Hybrid wireless network architectures for rural telecommunications: analysis of edge intelligence strategies.
DOI:
https://doi.org/10.62452/wfkgdc56Keywords:
Hybrid architectures, rural telecommunications, edge intelligence, wireless networks, rural connectivity, multi-objective optimizationAbstract
Rural telecommunications face unique challenges requiring technological solutions specifically adapted to low population density constraints, infrastructural limitations, and economic sustainability. This research developed a systematic characterization of hybrid wireless network architectures for rural contexts through documentary analysis of implementations in specialized literature. Descriptive-correlational methodology was applied integrating systematic content analysis with performance findings synthesis to identify patterns between technological components and effectiveness metrics. Results established four main configurations: cellular-cell-free with conjugate beamforming, 5G-LPWAN with virtual slicing for IoT, LiFi-WiFi with adaptive handover, and satellite-terrestrial with MEC. Edge intelligence strategies evaluation revealed five distinct approaches: MEET for cost distribution through connected vehicles, LEE for multi-objective energy-learning optimization, collaborative for distributed training, and multi-service for time-critical control. Theoretical implications establish that effective hybridization requires intelligent coordination specifically adapted to rural contexts, while practical implications provide specific guidance for system designers, telecommunications operators, and development organizations in implementing rural connectivity grounded in empirical evidence.
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Aijaz, A., Jiang, N., & Khan, A. (2023). Toward multi-service edge-intelligence paradigm: Temporal-adaptive prediction for time-critical control over wireless. IEEE Internet of Things Magazine, 6(1), 96–101. https://doi.org/10.1109/IOTM.001.2200139
Besjedica, T., Fertalj, K., Lipovac, V., & Zakarija, I. (2023). Evolution of hybrid LiFi–WiFi networks: A survey. Sensors, 23(9), 4252. https://doi.org/10.3390/s23094252
Chaoub, A., Giordani, M., Lall, B., Bhatia, V., Kliks, A., Mendes, L., Rabie, K., Saarnisaari, H., Singhal, A., Zhang, N., Dixit, S., & Zorzi, M. (2022). 6G for bridging the digital divide: Wireless connectivity to remote areas. IEEE Wireless Communications, 29(1), 160–168. https://doi.org/10.1109/MWC.001.2100137
Dai, Z., Xu, J., Xu, X., Li, R., & Zeng, Y. (2024). Performance analysis of hybrid cellular and cell-free MIMO network. https://doi.org/10.48550/arxiv.2406.01922
Davies, E., Chung, A., Broadbent, M., Macleod, A., & Race, N. (2022). 5G in the wild: Performance of C-band 5G-NR in rural low-power fixed wireless access. IEEE Future Networks World Forum (FNWF). Montreal, Canada.
El Falou, A., & Alouini, M.-S. (2023). Enhancement of rural connectivity by recycling TV towers with massive MIMO techniques. IEEE Communications Magazine, 61(4), 78–83. https://doi.org/10.1109/MCOM.003.2200257
Fourati, F., Alsamhi, S. H., & Alouini, M.-S. (2022). Bridging the urban-rural connectivity gap through intelligent space, air, and ground networks. http://arxiv.org/abs/2202.12683
Johnson, D. L., & Roux, K. (2008). Building rural wireless networks. Proceedings of the 2008 ACM Workshop on Wireless Networks and Systems for Developing Regions. San Francisco, USA.
Kaushik, A., & Al-Raweshidy, H. S. (2022). A hybrid latency- and power-aware approach for beyond fifth-generation Internet-of-Things edge systems. IEEE Access, 10, 87974–87989. https://doi.org/10.1109/ACCESS.2022.3200035
Letaief, K. B., Shi, Y., Lu, J., & Lu, J. (2022). Edge artificial intelligence for 6G: Vision, enabling technologies, and applications. IEEE Journal on Selected Areas in Communications, 40(1), 5–36. https://doi.org/10.1109/JSAC.2021.3126076
Li, X., Wang, S., Zhu, G., Zhou, Z., Huang, K., & Gong, Y. (2022). Data partition and rate control for learning and energy efficient edge intelligence. IEEE Transactions on Wireless Communications, 21(11), 9127–9142. https://doi.org/10.1109/TWC.2022.3173262
Lin, Y., Feng, W., Zhou, T., Wang, Y., Chen, Y., Ge, N., & Wang, C.-X. (2022). Integrating satellites and mobile edge computing for 6G wide-area edge intelligence: Minimal structures and systematic thinking. http://arxiv.org/abs/2208.07528
Liu, X., Yu, J., Liu, Y., Gao, Y., Mahmoodi, T., Lambotharan, S., & Tsang, D. H. K. (2022). Distributed intelligence in wireless networks. http://arxiv.org/abs/2208.00545
Marshall, A., Wilson, C. A., & Dale, A. (2023). New pathways to crisis resilience: Solutions for improved digital connectivity and capability in rural Australia. Media International Australia, 189(1), 24–42. https://doi.org/10.1177/1329878X231183292
Nguyen, N. T., Vu, Q. D., Lee, K., & Juntti, M. (2022). Hybrid relay-reflecting intelligent surface-assisted wireless communications. IEEE Transactions on Vehicular Technology, 71(6), 6228–6244. https://doi.org/10.1109/TVT.2022.3158686
Ogbodo, E. U., Abu-Mahfouz, A. M., & Kurien, A. M. (2022). A survey on 5G and LPWAN-IoT for improved smart cities and remote area applications: From the aspect of architecture and security. Sensors, 22(16), 6313. https://doi.org/10.3390/s22166313
Sun, Y., Xie, B., Zhou, S., & Niu, Z. (2022). MEET: Mobility-enhanced edge intelligence for smart and green 6G networks. https://doi.org/10.48550/arXiv.2210.15111
Zeng, L., Ye, S., Chen, X., & Yang, Y. (2024). Implementation of big AI models for wireless networks with collaborative edge computing. http://arxiv.org/abs/2404.17766
Zhang, T., Zu, G., Islam, T. U., Gossling, E., Babu, S., Qiao, D., & Zhang, H. (2024). Exploring wireless channels in rural areas: A comprehensive measurement study. http://arxiv.org/abs/2404.17434
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Copyright (c) 2025 Iván Alexander Neira-Reyes, Leonardo García-Correa, Elizabeth del Rocío Loor-Quimíz, Diana Carolina Decimavilla-Alarcón, Margarita del Rocio Pillajo-Mila (Autor/a)
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