Roi Mendez-Rial

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.

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.

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.

Radar aided beam alignment in MmWave V2I communications supporting antenna diversity

Millimeter wave (mmWave) communication is the only viable approach for high bandwidth connected vehicles exchanging raw sensor data. A main challenge for mmWave in connected vehicles, is that it requires frequent link reconfiguration in mobile environments, which is a source of high overhead. In this paper we introduce the concept of radar aided mmWave vehicular communication. Side information derived from radar mounted on the infrastructure operating in a given mmWave band is used to adapt the beams of the vehicular communication system operating in another millimeter wave band. We propose a set of algorithms to perform the beam alignment task in a vehicle-to-infrastructure (V2I) scenario, from extracting information from the radar signal to configuring the beams that illuminate the different antennas in the vehicle. Simulation results confirm that radar can be a useful source of side information that helps configure the mmWave V2I link.

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.

Anisotropic Inpainting of the Hypercube

Airborne pushbroom sensors produce images by acquiring scenes on a line-per-line basis. Depending on the motions of the aircraft carrying the sensor, the line integration time, and the targeted spatial resolution, missing areas may appear on geocorrected images. Missing pixels in geocorrected images are usually tackled by means of interpolation methods, such as nearest neighbor, but these cause visible artifacts that affect the visual quality of the result and also the performance of processing methods working on geocorrected images. We propose the use of an anisotropic diffusion inpainting method specifically devised for hyperspectral images, show some extreme examples, and discuss its convenience.

Adaptive hybrid precoding and combining in MmWave multiuser MIMO systems based on compressed covariance estimation

In this paper we propose an adaptive multi user (MU) single-cell hybrid precoding strategy that iteratively designs the precoders/combiners exploiting the reciprocity of time division duplex (TDD) millimeter wave systems. The minimum mean square error (MMSE) criterion is considered to design the combiners, which relies on second order statistics of the channel. The covariance of the received signal at the antenna array is estimated online from compressed measurements, leveraging the sparse nature of mmWave channels. The proposed method avoids the explicit estimation of the channel matrix associated to each user, reducing the training overhead, and achieving sum spectral efficiencies comparable to the ones obtained with block diagonalization.

Alien plant monitoring with ultralight airborne imaging spectroscopy.

Effective management of invasive plants requires a precise determination of their distribution. Remote sensing techniques constitute a promising alternative to field surveys and hyperspectral sensors (also known as imaging spectrometers, with a large number of spectral bands and high spectral resolution) are especially suitable when very similar categories are to be distinguished (e.g. plant species). A main priority in the development of this technology is to lower its cost and simplify its use, so that its demonstrated aptitude for many environmental applications can be truly realized. With this aim, we have developed a system for hyperspectral imaging (200 spectral bands in the 380–1000 nm range and circa 3 nm spectral resolution) operated on board ultralight aircraft (namely a gyrocopter), which allows a drastic reduction of the running costs and operational complexity of image acquisition, and also increases the spatial resolution of the images (circa 5–8 pixels/m2 at circa 65 km/h and 300 m height). The detection system proved useful for the species tested (Acacia melanoxylon, Oxalis pes-caprae, and Carpobrotus aff. edulis and acinaciformis), with user’s and producer’s accuracy always exceeding 90%. The detection accuracy reported corresponds to patches down to 0.125 m2 (50% of pixels 0.5×0.5 m in size), a very small size for many plant species, making it very effective for initial stages of invasive plant spread. In addition, its low operating costs, similar to those of a 4WD ground vehicle, facilitate frequent image acquisition. Acquired images constitute a permanent record of the status of the study area, with great amount of information that can be analyzed in the future for other purposes, thus greatly facilitating the monitoring of natural areas at detailed spatial and temporal scales for improved management.