Weighted sum-rate maximization using weighted MMSE for MIMO-BC beamforming design
By Søren Skovgaard Christensen, Rajiv Agarwal, E. de Carvalho, John M. Cioffi. IEEE Transactions on Wireless Communications, 2008
544 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/42477b66bb6e09540e2c88b5de702130567adfb7
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Citations in the corpus
- Fractional Programming for Communication Systems—Part I: Power Control and Beamforming (2018, background): “, Algorithm 5, is equivalent to the wellknown WMMSE algorithm [33], [34].”
- Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing (2019, methodology): “auxiliary variables {Υk}, we transform Problem P2-E3 as follows [34]:”
- Beam Division Multiple Access Transmission for Massive MIMO Communications (2015, methodology): “When the receiver treats the aggregate interference-plus-noise as Gaussian noise with covariance Kk, [29], we can derive the following ergodic achievable rate [30]:”
- Optimal Joint Base Station Assignment and Beamforming for Heterogeneous Networks (2014, methodology): “For this purpose, we introduce various constraints for the beamforming and base station assignment.”
- Fractional Programming for Communication Systems—Part II: Uplink Scheduling via Matching (2018, methodology): “We now compare the complexities of Algorithm 2 and the WMMSE method [27], [28] (which is modified to include scheduling as stated in Section VI).”
- Sparse Beamforming and User-Centric Clustering for Downlink Cloud Radio Access Network (2014, methodology): “eformulate it as an equivalent WMMSE problem and use the block coordinate descent method to reach a stationary point of (11). The equivalence between WSR maximization and WMMSE is first established in [11] for MIMO broadcast channel and later generalized to MIMO interfering channel in [12] and MIMO i”