The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Binary Power Control for Sum Rate Maximization over Multiple Interfering Links
Abstract
We consider allocating the transmit powers for a wireless multi-link (N-link) system, in order to maximize the total system throughput under interference and noise impairments, and short term power constraints. Employing dynamic spectral reuse, we allow for centralized control. In the two-link case, the optimal power allocation then has a remarkably simple nature termed binary power control: depending on the noise and channel gains, assign full power to one link and minimum to the other, or full power on both. Binary power control (BPC) has the advantage of leading towards simpler or even distributed power control algorithms. For N>2 we propose a strategy based on checking the corners of the domain resulting from the power constraints to perform BPC. We identify scenarios in which binary power allocation can be proven optimal also for arbitrary N. Furthermore, in the general setting for N>2, simulations demonstrate that a throughput performance with negligible loss, compared to the best non-binary scheme found by geometric programming, can be obtained by BPC. Finally, to reduce the complexity of optimal binary power allocation for large networks, we provide simple algorithms achieving 99% of the capacity promised by exhaustive binary search.
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Cites
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A simple distributed autonomous power control algorithm and its convergence
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Providing quality of service over a shared wireless link
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A tutorial on geometric programming
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Performance of optimum transmitter power control in cellular radio systems
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Power Control By Geometric Programming
Chiang, M., Tan, C. W., Palomar, D. P., O'neill, D., & Julian, D. (2007) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2007.05960 · Cited by 886
On the performance of adaptive modulation in cellular systems
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Calculus with Analytic Geometry
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Cited by
Learning to optimize: Training deep neural networks for wireless resource management
Sun, H., Chen, X., Shi, Q., Hong, M., Fu, X., & Sidiropoulos, N. D. (2017) · 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · doi:10.1109/spawc.2017.8227766 · Cited by 308
Metadata
- Authors
- Gjendemsjo, A., Gesbert, D., Oien, G. E., & Kiani, S. G.
- Venue
- IEEE Transactions on Wireless Communications
- Published
- 2008-08
- Volume
- 7
- Issue
- 8
- Pages
- 3164-3173
- ISSN
- 1536-1276 (ISSN-L)
- 1558-2248
- OpenAlex
- W2150586017
- Cited by
- 304
- References
- 12
- FWCI
- 22.71
- Type
- journal article
- Language
- en
- Open access
- closed
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