The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel
Abstract
Consider the MIMO interfering broadcast channel whereby multiple base stations in a cellular network simultaneously transmit signals to a group of users in their own cells while causing interference to the users in other cells. The basic problem is to design linear beamformers that can maximize the system throughput. In this paper we propose a linear transceiver design algorithm for weighted sum-rate maximization that is based on iterative minimization of weighted mean squared error (MSE). The proposed algorithm only needs local channel knowledge and converges to a stationary point of the weighted sum-rate maximization problem. Furthermore, we extend the algorithm to a general class of utility functions and establish its convergence. The resulting algorithm can be implemented in a distributed asynchronous manner. The effectiveness of the proposed algorithm is validated by numerical experiments.
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Cites
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Weighted sum-rate maximization using weighted MMSE for MIMO-BC beamforming design
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Self-citationApproaching the Capacity of Wireless Networks through Distributed Interference Alignment
Gomadam, K., Cadambe, V. R., & Jafar, S. A. (2008) · IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference · doi:10.1109/glocom.2008.ecp.817 · Cited by 731
Distributed interference compensation for wireless networks
Huang, J., Berry, R. A., & Honig, M. L. (2006) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2006.872889 · Cited by 726
Optimized signaling for mimo interference systems with feedback
Ye, S., & Blum, R. S. (2003) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2003.818339 · Cited by 323
Competition Versus Cooperation on the MISO Interference Channel
Larsson, E., & Jorswieck, E. (2008) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2008.080904 · Cited by 304
Optimal Resource Allocation for MIMO Ad Hoc Cognitive Radio Networks
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The MIMO Iterative Waterfilling Algorithm
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Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,768
Learning to Optimize: Training Deep Neural Networks for Interference Management
Sun, H., Chen, X., Shi, Q., Hong, M., Fu, X., & Sidiropoulos, N. D. (2018) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2866382 · Cited by 874
Self-citationDeep Learning for Intelligent Wireless Networks: A Comprehensive Survey
Mao, Q., Hu, F., & Hao, Q. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2846401 · Cited by 789
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
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Metadata
- Authors
- Shi, Q., Razaviyayn, M., Luo, Z.-Q., & He, C.
- Venue
- 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2011-05
- Pages
- 3060-3063
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2100755644
- Cited by
- 873
- References
- 22
- FWCI
- 82.20
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Shi, Q., Razaviyayn, M., Luo, Z.-Q., & He, C. (2011). An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp.2011.5946304
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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