In this paper, we propose a novel indoor localization algorithm that exploits the angle and delay information of the sparse channel paths at mmWave. We consider that the user and the access point (AP) are not perfectly synchronized, which results in an unknown clock offset for the estimated delays. The proposed algorithm comprises two stages where the initial stage is to estimate the unknown clock offset and the locations of the users by leveraging the properties of the indoor environment. Then, the initial location estimates of users are collected and used to learn the virtual anchor locations. Finally, we propose a one-shot anchor-based localization algorithm that outperforms the initial one.

Mm-wave communications use analog beamforming techniques, which steer the signal energy in a desired direction, to overcome the high path-loss at such frequencies. To determine the direction in which to steer, mm-wave standards such as IEEE 802.11ad specify beam training mechanisms for both access points as well as client stations. However, the overhead of the beam training limits scalability as the density of network deployments increases and mobile devices that require constantre-trainingare supported. We design SPIDER, a low-overhead beam-training mechanism where only access points actively participate in the training and stations perform passive compressive estimation of the angle-of-arrival. To this end, stations carry out phase-coherent measurements by switching through multiple receive beam patterns on a time-scale of tens of nanoseconds when receiving a packet preamble. Since no suitable testbed platforms exist that support such fast antenna reconfiguration, we design a high-performance, full-bandwidth FPGA-based testbed platform for flexible mm-wave experimentation, that we make available as open source. The performance analysis with this testbed shows that our algorithm achieves highly accurate angle estimation used to drive the beam steering decisions and reduces overhead by an order of magnitude compared to IEEE 802.11ad beam training.

Low overhead channel estimation based on compressive sensing (CS) has been widely investigated for hybrid wideband millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. The channel sparsifying dictionaries used in prior work are built from ideal array response vectors evaluated on discrete angles of arrival/departure. In addition, these dictionaries are assumed to be the same for all subcarriers, without considering the impacts of hardware impairments and beam squint. In this manuscript, we derive a general channel and signal model that explicitly incorporates the impacts of hardware impairments, practical pulse shaping functions, and beam squint, overcoming the limitations of mmWave MIMO channel and signal models commonly used in previous work. Then, we propose a dictionary learning (DL) algorithm to obtain the sparsifying dictionaries embedding hardware impairments, by considering the effect of beam squint without introducing it into the learning process. We also design a novel CS channel estimation algorithm under beam squint and hardware impairments, where the channel structures at different subcarriers are exploited to enable channel parameter estimation with low complexity and high accuracy. Numerical results demonstrate the effectiveness of the proposed DL and channel estimation strategy when applied to realistic mmWave channels.

Millimeter wave (mmWave) localization algorithms exploit the quasi-optical propagation of mmWave signals, which yields sparse angular spectra at the receiver. Geometric approaches to angle-based localization typically require to know the map of the environment and the location of the access points. Thus, several works have resorted to automated learning in order to infer a device’s location from the properties of received mmWave signals. However, collecting training data for such models is a significant burden. In this work, we propose a shallow neural network model to localize mmWave devices indoors. This model requires significantly fewer weights than those proposed in the literature. Therefore, it is amenable for implementation in resource-constrained hardware, and needs fewer training samples to converge. We also propose to relieve training data collection efforts by retrieving (inherently imperfect) location estimates from geometry-based mmWave localization algorithms. Even in this case, our results show that the proposed neural networks perform as good as or better than state-of-the-art algorithms.

WiFi location systems are remarkably accurate, with decimeter-level errors for recent CSI-based systems. However, such high accuracy is achieved under Line-of-Sight (LOS) conditions and with an access point (AP) density that is much higher than that typically found in current deployments that primarily target good coverage. In contrast, when many of the APs within range are in Non-Line-of-Sight (NLOS), the location accuracy degrades drastically. In this paper we present UbiLocate, a WiFi location system that copes well with common AP deployment densities and works ubiquitously, i.e., without excessive degradation under NLOS. UbiLocate demonstrates that meter-level median accuracy NLOS localization is possible through (i) an innovative angle estimator based on a Nelder-Mead search, (ii) a fine-grained time of flight ranging system with nanosecond resolution, and (iii) the accuracy improvements brought about by the increase in bandwidth and number of antennas of IEEE 802.11ac. In combination, they provide superior resolvability of multipath components, significantly improving location accuracy over prior work. We implement our location system on off-the-shelf 802.11ac devices and make the implementation, CSI-extraction tool and custom Fine Timing Measurement design publicly available to the research community. We carry out an extensive performance analysis of our system and show that it outperforms current state-of-the-art location systems by a factor of 2--3, both under LOS and NLOS.

A major source of difficulty when operating with large arrays at millimeter wave (mmWave) frequencies is to estimate the wideband channel, since the use of hybrid architectures acts as a compression stage for the received signal. Moreover, the channel has to be tracked and the antenna arrays regularly reconfigured to obtain appropriate beamforming gains when a mobile setting is considered. In this paper, we focus on the problem of channel tracking for frequency-selective mmWave channels, and propose two novel channel tracking algorithms. One of them exploits the sparsity of the mmWave channel, while the other one leverages prior statistical information about the channel parameters. We also propose a hybrid precoder and combiner design method to increase the received signal-to-noise ratio (SNR) during channel tracking, such that near-optimum data rates can be obtained with low-overhead. In our numerical results, we analyze the performance of our proposed algorithms for different system parameters. Simulation results show that our proposed channel tracking algorithms are able to achieve near-optimum data rates outperforming state-of-the-art methods.

Millimeter-Wave (mm-wave) technology is increasingly being considered for mobile devices and use cases such as vehicular communication. This requires suitable experimentation platforms to support systems-oriented research to tackle the multitude of problems and challenges of mm-wave communications in such environments. To this end, we introduce mm-FLEX, a flexible and modular open platform with real-time signal processing capabilities that supports a bandwidth of 2 GHz and is compatible with mm-wave standard requirements. mm-FLEX integrates an FPGA-based baseband processor with full-duplex capabilities together with mm-wave RF front-ends and phased antenna arrays that are fully configurable from the processor in real-time. To demonstrate the capabilities of mm-FLEX, we implement a scalable, ultra-fast beam alignment mechanism for IEEE 802.11ad systems. It is based on compressive estimation of the signal's angle-of-arrival by means of switching through multiple receive beam patterns on a nano-second time-scale while receiving a packet preamble. Our implementation is open source and is made publicly available to the research community.

Machine learning is a highly promising tool to design the physical layer of wireless communication systems, but it usually requires that a channel model is known. As data rates increase and wireless transceivers become more complex, the wireless channel, hardware imperfections, and their interactions become more difficult to model and compensate explicitly. New machine learning schemes for the physical layer do not require an explicit model but implicitly learn the end-to-end link including channel characteristics and non-linearities of the system directly from the training data.

Full-duplex (FD) communication is an enabling technology to increase spectral efficiency. The self- interference (SI) resulting from sharing time and frequency resources between different transceivers in FD mode must, however, be managed. At millimeter wave (mmWave) frequencies, FD communication is different than at sub-6 GHz frequencies, because non conventional MIMO architectures and large antenna arrays are often used. In this paper, we address a major challenge for FD mmWave systems: the design of hybrid precoders and combiners that simultaneously maximize the sum spectral efficiency and cancel the SI in the analog domain, to keep control of the signal level at the input of the analog- to-digital converters (ADCs). The optimal joint design is a very difficult problem, since it involves the optimization of eight precoding/combining matrices with several constraints, some of them non convex. We derive two suboptimal solutions which exhibit near- optimum spectral efficiency and significantly outperform prior work in terms of SI cancelation.

Future millimeter-wave networks will support very high densities of devices and access points. This vastly increases the overhead required for access point selection and beam training. Fortunately, the quasi-optical properties of millimeter-wave channels make location-based network optimization a highly promising technique to reduce control overhead in such millimeter-wave WLANs. In this paper, we extract channel state information from off-the-shelf routers, we use it to design a high accuracy location system, and then show how location information enables the optimization of network operations. The resulting scheme, named LEAP, can predict blockage, optimize access point association, and select the most suitable antenna beam patterns while significantly reducing the beam training overhead. We show that compared to standard state-of-the-art 802.11ad systems, LEAP's location driven management greatly improves network performance and link stability.

Millimeter-wave (mm-wave) location systems not only provide accurate positioning for location-based services but can also help optimize network operations, for example, through location-driven beam steering and access point association. In this paper, we design and evaluate localization schemes that exploit the characteristics of mm-wave communication systems. We propose two range-free algorithms belonging to the broad classes of triangulation and angle difference of arrival. The schemes work both with multiple anchors and with as few as a single anchor, under the only assumption that the floor plan and the positions of the mm-wave access points are known. Moreover, they are designed to be lightweight so that even computationally-constrained devices can run them. We evaluate our proposed algorithms against two benchmark approaches based on fingerprinting and angles of arrival, respectively. Our results, obtained both by means of simulations and through measurements involving commercial 60-GHz mm-wave devices, show that sub-meter accuracy is achieved in most of the cases, even in the presence of only a single access point. The availability of multiple access points substantially improves the localization accuracy, especially for large indoor spaces.

The very large bandwidth available in the 60 GHz band allows, in principle, to design highly accurate positioning systems. Integrating such systems with standard protocols (e.g., IEEE 802.11ad) is crucial for the deployment of location-based services, but it is also challenging and limits the design choices. Another key problem is that consumer-grade 60 GHz hardware only provides coarse channel state information, and has highly irregular beam shapes due to its cost-efficient design. In this paper, we explore the location accuracy that can be achieved using such hardware, without modifying the 802.11ad standard. We consider a typical 802.11ad indoor network with multiple access points (APs). Each AP collects the coarse signal-to-noise ratio of the directional beacons that clients transmit periodically. Given the irregular beam shapes, the challenge is to relate each beacon to a set of transmission angles that allows to triangulate a user. We design a location system based on particle filters along with linear programming and Fourier analysis. We implement and evaluate our algorithm on commercial off-the-shelf 802.11ad hardware in an office scenario with mobile human blockage. Despite the strong limitations of the hardware, our system operates in real-time and achieves sub-meter accuracy in 70% of the cases.

Millimeter wave (mmWave) communications are an essential component of 5G-and-beyond ultra-dense Gbit/s wireless networks, but also pose significant challenges related to the communication environment. Especially beam-training and tracking, device association, and fast handovers for highly directional mmWave links may potentially incur a high overhead. At the same time, such mechanisms would benefit greatly from accurate knowledge about the environment and device locations that can be provided through simultaneous localization and mapping (SLAM) algorithms. In this paper we tackle the above issues by proposing CLAM, a distributed mmWave SLAM algorithm that works with no initial information about the network deployment or the environment, and achieves low computational complexity thanks to a fundamental reformulation of the angle-differences-of-arrival mm Wave anchor location estimation problem. All information required by CLAM is collected by a mmWave device thanks to beam training and tracking mechanisms inherent to mm Wave networks, at no additional overhead. Our results show that CLAM achieves submeter accuracy in the great majority of cases. These results are validated via an extensive experimental measurement campaign carried out with 60-GHz mmWave hardware.

Beamforming is vital to overcome the high attenuation in wireless millimeter-wave networks. It enables nodes to steer their antennas in the direction of communication. To cope with complexity and overhead, the IEEE 802.11ad standard uses a sector codebook with distinct steering directions. In current off-the-shelf devices, we find codebooks with generic pre-defined beam patterns. While this approach is simple and robust, the antenna modules that are typically deployed in such devices are capable of generating much more precise antenna beams. In this paper, we adaptively adjust the sector codebook of IEEE 802.11ad devices to optimize the transmit beam patterns for the current channel. To achieve this, we propose a mechanism to extract full channel state information (CSI) regarding phase and magnitude from coarse signal strength readings on off-the-shelf IEEE 802.11ad devices. Since such devices do not expose the CSI directly, we generate a codebook with phase-shifted probing beams that enables us to obtain the CSI by combining strategically selected magnitude measurements. Using this CSI, transmitters dynamically compute a transmit beam pattern that maximizes the signal strength at the receiver. Thereby, we automatically exploit reflectors in the environment and improve the received signal quality. Our implementation of this mechanism on off-the-shelf devices demonstrates that adaptive codebook optimization achieves a significantly higher throughput of about a factor of two in typical real-world scenarios.

Millimeter-wave devices must use highly directional antennas to achieve GBit/s data rates over reasonable distances due to the high path loss. As a consequence, it is important to precisely align the antenna beams between sender and receiver. Even minor movement or rotation of a device can result in beam misalignment and thus a strong performance degradation. Existing work as well as standards such as IEEE 802.11ad tackle this issue by means of antenna sector probing. This comes at the expense of a significant overhead, which may significantly reduce the performance of millimeter-wave communication, particularly in mobile scenarios. In this paper, we present a mechanism that can track both movement and rotation of 60 GHz mobile devices with zero overhead. To this end, we transmit part of the preamble of each packet using a multi-lobe beampattern. Our approach does not require any additional control messages and is backward compatible with 802.11ad. We implement our scheme on a 60 GHz testbed using phased antenna arrays, and show that we reduce the angle error to less than 5° in most cases. We also perform simulations to validate our approach in a wide range of scenarios, achieving up to 2x throughput gain.

Millimeter wave (mm-wave) communication is a topic of intensive recent study, as it allows to significantly boost data rates of future 5G networks. In this paper, we focus on a mm-wave system consisting of a single Access Point (AP) and two User Equipments (UEs), where one UE requires high throughput, while the other is characterized by a low latency demand. Given that setup, we aim at optimally allocating the available AP hardware resources for the beam training phase and data communication, in order to efficiently serve both UEs via hybrid analog-digital beamforming. We evaluate an optimization framework with the objective to maximize the expected rate of one UE, for a given latency constraint set by the other UE. The optimal data rates are illustrated for different latency constraints and for different strategies of exploiting the full RF chain set at the AP side. We observe that our proposed access schemes outperform the basic TDMA approach by up to 22 %.

Device localization is a highly important functionality for a range of applications. It is particularly beneficial in mmWave networks, where it can be used to reduce the beam training overhead and anticipate handovers between access points. In this paper, we present JADE, an algorithm that estimates the location of a mobile user in an indoor space without any knowledge about the surrounding environment (floor plan, location of walls and presence of reflective surfaces) or about the location and number of access points available therein. JADE leverages the beam procedure used in pre-standard and commercial mmWave equipment to estimate the angle-of-arrival of multipath components of the signal sent by visible access points. This information is then employed to localize the mobile user, estimate the position of access points and finally form a map of the environment. No radar-like ranging operations are required for this. Our results demonstrate that JADE can localize a user with sub-meter accuracy in the broad majority of the cases, and that it even outperforms localization algorithms that require full knowledge of the environment and access point positions.

In order to cope with the severe path loss, millimeter-wave (mm-wave) systems exploit highly directional communication. As a consequence, even a slight beam mis-alignment between two communicating devices (for example, due to mobility) can generate a significant signal drop. This leads to frequent invocations of time-consuming mechanisms for beam re-alignment, which deteriorate system performance. In this paper, we propose smart beam training and tracking strategies for fast mm-wave link establishment and maintenance under node mobility. We leverage the ability of hybrid analog-digital transceivers to collect channel information from multiple spatial directions simultaneously and formulate a probabilistic optimization problem to model the temporal evolution of the mm-wave channel under mobility. In addition, we present for the first time a beam tracking algorithm that extracts information needed to update the steering directions directly from data packets, without the need for spatial scanning during the ongoing data transmission. Simulation results, obtained by a custom simulator based on ray tracing, demonstrate the ability of our beam training/tracking strategies to keep the communication rate only 10% below the optimal bound. Compared to the state of the art, our approach provides a 40% to 150% rate increase, yet requires lower complexity hardware.

Millimeter-wave (mm-wave) communication systems can provide much higher data rates than systems operating at lower frequencies, but achieving such rates over sufficiently large distances requires highly directional beamforming at both the transmitter and receiver. These antenna beams have to be aligned very precisely in order to obtain sufficient link margin. In this paper, we first propose a parallel-adaptive beam training protocol, which significantly accelerates the link establishment between mm-wave devices by exploiting the ability of hybrid analog-digital beamforming antennas to scan multiple spatial sectors simultaneously. Second, we deal with practical constraints of the mm-wave transceivers and design a novel greedy geometric algorithm to synthesize sector beam patterns featuring configurable beamwidth and multi-beam radiation as required by the proposed beam training protocol. These multi-beam patterns are then also used for concurrent data communication over multiple paths, in case several suitable directions are found during the beam training. Simulation results show that our algorithm is able to shape antenna patterns very close to those attained by a fully digital beamforming architecture, yet requires lower complexity hardware compared with the state-of-the-art solutions. Exploiting such multi-beam antenna patterns, our parallel beam training protocol can provide up to 82% effective rate increase and 70% search time decrease compared with existing sequential protocols. The acceleration of the beam training phase shifts the optimum balance between the search overhead and the achieved directivity gain so that the best performance is reached with a training load 30% to 60% lower than that of the sequential beam training.

Millimeter wave (mmWave) wireless technologies are expected to become key enablers of multi-gigabit wireless access in next-generation cellular and local area networks. Due to unfavorable radio propagation, mmWave systems will exploit large-scale MIMO and adaptive antenna arrays at both the transmitter and receiver to realize sufficient link margin. Unfortunately, power and cost requirements in mmWave radio frontends make the use of fully-digital beamforming very challenging. In this paper, we focus on hybrid analog-digital beamforming and address two relevant aspects of the initial access procedure at mmWave frequencies. First, we propose a beam training protocol which effectively accelerates the link establishment by exploiting the ability of mobile users to simultaneously receive from multiple directions. Second, we deal with practical constraints of mmWave transceivers and propose a novel, geometric approach to synthesize multi-beamwidth beam patterns that can be leveraged for simultaneous multi-direction scanning. Simulation results show that the proposed hybrid codebooks are able to shape beam patterns very close to those attained by a fully-digital beamforming architecture, yet require lower complexity hardware compared with the state of the art. Furthermore, the reduced duration of the beam training phase, in turn enabled by the multi-beam characteristics of our hybrid codebooks, provides a 25% to 70% increase in spectral efficiency compared to existing sequential scanning strategies.