京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第21219号 / 情博第672号 / 新制||情||116(附属図書館) / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 守倉 正博, 教授 原田 博司, 教授 大木 英司 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

To meet the latest requirements of the 6G standards, several techniques have been proposed in the open literature, such as millimeter waves, terahertz communication, and massive MIMO. In addition to these recent technologies, the use of unmanned aerial vehicles (UAVs) is strongly advocated for 6G networks, as the 6G standard will not be dedicated to broadband services, but will rather be oriented towards reduced geographical cellular coverage. In this context, the deployment of UAVs is considered a key solution for seamless connectivity and reliable coverage. Although UAVs are characterized by their high mobility and their ability to establish line-of-sight links, their use is still impeded by several factors such as weather conditions, their limited computing power, and, most importantly, their limited energy. In this work, we are aiming for the novel technology that enables indefinite wireless power transfer for UAVs using laser beams. We propose a novel UAV deployment strategy, based on which we analyze the overall performance of the system in terms of wireless coverage and provide some useful insights. To this end, we use tractable tools from stochastic geometry to model the complex communication system.

A wide range of contemporary technologies leveraging ubiquitous mobile phones have addressed the challenge of transportation mode recognition, which involves identifying how users move about, such as walking, cycling, driving a car, or taking a bus. This problem has found applications in various areas, including smart city transportation, carbon footprint calculation, and context-aware mobile assistants. Previous research has primarily focused on recognizing mobility modes using GPS and motion sensor data from smartphones. However, these approaches often necessitate the installation of specialized mobile applications on users’ devices to collect sensor data, resulting in power inefficiency and privacy concerns. In this study, we tackle these issues by presenting a user-independent system capable of distinguishing four forms of locomotion—walking, bus, car, and train—solely based on mobile data (4G) from smartphones. Our system was developed using data collected in three diverse locations (Mekkah, Jeddah, KAUST) in the Kingdom of Saudi Arabia. The underlying concept is to correlate phone speed with features extracted from Channel State Information (CSI), which includes information about Physical Cell ID, received signal strength, and other relevant data. The feature extraction process involves utilizing sliding windows over both the time and frequency domains. By employing statistical classification and boosting techniques, we achieved remarkable F-scores of 85%, 95%, 88%, and 70% for the car, bus, walking, and train modes, respectively. Moreover, we conducted an analysis of the handover rate in a one-tier network and compared the analytical results with real data. This investigation provided novel insights into the influence of transportation modes on handover rate, revealing the correlation between different modes of mobility and network connectivity. This work sets the stage for the development of more efficient and privacy-friendly solutions in transportation mode recognition and network optimization.

The main objective of this thesis is to develop a martingale-type solution to optimal consumption--investment choice problems ([Merton, 1969] and [Merton, 1971]) under time-varying incomplete preferences driven by externalities such as patience, socialization effects, and market volatility. The market is composed of multiple risky assets and multiple consumption goods, while in addition there are multiple fluctuating preference parameters with inexact values connected to imprecise tastes. Utility maximization becomes a multi-criteria problem with possibly function-valued criteria. To come up with a complete characterization of the solutions, first we motivate and introduce a set-valued stochastic process for the dynamics of multi-utility indices and formulate the optimization problem in a topological vector space. Then, we modify a classical scalarization method allowing for infiniteness and randomness in dimensions and prove results of equivalence to the original problem. Illustrative examples are given to demonstrate practical interests and method applicability progressively. The link between the original problem and a dual problem is also discussed, relatively briefly. Finally, by using Malliavin calculus with stochastic geometry, we find optimal investment policies to be generally set-valued, each of whose selectors admits a four-way decomposition involving an additional indecisiveness risk-hedging portfolio. Our results touch on new directions for optimal consumption--investment choices in the presence of incomparability and time inconsistency, also signaling potentially testable assumptions on the variability of asset prices. Simulation techniques for set-valued processes are studied for how solved optimal policies can be computed in practice. / 2025-05-12T00:00:00Z

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23327号 / 情博第763号 / 新制||情||130(附属図書館) / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 守倉 正博, 教授 原田 博司, 教授 大木 英司 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

During the last decade, the average mobile wireless data usage per person has tremendously increased. An even faster growth of the traffic demand is expected for the incoming years, due to several factors such as the increasing global population, the spread of the Internet of things (IoT), and the development of advanced technologies that require a higher amount of data. While mobile communication technologies have rapidly evolved to meet this need in the most usual situations, it is expected that the sixth generation (6G) of mobile connectivity will be the first one paying considerable attention to under-connected environments such as low-income, remote, or disaster-struck regions. Many specialized researchers and entrepreneurs are trying to design and implement alternative network architectures specifically meant for enhancing the performances of the current telecommunication (telecom) infrastructure. In particular, the use of aerial base stations (ABSs) has received considerable attention due to the main advantages of easy deployability and low-cost that are typical of unmanned aerial vehicles (UAVs), which are available in several fashions depending on the application; moreover, UAVs are also eligible to carry reflective intelligent surfaces (RISs), which represent a promising technology that allows to reflect signals towards specific directions. Another possibility that we have investigated consists in integrating the transceivers inside or atop existing rural wind turbine (WT) towers, in order to increase the coverage radius while avoiding the cost of building a separate telecom infrastructure. A powerful mathematical tool for evaluating the performance metrics of either terrestrial, aerial, or vertical heterogeneous wireless networks is stochastic geometry (SG), since it can be used to model the locations of the base stations (BSs) according to tractable spatial distributions (with either a fixed or a random cardinality) in order to imitate the typical deployments of the nodes made in realistic scenarios; in particular, in this work we focus on rural and post-disaster situations. SG makes use of point processes to model networks' topologies. The developed spatial models, in turn, allow us to analyze the quality of service (QoS) experienced by the typical user served by the proposed networks. To this extent, we creatively and efficiently studied our inhomogeneous systems by making use of what we call the indicator method, meaning that we do not subdivide the ground plane in multiple homogeneous sub-regions, but we use indicator functions to provide general expressions that are valid over the entire ground plane. To prove the effectiveness of the novel architectures, insightful comparisons with the conventional ones are presented.

In this thesis, we consider stationary random mosaics with a focus on the Poisson-Voronoi mosaic and the Poisson-Delaunay mosaic. We consider properties of stationary random mosaics in R2, such as mean value results of the typical cell. Further, we simulate various mean value results of the typical cell, a random neighbor of the typical cell, and the zero cell for the Poisson-Voronoi mosaic in R2. Some theory of point processes is introduced that is needed for random mosaics, including Palm theory, marked point processes, and the Pois point process. Finally, we consider an incremental flip-based algorithm for generating the Voronoi mosaic.

Unmanned aerial vehicle (UAV) networks, a flying platform, are a promising wireless communications infrastructure with wide-ranging applications in both commercial and military domain. Owing to the appealing characteristics, such as high mobility, high feasibility, and low cost, UAV networks can be applied in various scenarios, such as emergency communications, cellular networks, device-to-device (D2D) networks, and sensor networks, regardless of infrastructure and spatial constraints. To handle complicated missions, provide wireless coverage for a large range, and have a long lifetime, a UAV network may consist of a large amount of UAVs, working cooperatively as a swarm, also referred to as swarm UAV networks. Although high mobility and numerous UAVs offer high flexibility, high scalability, and performance enhancement for swarm UAV networks, they also incur some technical challenges. One of the major challenges is the routing protocol design. With high mobility, a dynamic network topology may be encountered. As a result, traditional routing protocols based on routing path discovery are not applicable in swarm UAV networks, as the discovered routing path may be outdated especially when the amount of UAVs is large causing considerable routing path discovery delay. Multicast is an essential and key technology in the scenarios, where swarm UAV networks are employed as aerial small base station (BSs), like relay or micro BS. Swarm UAV networks consisting of a large amount of UAVs will encounter severe multicast delay with existing multicast methods using acknowledgement (ACK) feedback and retransmissions. This issue will be deteriorated when a swarm UAV network is deployed far away from BSs, causing high packet loss. Data exchange is another major technical challenge in swarm UAV networks, where UAVs exchange data packets with each other, such as requesting and retrieving lost packets. Due to numerous UAVs, data exchange between UAVs can cause message and signaling storm, resulting in a long data exchange delay and severe ovehead. In this dissertation, I focus on developing novel routing protocols, multicast schemes, and data exchange schemes, enabling efficient, robust, and high-performance routing, multicast, and data exchange in swarm UAV networks. To be specific, two novel flooding-based routing protocols are designed in this dissertation, where random network coding (RNC) is utilized to improve the efficiency of the flooding-based routing in swarm UAV networks without relying on network topology information and routing path discovery. Using the property of RNC that as long as sufficient different versions of encoded packets/generations are accumulated, original packets could be decoded, RNC is naturally able to accelerate the routing process. This is because the use of RNC can reduce the number of encoded packets that are required to be delivered in some hop. In a hop, the receiver UAV may have already overheard some generations in previous hops, so that it only needs to receive fewer generations from the transmitter UAV in the current hop. To further expedite the flooding-based routing, the second flooding-based routing protocol is designed, where each forwarding UAV creates a new version of generation by linearly combining received generations rather than by decode original packets. Despite the flooding-based routing significantly hastened by RNC, the inherent drawback of the flooding-based routing is still unsolved, namely numerous hops. Aiming at reducing the amount of hops, a novel enhanced flooding-based routing protocol leveraging clustering is designed, where the whole UAV network will be partitioned into multiple clusters and in each cluster only one UAV will be selected as the representative of this cluster, participating in the flooding-based routing process. By this way, the number of hops is restricted by the number of representatives, since packets are only flooded between limited representatives rather than numerous UAVs. To address the multicast issue in swarm UAV networks, a novel multicast scheme is proposed based on clustering, where a UAV experiencing packet loss will retrieve the lost packets by requesting other UAVs in the same cluster without depending on retransmissions of BSs. In this way, the lost packet retrieval is carried out through short-distance data exchange between UAVs with reliable transmissions and a short delay. Tractable stochastic geometry tools are used to model swarm UAV networks with a dynamic network topology, based on which comprehensive analytical performance analysis is given. To enable efficient data exchange between UAVs in swarm UAV networks, a data exchange scheme is proposed utilizing unsupervised learning. With the proposed scheme, all UAVs are assigned to multiple clusters and a UAV can only carry out data exchange within its cluster. By this way, UAVs in different clusters perform data exchange in a parallel fashion to expedite data exchange. The agglomerative hierarchical clustering, a type of unsupervised learning, is used to conduct clustering in order to guarantee that UAVs in the same cluster are able to supply and supplement each other's lost packets. Additionally, a data exchange mechanism, including a novel random backoff procedure, is designed, where the priorities of UAVs in data exchange determined by the number of their lost packets or requested packets that they can provide. As a result, each request-reply process would be taken fully advantage, maximally supplying lost packets not only to the UAV sending request, but also to other UAVs in the same cluster. For all the developed technologies in this dissertation, their technical details and the corresponding system procedures are designed based on low-complexity and well-developed technologies, such as the carrier sense multiple access/collision avoidance (CSMA/CA), for practicability in practice and without loss of generality. Moreover, extensive simulation studies are conducted to demonstrate the effectiveness and superiority of the proposed and developed technologies. Additionally, system design insights are also explored and revealed through simulations. / Doctor of Philosophy / Compared to fixed infrastructures in wireless communications, unmanned aerial vehicle (UAV) networks possess some significant advantages, such as low cost, high mobility, and high feasibility, making UAV networks have a wide range of applications in both military and commercial fields. However, some characteristics of UAV networks, including dynamic network topology and numerous UAVs, may become technical barriers for wireless communications. One of the major challenges is the routing protocol design. Routing is the process of selecting a routing path, enabling data delivered from a node (source) to another desired node (destination). Traditionally, routing is performed based on routing path discovery, where control packets are broadcasted and the path, on which a control packet first reaches the destination, will be selected as routing path. However, in UAV networks, routing path discovery may experience a long delay, as control packets go through many UAVs. Besides, the discovered routing path may be outdated, as the topology of UAV networks change over time. Another key technology in wireless communications that may not work well in UAV networks is multicast, where a transmitter, like a base station (BS), broadcasts data to UAVs and all UAVs are required to receive this data. With numerous UAVs, multicast delay may be severe, since the transmitter will keep retransmitting a data packet to UAVs until all UAVs successfully receive the packet. This issue will be deteriorated when a UAV network is deployed far away from BSs, causing high packet loss. Data exchange between UAVs is a fundamental and important system procedure in UAV networks. A large amount of UAV in a UAV network will cause serious data exchange delay, as many UAVs have to compete for limited wireless resources to request or send data. In this dissertation, I focus on developing novel technologies and schemes for swarm UAV networks, where a large amount of UAVs exist to make UAV networks powerful and handle complicated missions, enable efficient, robust, and high-performance routing, multicast, and data exchange system procedures. To be specific, two novel flooding-based routing protocols are designed, where random network coding (RNC) is utilized to improve the efficiency of flooding-based routing without relying on any network topology information or routing path discovery. The use of RNC could naturally expedite flooding-based routing process. With RNC, a receiver can decode original packets as long as it accumulates sufficient encoded packets, which may be sent by different transmitters in different hops. As a result, in some hops, fewer generations may be required to be transmitted, as receivers have already received and accumulated some encoded in previous hops. To further improve the efficiency of flooding-based routing, another routing protocol using RNC is designed, where UAVs create new encoded packets by linearly combining received encoded packets rather than linearly combing original packets. Apparently, this method would be more efficient. UAVs do not need to collect sufficient encoded packets and decode original packets, while only linearly combining all received encoded packets. Although RNC could effectively improve the efficiency of flooding-based routing, the inherent drawback is still unsolved, which is a large amount of hops caused by numerous UAVs. Thus, an enhanced flooding-based routing protocol using clustering is designed, where the whole UAV network will be partitioned into multiple clusters. In each cluster only one UAV will be selected as the representative of this cluster, participating in the flooding-based routing process. By this way, the number of hops could be greatly reduced, as packets are only flooded between limited representatives rather than numerous UAVs. To address the multicast issue in swarm UAV networks, a novel multicast scheme is proposed, where a UAV experiencing packet loss will retrieve its lost packets by requesting other UAVs in the same cluster without depending on retransmissions of BSs. In this way, the lost packet retrieval is carried out through short-distance data exchange between UAVs with reliable transmissions and a short delay. Then, the optimal number of clusters and the performance of the proposed multicast scheme are investigated by tractable stochastic geometry tools. If all UAVs closely stay together in a swarm UAV network, long data exchange delay would be significant technical issue, since UAVs will cause considerable interference to each other and all UAVs will compete for spectrum access. To cope with that, a data exchange scheme is proposed leveraging unsupervised learning. To avoid interference between UAVs and a long-time waiting for spectrum access, all UAVs are assigned to multiple clusters and different clusters use different frequency bands to carry out data exchange simultaneously. The agglomerative hierarchical clustering, a type of unsupervised learning, is used to conduct clustering, guaranteeing that UAVs in the same cluster are able to supply and supplement each other's lost packets. Additionally, a data exchange mechanism is designed, facilitating that a UAV with more lost packets or more requested packets has a higher priority to carry out data exchange. In this way, each request-reply process would be taken fully advantage, maximally supplying lost packets not only to the UAV sending request, but also to other UAVs in the same cluster. For all the developed technologies in this dissertation, their technical details and the corresponding system procedures are designed based on low-complexity and well-developed technologies, such as the carrier sense multiple access/collision avoidance (CSMA/CA), for practicability in reality and without loss of generality. Moreover, extensive simulation studies are conducted to demonstrate the effectiveness and superiority of the developed technologies. Additionally, system design insights are also explored and revealed through simulations.

The development of autonomous ground and aerial vehicles has driven the requirement for radio access technologies (RATs) to support low latency applications. While onboard sensors such as Light Detection and Ranging (LIDAR), Radio Detection and Ranging (RADAR), and cameras can sense and assess the immediate space around the vehicle, RATs are crucial for the exchange of information on critical events, such as accidents and changes in trajectory, with other vehicles and surrounding infrastructure in a timely manner. Simulations and analytical models are critical in modelling and designing efficient networks. In this dissertation, we focus on (a) proposing and developing algorithms to improve the performance of decentralized vehicular communications in safety critical situations and (b) supporting these proposals with simulation and analysis of the two most popular RAT standards, the Dedicated Short Range Communications (DSRC) standard, and the Cellular vehicle-to-everything (C-V2X) standard. In our first contribution, we propose a risk based protocol for vehicles using the DSRC standard. The protocol allows a higher beacon transmission rate for vehicles that are at a higher risk of collision. We verify the benefits of the risk based protocol over conventional DSRC using ns-3 simulations. Two risk based beacon rate protocols are evaluated in our ns-3 simulator, one that adapts the beacon rate between 1 and 10 Hz, and another between 1 and 20 Hz. Our results show that both protocols improve the packet delivery ratio (PDR) performance by up to 45% in congested environments using the 1-10 Hz adaptive beacon rate protocol and by 38% using the 1-20 Hz adaptive scheme. The two adaptive beacon rate protocol simulation results also show that the likelihood of a vehicle collision due to missed packets decreases by up to 41% and 77% respectively, in a three lane dense highway scenario with 160 vehicles operating at different speeds. In our second contribution, we study the performance of a distance based transmission protocol for vehicular ad hoc network (VANET) using tools from stochastic geometry. We consider a risk based transmission protocol where vehicles transmit more frequently depending on the distance to adjacent vehicles. We evaluate two transmission policies, a listen more policy, in which the transmission rate of vehicles decreases as the inter-vehicular distance decreases, and a talk more policy, in which the transmission rate of vehicles increases as the distance to the vehicle ahead of it decreases. We model the layout of a highway using a 1-D Poisson Point process (PPP) and analyze the performance of a typical receiver in this highway setting. We characterize the success probability of a typical link assuming slotted ALOHA as the channel access scheme. We study the trends in success probability as a function of system parameters. Our third contribution includes improvements to the 3rd Generation Partnership Project (3GPP) Release 14 C-V2X standard, evaluated using a modified collision framework. In C-V2X basic safety messages (BSMs) are transmitted through Mode-4 communications, introduced in Release 14. Mode-4 communications operate under the principle of sensing-based semi-persistent scheduling (SPS), where vehicles sense and schedule transmissions without a base station present. We propose an improved adaptive semi-persistent scheduling, termed Ch-RRI SPS, for Mode-4 C-V2X networks. Specifically, Ch-RRI SPS allows each vehicle to dynamically adjust in real-time the BSM rate, referred to in the LTE standard as the resource reservation interval (RRI). Our study based on system level simulations demonstrates that Ch-RRI SPS greatly outperforms SPS in terms of both on-road safety performance, measured as collision risk, and network performance, measured as packet delivery ratio, in all considered C-V2X scenarios. In high density scenarios, e.g., 80 vehicles/km, Ch-RRI SPS shows a collision risk reduction of 51.27%, 51.20% and 75.41% when compared with SPS with 20 ms, 50 ms, and 100 ms RRI respectively. In our fourth and final contribution, we look at the tracking error and age-of-information (AoI) of the latest 3GPP Release 16 NR-V2X standard, which includes enhancements to the 3GPP Release 14 C-V2X standard. The successor to Mode-4 C-V2X, known as Mode-2a NR-V2X, makes slight changes to sensing-based semi-persistent scheduling (SPS), though vehicles can still sense and schedule transmissions without a base station present. We use AoI and tracking error, which is the freshness of the information at the receiver and the difference in estimated vs actual location of a transmitting vehicle respectively, to measure the impact of lost and outdated BSMs on a vehicle's ability to localize neighboring vehicles. In this work, we again show that such BSM scheduling (with a fixed RRI) suffers from severe under- and over- utilization of radio resources, which severely compromises timely dissemination of BSMs and increases the system AoI and tracking error. To address this, we propose an RRI selection algorithm that measures the age or freshness of messages from neighboring vehicles to select an RRI, termed Age of Information (AoI)-aware RRI (AoI-RRI) selection. Specifically, AoI-aware SPS (i) measures the neighborhood AoI (as opposed to channel availability) to select an age-optimal RRI and (ii) uses a modified SPS procedure with the chosen RRI to select BSM transmission opportunities that minimize the overall system AoI. We compare AoI-RRI SPS to Ch-RRI SPS and fixed RRI SPS for NR-V2X. Our experiments based on the Mode-2a NR-V2X standard implemented using system level simulations show both Ch-RRI SPS and AoI-RRI SPS outperform SPS in high density scenarios in terms of tracking error and age-of-information. / Doctor of Philosophy / An increasing number of vehicles are equipped with a large set of on-board sensors that enable and support autonomous capabilities. Such sensors, which include Light Detection and Ranging (LIDAR), Radio Detection and Ranging (RADAR), and cameras, are meant to increase passenger and driver safety. However, similar to humans, these sensors are limited to line-of-sight (LOS) visibility, meaning they cannot see beyond other vehicles, corners, and buildings. For this reason, efficient vehicular communications are essential to the next generation of vehicles and could significantly improve road safety. In addition, vehicular communications enable the timely exchange of critical information with other vehicles, cellular and roadside infrastructure, and pedestrians. However, unlike typical wireless and cellular networks, vehicular networks are expected to operate in a distributed manner, as there is no guarantee of the presence of cellular infrastructure. Accurate simulations and analytical models are critical in improving and guaranteeing the performance of the next generation of vehicular networks. In this dissertation, we propose and develop novel and practical distributed algorithms to enhance the performance of decentralized vehicular communications. We support these algorithms with computer simulations and analytical tools from the field of stochastic geometry.

Over the last few decades, wireless networks have morphed from traditional cellular/wireless local area networks (WLAN), into a wide range of applications, such as the Internet-of-Things (IoT), vehicular-to-everything (V2X), and smart grid communication networks. This transition has been facilitated by research and development efforts in academia and industry, which has resulted in the standardization of fifth-generation (5G) wireless networks. To meet the performance requirements of these diverse use-cases, 5G networks demand higher performance in terms of data rate, latency, security, and reliability, etc. At the physical layer, these performance enhancements are achieved by (a) optimizing spectrum utilization shared amongst multiple technologies (termed as spectrum sharing), and (b) leveraging advanced spatial signal processing techniques using large antenna arrays (termed as massive MIMO). In this dissertation, we focus on enhancing the performance of next-generation vehicular communication and spectrum sharing systems. In the first contribution, we present a novel pilot configuration design and adaptation mechanism for cellular vehicular-to-everything (C-V2X) networks. Drawing inspiration from 4G and 5G standards, the proposed approach is based on limited feedback of indices from a codebook comprised of quantized channel statistics information. We demonstrate significant rate improvements using our proposed approach in terrestrial and air-to-ground (A2G) vehicular channels. In the second contribution, we demonstrate the occurrence of cellular link adaptation failure due to channel state information (CSI) contamination, because of coexisting pulsed radar signals that act as non-pilot interference. To mitigate this problem, we propose a low-complexity semi-blind SINR estimation scheme that is robust and accurate in a wide range of interference and noise conditions. We also propose a novel dual CSI feedback mechanism for cellular systems and demonstrate significant improvements in throughput, block error rate, and latency, when sharing spectrum with a pulsed radar. In the third contribution, we develop fundamental insights on underlay radar-massive MIMO spectrum sharing, using mathematical tools from stochastic geometry. We consider a multi-antenna radar system, sharing spectrum with a network of massive MIMO base stations distributed as a homogeneous Poisson Point Process (PPP) outside a circular exclusion zone centered around the radar. We propose a tractable analytical framework, and characterize the impact of worst-case downlink cellular interference on radar performance, as a function of key system parameters. The analytical formulation enables network designers to systematically isolate and evaluate the impact of each parameter on the worst-case radar performance and complements industry-standard simulation methodologies by establishing a baseline performance for each set of system parameters, for current and future radar-cellular spectrum sharing deployments. Finally, we highlight directions for future work to advance the research presented in this dissertation and discuss its broader impacts across the wireless industry, and policy-making. / Doctor of Philosophy / The impact of today's technologies has been magnified by wireless networks, due to the standardization and deployment of fifth-generation (5G) cellular networks. 5G promises faster data speeds, lower latency and higher user security, among other desirable features. This has made it capable of meeting the performance requirements of key infrastructure such as smart grid and mission-critical networks, and novel consumer applications such as smart home appliances, smart vehicles, and augmented/virtual reality. In part, these capabilities have been achieved by (a) better spectrum utilization among various wireless technologies (called spectrum sharing), and (b) serving multiple users on the same resource using large multi-antenna systems (called massive MIMO). In this dissertation, we make three contributions that enhance the performance of vehicular communications and spectrum sharing systems. In the first contribution, we present a novel scheme wherein a vehicular communication link adapts to the channel conditions by controlling the resource overhead in real-time, to improve spectral utilization of data resources. The proposed scheme enhances those of current 4G and 5G networks, which are based on limited feedback of quantized channel statistics, fed back from the receiver to the transmitter. In the second contribution, we show that conventional link adaptation methods fail when 4G/5G networks share spectrum with pulsed radars. To mitigate this problem, we develop a comprehensive signal processing framework, consisting of a hybrid SINR estimation method that is robust and accurate in a wide range of interference and noise conditions. Concurrently, we also propose a scheme to pass additional information that captures the channel conditions in the presence of radar interference, and analyze its performance in detail. In the third contribution, we focus on characterizing the impact of 5G cellular interference on a radar system in shared spectrum, using mathematical tools from stochastic geometry. We model the worst-case interference scenario, and study the impact of the system parameters on the worst-case radar performance. In summary, this dissertation advances the state-of-the-art in vehicular communications and spectrum sharing, through (a) novel contributions in protocol design and (b) development of mathematical tools for performance characterization.