{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T09:35:00Z","timestamp":1774949700511,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Network"],"abstract":"<jats:p>Fifth-generation (5G) mobile networks must simultaneously satisfy stringent latency targets, high user density, and energy-aware operation across heterogeneous services. Cloud Radio Access Networks (C-RAN) provide architectural flexibility through centralized baseband processing, but they also introduce new control challenges related to fronthaul constraints, dynamic traffic variations, and joint radio\u2013compute coordination with Mobile Edge Computing (MEC). This paper proposes a unified AI-driven optimization framework for adaptive 5G C-RAN management, where the controller dynamically tunes key system decisions\u2014including functional split selection, TDD downlink ratio, user\u2013RU association, fronthaul load management, and MEC offloading proportion. To enable fair benchmarking under identical simulation settings, a static baseline policy is compared against five adaptive control strategies: Deep Q-Network (DQN), Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Multi-Objective Reinforcement Learning (MORL), and a Deterministic Service-Level Agreement (SLA)-aware controller Penalty-Constrained Hierarchical Action Controller (PCHAC). Performance evaluation across techno-economic and service KPIs shows that intelligent control significantly improves operational profit, tail-latency behavior, and energy efficiency while enhancing SLA compliance compared with non-adaptive operation. The results highlight the practicality of multi-objective and constraint-aware learning for next-generation C-RAN orchestration under scaling traffic demand.<\/jats:p>","DOI":"10.3390\/network6020020","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T08:09:18Z","timestamp":1774944558000},"page":"20","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Techno-Economic and SLA-Aware Control of 5G Cloud-RAN via Multi-Objective and Penalty-Constrained Reinforcement Learning"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4749-1423","authenticated-orcid":false,"given":"Sherif M.","family":"Aboul","sequence":"first","affiliation":[{"name":"Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11629, Egypt"},{"name":"Electrical Engineering Department, Faculty of Engineering, MTI University, Cairo 11439, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hala M.","family":"Abd El Kader","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11629, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8147-3778","authenticated-orcid":false,"given":"Esraa M.","family":"Eid","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11629, Egypt"},{"name":"Faculty of Computer Science, Benha National University, Cairo 13518, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shimaa S.","family":"Ali","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11629, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"34372","DOI":"10.1109\/ACCESS.2023.3264592","article-title":"A Survey of Scheduling in 5G URLLC and Outlook for Emerging 6G Systems","volume":"11","author":"Haque","year":"2023","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"110459","DOI":"10.1016\/j.comnet.2024.110459","article-title":"Latency Optimized C-RAN in Optical Backhaul and RF Fronthaul Architecture for beyond 5G Network: A Comprehensive Survey","volume":"247","author":"Bhandari","year":"2024","journal-title":"Comput. 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