Next-generation wireless networks promise to provide extremely high data rates, especially exploiting the so-called millimeter-wave frequency range. Gaining information from spectrum usage is becoming important to provide smart adaptation capabilities to future network protocol stacks. Issues such as deafness, misaligned antennas, or blockage may severely impact network performance, and their identification is crucial. Despite the complexity of full analytical models, machine learning techniques are progressively being considered to improve spectrum usage at higher layers. In this paper, we design a signal processing technique that uses narrowband physical layer energy traces, obtained from one or multiple channel sniffers. The proposed technique utilizes a combination of template matching and an Explicit Duration Hidden Markov Model (EDHMM) to correctly classify frames, while coping with the non-stationarity of the traces. This leads to a protocol level monitor that does not need to decode the channel at the physical layer, but just infers the type of packets that are exchanged based on sub-sampled energy traces. The performance of this framework is evaluated using off-the-shelf mm-wave wireless devices, quantifying its detection performance in the presence of one or multiple sniffers, and assessing the impact of physical layer parameters such as noise power and signal levels.

Both IEEE 802.15.3c and 802.11ad use the 60 GHz band for high datarate Wireless Personal/Local Area Network applications. These millimeter-wave communications use very directional antennas since the small wavelength allows to integrate many small antenna elements to form a beamforming antenna array, enabling very high spatial reuse as can be found in dense indoor and IoT settings. However, earlier work shows that current mmWave systems are not as directional as theory would suggest, with significant interference that may prevent spatial reuse. In this work, we propose a centralized system that allows the network to carry out the beamtraining process not only to maximize signal power, but also taking into account other stations in order to minimize interference. This system is designed to work with unmodified clients. We implement and validate our system on commercial off-the-shelf 60 GHz hardware, achieving an average throughput gain of 24.67% for TCP traffic, and up to a twofold throughput gain in specific cases.

This paper presents experiments to assess and understand the feasibility of millimeter-wave (mmWave) radios for aerial links at low altitude, namely drone communications. In this preliminary study, a Commercial Off-The-Shelf (COTS) mm Wave radio is attached to a DJI Matrice 600 Pro drone. The measurement campaign reveals that while the technology is promising, the range is extremely limited, i.e., approximately 30m. Our analysis shows that the poor range is likely due to low-directionality of today's COTS devices as well as poor rate adaptation in mobile environments. More antennas at both the transmitter and the receiver, along with better 3D beamforming, will be essential to boosting the communication range and thus making this a viable technology for use cases such as high definition real-time monitoring in disaster response scenarios or rapid deployment of multi-Gbps aerial links to expand connectivity in underserved areas.

Understanding the gap between theory and practice of 60 GHz networks is crucial. Theoretical models typically focus on the physical layer only and assume ideal hardware components. However, such assumptions do not hold in practice. For instance, the cost-effective design of commercial off-the-shelf products often results in hardware impairments, such as highly irregular beam shapes. Further, mobility and frame aggregation have a strong impact throughout the protocol stack. Taking these effects into account when designing 60 GHz networking mechanisms is critical. In this paper, we characterize the aforementioned effects and contribute a comprehensive dataset of real-world traces that capture them. Among other traces, our dataset includes measurements of actual beam patterns, the impact of the cases of the devices on propagation, exhaustive measurements on the degradation of link performance in case of mobility, and the behavior of aggregation at the physical layer. Our dataset is the most comprehensive study of this kind to date. As a result, our work allows researchers to take the aforementioned hardware impairments into account, which paves the way for comparable and realistic evaluations of 60 GHz networking mechanisms in the community.

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.

The directional nature of communication in millimeter-wave bands suggests that the frequency selectivity of the channel may be limited due to the absence of reflected paths. However, our measurement studies show that reflections often cause significant frequency selectivity in practical scenarios, which we can exploit to increase the otherwise limited range of 60 GHz networks. Specifically, we measure real-world indoor 60 GHz channels with a bandwidth of 2 GHz, and study their behavior with respect to techniques such as bitloading, subcarrier switch-off, and waterfilling. To this end, we consider Orthogonal Frequency-Division Multiplexing (OFDM) as defined in the IEEE 802.11ad standard and show that in contrast to common belief, these techniques are highly beneficial in millimeter-wave networks. We analyze this in practice for both horn antennas as well as an electronically-steerable phased antenna array. Most importantly, our practical results demonstrate that for the specific case of the 60 GHz band, this selectivity allows for a range extension of up to 50%. Hence, our approach enables us to alleviate one of the main limitations of millimeter-wave networks.

The lower layer characteristics of 60 GHz wireless networks impact the entire protocol stack. This includes effects such as sudden link failure due to blockage, high packet loss due to deafness, or suboptimal transmit rates due to antenna misalignment. To understand the resulting behaviors at the upper layers, it is key to have access to lower layer information. At the time of writing, 60 GHz experimentation hardware is either limited in terms of bandwidth, real-time capability, or does not support a full stack. In contrast, commercial off-the-shelf (COTS) hardware covers all of the above features but is a black box regarding the lower layers. Our demo exploits hidden monitoring capabilities of a certain COTS device which has become popular in the 60 GHz research community. The demo allows conference attendees to interact with 60 GHz links and observe how this impacts lower layer parameters. Moreover, the demo shows how such interactions relate to the performance at the upper layers.

Troubleshooting millimeter-wave (mm-wave) wireless networks is complex due to the directionality of the communication. Issues such as deafness, misaligned antennas, or blockage may severely impact network performance, and identifying them is crucial to improve network deployments. To this end, access to lower-layer information is important. However, commercial off-the-shelf mm-wave wireless devices typically do not provide such information. Even if they would, detecting effects such as deafness based on information of a single node that forms part of the network is typically hard. In this paper, we present the design and evaluation of an external sniffing device that can infer the aforementioned performance issues only using narrowband physical layer energy traces. Our sniffer does not need to decode any data, resulting in a simple but effective approach which also preserves privacy and works on encrypted networks. Our key contribution is a machine learning framework which enables automated energy trace analysis while coping with the non-stationarity of the traces. We evaluate its performance in practice using off-the-shelf wireless devices operating in the 60 GHz band. Our results show that the above framework correctly infers physical layer events in virtually all cases, thus providing valuable information to troubleshoot issues in mm-wave networks.

In this paper, we target single-anchor localization schemes for millimeter wave (MMW) systems. The schemes are designed to be lightweight, so that even computationally-constrained devices can support them. We identify the main propagation properties of MMW signals that have an impact on localization and design three algorithms that exploit these, namely a triangulation-validation procedure, an angle difference-of-arrival approach, and a scheme based on location fingerprinting. We evaluate the algorithms by means of simulations, and draw conclusions on their robustness. We then validate our results via measurements involving commercial pre- standard 60- GHz MMW hardware. Our experiments confirm that, by relying only on a single anchor and without requiring complex signal processing at the receiver, the algorithms can localize a node with high probability, and in many cases with sub-meter accuracy. We conclude by discussing how these algorithms complement each other in terms of robustness and localization success probability.

The performance of current consumer-grade devices for 60 GHz wireless networks is limited. While such networks promise both high data rates and uncomplicated spatial reuse, we find that commercially available devices based on the WiHD and WiGig standards may suffer from their cost-effective design. Very similar mechanisms are used in upcoming devices based on the IEEE 802.11ad standard. Hence, understanding them well is crucial to improve the efficiency and performance of next generation millimeter wave networks. In this paper, we present the first in-depth beamforming, interference, and frame level protocol analysis of off-the-shelf millimeter wave systems with phased antenna arrays. We focus on (a) the interference due to the lack of directionality of consumer-grade antennas, and (b) the degree of data aggregation of current devices. Regarding (a), our beam pattern measurements show strong side lobes that challenge the common conception of high spatial reuse in 60 GHz networks. We also show that reflections in realistic settings worsen this effect. Further, we measure weak directionality when beamforming towards the boundary of the transmission area of an antenna array. Regarding (b), we observe that devices only aggregate data if connections require high bandwidth, thus increasing medium usage time otherwise.