Advanced MIMO Systems Optimization

Últimos artículos

• Multi-Agent Orchestration of 3GPP Channel Estimators

  I. Zakir Ahmed, Hamid Sadjadpour · 25 de septiembre de 2026

  Pilot-aided channel estimation is a decisive block in orthogonal frequency-division multiplexing (OFDM) receivers for both 5G New Radio (5G-NR) and Long-Term Evolution (LTE). A large body of estimators exists, from simple least-squares (LS) interpolation to statistically optimal linear minimum-mean-…

• User-Level Handover Decision Making Based on Machine Learning Approaches

  Jo\~ao Lima, Alvaro Medeiros, Eduardo Aguiar, Vicente Angelo de Sousa Junior, Tarciana Guerra · 22 de septiembre de 2026

  This letter covers a broad comparison of methods for classification and regression applications for a user-level handover decision making in scenarios with adverse propagation conditions involving buildings, coverage holes, and shadowing effects. The simulation campaigns are based on network simulat…

• Interference-Driven Clustered Optimisation for FM Spectrum Coordination

  Federica Mangiatordi, Emiliano Pallotti · 21 de septiembre de 2026

  Cross-border FM spectrum coordination involves protecting foreign broadcasting services while preserving domestic coverage, amid increasingly large radio-planning datasets containing thousands of transmitters and millions of transmitter-pixel relationships. In such scenarios, conventional optimisati…

• Goal-oriented probabilistic forecasting for dynamic PRB allocation in 5G networks

  Oier Larumbe-Lizarraga, Roberto Pereira, Cristian J. Vaca-Rubio · 16 de septiembre de 2026

  Efficient physical resource block (PRB) allocation in 5G networks requires accurate demand forecasting. Conventional methods minimize symmetric error metrics (MAE, RMSE), ignoring the operational cost asymmetry where under-provisioning (service degradation) is far costlier than over-provisioning (wa…

• Efficient Graph Neural Networks for Multicarrier Wideband Hybrid Beamforming Optimization

  Beier Li, Mai Vu · 10 de septiembre de 2026

  6G wireless technology is poised to adopt higher and wider frequency bands, leveraging highly directional beamforming. However, the vast bandwidths amplify the impact of beam squinting. Traditional solutions, such as adding a true-time-delay filter to each antenna, are cost-prohibitive due to the re…

• Improving 5G AI-RAN MCS Selection by Predicting Retransmissions

  Tamerlan Aghayev, Maxime Elkael, Michele Polese, Reshma Prasad, Salvatore D'Oro, Yunseong Lee, Koichiro Furueda, Tommaso Melodia · 10 de septiembre de 2026

  Link Adaptation (LA) in 5G NR is inherently reactive, relying on channel measurements and HARQ feedback that may become quickly obsolete when the channel changes quickly. This data is also noisy, making it hard to track accurately, and has to be fed to real-time controllers with feedback-loop effect…

• Feasible but Not Safe: Constraint Violations and Report-Channel Attacks in Learned Cell-Free ISAC Association

  Mehdi Zafari, Iman Mohammadi, A. Lee Swindlehurst · 4 de septiembre de 2026

  Learning-based schedulers have been proposed to provide real-time user, target, and access point (AP) association in distributed cell-free integrated sensing and communication systems. In a typical approach, a graph neural network (GNN), trained on labels from a mixed-integer linear program, maps li…

• A Peer-Relative Representation Learning Framework for Energy Inefficiency Identification in Mobile Network Sites

  Eliud Nyakweba Koto, Jaco du Toit, Adham Stoltz, Johan du Preez · 4 de septiembre de 2026

  Energy consumption is one of the largest operational expenditure items for mobile network operators, yet site-level energy inefficiencies such as faulty cooling controllers, idle radio equipment, and parasitic auxiliary loads often remain undetected because no ground-truth inefficiency labels exist …

• WiSDoM: Wireless Sparse Decision Transformer with Mixture-of-Experts for Multi-Task Mobile Network Optimization

  Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci · 2 de septiembre de 2026

  Emerging 6G wireless networks are expected to operate across diverse deployment scenarios, where variations in network topology, user mobility, traffic demand, and radio conditions challenge the scalability of conventional radio resource management (RRM). While offline reinforcement learning (RL) me…

• AI/ML Life Cycle Management for Interoperable AI Native RAN

  Chu-Hsiang Huang, Yuan-Chih Fan Chiang, Chao-Kai Wen, Geoffrey Ye Li · 27 de agosto de 2026

  Artificial intelligence (AI) and machine learning (ML) are rapidly becoming integral to the 5G Radio Access Network (RAN), enabling beam management, channel state information (CSI) feedback, positioning, and mobility prediction. However, without a standardized life-cycle management (LCM) framework, …

• Agentic Autoresearch for Cell-Edge Power Control: Radically Redefining the Researcher's Role

  Ahmad Khan, Akram Bin Sediq, Sara Azadegi Naeini, Raviraj S. Adve · 27 de agosto de 2026

  Designing machine learning algorithms for wireless resource management is labour-intensive: the architecture, the loss function and the training recipe are all specified by hand. We demonstrate that this design layer can be surrendered to an autonomous agent in its entirety. We adopt the autoresearc…

• Multi-Agent Off-Policy Deep Reinforcement Learning for Smart Campus Coverage

  Omar Rady, Mohamed Ayman, Ali Arafa, Mohamed Shalma · 20 de agosto de 2026

  Deep reinforcement learning (DRL) has recently gained a great attention due to its real-time adaptation and effectiveness in complex optimization problems. This paper investigates the optimal deployment of millimeter-wave (mmWave) base stations (BSs) in a realistic, non-convex campus topology. The o…

• A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks

  Naveed Khan, Besan Al Sbeihi, Maryam Alshehhi, Nasir Saeed · 18 de agosto de 2026

  Foundation models are emerging as a transformative paradigm for AI-native sixth-generation (6G) wireless networks by enabling scalable, transferable, and data-efficient intelligence across diverse communication tasks. Unlike conventional deep learning models that are trained for individual applicati…

• Multi-perspective Imbalance-Conscious 6G Beamforming Optimization and Performance

  Chukwunonso Henry Nwokoye, Blessing Oluchi Iloka, Chikwue V. Umeugoji, Christopher Anene Egemba, Nnenna D. Duroha · 14 de agosto de 2026

  The study presents a systematic machine learning (ML) study of 6G-IoT beamforming optimization (6GBO) using supervised and unsupervised approaches. We compared the predictive power of network, environmental, device, and vision feature groups for 6GBO. Additionally, it addressed other unsupervised pe…

• FedCritic-MIMO: Communication-Efficient Serverless Federated Critic Learning for Massive-MIMO Resource Control in Open and Disaggregated 6G RANs

  Amin Farajzadeh, Melike Erol-Kantarci · 5 de agosto de 2026

  This paper proposes FedCritic-MIMO, a communication-efficient serverless federated multi-agent reinforcement learning framework for AI-native resource control across independently deployable cell-level controllers in open and disaggregated 6G RANs. Controllers share no trainer, retain local actors a…

• Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression

  Yigit Berkay Uslu, Navid NaderiAlizadeh, Mark Eisen, Alejandro Ribeiro · 27 de julio de 2026

  We consider resource allocation problems in multi-user wireless networks, where the goal is to optimize a network-wide utility function subject to constraints on the ergodic average performance of users. We demonstrate how a state-augmented graph neural network (GNN) parametrization for the resource…

• Physically Constrained Federated Additive Models for O-RAN SLA-Risk Prediction

  Aubida A. Al-Hameed, Mohammed M. H. Qazzaz, Maryam Hafeez, Syed A. Zaidi · 27 de julio de 2026

  Proactive service assurance in O-RAN requires predicting per-slice SLA violations before they occur. The prediction model must be auditable by operators and must train across base stations without pooling per-slice KPIs, which are commercially sensitive because slices are leased to individual tenant…

• Lightweight PCGAE-Net: Parallel CrossGate Attention and Bottleneck AutoEncoder for Efficient 5G Channel Prediction

  Uma Kishore Godavarti, K. Giridhar, Vanani Prince Dharmendrabhai, Anchit Panday, Madhan Raj Kanagarathinam · 25 de junio de 2026

  Accurate channel state information (CSI) prediction is essential for proactive beamforming and resource management in 5G massive MIMO systems, yet the deployment of high-accuracy transformer-based predictors on base-station hardware remains challenging because the most capable models carry upwards o…

• LOLLA: Deep Reinforcement Learning for Closed-Loop Link Adaptation Towards a GPU-Accelerated AI-RAN

  Rui Wang, Linchao Zhang, Qiang Liu, Kun Yang · 23 de junio de 2026

  Outer-loop link adaptation (OLLA) is widely deployed in 5G NR to track channel variations, yet its reliance on first-order, single-bit feedback degrades performance significantly under high-mobility and fast-varying channels. This paper presents LOLLA (Learned Outer-Loop Link Adaptation), a deep rei…

• Physical-AI: From Channel Awareness to Environmental Intelligence in 6G Wireless Networks

  Farooque Hassan Kumbhar, Kapal Dev, Sunder Ali Khowaja, Alexandros-Apostolos A. Boulogeorgos, Mehdi Bennis, Yuanwei Liu · 23 de junio de 2026

  Conventional wireless networks rely on instantaneous channel state information (CSI) and react to channel variations without explicitly modeling the physical environment, limiting their ability to handle blockage, mobility, and interference in dynamic deployments. Paradigms such as Integrated Sensin…

• Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers

  Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci · 4 de junio de 2026

  Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimization-driven radio resource management (RRM) toward artificial intelligence (AI)-driven RRM. AI-driven approaches can l…

• FedCritic: Serverless Federated Critic Learning-based Resource Allocation for Multi-Cell OFDMA in 6G

  Amin Farajzadeh, Melike Erol-Kantarci · 21 de mayo de 2026

  In sixth-generation (6G) ultra-dense networks, aggressive frequency reuse amplifies inter-cell interference (ICI), making multi-cell orthogonal frequency-division multiple access (OFDMA) scheduling and power control strongly coupled across neighboring cells. We study distributed downlink resource ma…

• Multi-Block Attention for Efficient Channel Estimation in IRS-Assisted mmWave MIMO

  Mehrdad Momen-Tayefeh, Mehrshad Momen-Tayefeh, Maryam Sabbaghian · 15 de mayo de 2026

  Intelligent Reflecting Surfaces (IRSs) are a promising technology for enhancing the spectral and energy efficiency of millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. In these systems, accurate channel estimation remains challenging due to the passive nature of IRS elements an…

• Agentic AI-Based Joint Computing and Networking via Mixture of Experts and Large Language Models

  Robert-Jeron Reifert, Alaa Alameer Ahmad, Hayssam Dahrouj, Aydin Sezgin · 6 de mayo de 2026

  Future sixth-generation (6G) mobile networks are envisioned to be equipped with a diverse set of powerful, yet highly specialized, optimization experts. Such a promising vision is concurrently expected to give rise to the need for scalable mechanisms that can select, combine, and orchestrate such ex…

• Data driven approach for Outdoor Channel Prediction in 5G and Beyond

  A. Sathi Babu, V. Udaya Sankar, Vishnu Ram OV · 6 de mayo de 2026

  An evolution of Wireless Communications towards 5G and beyond provides improved user experience in terms of quality of services. Understanding and estimating Channel information plays crucial role in providing better user experience. Traditional methods of channel estimation involves periodically se…