A Vector-Perturbation Technique for Near-Capacity Multiantenna Multiuser Communication—Part I: Channel Inversion and Regularization

Peel, C.B.; Hochwald, Bertrand M.; Swindlehurst, A. Lee

doi:10.1109/tcomm.2004.840638

Cited by 1,475 publications

(1,296 citation statements)

References 11 publications

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“…In the first stage, based on the MMSE-CI [8] which takes the OCI plus noise into account, we find a set of orthonormal basis vectors of the MUI suppression precoding matrix. From (2), we can see that the OCI plus noise is correlated.…”

Section: ⅲ Proposed Mmse Based Successive Precodingmentioning

confidence: 99%

“…[|| || ] / / s t r t P K N P α = = ⋅ w E [8] , and the other users' channel e, j H % is defined as e, e,1 e,2 e, 1…”

Section: ⅲ Proposed Mmse Based Successive Precodingmentioning

confidence: 99%

“…In contrast to the SZFDPC, the proposed method computes an orthonormal vector set of the MUI suppression precoding matrix based on the MMSE channel inversion (MMSE-CI) method in [8] which takes the OCI plus noise into account. Then, applying the transmit combining matrix obtained under an MMSE criterion to the orthonormal vector set, the proposed scheme is able to increase the signal-to-interference-plus-noise ratio (SINR) at each user's receiver.…”

Section: ⅰ Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MMSE Based Nonlinear Precoding for Multiuser MIMO Broadcast Channels with Inter-Cell Interference

Lee¹,

Sung²,

Lee³

2016

The Journal of Korean Institute of Communications and Informati

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In this paper, we investigate a minimum mean-squared error based nonlinear successive precoding method as a practical solution of dirty paper coding for multiuser downlink channels where each user has more than one antenna in the presence of other cell interference (OCI). Unlike conventional zero-forcing (ZF) based methods, the proposed scheme takes the OCI plus noise into account when suppressing the inter-cell multiuser interference, which results in improvement of the received signal-to-interference-plus-noise ratio. Simulation results show that the proposed scheme outperforms conventional methods in terms of sum rate for various OCI configurations.

show abstract

Section: ⅲ Proposed Mmse Based Successive Precodingmentioning

confidence: 99%

“…[|| || ] / / s t r t P K N P α = = ⋅ w E [8] , and the other users' channel e, j H % is defined as e, e,1 e,2 e, 1…”

Section: ⅲ Proposed Mmse Based Successive Precodingmentioning

confidence: 99%

Section: ⅰ Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MMSE Based Nonlinear Precoding for Multiuser MIMO Broadcast Channels with Inter-Cell Interference

Lee¹,

Sung²,

Lee³

2016

The Journal of Korean Institute of Communications and Informati

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show abstract

“…In this section, the optimal power control policy is derived for the case of channel inversion (CI) beamforming [3].…”

Section: Power Control With Directional Water-filling For Channementioning

confidence: 99%

“…Online policies are then derived based on observations made from the solution of the offline problem. Two multiuser beamforming schemes are considered, namely, the channel inversion (CI) [3] and the maximal ratio transmit (MRT) [4] beamforming schemes. In the CI beamforming case, the offline power control problem can be formulated as a convex optimization problem, whose solution is given by the directional water-filling algorithm [5].…”

Section: Introductionmentioning

confidence: 99%

Downlink multiuser beamforming and power control for base stations empowered by renewable energy

Wang

Hong

Chen

2013

2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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Abstract-This work examines offline and online power control policies for efficient usage of renewable energy in the downlink of a multi-antenna wireless system. Two multiuser beamforming schemes are considered, namely, channel inversion (CI) and maximal ratio transmit (MRT) beamforming schemes. Power control policies are derived for both schemes, respectively, with the goal of maximizing the sum throughput by a deadline subject to energy causality and battery storage constraints. With CI beamforming, the power control problem can be formulated as a convex optimization problem, whose solution can be obtained using the directional water-filling algorithm. With MRT beamforming, the power control problem becomes non-convex, due to interference between the signals intended for different users, and, thus, is difficult to solve exactly. However, an efficient solution can be obtained by performing successive approximation into a sequence of geometric programming problems, i.e., by employing the condensation method. Offline power control policies are first derived assuming non-causal knowledge of the energy arrival and channel coefficients over time. Online power control policies are then proposed based on observations gained from the offline policy. The performance of the proposed schemes are demonstrated through numerical simulations.

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Linear and Dirty‐Paper Techniques for the Multiuser MIMO Downlink

Peel¹,

Spencer

Swindlehurst

et al. 2005

Space‐Time Processing for MIMO Communications

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A Vector-Perturbation Technique for Near-Capacity Multiantenna Multiuser Communication—Part I: Channel Inversion and Regularization

Cited by 1,475 publications

References 11 publications

MMSE Based Nonlinear Precoding for Multiuser MIMO Broadcast Channels with Inter-Cell Interference

MMSE Based Nonlinear Precoding for Multiuser MIMO Broadcast Channels with Inter-Cell Interference

Downlink multiuser beamforming and power control for base stations empowered by renewable energy

Linear and Dirty‐Paper Techniques for the Multiuser MIMO Downlink

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