Transmitter Optimization for the Multi-Antenna Downlink With Per-Antenna Power Constraints

By Wei Yu, Tian Lan. IEEE Transactions on Signal Processing, 2007

848 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/30b91150bc3bf71e87d42babbdf84195e96d50fd

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Citations in the corpus

Multi-Cell MIMO Cooperative Networks: A New Look at Interference (2010, background): “The uplink-downlink duality result can be generalized to accommodate the per-antenna power constraint [59] as mentioned in Section III.”
Network coordination for spectrally efficient communications in cellular systems (2006, background): “We mention that a minimum mean square error (MMSE) prefilter is also possible [ 15 , 16], which should have performance beyond that of the zero-forcing scheme.”
Optimal Resource Allocation for MIMO Ad Hoc Cognitive Radio Networks (2008, methodology): “to the ones presented in [28] and [32].”
Spatially Selective Artificial-Noise Aided Transmit Optimization for MISO Multi-Eves Secrecy Rate Maximization (2013, background): “In order to operat within the linear region of each PA, one may want to limit the per-antenna peak power [22], [28].”
Weighted sum-rate maximization using weighted MMSE for MIMO-BC beamforming design (2008, background): “This makes linear downlink transmission techniques (also called beamforming) an attractive alternative because of their simplicity [6], [7], [8], [9].”
Zero-Forcing Precoding and Generalized Inverses (2008, background): “This solution can be found as follows: Let for be a (possibly high rank) optimal solution to (34).”
Coordinated Scheduling and Power Allocation in Downlink Multicell OFDMA Networks (2009, background): “Ideally, if both data and channels of all users could be shared in real time, adjacent base stations could act as a large distributed antenna array and could employ joint beamforming, scheduling, and data encoding to simultaneously serve multiple cochannel users [8]–[12].”
An Overview of Massive MIMO: Benefits and Challenges (2014, background): “required to achieve a desired rate with per-antenna power constraints [54], [55].”
Joint Transmit Beamforming for Multiuser MIMO Communications and MIMO Radar (2019, background): “The per-antenna power constraint settles with the common practice that radar waveforms should be transmitted with their maximal available power [50], and has also been applied in multi-antenna communication systems [51]–[53].”
Scalable Cell-Free Massive MIMO Systems (2019, methodology): “To take into account that the matrix Z k may not be strictly positive deﬁnite, a general form of the solution is used with ( · ) † denoting the matrix pseudoinverse [49].”
Per-Antenna Constant Envelope Precoding for Large Multi-User MIMO Systems (2012, background): “Most reported work on per-antenna communication consider an average-only or a peak-only power constraint (see [6], [7] and references therein).”
Generic Optimization of Linear Precoding in Multibeam Satellite Systems (2011, methodology): “Summarizing the above results, we propose an efficient algorithm using similar subgradient method in [32] on top of this page.”
Optimal Multiuser Transmit Beamforming: A Difficult Problem with a Simple Solution Structure [Lecture Notes] (2014, methodology): “The reformulated SINR constraint in (3) is a second-order cone constraint, which is a convex type of constraint [4]–[6], and it is easy to show that Slater’s constraint qualification is fulfilled [7].”
Convex Optimization-Based Beamforming (2010, background): “For example, individual power constraints per antenna are considered in [54], where an efficient algorithm based on a generalization of the virtual uplink duality is developed.”
Optimal Resource Allocation in Coordinated Multi-Cell Systems (2013, background): “case (although an outer optimization procedure can be applied to take care of general power constraints [59, 308]).”
Max-Min SINR Coordinated Multipoint Downlink Transmission—Duality and Algorithms (2012, background): “Uplink-downlink duality [21], [23]–[26] is often used to convert transmit beamforming problems into equivalent uplink receive beamforming problems [14]–[19], [21], [22], [24], [25].”