Nuria González Prelcic

Channel estimation in millimeter wave MIMO systems with one-bit quantization

We develop channel estimation agorithms for millimeter wave (mmWave) multiple input multiple output (MIMO) systems with one-bit analog-to-digital converters (ADCs). Since the mmWave MIMO channel is sparse due to the propagation characteristics, the estimation problem is formulated as a one-bit compressed sensing problem. We propose a modified EM algorithm that exploits sparsity and has better performance than the conventional EM algorithm. We also present a second solution using the generalized approximate message passing (GAMP) algorithm to solve this optimization problem. The simulation results show that GAMP can reduce mean squared error in the important low and medium SNR regions.

Channel Estimation for Hybrid Architecture-Based Wideband Millimeter Wave Systems

Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the hybrid precoders and combiners, though, is usually based on the knowledge of the channel. Prior work on mmWave channel estimation with hybrid architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for hybrid architectures-based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing-based solutions for the wideband mmWave channel estimation problem for hybrid architectures. First, we leverage the sparse structure of the frequency-selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains. Our solutions are suitable for both single carrier-frequency domain equalization and orthogonal frequency-division multiplexing systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead. Leveraging the hybrid architecture at the transceivers gives further improvement in estimation error performance and achievable rates.

Linear boundary extensions for finite length signals and paraunitary two-channel filterbanks

In this paper, we introduce a novel and general matrix formulation of artificial linear boundary extension methods for removing border effects inherent to any paraunitary two-channel size-limited filterbank. This new characterization of the transformation operator allows us to prove that perfect reconstruction (PR) of finite signals can be ensured under some conditions without using extra subband coefficients; in other words, we characterize the signal extension methods that lead to nonexpansive transforms. The necessary and sufficient condition we find allows us to show that some traditional extension techniques that are being used in an expansive way, such as the polynomial extension, lead in fact to nonexpansive invertible transforms; moreover, we can also prove that in contradiction to previous literature, not every transformation matrix associated with a linear extension is invertible even if using prototype filters of the same length. Apart from these invertibility criteria, we propose the first algorithm for the design of all linear extensions and their associated biorthogonal boundary filters that lead to nonexpansive and invertible transforms. Analogously, we provide the first method for the design of all linear extensions that yield orthogonal transforms: We construct an infinite number of orthogonal extensions, apart from the commonly used periodic extension, and their associated orthogonal boundary filters. The final contribution of the paper is a new algorithm for the design of smooth orthogonal extensions, which keep the orthogonality property and overcome the main drawback of periodization, that is, the introduction of subband coefficients of great amplitude near the boundaries in the transform domain.

Wavelet packet-based subband adaptive equalization☆

This paper proposes adaptive algorithms for channel equalization in the wavelet packet transform domain. Adaptation to highly correlated time-varying channels is achieved by the use of the orthogonal tree-structured time-varying filter bank associated to this transform. The filter bank structure, that is, the type of decomposition, is obtained, for a fixed number of bands, as the one that leads to a quasi-optimum convergence rate of the adaptive filters in the different subbands. Then, we propose an NLMS-type adaptive strategy for two possible implementations, a block algorithm, BWPKNLMS, and a non-block approach, WPKNLMS. A theoretical analysis of both schemes is also provided: we obtain the mean squared error after the adaptive algorithm has converged and we find the expression for the optimal solution, to finally show that faster convergence is achieved. Experimental results for the learning curves show the efficiency of the proposed schemes and illustrate the previously presented theoretical results.

A fast noise-scaling algorithm for uniform quantization in audio coding schemes

A new bit assignment algorithm is presented. Its goals are the simultaneous assignment on all subbands in a few steps of an iterative calculus, the use of memory to achieve a better speed of convergence and the consideration of a deformable error curve. The basis of the algorithm is discussed and also other considerations that are likely to arise in practice. Finally, an example of its performance is given.

Time-domain channel estimation for wideband millimeter wave systems with hybrid architecture

Millimeter wave (mmWave) systems will likely employ large antennas at both the transmitter and receiver for directional beamforming. Hybrid analog/digital MIMO architectures have been proposed previously for leveraging both array gain and multiplexing gain, while reducing the power consumption in analog-to-digital converters. Channel knowledge is needed to design the hybrid precoders/combiners, which is difficult to obtain due to the large antenna arrays and the frequency selective nature of the channel. In this paper, we propose a sparse recovery based time-domain channel estimation technique for hybrid architecture based frequency selective mmWave systems. The proposed compressed sensing channel estimation algorithm is shown to provide good estimation error performance, while requiring small training overhead. The simulation results show that using multiple RF chains at the receiver and the transmitter further reduces the training overhead.

An adaptive tree search algorithm with application to multiresolution based perceptive audio coding

Search algorithms for selecting signal decompositions based on the minimization of a cost functional have been proposed in the literature, in addition to several additive and non-additive information costs. We introduce a new cost function and a search algorithm built from a perceptual criterion. Their efficiency is demonstrated with results showing higher quality compressed audio signals than preliminary approaches at similar bit-rates.

5G in space: PHY-layer design for satellite communications using non-orthogonal multi-carrier transmission

For the 5th generation of terrestrial mobile communications, Multi-Carrier (MC) transmission based on non-orthogonal waveforms is a promising technology component compared to orthogonal frequency division multiplex (OFDM) in order to achieve higher throughput and enable flexible spectrum management. Coverage extension and service continuity can be provided considering satellites as additional components in future networks by allowing vertical handover to terrestrial radio interfaces. In this paper, the properties of Filter Bank Multicarrier (FBMC) as potential MC transmission scheme is discussed taking into account the requirements for the satellite-specific PHY-Layer like non-linear distortions due to High Power Amplifiers (HPAs). The performance for specific FBMC configurations is analyzed in terms of peak-to-average power ratio (PAPR), computational complexity, non-linear distortions as well as carrier frequency offsets sensitivity (CFOs). Even though FBMC and OFDM have similar PAPR and suffer comparable spectral regrowth at the output of the non linear amplifier, simulations on link level show that FBMC still outperforms OFDM in terms of CFO sensitivity and symbol error rate in the presence of non-linear distortions.

A flexible tiling of the time axis for adaptive wavelet packet decompositions

A segmentation procedure of time sequences based on a time-frequency analysis is presented. The use of both a wavelet packet transform and the original time signal provides a set of spectral and time parameters that allows the algorithm to locate some proper break points to split the input frame into a discrete number of smaller segments. Some examples showing the performance of the method are also presented. An application to wavelet-based audio coding is also discussed.