Performance and Security Enhancements in Practical Millimeter-Wave Communication Systems

Millimeter-wave (mm-wave) communication systems achieve extremely high data rates and provide interference-free transmissions. to overcome high attenuations, they employ directional antennas that focus their energy in the intended direction. Transmissions can be steered such that signals only propagate within a specific area-of-interest. Although these advantages are well-known, they are not yet available in practical networks. IEEE 802.11ad, the recent standard for communications in the unlicensed 60 GHz band, exploits a subset of the directional propagation effects only. Despite the large available spectrum, it does not outperform other developments in the prevalent sub-6 GHz bands. This underutilization of directional communications causes unnecessary performance limitations and leaves a false sense of security. For example, standard compliant beam training is very time consuming. It uses suboptimal beam patterns, and is unprotected against malicious behaviors. Furthermore, no suitable research platform exists to validate protocols in realistic environments. To address these challenges, we develop a holistic evaluation framework and enhance the performance and security in practical mm-wave communication systems. Besides signal propagation analyses and environment simulations, our framework enables practical testbed experiments with off-the-shelf devices. We provide full access to a tri-band router’s operating system, modify the beam training operation in the Wi-Fi firmware, and create arbitrary beam patterns with the integrated antenna array. This novel approach allows us to implement custom algorithms such as a compressive sector selection that reduces the beam training overhead by a factor of 2.3. By aligning the receive beam, our adaptive beam switching algorithm mitigates interference from lateral directions and achieves throughput gains of up to 60%. With adaptive beam optimization, we estimate the current channel conditions and generate directional beams that implicitly exploit potential reflections in the environment. These beams increase the received signal strength by about 4.4 dB. While intercepting a directional link is assumed to be challenging, our experimental studies show that reflections on small-scale objects are sufficient to enable eavesdropping from afar. Additionally, we practically demonstrate that injecting forged feedback in the beam training enables Man-in-the Middle attacks. With only 7.3% overhead, our authentication scheme protects against this beam stealing and enforces responses to be only accepted from legitimate devices. By making beam training more efficient, effective, and reliable, our contributions finally enable practical applications of highly directional transmissions

A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications

The commercial availability of low-cost millimeter wave (mmWave) communication and radar devices is starting to improve the penetration of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices. This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We first overview key concepts about mmWave signal propagation and system design. Then, we provide a detailed account of approaches and algorithms for localization and sensing enabled by mmWaves. We consider several dimensions in our analysis, including the main objectives, techniques, and performance of each work, whether each research reached some degree of implementation, and which hardware platforms were used for this purpose. We conclude by discussing that better algorithms for consumer-grade devices, data fusion methods for dense deployments, as well as an educated application of machine learning methods are promising, relevant and timely research directions.Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys & Tutorials (IEEE COMST

Optimized Live 4K Video Multicast

4K videos are becoming increasingly popular. However, despite advances in wireless technology, streaming 4K videos over mmWave to multiple users is facing significant challenges arising from directional communication, unpredictable channel fluctuation and high bandwidth requirements. This paper develops a novel 4K layered video multicast system. We (i) develop a video quality model for layered video coding, (ii) optimize resource allocation, scheduling, and beamforming based on the channel conditions of different users, and (iii) put forward a streaming strategy that uses fountain code to avoid redundancy across multicast groups and a Leaky-Bucket-based congestion control. We realize an end-to-end system on commodity-off-the-shelf (COTS) WiGig devices. We demonstrate the effectiveness of our system with extensive testbed experiments and emulation

Towards Adaptive, Self-Configuring Networked Unmanned Aerial Vehicles

Networked drones have the potential to transform various applications domains; yet their adoption particularly in indoor and forest environments has been stymied by the lack of accurate maps and autonomous navigation abilities in the absence of GPS, the lack of highly reliable, energy-efficient wireless communications, and the challenges of visually inferring and understanding an environment with resource-limited individual drones. We advocate a novel vision for the research community in the development of distributed, localized algorithms that enable the networked drones to dynamically coordinate to perform adaptive beam forming to achieve high capacity directional aerial communications, and collaborative machine learning to simultaneously localize, map and visually infer the challenging environment, even when individual drones are resource-limited in terms of computation and communication due to payload restrictions

Design and Analysis of Beamforming in mmWave Networks

To support increasing data-intensive wireless applications, millimeter-wave (mmWave) communication emerges as the most promising wireless technology that offers high data rate connections by exploiting a large swath of spectrum. Beamforming (BF) that focuses the radio frequency power in a narrow direction, is adopted in mmWave communication to overcome the hostile path loss. However, the distinct high directionality feature caused by BF poses new challenges: 1) Beam alignment (BA) latency which is a processing delay that both the transmitter and the receiver align their beams to establish a reliable link. Existing BA methods incur significant BA latency on the order of seconds for a large number of beams; 2) Medium access control (MAC) degradation. To coordinate the BF training for multiple users, 802.11ad standard specifies a new MAC protocol in which all the users contend for BF training resources in a distributed manner. Due to the “deafness” problem caused by directional transmission, i.e., a user may not sense the transmission of other users, severe collisions occur in high user density scenarios, which significantly degrades the MAC performance; and 3) Backhaul congestion. All the base stations (BSs) in mmWave dense networks are connected to backbone network via backhaul links, in order to access remote content servers. Although BF technology can increase the data rate of the fronthaul links between users and the BS, the congested backhaul link becomes a new bottleneck, since deploying unconstrained wired backhaul links in mmWave dense networks is infeasible due to high costs. In this dissertation, we address each challenge respectively by 1) proposing an efficient BA algorithm; 2) evaluating and enhancing the 802.11ad MAC performance; and 3) designing an effective backhaul alleviation scheme. Firstly, we propose an efficient BA algorithm to reduce processing latency. The existing BA methods search the entire beam space to identify the optimal transmit-receive beam pair, which leads to significant latency. Thus, an efficient BA algorithm without search- ing the entire beam space is desired. Accordingly, a learning-based BA algorithm, namely hierarchical BA (HBA) algorithm is proposed which takes advantage of the correlation structure among beams such that the information from nearby beams is extracted to iden- tify the optimal beam, instead of searching the entire beam space. Furthermore, the prior knowledge on the channel fluctuation is incorporated in the proposed algorithm to further accelerate the BA process. Theoretical analysis indicates that the proposed algorithm can effectively identify the optimal beam pair with low latency. Secondly, we analyze and enhance the performance of BF training MAC (BFT-MAC) in 802.11ad. Existing analytical models for traditional omni-directional systems are un- suitable for BFT-MAC due to the distinct directional transmission feature in mmWave networks. Therefore, a thorough theoretical framework on BFT-MAC is necessary and significant. To this end, we develop a simple yet accurate analytical model to evaluate the performance of BFT-MAC. Based on our analytical model, we derive the closed-form expressions of average successful BF training probability, the normalized throughput, and the BF training latency. Asymptotic analysis indicates that the maximum normalized throughput of BFT-MAC is barely 1/e. Then, we propose an enhancement scheme which adaptively adjusts MAC parameters in tune with user density. The proposed scheme can effectively improve MAC performance in high user density scenarios. Thirdly, to alleviate backhaul burden in mmWave dense networks, edge caching that proactively caches popular contents at the edge of mmWave networks, is employed. Since the cache resource of an individual BS can only store limited contents, this significantly throttles the caching performance. We propose a cooperative edge caching policy, namely device-to-device assisted cooperative edge caching (DCEC), to enlarge cached contents by jointly utilizing cache resources of adjacent users and BSs in proximity. In addition, the proposed caching policy brings an extra advantage that the high directional transmission in mmWave communications can naturally tackle the interference issue in the cooperative caching policy. We theoretically analyze the performance of DCEC scheme taking the network density, the practical directional antenna model and the stochastic information of network topology into consideration. Theoretical results demonstrate that the proposed policy can achieve higher performance in offloading the backhaul traffic and reducing the content retrieval delay, compared with the benchmark policy. The research outcomes from the dissertation can provide insightful lights on under- standing the fundamental performance of the mmWave networks from the perspectives of BA, MAC, and backhaul. The schemes developed in the dissertation should offer practical and efficient solutions to build and optimize the mmWave networks

Network Management and Control for mmWave Communications

Millimeter-wave (mmWave) is one of the key technologies that enables the next wireless generation. mmWave offers a much higher bandwidth than sub-6GHz communications which allows multi-gigabit-per-second rates. This also alleviates the scarcity of spectrum at lower frequencies, where most devices connect through sub-6GHz bands. However new techniques are necessary to overcome the challenges associated with such high frequencies. Most of these challenges come from the high spatial attenuation at the mmWave band, which requires new paradigms that differ from sub-6GHz communications. Most notably mmWave telecommunications are characterized by the need to be directional in order to extend the operational range. This is achieved by using electronically steerable antenna arrays, that focus the energy towards the desired direction by combining each antenna element constructively or destructively. Additionally, most of the energy comes from the Line Of Sight (LOS) component which gives mmWave a quasi-optical behaviour where signals can reflect off walls and still be used for communication. Some other challenges that directional communications bring are mobility tracking, blockages and misalignments due to device rotation. The IEEE 802.11ad amendment introduced wireless telecommunications in the unlicensed 60 GHz band. It is the first standard to address the limitations of mmWave. It does so by introducing new mechanisms at the Medium Access Control (MAC) and Physical (PHY) layers. It introduces multi-band operation, relay operation mode, hybrid channel access scheme, beam tracking and beam forming among others. In this thesis we present a series of works that aim to improve mmWave telecommunications. First we give an overview of the intrinsic challenges of mmWave telecommunications, by explaining the modifications to the MAC and PHY layers. This sets the base for the rest of the thesis. Then do a comprehensive study on how mmWave behaves with existing technologies, namely TCP. TCP is unable to distinguish losses caused by congestion or by transmission errors caused by channel degradation. Since mmWave is affected by blockages more than sub-6GHz technologies, we propose a set of parameters that improve the channel quality even for mobile scenarios. The next job focuses on reducing the initial access overhead of mmWave by using sub-6GHz information to steer towards the desired direction. We start this work by doing a comprehensive High Frequency (HF) and Low Frequency (LF) correlation, analyzing the similarity of the existing paths between the two selected frequencies. Then we propose a beam steering algorithm that reduces the overhead to one third of the original time. Once we have studied how to reduce the initial access overhead, we propose a mechanism to reduce the beam tracking overhead. For this we propose an open platform based on a Field Programmable Gate Arrays (FPGA) where we implement an algorithm that completely removes the need to train on the Station (STA) side. This is achieved by changing beam patterns on the STA side while the Access Point (AP) is sending the preamble. We can change up to 10 beam patterns without losing connection and we reduce the overhead by a factor of 8.8 with respect to the IEEE 802.11ad standard. Finally we present a dual band location system based on Commercial-Off-The-Shelve (COTS) devices. Locating the STA can improve the quality of the channel significantly, since the AP can predict and react to possible blockages. First we reverse engineer existing 60 GHz enabled COTS devices to extract Channel State Information (CSI) and Fine Timing Measurements (FTM) measurements, from which we can estimate angle and distance. Then we develop an algorithm that is able to choose between HF and LF in order to improve the overall accuracy of the system. We achieve less than 17 cm of median error in indoor environments, even when some areas are Non Line Of Sight (NLOS).This work has been supported by IMDEA Networks Institute.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Matthias Hollick.- Secretario: Vincenzo Mancuso.- Vocal: Paolo Casar