Linear precoding via conic optimization for fixed MIMO receivers

By Ami Wiesel, Yonina C. Eldar, Shlomo Shamai. IEEE Transactions on Signal Processing, 2006

1,024 citations recorded by the source corpus.

In this paper, the problem of designing linear precoders for fixed multiple-input-multiple-output (MIMO) receivers is considered. Two different design criteria are considered. In the first, the transmitted power is minimized subject to signal-to-interference-plus-noise-ratio (SINR) constraints. In the second, the worst case SINR is maximized subject to a power constraint. It is shown that both problems can be solved using standard conic optimization packages. In addition, conditions are developed for the optimal precoder for both of these problems, and two simple fixed-point iterations are proposed to find the solutions that satisfy these conditions. The relation to the well-known uplink-downlink duality in the context of joint transmit beamforming and power control is also explored. The proposed precoder design is general, and as a special case, it solves the transmit rank-one beamforming problem. Simulation results in a multiuser system show that the resulting precoders can significantly outperform existing linear precoders.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ad8f210075d9af43484ccdd04e7c4c2a8e515381

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Citations in the corpus

Optimal Design of Energy-Efficient Multi-User MIMO Systems: Is Massive MIMO the Answer? (2014, background): “…condition is met if and only if the uplink power allocation vector p (ul) = [ p where the ( k, (cid:96) ) th element of The power allocation in (8) is computed directly for MRC and ZF detection, while it is a ﬁxed-point equation for MMSE detection since also G depends on the power allocation [23].”
Optimal Resource Allocation for MIMO Ad Hoc Cognitive Radio Networks (2008, background): “The proof follows closely the ones in [28] and [32] developed for an analogous claim in the multiantenna downlink setup.”
Outage Constrained Robust Transmit Optimization for Multiuser MISO Downlinks: Tractable Approximations by Conic Optimization (2011, methodology): “There are three parallel solution approaches to the problem, namely, uplink-downlink duality [10], [11], semideﬁnite relaxation (SDR) [5], [12], and the second-order cone programming (SOCP) formulation [13].”
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].”
Exploiting Multi-Antennas for Opportunistic Spectrum Sharing in Cognitive Radio Networks (2007, methodology): “At last, it is noted that the SOCP and SDP have also been applied for design of downlink precoding for MIMO-BC channels (e.g., [31]-[32]).”
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].”
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Dynamic Resource Allocation in Cognitive Radio Networks (2010, methodology): “The above technique has also been applied in [27] for (P4) without the PIPCs.”
Zero-Forcing Precoding and Generalized Inverses (2008, background): “In particular, linear precoding schemes which seem to provide a promising tradeoff between performance and complexity were proposed in [2]–[5].”
Convex Optimization-Based Beamforming (2010, methodology): “An alternative solution using quasi convexity is described in [53].”
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].”
Large System Analysis of Linear Precoding in Correlated MISO Broadcast Channels Under Limited Feedback (2009, background): “Under the assumption of large K and for any rotationally-invariant channel distribution, [9] derives the regularization term that maximizes the signal-to-interference plus noise ratio (SINR).”
An introduction to convex optimization for communications and signal processing (2006, background): “This section gives an overview of the approaches in [10] and [11] to illustrate the application of convex optimization.”
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].”
Beamforming Optimization for Wireless Network Aided by Intelligent Reflecting Surface With Discrete Phase Shifts (2019, neutral): “Note that (P3) is the conventional power minimization problem in the multiuser MISO downlink broadcast channel, which can be efficiently and optimally solved by using the fixedpoint iteration algorithm based on the uplink-downlink duality [41]–[43].”