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.

Millimeter-wave (mmWave) technology is an important element to increase the throughput and reduce latency of future wireless networks. At the same time, its high bandwidth and highly directional antennas allow for unprecedented accuracy in wireless sensing and localization applications. In this paper, we thoroughly analyze mmWave localization and find that it is either extremely accurate or has a very high error, since there is significant mmWave coverage via reflections and even through walls. As a consequence, sub-6 GHz technology can not only provide (coarse) localization where mmWave is not available, but is also critical to decide among multiple candidate antennas and APs for accurate mmWave localization.

Millimeter-wave technology provides the necessary improvements in capacity and performance for the next generation of wireless networks. The new IEEE 802.11ay amendment extends IEEE 802.11ad to offer 100 Gbit/s connectivity in the unlicensed 60 GHz band through technical advancements such as Multiple-Input and Multiple-Output (MIMO), channel bonding and aggregation. Additionally, it offers improvements to the Beamforming Training (BFT) process in order to increase its efficiency and accuracy. One new technique defined by IEEE 802.11ay is Group Beamforming, which allows to simultaneously train all stations, and significantly reduces training overhead, especially in very dense networks. In this paper, we provide an implementation of IEEE 802.11ay in ns-3 and perform, to the best of our knowledge, the first detailed system-level evaluation of the performance of the novel IEEE 802.11ay protocol. We specifically study the performance of Group Beamforming and compare it against the legacy 802.11ad BFT. We explore how different BFT approaches scale in large networks, identify the possible problems and evaluate at how the BFT process influences the performance of the network overall. Our analysis shows that Group Beamforming can outperform the legacy approach, resulting in lower overhead and improved network performance. However, we also found that the Access Point (AP) training is quite vulnerable to interference in dense networks, introducing severe limitations to the performance, especially in large rooms where precise BFT is crucial to maintain the communication link. Therefore, we propose several improvements to Group Beamforming that improve performance and provide robust beamforming even in very dense scenarios.

Millimeter-wave systems not only provide high data rates and low latency, but the very large bandwidth also allows for highly accurate environment sensing. Such properties are extremely useful for smart factory scenarios. At the same time, reusing existing communication links for passive object localization is significantly more challenging than radar-based approaches due to the sparsity of the millimeter-wave multi-path environment and the weakness of the reflected paths compared to the line-of-sight path. In this paper, we explore the passive object localization accuracy that can be achieved with IEEE 802.11ad devices. We use commercial Access Points (APs) whereas the station design is based on a full-bandwidth 802.11ad compatible FPGA-based platform with a phased antenna array. The stations exploit the preamble of the beam training packets of the APs to obtain Channel Impulse Response (CIR) measurements for all antenna patterns. With this, we determine distance and angle information for the different multi-path components in the environment to passively localize a mobile object. We evaluate our system with multiple APs and a moving robot with a metallic surface. Our system operates in real-time and achieves 6.5cm mean error accuracy and sub-meter accuracy in 100% of the cases.

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.

Millimeter-wave (mm-wave) bands will play an essential role in 5G mobile networks in supporting the increasing demand for higher data rates. Communications at mm-wave frequencies pose unique challenges. The high propagation loss and unfavorable atmospheric absorption make the channel quality highly variable - short communication ranges and blockage through obstacles may prevent communication altogether. The use of directional antennas helps to achieve higher communication ranges and provides better spatial reuse and lower interference compared to omni-directional communications. At the same time, this introduces the problem of beam misalignment. Mm-wave research has primarily focused on the PHY and MAC layers, whereas the transport layer aspects of mm-wave systems require further attention. In this article, we analyze the behavior of TCP in mm-wave networks and study its impact on system-level performance. Through extensive simulations, we show the effect of different types of blockages on the behavior of the congestion control in the presence of handovers, and when small, medium and long flows coexist. Protocols like CUBIC that target high throughput benefit significantly when jointly optimizing link layers buffers and timeouts. While the optimization fosters prompt reaction to short-term blockages, the performance of such protocols significantly decreases when obstacles degrade the channel quality for longer time periods. Hybrid-designs like TCP YeAH are more robust to blockage, but fail to recover quickly and to ramp up to the link capacity after timeouts.

Precise localization is becoming an integral part of mobile network architectures, not only to provide location-based services but also to optimize the operation of the network itself through suitable context information. Location systems are of particular importance for indoor settings where GPS may be unavailable. While upcoming 5G systems will provide improved location accuracy, for a long time to come many areas will only have LTE coverage, and ubiquitous localization will thus also have to rely on LTE technology. To evaluate the location accuracy that can be achieved with current mobile systems, we implement a localization algorithm in a standard-compliant LTE testbed based on software-defined radios. We assess the localization accuracy in representative indoor scenarios. Despite a bandwidth of only 20MHz, the results show a good median error around 2m, but significantly larger errors may occur in non-line-of-sight cases. Nevertheless, the accuracy is sufficient for a range of potential applications.