Geert Leus
Delft University of Technology
661 Papers
4.4K Citations
Geert Leus is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Computer science & Communication channel. The author has an hindex of 62, co-authored 609 publications. Previous affiliations of Geert Leus include Northeastern University & IMEC.
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Papers
Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems
TL;DR: An adaptive algorithm to estimate the mmWave channel parameters that exploits the poor scattering nature of the channel is developed and a new hybrid analog/digital precoding algorithm is proposed that overcomes the hardware constraints on the analog-only beamforming, and approaches the performance of digital solutions.
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Spectrum Sensing for Cognitive Radio : State-of-the-Art and Recent Advances
TL;DR: Cognitive radio is introduced to exploit underutilized spectral resources by reusing unused spectrum in an opportunistic manner and the idea of using learning and sensing machines to probe the radio spectrum was envisioned several decades earlier.
1.1K
Limited Feedback Hybrid Precoding for Multi-User Millimeter Wave Systems
TL;DR: In this article, a low-complexity hybrid analog/digital precoding for downlink multiuser mmWave systems is proposed, which involves a combination of analog and digital processing that is inspired by the power consumption of complete radio frequency and mixed signal hardware.
1.1K
Limited Feedback Hybrid Precoding for Multi-User Millimeter Wave Systems
TL;DR: Analytical and simulation results show that the proposed techniques offer higher sum rates compared with analog-only beamforming solutions, and approach the performance of the unconstrained digital beamforming with relatively small codebooks.
988
Optimal training design for MIMO OFDM systems in mobile wireless channels
TL;DR: It is shown that the optimal pilot sequences derived in this paper outperform both the orthogonal and random pilot sequences and that a considerable gain in signal-to-noise ratio (SNR) can be obtained by using the RLS algorithm, especially in slowly time-varying channels.