Transmitter Beam Selection in Millimeter-Wave MIMO with In-Band Position-Aiding

Emerging wireless communication systems will be characterized by a tight coupling between communication and positioning. This is particularly apparent in millimeter-wave (mm-wave) communications, where devices use a large number of antennas, and the propagation is well described by geometric channel models. For mm-wave communications, initial access, consisting in the beam selection and alignment of two devices, is challenging and time consuming in the absence of location information. Conversely, accurate positioning relies on high-quality communication links with proper beam alignment. This paper studies this interaction and proposes a new position-aided transmitter beam selection protocol, which considers the problem of joint communication and positioning in scenarios with direct line-of-sight and scattering. Simulation results show significant reductions in latency with respect to a standard protocol

Optimal Precoders for Tracking the AoD and AoA of a mm-Wave Path

In millimeter-wave channels, most of the received energy is carried by a few paths. Traditional precoders sweep the angle-of-departure (AoD) and angle-of-arrival (AoA) space with directional precoders to identify directions with largest power. Such precoders are heuristic and lead to sub-optimal AoD/AoA estimation. We derive optimal precoders, minimizing the Cram\'{e}r-Rao bound (CRB) of the AoD/AoA, assuming a fully digital architecture at the transmitter and spatial filtering of a single path. The precoders are found by solving a suitable convex optimization problem. We demonstrate that the accuracy can be improved by at least a factor of two over traditional precoders, and show that there is an optimal number of distinct precoders beyond which the CRB does not improve.Comment: Resubmission to IEEE Trans. on Signal Processing. 12 pages and 9 figure

On the trade-off between uncertainty and delay in UWB and 5G localization

Location-aware technologies in combination with emerging wireless communication systems\ua0have revolutionized many aspects of our daily lives by means of applications within\ua0the commercial, public and military sectors. Ultra-wideband (UWB) and 5G stand\ua0out as emerging radio frequency (RF) based technologies that tackle the limitations of\ua0Global Positioning System solutions. The thrive in search for better accuracy involves\ua0improved ranging algorithms, higher transmission powers, network densification, larger\ua0bandwidths, and the use of cooperation among nodes in the network. However, practical\ua0implementations introduce communication related constraints. In this thesis, we study\ua0the trade-off between localization accuracy and communication constraints in terms of\ua0delay. This trade-off is investigated and quantified for two of the most rapidly growing\ua0RF technologies for high precision positioning: UWB and 5G.In UWB, we investigate the trade-off between medium access control (MAC) delay and\ua0accuracy based on a two-way-ranging and a spatial time division multiple access scheme.\ua0We quantify this relationship by deriving lower bounds on localization accuracy and MAC\ua0delay during the measurements phase, which is often neglected in the analyses. We find\ua0that the traditional means to improve accuracy such as increased number of anchors,\ua0increased communication range, and cooperation among nodes, come at a significant cost\ua0in terms of delay, which can be mitigated by means of techniques such as selective ranging\ua0and eavesdropping. We summarize and generalize our findings by characterizing the\ua0position error and delay lower bounds by deriving asymptotic scaling laws. These scaling\ua0laws are presented for dense noncooperative and cooperative networks in combination\ua0with delay mitigation techniques. Moreover, we introduce a delay/accuracy trade-off\ua0parameter, which can uniquely quantify the trade-off as a function of the agent and\ua0anchor density. Finally, we consider the problem of fast link scheduling and propose an\ua0optimization strategy to perform robust ranging scheduling with localization constraints.\ua0We propose two MAC-aware link selection heuristic approximation approaches which\ua0show similar performance as the optimal solution, but alleviate the problem complexity.In 5G, we analyze the interplay between communication and positioning within the initial\ua0access procedure between a transmitter and a receiver in a millimeter-wave multipleinput\ua0multiple-output system. We exploit the ability of the receiver to determine its\ua0location during the beam selection process and thus, improve the subsequent selection\ua0of beams within initial access. First, assuming that only the transmitter has beamforming\ua0capabilities, we propose an in-band position-aided transmitter beam selection\ua0protocol for scenarios with direct line-of-sight and scattering. Then, we extend the work\ua0and propose an in-band position-aided beam selection protocol where we also allow for\ua0the receiver to perform beamforming in scenarios with line-of-sight, reflected paths, and\ua0possible beam alignment errors. Both protocols show similar performance compared to\ua0their conventional counterparts in terms of final achieved signal-to-noise ratio, but they\ua0are significantly faster and can additionally provide the position and orientation of the\ua0devices in an accurate manner

Adaptive beamwidth optimization under Doppler ICI and positioning errors at mmWave bands

The growing trends towards massive antenna arrays with focusing capabilities has enabled the use of higher frequencies at the cost of more complex systems. In the particular case of vehicular communications, millimeter-wave (mmWave) communications are expected to unleash a set of advanced use cases with stringent spectrum needs. However, dealing with very directive patterns and high frequencies entail additional challenges such as beam misalignment and Doppler effect. This paper presents a beam optimization procedure for vehicle-to-network (V2N) systems in which a base station communicates with high-speed users. Aided by the a priori knowledge of the vehicle location, the base station is able to estimate the average signal-to-interference-plus-noise ratio (SINR) until the next beam refresh considering the positioning accuracy and the Doppler inter-carrier interference (ICI). The estimation includes the antenna beamwidth, which can be optimized to maximize the achievable throughput. The numerical results indicate that the SINR can be significantly enhanced compared to beam sweeping with identical hierarchical codebooks while reducing the probability of outage.This work was partly funded by the Spanish Ministerio de Economía y Competitividad under the projects PID2019-107885GB- C31 and MDM2016-0600, the Catalan Research Group 2017 SGR 219, and “Industrial Doctorate” programme of the Agència de Gestió d’Ajuts Universitaris i de Recerca (2018-DI-084). The Spanish Ministerio de Universidades contributes via a predoctoral grant to the first author (FPU17/05561).Peer ReviewedPostprint (published version

5G Downlink Multi-Beam Signal Design for LOS Positioning

In this work, we study optimal transmit strategies for minimizing the positioning error bound in a line-of-sight scenario, under different levels of prior knowledge of the channel parameters. For the case of perfect prior knowledge, we prove that two beams are optimal, and determine their beam directions and optimal power allocation. For the imperfect prior knowledge case, we compute the optimal power allocation among the beams of a codebook for two different robustness-related objectives, namely average or maximum squared position error bound minimization. Our numerical results show that our low-complexity approach can outperform existing methods that entail higher signaling and computational overhead.Comment: accepted for publication at IEEE GLOBECOM 201

Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless Telecommunication Performance

The pervasive nature of wireless telecommunication has made it the foundation for mainstream technologies like automation, smart vehicles, virtual reality, and unmanned aerial vehicles. As these technologies experience widespread adoption in our daily lives, ensuring the reliable performance of cellular networks in mobile scenarios has become a paramount challenge. Beamforming, an integral component of modern mobile networks, enables spatial selectivity and improves network quality. However, many beamforming techniques are iterative, introducing unwanted latency to the system. In recent times, there has been a growing interest in leveraging mobile users' location information to expedite beamforming processes. This paper explores the concept of contextual beamforming, discussing its advantages, disadvantages and implications. Notably, the study presents an impressive 53% improvement in signal-to-noise ratio (SNR) by implementing the adaptive beamforming (MRT) algorithm compared to scenarios without beamforming. It further elucidates how MRT contributes to contextual beamforming. The importance of localization in implementing contextual beamforming is also examined. Additionally, the paper delves into the use of artificial intelligence schemes, including machine learning and deep learning, in implementing contextual beamforming techniques that leverage user location information. Based on the comprehensive review, the results suggest that the combination of MRT and Zero forcing (ZF) techniques, alongside deep neural networks (DNN) employing Bayesian Optimization (BO), represents the most promising approach for contextual beamforming. Furthermore, the study discusses the future potential of programmable switches, such as Tofino, in enabling location-aware beamforming

Beyond 5G RIS mmWave Systems: Where Communication and Localization Meet

Upcoming beyond fifth generation (5G) communications systems aim at further enhancing key performance indicators and fully supporting brand-new use cases by embracing emerging techniques, e.g., reconfigurable intelligent surface (RIS), integrated communication, localization, and sensing, and mmWave/THz communications. The wireless intelligence empowered by state-of-the-art artificial intelligence techniques has been widely considered at the transceivers, and now the paradigm is deemed to be shifted to the smart control of radio propagation environment by virtue of RISs. In this paper, we argue that to harness the full potential of RISs, localization and communication must be tightly coupled. This is in sharp contrast to 5G and earlier generations, where localization was a minor additional service. To support this, we first introduce the fundamentals of RIS mmWave channel modeling, followed by RIS channel state information acquisition and link establishment. Then, we deal with the connection between localization and communications, from a separate and joint perspective