Nuria Gonzalez-Prelcic

An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems

Communication at millimeter wave (mmWave) frequencies is defining a new era of wireless communication. The mmWave band offers higher bandwidth communication channels versus those presently used in commercial wireless systems. The applications of mmWave are immense: wireless local and personal area networks in the unlicensed band, 5G cellular systems, not to mention vehicular area networks, ad hoc networks, and wearables. Signal processing is critical for enabling the next generation of mmWave communication. Due to the use of large antenna arrays at the transmitter and receiver, combined with radio frequency and mixed signal power constraints, new multiple-input multiple-output (MIMO) communication signal processing techniques are needed. Because of the wide bandwidths, low complexity transceiver algorithms become important. There are opportunities to exploit techniques like compressed sensing for channel estimation and beamforming. This article provides an overview of signal processing challenges in mmWave wireless systems, with an emphasis on those faced by using MIMO communication at higher carrier frequencies.

MIMO Precoding and Combining Solutions for Millimeter-Wave Systems

Millimeter-wave communication is one way to alleviate the spectrum gridlock at lower frequencies while simultaneously providing high-bandwidth communication channels. MmWave makes use of MIMO through large antenna arrays at both the base station and the mobile station to provide sufficient received signal power. This article explains how beamforming and precoding are different in MIMO mmWave systems than in their lower-frequency counterparts, due to different hardware constraints and channel characteristics. Two potential architectures are reviewed: hybrid analog/digital precoding/combining and combining with low-resolution analog- to-digital converters. The potential gains and design challenges for these strategies are discussed, and future research directions are highlighted.

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.

Millimeter-Wave Vehicular Communication to Support Massive Automotive Sensing

As driving becomes more automated, vehicles are being equipped with more sensors generating even higher data rates. Radars are used for object detection, visual cameras as virtual mirrors, and LIDARs for generating high resolution depth associated range maps, all to enhance the safety and efficiency of driving. Connected vehicles can use wireless communication to exchange sensor data, allowing them to enlarge their sensing range and improve automated driving functions. Unfortunately, conventional technologies, such as DSRC and 4G cellular communication, do not support the gigabit-per-second data rates that would be required for raw sensor data exchange between vehicles. This article makes the case that mmWave communication is the only viable approach for high bandwidth connected vehicles. The motivations and challenges associated with using mmWave for vehicle-to-vehicle and vehicle-to-infrastructure applications are highlighted. A high-level solution to one key challenge - the overhead of mmWave beam training - is proposed. The critical feature of this solution is to leverage information derived from the sensors or DSRC as side information for the mmWave communication link configuration. Examples and simulation results show that the beam alignment overhead can be reduced by using position information obtained from DSRC.

Investigating the IEEE 802.11ad Standard for Millimeter Wave Automotive Radar

Millimeter wave (mmWave) technology is widely used for automotive radar applications, like adaptive cruise control and obstacle detection. Unlike conventional radar waveforms which are usually propriety, this paper explores the use of a consumer wireless local area network (WLAN) waveform in the 60GHz unlicensed mmWave band for automotive radar applications. In particular, this paper develops a joint framework of long range automotive radar (LRR) and vehicle-to-vehicle communication (V2V) at 60 GHz by exploiting the special data-aided structure (repeated Golay complimentary sequences) of an IEEE 802.11ad single carrier physical layer (SCPHY) frame. This framework leverages the signal processing algorithms used in the typical WLAN receiver for time and frequency synchronization to perform radar parameter estimation. The initial simulation results show that it is possible to achieve the desired range accuracy of 0.1 m with a very high probability of detection (above 99%) using the preamble of a SCPHY frame. Furthermore, the velocity estimation algorithm achieves the desired accuracy of 0.1 m/s at high SNR using the preamble and pilot words of only a single frame.

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.

Design of universal multicoset sampling patterns for compressed sensing of multiband sparse signals

Many problems in digital communications involve wideband radio signals. As the most recent example, the impressive advances in Cognitive Radio systems make even more necessary the development of sampling schemes for wideband radio signals with spectral holes. This is equivalent to considering a sparse multiband signal in the framework of Compressive Sampling theory. Starting from previous results on multicoset sampling and recent advances in compressive sampling, we analyze the matrix involved in the corresponding reconstruction equation and define a new method for the design of universal multicoset codes, that is, codes guaranteeing perfect reconstruction of the sparse multiband signal.