Compressed Sensing for Sparse Channel Estimation in Next Generation Radios
Addressing Hardware Impairments
H. Masoumi (TU Delft - Signal Processing Systems)
R. Van de Plas – Promotor (TU Delft - Team Raf Van de Plas)
N.J. Myers – Copromotor (TU Delft - Team Nitin Myers)
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Abstract
Millimeter wave (mmWave) bands, currently used in 5G, offer significant spectrum to enable Gbps data rates for emerging data-hungry applications. At mmWave and higher bands, however, high scattering and atmospheric attenuation result in low received power. To address this issue, base stations employ large antenna arrays and periodically configure these arrays by learning the propagation environment, called the wireless channel. The use of such large arrays, however, makes the learning process, i.e., wireless channel estimation, extremely challenging. This is because, on the one hand, the training overhead of classical channel estimation methods increases significantly with the array dimensions. On the other hand, compressed sensing (CS)based methods for fast channel estimation suffer from a poor signal-to-noise ratio (SNR) in the received measurements. Moreover, radio frequency (RF) impairments, which are much more severe in mmWave systems than in low-frequency systems, further complicate channel estimation. For example, low-resolution RF phase shifters, phase noise at the oscillator, and in-phase/quadrature (IQ) mismatch at the down-conversion are among the practically significant impairments....