Solution of the multiuser downlink beamforming problem with individual SINR constraints

By M. Schubert, Holger Boche. IEEE Transactions on Vehicular Technology, 2004

1,242 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d667973eba3653d72ca0e807711277121d56f44e

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Citations in the corpus

Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming (2018, methodology): “in the multiuser MISO downlink broadcast channel, which can be efficiently solved by using second-order cone program (SOCP) [22], SDP [23], or a fixed-point iteration algorithm based on the uplink-downlink duality [24], [25].”
Convex Optimization-Based Beamforming (2010, methodology): “Unfortunately, to our knowledge, there is no convex formulation of the resulting optimization problem, which however can still be solved very efficiently using the virtual uplink formulation and a fixed-point iteration, see [48].”
Outage Constrained Robust Transmit Optimization for Multiuser MISO Downlinks: Tractable Approximations by Conic Optimization (2011, background): “2Specifically, problem (2) can be reformulated as a semidefinite program (SDP), which is polynomial-time solvable [35], [36]; see also the classic contributions [31], [37] related to this topic.”
Linear precoding via conic optimization for fixed MIMO receivers (2006, methodology): “A more detailed proof on this duality in the case of HTx = I can be found in [49].”
Sum-Rate Maximization for Linearly Precoded Downlink Multiuser MISO Systems With Partial CSIT: A Rate-Splitting Approach (2016, background): “While the mode l in (2) can be seen as a super-position of MU beamforming [34] and multicast beamforming [35] 2, the transmission of common message has a fundamentally different purpose here.”
Exploiting Known Interference as Green Signal Power for Downlink Beamforming Optimization (2015, methodology): “hat the beneﬁts of the proposed technique extend to higher order modulation. To focus on the proposed concept, we compare the proposed CI optimization solely to the conventional optimization of [16], [31], while it is clear the the beneﬁts of the proposed concept extend to modiﬁed optimization desig”
Joint Transmit Beamforming and Receive Power Splitting for MISO SWIPT Systems (2013, methodology): “Problem (17) has been well studied in the literature and can be efficiently solved by existing techniques [21], [22], [23].”
Optimal Resource Allocation in Coordinated Multi-Cell Systems (2013, methodology): “The duality can in this case be utilized to design iterative fixed-point algorithms that quickly find the optimal dual variables and thereby solve both the downlink and uplink problems [42, 59, 208, 226, 227, 296].”
Dynamic Resource Allocation in Cognitive Radio Networks (2010, methodology): “IMO-MAC [25]. With this duality result, the SINR balancing problem (P4) for the CR MISO-BC can be converted into an equivalent problem for the dual SIMO-MAC, where the efﬁcient iterative algorithm in [28] can be directly applied. The interested readers may refer to [25] for the details of this metho”
Multi-Antenna Wireless Powered Communication With Energy Beamforming (2013, methodology): “It is worth noting thatW̃ is the optimal MMSE receiver corresponding to the power allocation p̃, as shown in [7], [10], which maximizes γk(p̃,wk), ∀k.”
Multiuser MISO Beamforming for Simultaneous Wireless Information and Power Transfer (2013, methodology): “ Problem (6) can be solved by the standard interior point method via transforming it into a second-order cone program (SOCP) [12] or by an uplink-downlink duality based ﬁxed-point iteration algorithm [11]. Next, we consider the other extreme case with no ID receivers, i.e., KI = φ, where by setting ”
An introduction to convex optimization for communications and signal processing (2006, background): “For example, an iterative optimization of ρ i and wi leads to the optimal solution for the downlink [6], [7], [9].”
A Deep Learning Framework for Optimization of MISO Downlink Beamforming (2019, methodology): “Before we present the BNN for the SINR balancing problem P1, we first introduce the following lemma to describe the uplink-downlink duality of problem P1 [12].”
Multi-Cell MIMO Cooperative Networks: A New Look at Interference (2010, methodology): “The optimality of this algorithm can be established for the single-cell network using several different techniques based on convex optimization methods [97], [98], [99], [100], [101].”
Cooperative Multicell Precoding: Rate Region Characterization and Distributed Strategies With Instantaneous and Statistical CSI (2010, positive): “The proposed beamforming approach is inspired by uplink-downlink duality for broadcast channels [19] and the transmit power is allocated heuristically by solving local optimization problems.”
Intelligent Reflecting Surface: A Programmable Wireless Environment for Physical Layer Security (2019, neutral): “Note that γ ∗ is the optimal value of the objective function in (P7), and we know γ ∗ k = γ ∗ holds for all 1 ≤ k ≤ K [45].”
Generalized Design of Multi-User MIMO Precoding Matrices (2008, background): “For the optimization of such systems it is often assumed that the users are equipped with only one antenna, [12], [13], [14], [8], [9].”