Cristian Rusu

Hybrid MIMO Architectures for Millimeter Wave Communications: Phase Shifters or Switches?

Hybrid analog/digital multiple-input multiple-output architectures were recently proposed as an alternative for fully digital-precoding in millimeter wave wireless communication systems. This is motivated by the possible reduction in the number of RF chains and analog-to-digital converters. In these architectures, the analog processing network is usually based on variable phase shifters. In this paper, we propose hybrid architectures based on switching networks to reduce the complexity and the power consumption of the structures based on phase shifters. We define a power consumption model and use it to evaluate the energy efficiency of both structures. To estimate the complete MIMO channel, we propose an open-loop compressive channel estimation technique that is independent of the hardware used in the analog processing stage. We analyze the performance of the new estimation algorithm for hybrid architectures based on phase shifters and switches. Using the estimate, we develop two algorithms for the design of the hybrid combiner based on switches and analyze the achieved spectral efficiency. Finally, we study the tradeoffs between power consumption, hardware complexity, and spectral efficiency for hybrid architectures based on phase shifting networks and switching networks. Numerical results show that architectures based on switches obtain equal or better channel estimation performance to that obtained using phase shifters, while reducing hardware complexity and power consumption. For equal power consumption, all the hybrid architectures provide similar spectral efficiencies.

Channel estimation and hybrid combining for mmWave: Phase shifters or switches?

Precoding/combining and large antenna arrays are essential in millimeter wave (mmWave) systems. In traditional MIMO systems, precoding/combining is usually done digitally at baseband with one radio frequency (RF) chain and one analog-to-digital converter (ADC) per antenna. The high cost and power consumption of RF chains and ADCs at mmWave frequencies make an all-digital processing approach prohibitive. When only a limited number of RF chains is available, hybrid architectures that split the precoding/combining processing into the analog and digital domains are attractive. A previously proposed hybrid solution employs phase shifters and mixers in the RF precoding/combining stage. It obtains near optimal spectral efficiencies with a reduced number of RF channels. In this paper we propose a different hybrid architecture, which simplifies the hardware at the receiver by replacing the phase shifters with switches. We present a new approach for compressed sensing based channel estimation for the hybrid architectures. Given the channel estimate, we propose a novel algorithm that jointly designs the antenna subsets selected and the baseband combining. Using power consumption calculations and achievable rates, we compare the performance of hybrid combining with antenna switching and phase shifting, showing that antenna selection is preferred in a range of operating conditions.

H0LiCOW – IV. Lens mass model of HE 0435−1223 and blind measurement of its time-delay distance for cosmology

Strong gravitational lenses with measured time delays between the multiple images allow a direct measurement of the time-delay distance to the lens, and thus a measure of cosmological parameters, particularly the Hubble constant, H-0. We present a blind lens model analysis of the quadruply imaged quasar lens HE 0435-1223 using deep Hubble Space Telescope imaging, updated time-delay measurements from the COSmological MOnitoring of GRAvItational Lenses (COSMOGRAIL), a measurement of the velocity dispersion of the lens galaxy based on Keck data, and a characterization of the mass distribution along the line of sight. HE 0435-1223 is the third lens analysed as a part of the H-0 Lenses in COSMOGRAILs Wellspring (HOLiCOW) project. We account for various sources of systematic uncertainty, including the detailed treatment of nearby perturbers, the parametrization of the galaxy light and mass profile, and the regions used for lens modelling. We constrain the effective time delay distance to be D-Delta t = 2612(191)(+208) Mpc, a precision of 7.6 per cent. From HE 0435-1223 alone, we infer a Hubble constant of H-0 = 73.1(6.0)(+5.7) km s(-1) Mpc(-1) assuming a flat ACDM cosmology. The cosmographic inference based on the three lenses analysed by HOLiCOW to date is presented in a companion paper (HOLiCOW Paper V).

Iterative reweighted l1 design of sparse FIR filters

Sparse FIR filters have lower implementation complexity than full filters, while keeping a good performance level. This paper describes a new method for designing 1D and 2D sparse filters in the minimax sense using a mixture of reweighted l1 minimization and greedy iterations. The combination proves to be quite efficient; after the reweighted l1 minimization stage introduces zero coefficients in bulk, a small number of greedy iterations serve to eliminate a few extra coefficients. Experimental results and a comparison with the latest methods show that the proposed method performs very well both in the running speed and in the quality of the solutions obtained.

Low complexity hybrid sparse precoding and combining in millimeter wave MIMO systems

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication with large antenna arrays has been proposed to enable gigabit per second communication for next generation cellular systems and local area networks. A key difference relative to lower frequency solutions is that in mmWave systems, precoding/combining can not be performed entirely at digital baseband, due to the high cost and power consumption of some components of the radio frequency (RF) chain. In this paper we develop a low complexity algorithm for finding hybrid precoders that split the precoding/combining process between the analog and digital domains. Our approach exploits sparsity in the received signal to formulate the design of the precoder/combiners as a compressed sensing optimization problem. We use the properties of the matrix containing the array response vectors to find first an orthonormal analog precoder, since sparse approximation algorithms applied to orthonormal sensing matrices are based on simple computations of correlations. Then, we propose to perform a local search to refine the analog precoder and compute the baseband precoder. We present numerical results demonstrate substantial improvements in complexity while maintaining good spectral efficiency.

Stagewise K-SVD to Design Efficient Dictionaries for Sparse Representations

The problem of training a dictionary for sparse representations from a given dataset is receiving a lot of attention mainly due to its applications in the fields of coding, classification and pattern recognition. One of the open questions is how to choose the number of atoms in the dictionary: if the dictionary is too small then the representation errors are big and if the dictionary is too big then using it becomes computationally expensive. In this letter, we solve the problem of computing efficient dictionaries of reduced size by a new design method, called Stagewise K-SVD, which is an adaptation of the popular K-SVD algorithm. Since K-SVD performs very well in practice, we use K-SVD steps to gradually build dictionaries that fulfill an imposed error constraint. The conceptual simplicity of the method makes it easy to apply, while the numerical experiments highlight its efficiency for different overcomplete dictionaries.

Dictionary-free hybrid precoders and combiners for mmWave MIMO systems

The high cost and power consumption of the radio frequency chain and data converters at mmWave frequencies introduce hardware limitations into the design of MIMO precoders and combiners. MmWave hybrid precoding overcomes this limitation by dividing the spatial signal processing between the radio frequency and baseband domains. Analog networks of phase shifters have been proposed to implement the radio frequency precoders, since they achieve a good compromise between complexity and performance. In this paper, we propose a low complexity hybrid precoding design for the architecture based on phase shifters. The new method is a greedy algorithm based on the orthogonal matching pursuit algorithm, but replacing the costly correlation operations over a dictionary with the element-wise normalization of the first singular vector of the residual. The main advantage is that the design avoids any assumption on the antenna array geometry. Additionally, numerical results show the superiority of the proposed method in terms of achievable spectral efficiency over other previous solutions.

Low Complexity Hybrid Precoding Strategies for Millimeter Wave Communication Systems

Millimeter communication systems use large antenna arrays to provide good average received power and to take advantage of multi-stream MIMO communication. Unfortunately, due to power consumption in the analog front-end, it is impractical to perform beamforming and fully digital precoding at baseband. Hybrid precoding/combining architectures have been proposed to overcome this limitation. The hybrid structure splits the MIMO processing between the digital and analog domains, while keeping the performance close to that of the fully digital solution. In this paper, we introduce and analyze several algorithms that efficiently design hybrid precoders and combiners starting from the known optimum digital precoder/combiner, which can be computed when perfect channel state information is available. We propose several low complexity solutions which provide different trade-offs between performance and complexity. We show that the proposed iterative solutions perform better in terms of spectral efficiency and/or are faster than previous methods in the literature. All of them provide designs which perform close to the known optimal digital solution. Finally, we study the effects of quantizing the analog component of the hybrid design and show that even with coarse quantization, the average rate performance is good.

Adaptive One-Bit Compressive Sensing with Application to Low-Precision Receivers at mmWave

Multiple input multiple output (MIMO) systems employing large antenna arrays are the basic architecture for millimeter wave (mmWave) systems. Due to the higher bandwidths to be used at mmWave, the corresponding sampling rates of high-resolution analog-to-digital converters (ADCs) are also very high, so that ADCs become the most power hungry devices in the reception chain. One solution is to employ low resolution, i.e. one-bit, ADCs. We develop an adaptive one-bit compressed sensing scheme that can be used at low-resolution mmWave receivers for channel estimation. The simulation results show that the adaptive one-bit compressed sensing scheme outperforms the fixed one in the context of mmWave channel estimation.

Explicit Shift-Invariant Dictionary Learning

In this letter we give efficient solutions to the construction of structured dictionaries for sparse representations. We study circulant and Toeplitz structures and give fast algorithms based on least squares solutions. We take advantage of explicit circulant structures and we apply the resulting algorithms to shift-invariant learning scenarios. Synthetic experiments and comparisons with state-of-the-art methods show the superiority of the proposed methods.