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## MIMO Broadcast Channels with Gaussian CSIT and Application to Location based CSIT

### Citations

76 |
An iteratively weighted MMSE approach to distributed sum-utility maximization for MIMO interfering broadcast channel
- Shi, Razaviyayn, et al.
- 2011
(Show Context)
Citation Context ...correspond to the Kim-Giannakis discussion below. C. Optimal Lagrange Multiplier The optimal λ for the update of gk in (8) can be found using a (bisection) line search on ∑K k=1 ||gk||2 − P = 0 as in =-=[10]-=-. Alternatively, it can be updated analytically as in [8], [9] by exploiting ∑ k g H k ∂WSMSE ∂g∗k = 0. This leads to the same result as in [11] where the problem of introducing and finding a Lagrange... |

58 | Weighted sum-rate maximization using weighted MMSE for MIMOBC beamforming design
- Christensen, Argawal, et al.
- 2008
(Show Context)
Citation Context ...k)(1− gHk HHk fk) + ∑ i 6=k f H k Hkgig H i H H k fk + ||fk||2 = 1−fHk Hkgk−gHk HHk fk+ ∑ i f H k Hkgig H i H H k fk+||fk||2. (5) The WSR(g) is a non-convex and complicated function of g. Inspired by =-=[7]-=-, we introduced in [8], [9] an augmented cost function, the Weighted Sum MSE, WSMSE(g, f , w) = K∑ k=1 uk(wk ek(fk,g)− lnwk) + λ( K∑ k=1 ||gk||2 − P ) (6) where λ is a Lagrange multiplier and P is the... |

53 |
A leakage-based precoding scheme for downlink multi-user MIMO channels
- Sadek, Tarighat, et al.
- 2007
(Show Context)
Citation Context ...two indicated matrices and is proportional to the ”LMMSE” gk in (8), with max eigenvalue σk = σmax(H H k R̂ −1 k Hk, Âk +λI). This can be viewed as an optimally weighted version of the SLNR solution =-=[15]-=- which takes as Tx filter g ′ k = Vmax(H H k Hk, ∑ i 6=kH H i Hi + I). Let σ(1)k = g ′H k H H k R̂ −1 k Hkg ′ k and σ (2) k = g ′H k Âkg ′ k. The advantage of formulation (16) is that it allows strai... |

44 | A coordinated approach to channel estimation in large-scale multiple-antenna systems,” Submitted to
- Yin, Gesbert, et al.
(Show Context)
Citation Context ...he transmit side channel correlation matrix, and worked out in more detail for single user (SU) MIMO, e.g. [1], [2]. The use of covariance CSIT has recently reappeared in the context of Massive MIMO, =-=[3]-=-, [4] where a not so rich propagation environment leads to subspaces (slow CSIT) for the channel vectors so that the fast CSIT can be reduced to the smaller dimension of the subspace, which is especia... |

43 | On the capacity achieving covariance matrix for Rician MIMO channels : an asymptotic approach
- Dumont, Loubaton, et al.
- 2006
(Show Context)
Citation Context ...m in section II.D, replacing R̃k, R̃k by E R̃k, E R̃k, and hence HHk Hk by Rt,k and expressions of the form H H k R −1Hk by Rt,kR −1. A. Large MIMO Asymptotics Refinement The SU MIMO asymptotics from =-=[17]-=-, [18] (in which both M,N → ∞, which tends to give more precise approximations when M is not so large) for a term of the form ln det(QHHH+ I) (as in (32)) correspond to replacing HHk Hk in the R̃k and... |

41 | Rate maximization in multiantenna broadcast channels with linear preprocessing
- Stojnic, Vikalo, et al.
- 2004
(Show Context)
Citation Context ...(bisection) line search on ∑K k=1 ||gk||2 − P = 0 as in [10]. Alternatively, it can be updated analytically as in [8], [9] by exploiting ∑ k g H k ∂WSMSE ∂g∗k = 0. This leads to the same result as in =-=[11]-=- where the problem of introducing and finding a Lagrange multiplier was avoided by reparameterizing the stream powers to satisfy the power constraint. If we reparameterize using normalized BF vectors ... |

39 | Giannakis, “Optimal resource allocation for MIMO ad hoc cognitive radio networks
- Kim, B
- 2011
(Show Context)
Citation Context ... ||fk||2 ∑K m=1 p ′ m . (12) This leads to the same Lagrange multiplier expression obtained in [7] on the basis of a heuristic that was introduced in [12] as was pointed out in [13]. D. Kim-Giannakis =-=[14]-=- Let Qk = gkgHk be the transmit covariance for stream k. The WSR can be rewritten as WSR = K∑ k=1 uk[ln det(Rk)− ln det(Rk)] (13) where Rk = Hk( ∑ iQi)H H k + INk , and Rk = Hk( ∑ i6=kQi)H H k + INk .... |

38 |
Asymptotic eigenvalue distributions and capacity for MIMO channels under correlated fading
- Martin, Ottersten
- 2004
(Show Context)
Citation Context ...ulting quadratic appearances of the channel. 3) Higher-Order Taylor Series Expansions: One possibility is to go to the next (second) order term in the Taylor series expansion of the log as in (15) in =-=[19]-=-. VIII. PROPAGATION CHANNEL MODEL Fig. 1. MIMO transmission with M transmit and N receive antennas. A. Specular Wireless MIMO Channel Model Consider a MIMO transmission configuration as depicted in Fi... |

32 | The effects of local scattering on direction of arrival estimation with MUSIC
- Astély, Ottersten
- 1999
(Show Context)
Citation Context ...uency (subcarrier), through the Tx (and Rx) filter(s). B. Narrow AoD Aperture (NADA) case The idea here is to focus on the category of mobiles for which the angular spread seen from the BS is limited =-=[21]-=-. This is a small generalization of the LoS case. In the NADA case, the MIMO channel H is of the form H = ∑ i Ai hr(φi)h H t (θi) ≈ B AH , A = [ ht(θ) ḣt(θ) ] . (37) In the case of narrow AoD spread,... |

17 |
Asymptotic Mutual Information Statistics of Separately Correlated Rician Fading MIMO
- Taricco
(Show Context)
Citation Context ...ection II.D, replacing R̃k, R̃k by E R̃k, E R̃k, and hence HHk Hk by Rt,k and expressions of the form H H k R −1Hk by Rt,kR −1. A. Large MIMO Asymptotics Refinement The SU MIMO asymptotics from [17], =-=[18]-=- (in which both M,N → ∞, which tends to give more precise approximations when M is not so large) for a term of the form ln det(QHHH+ I) (as in (32)) correspond to replacing HHk Hk in the R̃k and R̃k i... |

11 |
Transmit Wiener Filter for the Downlink
- Joham, Kusume, et al.
- 2002
(Show Context)
Citation Context ... SINRk = |fkHkg′k|2p ′ k∑K i=1,6=k |fkHkg′i|2p′i + ||fk||2 ∑K m=1 p ′ m . (12) This leads to the same Lagrange multiplier expression obtained in [7] on the basis of a heuristic that was introduced in =-=[12]-=- as was pointed out in [13]. D. Kim-Giannakis [14] Let Qk = gkgHk be the transmit covariance for stream k. The WSR can be rewritten as WSR = K∑ k=1 uk[ln det(Rk)− ln det(Rk)] (13) where Rk = Hk( ∑ iQi... |

8 |
Sparse variational Bayesian SAGE algorithm with application to the estimation of multipath wireless channels
- Shutin, Fleury
- 2011
(Show Context)
Citation Context ...N receive antennas. A. Specular Wireless MIMO Channel Model Consider a MIMO transmission configuration as depicted in Fig. 1. We get for the matrix impulse response of the timevarying channel h(t, τ) =-=[20]-=- h(t, τ) = Np∑ i=1 Ai(t) e j2pi fi t hr(φi)h H t (θi) p(τ − τi) . (35) The channel impulse response h has per path a rank 1 contribution in 4 dimensions (Tx and Rx spatial multi-antenna dimensions, de... |

4 |
Sum rate maximization in the noisy MIMO interfering broadcast channel with partial CSIT via the expected weighted MSE
- Negro, Ghauri, et al.
- 2012
(Show Context)
Citation Context ...n be avoided. In any case, we shall consider the ergodic sum rate as optimization criterion. The contributions here are significantly better partial CSIT approaches compared to the EWSMSE approach in =-=[5]-=-, and present deterministic alternatives to the stochastic approximation solution of [6]. We first treat the general Gaussian CSIT case. Then we focus on a location aided CSIT case with zero mean and ... |

4 |
A stochastic weighted MMSE approach to sum rate maximization for a MIMO interference channel
- Razaviyayn, Sanjabi, et al.
- 2013
(Show Context)
Citation Context ...ion. The contributions here are significantly better partial CSIT approaches compared to the EWSMSE approach in [5], and present deterministic alternatives to the stochastic approximation solution of =-=[6]-=-. We first treat the general Gaussian CSIT case. Then we focus on a location aided CSIT case with zero mean and identity plus rank one Tx side covariance matrix and no Rx side correlations. The goal h... |

3 |
Deterministic Annealing Design and Analysis of the Noisy
- Negro, Ghauri, et al.
- 2011
(Show Context)
Citation Context ... f H k Hkgig H i H H k fk + ||fk||2 = 1−fHk Hkgk−gHk HHk fk+ ∑ i f H k Hkgig H i H H k fk+||fk||2. (5) The WSR(g) is a non-convex and complicated function of g. Inspired by [7], we introduced in [8], =-=[9]-=- an augmented cost function, the Weighted Sum MSE, WSMSE(g, f , w) = K∑ k=1 uk(wk ek(fk,g)− lnwk) + λ( K∑ k=1 ||gk||2 − P ) (6) where λ is a Lagrange multiplier and P is the Tx power constraint. After... |

1 |
Spatial Transmit Prefiltering for Frequency-Flat MIMO Transmission with Mean and Covariance
- Francisco, Slock
- 2005
(Show Context)
Citation Context ...torical precedent of MU MIMO), in which the channel outer product was typically replaced by the transmit side channel correlation matrix, and worked out in more detail for single user (SU) MIMO, e.g. =-=[1]-=-, [2]. The use of covariance CSIT has recently reappeared in the context of Massive MIMO, [3], [4] where a not so rich propagation environment leads to subspaces (slow CSIT) for the channel vectors so... |