arXiv subjects

Robert W. Heath Jr

Publications and source records attributed to Robert W. Heath Jr.

At least 91 records · Page 5Linked to original sources

arXiv2019

Optimization of Power Transfer Efficiency and Energy Efficiency for Wireless-Powered Systems with Massive MIMO

Massive MIMO is attractive for wireless information and energy transfer due to its ability to focus energy towards desired spatial locations. In this paper, the overall power transfer efficiency (PTE) and the energy efficiency (EE) of a wireless-powered massive MIMO system is investigated where a multi-antenna base-station (BS) uses wireless energy transfer to charge single-antenna energy harvesting users on the downlink. The users may exploit the harvested energy to transmit information to the BS on the uplink. The overall system performance is analyzed while accounting for the nonlinear nature of practical energy harvesters. First, for wireless energy transfer, the PTE is characterized using a scalable model for the BS circuit power consumption. The PTE-optimal number of BS antennas and users are derived. Then, for wireless energy and information transfer, the EE performance is characterized. The EE-optimal BS transmit power is derived in terms of the key system parameters such as the number of BS antennas and the number of users. As the number of antennas becomes large, increasing the transmit power improves the energy efficiency for moderate to large number of antennas. Simulation results suggest that it is energy efficient to operate the system in the massive antenna regime.

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arXiv2019

FALP: Fast beam alignment in mmWave systems with low-resolution phase shifters

Millimeter wave (mmWave) systems can enable high data rates if the link between the transmitting and receiving radios is configured properly. Fast configuration of mmWave links, however, is challenging due to the use of large antenna arrays and hardware constraints. For example, a large amount of training overhead is incurred by exhaustive search-based beam alignment in typical mmWave phased arrays. In this paper, we present a framework called FALP for Fast beam Alignment with Low-resolution Phase shifters. FALP uses an efficient set of antenna weight vectors to acquire channel measurements, and allows faster beam alignment when compared to exhaustive scan. The antenna weight vectors in FALP can be realized in ultra-low power phase shifters whose resolution can be as low as one-bit. From a compressed sensing (CS) perspective, the CS matrix designed in FALP satisfies the restricted isometry property and allows CS algorithms to exploit the fast Fourier transform. The proposed framework also establishes a new connection between channel acquisition in phased arrays and magnetic resonance imaging.

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arXiv2019

Analysis of Intelligent Vehicular Relaying in Urban 5G+ Millimeter-Wave Cellular Deployments

The capability of smarter networked devices to dynamically select appropriate radio connectivity options is especially important in the emerging millimeter-wave (mmWave) systems to mitigate abrupt link blockage in complex environments. To enrich the levels of diversity, mobile mmWave relays can be employed for improved connection reliability. These are considered by 3GPP for on-demand densification on top of the static mmWave infrastructure. However, performance dynamics of mobile mmWave relaying is not nearly well explored, especially in realistic conditions, such as urban vehicular scenarios. In this paper, we develop a mathematical framework for the performance evaluation of mmWave vehicular relaying in a typical street deployment. We analyze and compare alternative connectivity strategies by quantifying the performance gains made available to smart devices in the presence of mmWave relays. We identify situations where the use of mmWave vehicular relaying is particularly beneficial. Our methodology and results can support further standardization and deployment of mmWave relaying in more intelligent 5G+ "all-mmWave" cellular networks.

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arXiv2019

Message passing-based link configuration in short range millimeter wave systems

Millimeter wave (mmWave) communication in typical wearable and data center settings is short range. As the distance between the transmitter and the receiver in short range scenarios can be comparable to the length of the antenna arrays, the common far field approximation for the channel may not be applicable. As a result, dictionaries that result in a sparse channel representation in the far field setting may not be appropriate for short distances. In this paper, we develop a novel framework to exploit the structure in short range mmWave channels. The proposed method splits the channel into several subchannels for which the far field approximation can be applied. Then, the structure within and across different subchannels is leveraged using message passing. We show how information about the antenna array geometry can be used to design message passing factors that incorporate structure across successive subchannels. Simulation results indicate that our framework can be used to achieve better beam alignment with fewer channel measurements when compared to standard compressed sensing-based techniques that do not exploit structure across subchannels.

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arXiv2019

A Geometry-aided Message Passing Method for AoA-Based Short Range MIMO Channel Estimation

Short range channels commonly arise in millimeter wave (mmWave) wearable settings, where the length of the antenna arrays can be comparable to the distance between the radios. Conventional mmWave MIMO channel estimation techniques based on the far field assumption may perform poorly in short range settings due to the large angular spread and, hence, high available rank. We propose a geometry-aided message passing algorithm that exploits structure in short range line-of-sight (LoS) channels for spatial sub-Nyquist channel estimation. Our approach parametrizes the channel using angle-of-arrivals (AoAs) that are locally defined for subarrays of an antenna array. Furthermore, it leverages the dependencies between the local AoAs using factors based on the array geometry. We show that the LoS MIMO channel can be reconstructed using the derived local AoA estimates and the known transceiver geometry. The proposed approach achieves a reasonable rate with greatly reduced pilot transmissions when compared to exhaustive beam search-based local AoA estimation.

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arXiv2019

Adaptive Virtual Waveform Design for Millimeter-Wave Joint Communication-Radar

Joint communication and radar (JCR) waveforms with fully digital baseband generation and processing can now be realized at the millimeter-wave (mmWave) band. Prior work has proposed a mmWave wireless local area network (WLAN)-based JCR that exploits the WLAN preamble for radars. The performance of target velocity estimation, however, was limited. In this paper, we propose a virtual waveform design for an adaptive mmWave JCR. The proposed system transmits a few non-uniformly placed preambles to construct several receive virtual preambles for enhancing velocity estimation accuracy, at the cost of only a small reduction in the communication data rate. We evaluate JCR performance trade-offs using the Cramer-Rao Bound (CRB) metric for radar estimation and a novel distortion minimum mean square error (MMSE) metric for data communication. Additionally, we develop three different MMSE-based optimization problems for the adaptive JCR waveform design. Simulations show that an optimal virtual (non-uniform) waveform achieves a significant performance improvement as compared to a uniform waveform. For a radar CRB constrained optimization, the optimal radar range of operation and the optimal communication distortion MMSE (DMMSE) are improved. For a communication DMMSE constrained optimization with a high DMMSE constraint, the optimal radar CRB is enhanced. For a weighted MMSE average optimization, the advantage of the virtual waveform over the uniform waveform is increased with decreased communication weighting. Comparison of MMSE-based optimization with traditional virtual preamble count-based optimization indicated that the conventional solution converges to the MMSE-based one only for a small number of targets and a high signal-to-noise ratio.

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arXiv2019

MmWave MU-MIMO for Aerial Networks

Millimeter wave offers high bandwidth for air-to-air (A2A) communication. In this paper, we evaluate the rate performance of a multiuser MIMO (MU-MIMO) configuration where several aircraft communicate with a central hub. We consider a hybrid subarray architecture, single path channels, and realistic atmospheric attenuation effects. We propose a mathematical framework for the analysis of millimeter wave (mmWave) MU-MIMO networks. Via Monte Carlo simulation, we demonstrate that mmWave is a promising technology for delivering gigabit connectivity in next-generation aerial networks.

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arXiv2019

Spatial Channel Covariance Estimation for Hybrid Architectures Based on Tensor Decompositions

Spatial channel covariance information can replace full instantaneous channel state information for the analog precoder design in hybrid analog/digital architectures. Obtaining spatial channel covariance estimation, however, is challenging in the hybrid structure due to the use of fewer radio frequency (RF) chains than the number of antennas. In this paper, we propose a spatial channel covariance estimation method based on higher-order tensor decomposition for spatially sparse time-varying frequency-selective channels. The proposed method leverages the fact that the channel can be represented as a low-rank higher-order tensor. We also derive the Cramér-Rao lower bound on the estimation accuracy of the proposed method. Numerical results and theoretical analysis show that the proposed tensor-based approach achieves higher estimation accuracy in comparison with prior compressive-sensing-based approaches or conventional angle-of-arrival estimation approaches. Simulation results reveal that the proposed approach becomes more beneficial at low signal-to-noise (SNR) region.

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arXiv2019

Framework for a Perceptive Mobile Network using Joint Communication and Radar Sensing

In this paper, we develop a framework for a novel perceptive mobile/cellular network that integrates radar sensing function into the mobile communication network. We propose a unified system platform that enables downlink and uplink sensing, sharing the same transmitted signals with communications. We aim to tackle the fundamental sensing parameter estimation problem in perceptive mobile networks, by addressing two key challenges associated with sophisticated mobile signals and rich multipath in mobile networks. To extract sensing parameters from orthogonal frequency division multiple access (OFDMA) and spatial division multiple access (SDMA) communication signals, we propose two approaches to formulate it to problems that can be solved by compressive sensing techniques. Most sensing algorithms have limits on the number of multipath signals for their inputs. To reduce the multipath signals, as well as removing unwanted clutter signals, we propose a background subtraction method based on simple recursive computation, and provide a closed-form expression for performance characterization. The effectiveness of these methods is validated in simulations.

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arXiv2019

Online Learning for Position-Aided Millimeter Wave Beam Training

Accurate beam alignment is essential for beam-based millimeter wave communications. Conventional beam sweeping solutions often have large overhead, which is unacceptable for mobile applications like vehicle-to-everything. Learning-based solutions that leverage sensor data like position to identify good beam directions are one approach to reduce the overhead. Most existing solutions, though, are supervised-learning where the training data is collected beforehand. In this paper, we use a multi-armed bandit framework to develop online learning algorithms for beam pair selection and refinement. The beam pair selection algorithm learns coarse beam directions in some predefined beam codebook, e.g., in discrete angles separated by the 3dB beamwidths. The beam refinement fine-tunes the identified directions to match the peak of the power angular spectrum at that position. The beam pair selection uses the upper confidence bound (UCB) with a newly proposed risk-aware feature, while the beam refinement uses a modified optimistic optimization algorithm. The proposed algorithms learn to recommend good beam pairs quickly. When using 16x16 arrays at both the transmitter and receiver, it can achieve on average 1dB gain over the exhaustive search (over 271x271 beam pairs) on the unrefined codebook within 100 time-steps with a training budget of only 30 beam pairs.

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arXiv2018

Double-Sequence Frequency Synchronization for Wideband Millimeter-Wave Systems with Few-Bit ADCs

In this paper, we propose and evaluate two novel double-sequence low-resolution frequency synchronization methods in millimeter-wave (mmWave) systems. In our system model, the base station uses analog beams to send the synchronization signal with infinite-resolution digital-to-analog converters. The user equipment employs a fully digital front end to detect the synchronization signal with low-resolution analog-to-digital converters (ADCs). The key ingredient of the proposed methods is the custom designed synchronization sequence pairs, from which there exists an invertible function (a ratio metric) of the carrier frequency offset (CFO) to be estimated. We use numerical examples to show that the ratio metric is robust to the quantization distortion. Further, we analytically characterize the CFO estimation performances of our proposed designs assuming a single user. To implement our proposed methods in a multi-user scenario, we propose to optimize the double-sequence design parameters such that: (i) for each individual user, the impact of the quantization distortion on the CFO estimation accuracy is minimized, and (ii) the resulting frequency range of estimation can capture as many users' CFOs as possible. Numerical results reveal that our proposed algorithms provide a flexible means to estimate CFO in a variety of low-resolution settings.

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arXiv2018

Swift-Link: A compressive beam alignment algorithm for practical mmWave radios

Next generation wireless networks will exploit the large amount of spectrum available at millimeter wave (mmWave) frequencies. Design of mmWave systems, however, is challenging due to strict power, cost and hardware constraints at higher bandwidths. To achieve a good SNR for communication, mmWave systems use large antenna arrays. Beamforming with highly directional beams is one way to use the antennas. As the channel changes over time, the beams that maximize the SNR have to be estimated quickly to reduce the training overhead. Prior work has exploited the observation that mmWave channels are sparse to perform compressed sensing (CS) based beam alignment with few channel measurements. Most of the existing CS-based algorithms, however, assume perfect synchronization and fail in the presence of carrier frequency offset (CFO). This paper presents Swift-Link, a fast beam alignment algorithm that is robust against the offset. Swift-Link includes a novel randomized beam training sequence that minimizes the beam alignment errors due to CFO and a low-complexity algorithm that corrects these errors. Even with strict hardware constraints, our algorithm uses fewer channel measurements than comparable CS algorithms and has analytical guarantees. Swift-Link requires a small output dynamic range at the analog-to-digital converter compared to beam-scanning techniques.

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arXiv2018

Linear Receivers in Non-stationary Massive MIMO Channels with Visibility Regions

In a massive MIMO system with large arrays, the channel becomes spatially non-stationary. We study the impact of spatial non-stationarity characterized by visibility regions (VRs) where the channel energy is significant on a portion of the array. Relying on a channel model based on VRs, we provide expressions of the signal-to-interference-plus-noise ratio (SINR) of conjugate beamforming (CB) and zero-forcing (ZF) precoders. We also provide an approximate deterministic equivalent of the SINR of ZF precoders. We identify favorable and unfavorable multi-user configurations of the VRs and compare the performance of both stationary and non-stationary channels through analysis and numerical simulations.

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arXiv2018

Multibeam for Joint Communication and Sensing Using Steerable Analog Antenna Arrays

Beamforming has great potential for joint communication and sensing (JCAS), which is becoming a demanding feature on many emerging platforms such as unmanned aerial vehicles and smart cars. Although beamforming has been extensively studied for communication and radar sensing respectively, its application in the joint system is not straightforward due to different beamforming requirements by communication and sensing. In this paper, we propose a novel multibeam framework using steerable analog antenna arrays, which allows seamless integration of communication and sensing. Different to conventional JCAS schemes that support JCAS using a single beam, our framework is based on the key innovation of multibeam technology: providing fixed subbeam for communication and packet-varying scanning subbeam for sensing, simultaneously from a single transmitting array. We provide a system architecture and protocols for the proposed framework, complying well with modern packet communication systems with multicarrier modulation. We also propose low-complexity and effective multibeam design and generation methods, which offer great flexibility in meeting different communication and sensing requirements. We further develop sensing parameter estimation algorithms using conventional digital Fourier transform and 1D compressive sensing techniques, matching well with the multibeam framework. Simulation results are provided and validate the effectiveness of our proposed framework, beamforming design methods and the sensing algorithms.

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arXiv2018

Extremely Large Aperture Massive MIMO: Low Complexity Receiver Architectures

This paper focuses on new communication paradigms arising in massive multiple-input-multiple-output systems where the antenna array at the base station is of extremely large dimension (xMaMIMO). Due to the extreme dimension of the array, xMaMIMO is characterized by spatial non-stationary field properties along the array; this calls for a multi-antenna transceiver design that is adapted to the array dimension but also its non-stationary properties. We address implementation aspects of xMaMIMO, with computational efficiency as our primary objective. To reduce the computational burden of centralized schemes, we distribute the processing into smaller, disjoint subarrays. Then, we consider several low-complexity data detection algorithms as candidates for uplink communication in crowded xMaMIMO systems. Drawing inspiration from coded random access, one of the main contributions of the paper is the design of low complexity scheme that exploits the non-stationary nature of xMaMIMO systems and where the data processing is decentralized. We evaluate the bit-error-rate performance of the transceivers in crowded xMaMIMO scenarios. The results confirm their practical potential.

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arXiv2018

Directional Frame Timing Synchronization in Wideband Millimeter-Wave Systems with Low-Resolution ADCs

In this paper, we propose and evaluate a novel beamforming strategy for directional frame timing synchronization in wideband millimeter-wave (mmWave) systems operating with low-resolution analog-to-digital converters (ADCs). In the employed system model, we assume multiple radio frequency chains equipped at the base station to simultaneously form multiple synchronization beams in the analog domain. We formulate the corresponding directional frame timing synchronization problem as a max-min multicast beamforming problem under low-resolution quantization. We first show that the formulated problem cannot be effectively solved by conventional single-stream beamforming based approaches due to large quantization loss and limited beam codebook resolution. We then develop a new multi-beam probing based directional synchronization strategy, targeting at maximizing the minimum received synchronization signal-to-quantization-plus-noise ratio (SQNR) among all users. Leveraging a common synchronization signal structure design, the proposed approach synthesizes an effective composite beam from the simultaneously probed beams to better trade off the beamforming gain and the quantization distortion. Numerical results reveal that for wideband mmWave systems with low-resolution ADCs, the timing synchronization performance of our proposed method outperforms the existing approaches due to the improvement in the received synchronization SQNR.

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arXiv2018

Connectivity and Blockage Effects in Millimeter-Wave Air-To-Everything Networks

Millimeter-wave (mmWave) offers high data rate and bandwidth for air-to-everything (A2X) communications including air-to-air, air-to-ground, and air-to-tower. MmWave communication in the A2X network is sensitive to buildings blockage effects. In this paper, we propose an analytical framework to define and characterise the connectivity for an aerial access point (AAP) by jointly using stochastic geometry and random shape theory. The buildings are modelled as a Boolean line-segment process with fixed height. The blocking area for an arbitrary building is derived and minimized by optimizing the altitude of AAP. A lower bound on the connectivity probability is derived as a function of the altitude of AAP and different parameters of users and buildings including their densities, sizes, and heights. Our study yields guidelines on practical mmWave A2X networks deployment.

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arXiv2018

Algorithms for the Construction of Incoherent Frames Under Various Design Constraints

Unit norm finite frames are generalizations of orthonormal bases with many applications in signal processing. An important property of a frame is its coherence, a measure of how close any two vectors of the frame are to each other. Low coherence frames are useful in compressed sensing applications. When used as measurement matrices, they successfully recover highly sparse solutions to linear inverse problems. This paper describes algorithms for the design of various low coherence frame types: real, complex, unital (constant magnitude) complex, sparse real and complex, nonnegative real and complex, and harmonic (selection of rows from Fourier matrices). The proposed methods are based on solving a sequence of convex optimization problems that update each vector of the frame. This update reduces the coherence with the other frame vectors, while other constraints on its entries are also imposed. Numerical experiments show the effectiveness of the methods compared to the Welch bound, as well as other competing algorithms, in compressed sensing applications.

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