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Impact of Link Adaptation Strategies in Ray-Traced 5G Scenarios

Year 2025, Volume: 1 Issue: 2, 29 - 35, 29.09.2025

Abstract

Efficient link adaptation is critical for maintaining reliable and high-throughput wireless communication, especially in 5G networks where dense deployments and diverse mobility patterns increase the complexity of radio conditions. This study evaluates the performance of Inner-Loop Link Adaptation (ILLA) and Outer-Loop Link Adaptation (OLLA) mechanisms under realistic scenarios involving imperfect channel estimates and noisy CQI feedback. A ray tracing-based simulation environment was developed using Sionna to model the effects of multipath propagation, shadowing, and geometric obstructions in an actual urban area. Results show that while ILLA reacts quickly to instantaneous SINR variations, it struggles to maintain the target BLER under feedback uncertainty. In contrast, the OLLA-enhanced system achieves more stable BLER control and higher spectral efficiency by compensating for non-idealities of noisy channel through HARQ-based offset tuning. The findings highlight the importance of robust and adaptive link control mechanisms in 5G and beyond wireless systems, particularly under unreliable feedback conditions

References

  • [1] G. Ku and J. M. Walsh, “Resource allocation and link adaptation in LTE and LTE advanced: A tutorial,” IEEE communications surveys & tutorials, vol. 17, no. 3, pp. 1605–1633, 2014.
  • [2] E. Makridis, “Reinforcement learning for link adaptation in 5G-NR networks.” 2020. [Online]. Available: https://www.diva-portal.org/smash/record.jsf?pid=diva2:1527910.
  • [3] F. Blanquez-Casado, G. Gomez, M. D. C. Aguayo-Torres, and J. T. Entrambasaguas, “eOLLA: an enhanced outer loop link adaptation for cellular networks,” J Wireless Com Network, vol. 2016, no. 1, Dec. 2016, doi: 10.1186/s13638-016-0518-3.
  • [4] A. Duran, M. Toril, F. Ruiz, and A. Mendo, “Self-optimization algorithm for outer loop link adaptation in LTE,” IEEE Communications letters, vol. 19, no. 11, pp. 2005–2008, 2015.
  • [5] T. Ohseki and Y. Suegara, “Fast outer-loop link adaptation scheme realizing low-latency transmission in LTE-advanced and future wireless networks,” in 2016 IEEE radio and wireless symposium (RWS), IEEE, 2016, pp. 1–3.
  • [6] V. Saxena, H. Tullberg, and J. Jaldén, “Reinforcement Learning for Efficient and Tuning-Free Link Adaptation,” May 05, 2021, arXiv: arXiv:2010.08651. doi: 10.48550/arXiv.2010.08651.
  • [7] F. Geiser et al., “DRLLA: Deep Reinforcement Learning for Link Adaptation,” Telecom, vol. 3, no. 4, Art. no. 4, Dec. 2022, doi: 10.3390/telecom3040037.
  • [8] P. Kela, T. Höhne, T. Veijalainen, and H. Abdulrahman, “Reinforcement Learning for Delay Sensitive Uplink Outer-Loop Link Adaptation,” in 2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), Jun. 2022, pp. 59–64. doi: 10.1109/EuCNC/6GSummit54941.2022.9815746.
  • [9] P. Bertrand, J. Jiang, and A. Ekpenyong, “Link Adaptation Control in LTE Uplink,” in 2012 IEEE Vehicular Technology Conference (VTC Fall), Sep. 2012, pp. 1–5. doi: 10.1109/VTCFall.2012.6399063.
  • [10] S. Wu, C. Chakrabarti, and A. Alkhateeb, “Proactively predicting dynamic 6G link blockages using LiDAR and in-band signatures,” IEEE open journal of the communications society, vol. 4, pp. 392–412, 2023.
  • [11] H. S. Sucuoglu, “Development of Real-Time Fire Detection Robotic System with Hybrid-Cascade Machine Learning Detection Structure,” Processes, vol. 13, no. 6, p. 1712, 2025.
  • [12] J. Hoydis et al., “Sionna: An Open-Source Library for Next-Generation Physical Layer Research,” Mar. 20, 2023, arXiv: arXiv:2203.11854. doi: 10.48550/arXiv.2203.11854.
  • [13] S. N. Donthi and N. B. Mehta, “An accurate model for EESM and its application to analysis of CQI feedback schemes and scheduling in LTE,” IEEE Transactions on Wireless Communications, vol. 10, no. 10, pp. 3436–3448, 2011.
  • [14] A. Sampath, P. S. Kumar, and J. M. Holtzman, “On setting reverse link target SIR in a CDMA system,” in 1997 IEEE 47th Vehicular Technology Conference. Technology in Motion, IEEE, 1997, pp. 929–933. Accessed: Jul. 13, 2025. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/600465/.
  • [15] K. I. Pedersen et al., “Frequency domain scheduling for OFDMA with limited and noisy channel feedback,” in 2007 IEEE 66th Vehicular Technology Conference, IEEE, 2007, pp. 1792–1796.
  • [16] OpenStreetMap Foundation, “OpenStreetMap.” Accessed: Dec. 10, 2024. [Online]. Available: https://www.openstreetmap.org.
  • [17] Blender Foundation, “Blender.” Accessed: Dec. 15, 2024. [Online]. Available: https://www.blender.org.
  • [18] W. Jakob et al., Mitsuba 3 Physically Based Renderer. (2022). Accessed: Feb. 02, 2025. [Online]. Available: https://mitsuba-renderer.org.
  • [19] J. Hoydis et al., Sionna. (2022). [Online]. Available: https://nvlabs.github.io/sionna/.
  • [20] ITU–R P.2040–3, “Effects of building materials and structures on radiowave propagation above about 100 MHz,” Geneva, 2023.

Işın İzlemeli 5G Senaryolarında Bağlantı Uyarlama Stratejilerinin Etkisi

Year 2025, Volume: 1 Issue: 2, 29 - 35, 29.09.2025

Abstract

Verimli bağlantı adaptasyonu, özellikle yoğun dağıtımların ve çeşitli mobilite modellerinin radyo koşullarının karmaşıklığını artırdığı 5G ağlarında, güvenilir ve yüksek verimli kablosuz iletişimin sürdürülmesi için kritik öneme sahiptir. Bu çalışma, kusurlu kanal tahminleri ve gürültülü CQI geri bildirimi içeren gerçekçi senaryolar altında İç Döngü Bağlantı Uyarlaması (ILLA) ve Dış Döngü Bağlantı Uyarlaması (OLLA) mekanizmalarının performansını değerlendirmektedir. Gerçek bir kentsel alanda çoklu yol yayılımının, gölgelemenin ve geometrik engellerin etkilerini modellemek için Sionna kullanılarak ışın izleme tabanlı bir simülasyon ortamı geliştirilmiştir. Sonuçlar, ILLA'nın anlık SINR değişimlerine hızlı tepki verirken, geri bildirim belirsizliği altında hedef BLER'i korumakta zorlandığını göstermektedir. Buna karşılık, OLLA ile geliştirilmiş sistem, HARQ tabanlı ofset ayarı yoluyla gürültülü kanalın ideal olmayan özelliklerini telafi ederek daha kararlı BLER kontrolü ve daha yüksek spektral verimlilik elde etmektedir. Bulgular, özellikle güvenilmez geri bildirim koşulları altında, 5G ve ötesi kablosuz sistemlerde sağlam ve adaptif bağlantı kontrol mekanizmalarının önemini vurgulamaktadır.

References

  • [1] G. Ku and J. M. Walsh, “Resource allocation and link adaptation in LTE and LTE advanced: A tutorial,” IEEE communications surveys & tutorials, vol. 17, no. 3, pp. 1605–1633, 2014.
  • [2] E. Makridis, “Reinforcement learning for link adaptation in 5G-NR networks.” 2020. [Online]. Available: https://www.diva-portal.org/smash/record.jsf?pid=diva2:1527910.
  • [3] F. Blanquez-Casado, G. Gomez, M. D. C. Aguayo-Torres, and J. T. Entrambasaguas, “eOLLA: an enhanced outer loop link adaptation for cellular networks,” J Wireless Com Network, vol. 2016, no. 1, Dec. 2016, doi: 10.1186/s13638-016-0518-3.
  • [4] A. Duran, M. Toril, F. Ruiz, and A. Mendo, “Self-optimization algorithm for outer loop link adaptation in LTE,” IEEE Communications letters, vol. 19, no. 11, pp. 2005–2008, 2015.
  • [5] T. Ohseki and Y. Suegara, “Fast outer-loop link adaptation scheme realizing low-latency transmission in LTE-advanced and future wireless networks,” in 2016 IEEE radio and wireless symposium (RWS), IEEE, 2016, pp. 1–3.
  • [6] V. Saxena, H. Tullberg, and J. Jaldén, “Reinforcement Learning for Efficient and Tuning-Free Link Adaptation,” May 05, 2021, arXiv: arXiv:2010.08651. doi: 10.48550/arXiv.2010.08651.
  • [7] F. Geiser et al., “DRLLA: Deep Reinforcement Learning for Link Adaptation,” Telecom, vol. 3, no. 4, Art. no. 4, Dec. 2022, doi: 10.3390/telecom3040037.
  • [8] P. Kela, T. Höhne, T. Veijalainen, and H. Abdulrahman, “Reinforcement Learning for Delay Sensitive Uplink Outer-Loop Link Adaptation,” in 2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), Jun. 2022, pp. 59–64. doi: 10.1109/EuCNC/6GSummit54941.2022.9815746.
  • [9] P. Bertrand, J. Jiang, and A. Ekpenyong, “Link Adaptation Control in LTE Uplink,” in 2012 IEEE Vehicular Technology Conference (VTC Fall), Sep. 2012, pp. 1–5. doi: 10.1109/VTCFall.2012.6399063.
  • [10] S. Wu, C. Chakrabarti, and A. Alkhateeb, “Proactively predicting dynamic 6G link blockages using LiDAR and in-band signatures,” IEEE open journal of the communications society, vol. 4, pp. 392–412, 2023.
  • [11] H. S. Sucuoglu, “Development of Real-Time Fire Detection Robotic System with Hybrid-Cascade Machine Learning Detection Structure,” Processes, vol. 13, no. 6, p. 1712, 2025.
  • [12] J. Hoydis et al., “Sionna: An Open-Source Library for Next-Generation Physical Layer Research,” Mar. 20, 2023, arXiv: arXiv:2203.11854. doi: 10.48550/arXiv.2203.11854.
  • [13] S. N. Donthi and N. B. Mehta, “An accurate model for EESM and its application to analysis of CQI feedback schemes and scheduling in LTE,” IEEE Transactions on Wireless Communications, vol. 10, no. 10, pp. 3436–3448, 2011.
  • [14] A. Sampath, P. S. Kumar, and J. M. Holtzman, “On setting reverse link target SIR in a CDMA system,” in 1997 IEEE 47th Vehicular Technology Conference. Technology in Motion, IEEE, 1997, pp. 929–933. Accessed: Jul. 13, 2025. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/600465/.
  • [15] K. I. Pedersen et al., “Frequency domain scheduling for OFDMA with limited and noisy channel feedback,” in 2007 IEEE 66th Vehicular Technology Conference, IEEE, 2007, pp. 1792–1796.
  • [16] OpenStreetMap Foundation, “OpenStreetMap.” Accessed: Dec. 10, 2024. [Online]. Available: https://www.openstreetmap.org.
  • [17] Blender Foundation, “Blender.” Accessed: Dec. 15, 2024. [Online]. Available: https://www.blender.org.
  • [18] W. Jakob et al., Mitsuba 3 Physically Based Renderer. (2022). Accessed: Feb. 02, 2025. [Online]. Available: https://mitsuba-renderer.org.
  • [19] J. Hoydis et al., Sionna. (2022). [Online]. Available: https://nvlabs.github.io/sionna/.
  • [20] ITU–R P.2040–3, “Effects of building materials and structures on radiowave propagation above about 100 MHz,” Geneva, 2023.
There are 20 citations in total.

Details

Primary Language English
Subjects Electrical Engineering (Other)
Journal Section Research Article
Authors

Önem Yıldız 0000-0003-0675-6637

Publication Date September 29, 2025
Submission Date July 13, 2025
Acceptance Date August 6, 2025
Published in Issue Year 2025 Volume: 1 Issue: 2

Cite

APA Yıldız, Ö. (2025). Impact of Link Adaptation Strategies in Ray-Traced 5G Scenarios. Innovative Approaches to Engineering Problems, 1(2), 29-35.