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Mats Bengtsson

Researcher at Royal Institute of Technology

Publications -  268
Citations -  7786

Mats Bengtsson is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: MIMO & Precoding. The author has an hindex of 42, co-authored 259 publications receiving 7096 citations. Previous affiliations of Mats Bengtsson include Linköping University & University of Oulu.

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IgG Endopeptidase in Highly Sensitized Patients Undergoing Transplantation.

TL;DR: Assessment of two independently performed open‐label, phase 1–2 trials that assessed the efficacy of IdeS with regard to desensitization and transplantation of a kidney from an HLA‐incompatible donor concluded that IdeS reduced or eliminated donor‐specific antibodies and permitted HLA- incompatible transplantation in 24 of 25 patients.
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Pain analysis in patients with fibromyalgia. Effects of intravenous morphine, lidocaine, and ketamine

TL;DR: The hypothesis that the NMDA receptors are involved in pain mechanisms in fibromyalgia is supported and central sensitization is present in FM and that tender points represent secondary hyperalgesia is suggested.
Proceedings ArticleDOI

Second order statistics of NLOS indoor MIMO channels based on 5.2 GHz measurements

TL;DR: It is shown that for NLOS indoor scenarios, the MIMO channel covariance matrix can be well approximated by a Kronecker product of the covariance matrices describing the correlation at the transmitter and receiver side respectively.
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Capacity Limits and Multiplexing Gains of MIMO Channels with Transceiver Impairments

TL;DR: It is proved analytically that such physical MIMO channels have a finite upper capacity limit, for any channel distribution and SNR, and it is proved that the relative capacity gain of employing MIMo is at least as large as with ideal transceivers.

Optimal Downlink Beamforming Using Semidefinite Optimization

TL;DR: An optimal transmit strategy for simultaneous transmission to several co-channel users is formulated and it is shown how the solution can be calculated efficiently using interior point methods for semidefinite optimization.