On eigenvectors of the discrete Fourier transform over finite Gaussian fields

Основная статьяThe problem of furnishing orthogonal systems of eigenvectors for the discrete Fourier transform (DFT) is fundamental to image processing with applications in image compression and digital watermarking. This paper studies some properties of such systems for DFT over finite fields that may be considered as ”finite complex planes”. Some applications for multiuser communication schemes are also considered

Target Sidelobes Removal via Sparse Recovery in the Subband Domain of an OFDM RadCom System

In this paper, the problem of target masking induced by sidelobes arising in an OFDM RadCom System is considered. To fully exploit the waveform structure and address practical scenarios, we propose to deal with the sidelobes in the subband domain via sparse recovery. Accordingly, we design a sparsifying dictionary modeling at the same time the target's peak and pedestal. Results on synthetic data show that our approach allows one to remove not only the target random sidelobes but also range ambiguities arising when all subbands are not active

Toward Massive MIMO 2.0: Understanding Spatial Correlation, Interference Suppression, and Pilot Contamination

Since the seminal paper by Marzetta from 2010, Massive MIMO has changed from being a theoretical concept with an infinite number of antennas to a practical technology. The key concepts are adopted into the 5G New Radio Standard and base stations (BSs) with M = 64 fully digital transceivers have been commercially deployed in sub-6GHz bands. The fast progress was enabled by many solid research contributions of which the vast majority assume spatially uncorrelated channels and signal processing schemes developed for single-cell operation. These assumptions make the performance analysis and optimization of Massive MIMO tractable but have three major caveats: 1) practical channels are spatially correlated; 2) large performance gains can be obtained by multicell processing, without BS cooperation; 3) the interference caused by pilot contamination creates a finite capacity limit, as M → ∞. There is a thin line of papers that avoided these caveats, but the results are easily missed. Hence, this tutorial article explains the importance of considering spatial channel correlation and using signal processing schemes designed for multicell networks. We present recent results on the fundamental limits of Massive MIMO, which are not determined by pilot contamination but the ability to acquire channel statistics. These results will guide the journey towards the next level of Massive MIMO, which we call "Massive MIMO 2.0"