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Ivan Gaspar

Researcher at Dresden University of Technology

Publications -  36
Citations -  3062

Ivan Gaspar is an academic researcher from Dresden University of Technology. The author has contributed to research in topics: Orthogonal frequency-division multiplexing & Physical layer. The author has an hindex of 18, co-authored 36 publications receiving 2655 citations. Previous affiliations of Ivan Gaspar include Vodafone & Intel.

Papers
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Journal ArticleDOI

5GNOW: non-orthogonal, asynchronous waveforms for future mobile applications

TL;DR: New key PHY layer technology components such as a unified frame structure, multicarrier waveform design including a filtering functionality, sparse signal processing mechanisms, a robustness framework, and transmissions with very short latency enable indeed an efficient and scalable air interface supporting the highly varying set of requirements originating from the 5G drivers.
Journal ArticleDOI

Generalized Frequency Division Multiplexing for 5th Generation Cellular Networks

TL;DR: The flexible nature of GFDM makes this waveform a suitable candidate for future 5G networks, and its main characteristics are analyzed.
Journal ArticleDOI

Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems

TL;DR: The results in this paper clearly demonstrate that deep CNN can efficiently exploit channel correlation to improve the estimation performance for mmWave massive MIMO systems.
Proceedings ArticleDOI

Generalized frequency division multiplexing: Analysis of an alternative multi-carrier technique for next generation cellular systems

TL;DR: This paper shows that based on the FFT/IFFT algorithm, the GFDM scheme can be implemented with reasonable computational effort and presents the benefits of the pulse shaped carriers in GFDM.
Proceedings ArticleDOI

Low Complexity GFDM Receiver Based on Sparse Frequency Domain Processing

TL;DR: A low complexity design for demodulating GFDM signals based on a sparse representation of the pulse-shaping filter in frequency domain and the results show, that for high-order QAM signaling, the error performance can be significantly improved with interference cancellation at reasonable computational cost.