. Debbah, H. V . Poor, and C. Yuen, “Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions,”IEEE Communications Surveys & Tutorials, vol. 26, no. 1, pp. 196–257, 2024, doi: 10.1109/COMST.2023.3309529

work page arXiv 2024
[2]

Generative AI for Immersive Communication: The Next Fron- tier in Internet-of-Senses Through 6G,

N. Sehad, L. Bariah, W. Hamidouche, H. Hellaoui, R. Jantti, and M. Debbah, “Generative AI for Immersive Communication: The Next Fron- tier in Internet-of-Senses Through 6G,”IEEE Communications Magazine, vol. TBD, no. TBD, pp. 1–13, 2024, doi: 10.1109/MCOM.001.2400199

work page doi:10.1109/mcom.001.2400199 2024
[3]

The Evolution of RAN (Radio Access Network), D-RAN, C-RAN, V-RAN, and O-RAN,

J. L. Frauendorf and ´E. Almeida de Souza, “The Evolution of RAN (Radio Access Network), D-RAN, C-RAN, V-RAN, and O-RAN,” in The Architectural and Technological Revolution of 5G, Springer, Cham,

work page
[4]

Recent Progress on C-RAN Centralization and Cloudification,

I. Chih-Lin, J. Huang, R. Duan, C. Cui, J. X. Jiang, and L. Li, “Recent Progress on C-RAN Centralization and Cloudification,”IEEE Access, vol. 2, pp. 1030–1039, 2014, doi: 10.1109/ACCESS.2014.2351411

work page arXiv 2014
[5]

AI-RAN in 6G Networks: State-of- the-Art and Challenges,

N. A. Kaim Khani and S. Schmid, “AI-RAN in 6G Networks: State-of- the-Art and Challenges,”IEEE Open Journal of the Communications So- ciety, vol. PP, pp. 1–1, Jan. 2023, doi: 10.1109/OJCOMS.2023.3343069

work page arXiv 2023
[6]

Digital- Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions,

L. U. Khan, W. Saad, D. Niyato, Z. Han, and C. S. Hong, “Digital- Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions,” IEEE Communications Magazine, vol. 60, no. 1, pp. 74–80, 2022, doi: 10.1109/MCOM.001.21143

work page doi:10.1109/mcom.001.21143 2022
[7]

Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities,

H. Zhou, C. Hu, Y . Yuan, Y . Cui, Y . Jin, C. Chen, H. Wu, D. Yuan, L. Jiang, D. Wu, X. Liu, C. Zhang, X. Wang, and J. Liu, “Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities,”IEEE Com- munications Surveys & Tutorials, vol. TBD, no. TBD, pp. 1–1, 2024, doi: 10.1109/COMST.2024.3465447

work page arXiv 2024
[8]

xLAM: A Family of Large Action Models to Empower AI Agent Systems,

J. Zhang, T. Lan, M. Zhu, Z. Liu, T. Hoang, S. Kokane, W. Yao, J. Tan, A. Prabhakar, H. Chen, Z. Liu, Y . Feng, T. Awalgaonkar, R. Murthy, E. Hu, Z. Chen, R. Xu, J. C. Niebles, S. Heinecke, H. Wang, S. Savarese, and C. Xiong, “xLAM: A Family of Large Action Models to Empower AI Agent Systems,”arXiv Preprint, arXiv:2409.03215, 2024. [Online]. Available: ht...

arXiv 2024

Show all 16 references

[9]

Bitcoin: A Peer-to-Peer Electronic Cash System,

S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,” 2008. [Online]. Available: https://bitcoin.org/bitcoin.pdf. [Accessed: Feb. 27, 2025]

2008
[10]

A Prospective Look: Key Enabling Technologies, Applications and Open Research Topics in 6G Networks,

L. Bariah, L. Mohjazi, S. Muhaidat, P. C. Sofotasios, G. K. Kurt, H. Yanikomeroglu, and O. A. Dobre, “A Prospective Look: Key Enabling Technologies, Applications and Open Research Topics in 6G Networks,” IEEE Access, vol. 8, pp. 174792–174820, 2020, doi: 10.1109/AC- CESS.2020.3019590

2020
[11]

System-Level Simulation and Performance Evaluation for 6G Ultra Massive MIMO,

J. Guo, L. Gao, N. Li, S. Yang, J. Zhu, X. She, J. Wang, and P. Chen, “System-Level Simulation and Performance Evaluation for 6G Ultra Massive MIMO,” inProc. 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), 2023, pp. 1–6, doi: 10.1109/VTC2023- Spring57618.2023.10199530

2023
[12]

A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications,

Z. Wang, J. Zhang, H. Du, D. Niyato, S. Cui, B. Ai, M. Debbah, K. B. Letaief, and H. V . Poor, “A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications,”IEEE Communications Surveys & Tutorials, vol. 26, no. 3, pp. 1560–1605, 2024, doi...

2024
[13]

Federated Learning for 6G Networks: Navigating Privacy Benefits and Challenges,

C. Sandeepa, E. Zeydan, T. Samarasinghe, and M. Liyanage, “Federated Learning for 6G Networks: Navigating Privacy Benefits and Challenges,” IEEE Open Journal of the Communications Society, vol. 6, pp. 90–129, 2025, doi: 10.1109/OJCOMS.2024.3513832

2025
[14]

Artificial Intelligence (AI)/Machine Learning (ML) for NG- RAN,

3GPP, “Artificial Intelligence (AI)/Machine Learning (ML) for NG- RAN,” 3GPP TSG RAN Meeting #94e, RP-213602, Dec. 2021. [Online]. Available: https://www.3gpp.org/ftp/tsg ran/TSG RAN/TSGR 94e/Docs/RP-213602.zip

2021
[15]

Study on Security and Privacy of Artificial Intelligence/Machine Learning (AI/ML)-Based Services and Applications in 5G,

3GPP, “Study on Security and Privacy of Artificial Intelligence/Machine Learning (AI/ML)-Based Services and Applications in 5G,” 3GPP TR 33.898, June 2023. [Online]. Available: https://www.3gpp.org/dynareport/ 33898.htm

2023
[2023]

DOI: https://doi.org/10.1007/978-3-031-10650-7 10

doi

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions,

T. Gong, P. Gavriilidis, R. Ji, C. Huang, G. C. Alexandropoulos, L. Wei, Z. Zhang, M. Debbah, H. V . Poor, and C. Yuen, “Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions,”IEEE Communications Surveys & Tutorials, vol. 26, no. 1, pp. 196–257, 2024, doi: 10.1109/COMST.2023.3309529

work page arXiv 2024

[2] [2]

Generative AI for Immersive Communication: The Next Fron- tier in Internet-of-Senses Through 6G,

N. Sehad, L. Bariah, W. Hamidouche, H. Hellaoui, R. Jantti, and M. Debbah, “Generative AI for Immersive Communication: The Next Fron- tier in Internet-of-Senses Through 6G,”IEEE Communications Magazine, vol. TBD, no. TBD, pp. 1–13, 2024, doi: 10.1109/MCOM.001.2400199

work page doi:10.1109/mcom.001.2400199 2024

[3] [3]

The Evolution of RAN (Radio Access Network), D-RAN, C-RAN, V-RAN, and O-RAN,

J. L. Frauendorf and ´E. Almeida de Souza, “The Evolution of RAN (Radio Access Network), D-RAN, C-RAN, V-RAN, and O-RAN,” in The Architectural and Technological Revolution of 5G, Springer, Cham,

work page

[4] [4]

Recent Progress on C-RAN Centralization and Cloudification,

I. Chih-Lin, J. Huang, R. Duan, C. Cui, J. X. Jiang, and L. Li, “Recent Progress on C-RAN Centralization and Cloudification,”IEEE Access, vol. 2, pp. 1030–1039, 2014, doi: 10.1109/ACCESS.2014.2351411

work page arXiv 2014

[5] [5]

AI-RAN in 6G Networks: State-of- the-Art and Challenges,

N. A. Kaim Khani and S. Schmid, “AI-RAN in 6G Networks: State-of- the-Art and Challenges,”IEEE Open Journal of the Communications So- ciety, vol. PP, pp. 1–1, Jan. 2023, doi: 10.1109/OJCOMS.2023.3343069

work page arXiv 2023

[6] [6]

Digital- Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions,

L. U. Khan, W. Saad, D. Niyato, Z. Han, and C. S. Hong, “Digital- Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions,” IEEE Communications Magazine, vol. 60, no. 1, pp. 74–80, 2022, doi: 10.1109/MCOM.001.21143

work page doi:10.1109/mcom.001.21143 2022

[7] [7]

Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities,

H. Zhou, C. Hu, Y . Yuan, Y . Cui, Y . Jin, C. Chen, H. Wu, D. Yuan, L. Jiang, D. Wu, X. Liu, C. Zhang, X. Wang, and J. Liu, “Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities,”IEEE Com- munications Surveys & Tutorials, vol. TBD, no. TBD, pp. 1–1, 2024, doi: 10.1109/COMST.2024.3465447

work page arXiv 2024

[8] [8]

xLAM: A Family of Large Action Models to Empower AI Agent Systems,

J. Zhang, T. Lan, M. Zhu, Z. Liu, T. Hoang, S. Kokane, W. Yao, J. Tan, A. Prabhakar, H. Chen, Z. Liu, Y . Feng, T. Awalgaonkar, R. Murthy, E. Hu, Z. Chen, R. Xu, J. C. Niebles, S. Heinecke, H. Wang, S. Savarese, and C. Xiong, “xLAM: A Family of Large Action Models to Empower AI Agent Systems,”arXiv Preprint, arXiv:2409.03215, 2024. [Online]. Available: ht...

arXiv 2024