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User Association for Load Balancing in Heterogeneous Cellular Networks
Open accessAbstract
For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rate for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor Aj≥ 1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values Ajare chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a maximizing received power association.
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Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
Cited by
Andrews, J. G., Buzzi, S., Choi, W., Hanly, S. V., Lozano, A., Soong, A. C. K., & Zhang, J. C. (2014) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2014.2328098 · Cited by 8,106
Self-citationA Survey of 5G Network: Architecture and Emerging Technologies
Gupta, A., & Jha, R. K. (2015) · IEEE Access · doi:10.1109/access.2015.2461602 · Cited by 2,420
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Ren, J., Yu, G., He, Y., & Li, G. Y. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2904244 · Cited by 685
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
Sun, Y., Peng, M., Zhou, Y., Huang, Y., & Mao, S. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2924243 · Cited by 678
Convergence Time Optimization for Federated Learning Over Wireless Networks
Chen, M., Poor, H. V., Saad, W., & Cui, S. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3042530 · Cited by 403
Metadata
- Authors
- Ye, Q., Rong, B., Chen, Y., Al-Shalash, M., Caramanis, C., & Andrews, J. G.
- Venue
- IEEE Transactions on Wireless Communications
- Published
- 2013-06
- Volume
- 12
- Issue
- 6
- Pages
- 2706-2716
- ISSN
- 1536-1276 (ISSN-L)
- OpenAlex
- W2010630450
- Cited by
- 1,228
- References
- 1
- FWCI
- 87.67
- Type
- journal article
- Language
- en
- Open access
- green
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- Ye, Q., Rong, B., Chen, Y., Al-Shalash, M., Caramanis, C., & Andrews, J. G. (2013). User Association for Load Balancing in Heterogeneous Cellular Networks. IEEE Transactions on Wireless Communications. https://doi.org/10.1109/twc.2013.040413.120676
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