MIMO BC/MAC MSE duality with imperfect transmitter and perfect receiver CSI

Joham, Michael; Vonbun, Michael; Utschick, Wolfgang

doi:10.1109/spawc.2010.5670866

Cited by 16 publications

(15 citation statements)

References 22 publications

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“…it is possible to achieve identical MSEs for all the users in the BC as in the MAC, i.e., MSE BC k = MSE MAC k ∀k. Moreover, the average transmit power is preserved [35]. Note that even not always explicitly remarked in the notation, the MAC receive filters and precoders are functions of the partial CSIT v and the channel, respectively, as the corresponding BC precoders and receive filters.…”

Section: B Bc/mac Mse Dualitymentioning

confidence: 99%

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QoS constrained power minimization in the MISO broadcast channel with imperfect CSI

et al. 2017

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Abstract-We consider the design of linear precoders and receivers in a Multiple-Input Single-Output (MISO) Broadcast Channel (BC). We aim at minimizing the transmit power while fullfiling a set of per-user Quality-of-Service (QoS) constraints expressed in terms of per-user average rate requirements. The Channel State Information (CSI) is assumed to be perfectly known at the receivers but only partially at the transmitter. To solve the problem we transform the QoS constraints into Minimum Mean Square Error (MMSE) constraints. We then leverage the MSE duality between the BC and the Multiple Access Channel (MAC), as well as standard interference functions in the dual MAC, to perform power minimization by means of an Alternating Optimization (AO) algorithm. Problem feasibility is also studied to determine whether the QoS constraints can be fulfilled or not. Finally, we present an algorithm to balance the average rates and manage situations that may be unfeasible or lead to an unacceptably high transmit power.Index Terms-Broadcast Channels, imperfect CSI, MSE duality, QoS constraints, rate balancing, interference functions.

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Section: B Bc/mac Mse Dualitymentioning

confidence: 99%

“…1) N ≥ K: This is the case where the number of transmit antennas is greater than or equal to the number of users. We start searching for an upper bound for f k (ξ; ε), or equivalently, a lower bound for the inverse term in (35). To do so, we introduce the following matrices Bk = [ϕ i1 , .…”

Section: Appendix C Proof For the Condition (30)mentioning

confidence: 99%

QoS constrained power minimization in the MISO broadcast channel with imperfect CSI

et al. 2017

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“…3 The algorithm performs a preconditioned gradient descent step in each iteration, followed by an orthogonal projection onto the constraint set. The gradient descent step for the eigenvalue matrixΦ can be expressed as…”

Section: Gradient Projection Based Precoder Designmentioning

confidence: 99%

“…The details of the derivation are presented in Appendix B and the first order eigenvalue derivatives are given by [cf. 3 Alternatively, we could exploit (36) for a precoder based gradient projection method (e.g., see [20]) where the unit-norm eigenvectors u i and v i are defined via the eigenvalue decompositions of S and O, respectively. With (26) and Proposition 2, above matrix derivatives are…”

Section: Gradient Projection Based Precoder Designmentioning

confidence: 99%

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Stochastic transceiver design in point-to-point MIMO channels with imperfect CSI

Grundinger

Joham

Utschick

2011

2011 International ITG Workshop on Smart Antennas

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The problem of stochastically robust minimum mean square error (MMSE) transceiver design is addressed for multiple-input multiple-output (MIMO) point-to-point channels with different imperfect channel state information (CSI) at the receiver and the transmitter. While the receiver has distribution knowledge of the doubly correlated Gaussian channel that is conditioned on pilot-based training observations (partial CSIRx), the transmitter has either conditional distribution knowledge about the receiver's observations based on feedback (partial CSITx), or only unconditioned distribution knowledge (statistical CSITx). In case of partial CSITx, the design is based on an alternating optimization of the transmit and receive filter. For statistical CSITx, a novel closed-form expression for the expected MMSE is calculated and the structure of the optimal precoder is determined. This enables us to employ an efficient gradient projection method for the robust precoder design.

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Multi-User Downlink Communication

Grundinger¹

2019

Statistical Robust Beamforming for Broadcast Channels and Applications in Satellite Communication

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MIMO BC/MAC MSE duality with imperfect transmitter and perfect receiver CSI

Cited by 16 publications

References 22 publications

QoS constrained power minimization in the MISO broadcast channel with imperfect CSI

QoS constrained power minimization in the MISO broadcast channel with imperfect CSI

Stochastic transceiver design in point-to-point MIMO channels with imperfect CSI

Multi-User Downlink Communication

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