{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T09:07:52Z","timestamp":1779959272100,"version":"3.53.1"},"reference-count":41,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T00:00:00Z","timestamp":1779840000000},"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>A coupled multi-stage learning framework is presented for joint bit error rate (BER) prediction and beam angle optimization in massive multiple-input multiple-output (MIMO) systems under a controlled simulation protocol. Unlike purely sequential benchmarking pipelines, the proposed method jointly coordinates BER prediction and beam-angle selection through a shared latent representation, an uncertainty-guided refinement mechanism, a cross-stage consistency loss and alternating optimization. Ten diverse approaches are systematically evaluated across two task-specific stages: Stage 1 examines six classical and adapted methods for BER prediction, including polynomial regression and deep unfolding networks; Stage 2 investigates four machine-learning and generative adversarial network (GAN)-based approaches for angle optimization, including conditional GANs and the proposed Direct-Angle neural network. Stage 3 couples the best-performing methods into a unified hybrid architecture through a shared encoder, explicit consistency regularization and alternating cross-stage updates, thereby producing an integrated beamforming decision strategy rather than an independent cascade. It is shown through the evaluation that the coupled hybrid framework achieves 96.0% overall angle-selection accuracy, a mean BER of 8.0\u00d710\u22125 and 100% BER tolerance compliance within \u00b13 dB. In this framework, a differentiable BER surrogate initialized from a second-degree polynomial-regression teacher is coupled with the proposed Direct-Angle-NN for angle optimization. Relative to the strongest reimplemented literature baseline under the same controlled simulation assumptions, a 33.3% reduction in mean BER is achieved. Ablation experiments show that the coupling mechanism provides a modest but consistent improvement over the decoupled sequential baseline, increasing angle-selection accuracy from 93.5% to 96.0% and reducing mean BER from 1.05\u00d710\u22124 to 8.0\u00d710\u22125; the shared encoder accounts for the largest part of this gain while the consistency loss adds 0.6 percentage points. These results indicate that the shared encoder, consistency regularization and uncertainty-guided refinement improve the final beamforming decision, although the gain should be interpreted as incremental rather than as a large architectural breakthrough. A spectral efficiency of 38.0 bps\/Hz and an energy efficiency of 0.466 Gbps\/W are achieved with a power consumption of only 32.6 W. The theoretical discussion is presented as an analytical characterization of BER sensitivity, complemented by a computational-complexity assessment and empirical convergence diagnostics for the alternating optimization, rather than as a formal optimality proof.\u00a0The effectiveness of the framework across multiple performance metrics is supported by Monte Carlo simulations, while the limitations of the current setup, including perfect CSI, uncoded QPSK, ideal hardware assumptions and a fixed beam codebook, are explicitly discussed. The complete simulation framework, including code and trained models, can be made available by the corresponding author upon reasonable request to facilitate reproducible research in massive MIMO optimization.<\/jats:p>","DOI":"10.3390\/network6020035","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T12:31:56Z","timestamp":1779885116000},"page":"35","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Coupled Multi-Stage Hybrid Framework for BER Prediction and Beam Angle Optimization in Massive MIMO Systems: Combining Classical Regression with Coupled Deep Learning Approaches"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1562-5543","authenticated-orcid":false,"given":"Iacovos","family":"Ioannou","sequence":"first","affiliation":[{"name":"Department of Computer Science, Philips University, 2001 Strovolos, Cyprus"},{"name":"Department of Computer Science, University of Cyprus, 1678 Nicosia, Cyprus"},{"name":"CYENS Centre of Excellence, 1016 Nicosia, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5930-8814","authenticated-orcid":false,"given":"Michael","family":"Georgiades","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Neapolis University Pafos, 8042 Pafos, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prabagarane","family":"Nagaradjane","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, SSN College of Engineering, Rajiv Gandhi Salai, Kalavakkam, Chennai 603110, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3600-8090","authenticated-orcid":false,"given":"Ala","family":"Khalifeh","sequence":"additional","affiliation":[{"name":"School of Computing, German Jordanian University, Amman 11180, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1852-8642","authenticated-orcid":false,"given":"Christophoros","family":"Christophorou","sequence":"additional","affiliation":[{"name":"School of Sciences, UCLan Cyprus, 12-14 University Avenue, 7080 Pyla, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1513-6018","authenticated-orcid":false,"given":"Marios","family":"Raspopoulos","sequence":"additional","affiliation":[{"name":"School of Sciences, UCLan Cyprus, 12-14 University Avenue, 7080 Pyla, Cyprus"},{"name":"INSPIRE Research Centre, UCLan Cyprus, 12-14 University Avenue, 7080 Pyla, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8647-0860","authenticated-orcid":false,"given":"Vasos","family":"Vassiliou","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Cyprus, 1678 Nicosia, Cyprus"},{"name":"CYENS Centre of Excellence, 1016 Nicosia, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1561\/2000000093","article-title":"Massive MIMO networks: Spectral, energy, and hardware efficiency","volume":"11","author":"Hoydis","year":"2017","journal-title":"Found. 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