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Distributed interference compensation for wireless networks
Abstract
We consider a distributed power control scheme for wireless ad hoc networks, in which each user announces a price that reflects compensation paid by other users for their interference. We present an asynchronous distributed algorithm for updating power levels and prices. By relating this algorithm to myopic best response updates in a fictitious game, we are able to characterize convergence using supermodular game theory. Extensions of this algorithm to a multichannel network are also presented, in which users can allocate their power across multiple frequency bands.
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Cited by
Shi, Q., Razaviyayn, M., Luo, Z.-Q., & He, C. (2011) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2011.2147784 · Cited by 2,129
Shi, Q., Razaviyayn, M., Luo, Z.-Q., & He, C. (2011) · 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2011.5946304 · Cited by 873
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks
Nasir, Y. S., & Guo, D. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2933973 · Cited by 660
Foundations of MIMO Communication
Heath Jr, R. W., & Lozano, A. (2018) · Cambridge University Press eBooks · doi:10.1017/9781139049276 · Cited by 426
Spatial Deep Learning for Wireless Scheduling
Cui, W., Shen, K., & Yu, W. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2904352 · Cited by 265
Metadata
- Authors
- Huang, J., Berry, R. A., & Honig, M. L.
- Venue
- IEEE Journal on Selected Areas in Communications
- Published
- 2006-05
- Volume
- 24
- Issue
- 5
- Pages
- 1074-1084
- ISSN
- 0733-8716 (ISSN-L)
- OpenAlex
- W154102921
- Cited by
- 726
- References
- 0
- FWCI
- 73.07
- Type
- journal article
- Language
- en
- Open access
- closed
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