Parallel concatenated convolutional codes (PCCCs), called "turbo codes" by their discoverers, have been shown to perform close to the Shannon bound at bit error rates (BERs) between 1e-4 and 1e-6. Serial concatenated convolutional codes (SCCCs), which perform better than PCCCs at BERs lower than 1e-6, were developed borrowing the same principles as PCCCs, including code concatenation, pseudorandom interleaving and iterative decoding. The first part of this dissertation introduces the fundamentals of concatenated convolutional codes. The theoretical and simulated BER performance of PCCC and SCCC are discussed. Encoding and decoding structures are explained, with emphasis on the Log-MAP decoding algorithm and the general soft-input soft-output (SISO) decoding module. Sliding window techniques, which can be employed to reduce memory requirements, are also briefly discussed. The second part of this dissertation presents four major contributions to the field of concatenated convolutional coding developed through this research. First, the effects of quantization and fixed point arithmetic on the decoding performance are studied. Analytic bounds and modular renormalization techniques are developed to improve the efficiency of SISO module implementation without compromising the performance. Second, a new stopping criterion, SDR, is discovered. It is found to perform well with lowest cost when evaluating its complexity and performance in comparison with existing criteria. Third, a new type-II code combining automatic repeat request (ARQ) technique is introduced which makes use of the related PCCC and SCCC. Fourth, a new code-assisted synchronization technique is presented, which uses a list approach to leverage the simplicity of the correlation technique and the soft information of the decoder. In particular, the variant that uses SDR criterion achieves superb performance with low complexity. Finally, the third part of this dissertation discusses the FPGA-based implementation of the turbo decoder, which is the fruit of cooperation with fellow researchers. / Ph. D.

Among the many advances and innovations in wireless technologies over the past twenty years, MIMO is perhaps among the most successful. MIMO technology has been evolving over the past two decades. Today, the number of antennas equipped at a base station (BS) or an access point (AP) is increasing, which forms what we call ``many-antenna'' MIMO systems. Many-antenna MIMO will have significant impacts on modern wireless communications, as it will allow numerous wireless applications to operate on the vastly underexplored mid-band and high-band spectrum and is able to deliver ultra-high throughput. Although there are considerable efforts on many-antenna MIMO systems, most of them came from physical (PHY) layer information-theoretic exploitation. There is a lack of investigation of many-antenna MIMO from a networking perspective. On the other hand, new knowledge and understanding begin to emerge at the PHY layer, such as the rank-deficient channel phenomenon. This calls for new theories and models for many-antenna MIMO in a networking environment. In addition, the problem space for many-antenna MIMO systems is much broader and more challenging than conventional MIMO. Reusing existing solutions designed for conventional MIMO systems may suffer from inferior performance or require excessive computation time. The goal of this dissertation is to advance many-antenna MIMO techniques for networking research. We focus on the following two critical areas in the context of many-antenna MIMO networks: (i) DoF-based modeling and (ii) real-time optimization. This dissertation consists of two parts that study these two areas. In the first part, we aim to develop new DoF models and theories under general channel rank conditions for many-antenna MIMO networks, and we explored efficient DoF allocation based on our new DoF model. The main contributions of this part are summarized as follows. New DoF models and theories under general channel rank conditions: Existing DoF-based models in networking community assume that the channel matrix is of full rank. However, this assumption no longer holds when the number of antennas becomes many and the propagation environment is not ideal. In this study, we develop a novel DoF model under general channel rank conditions. In particular, we find that for IC, shared DoF consumption at both transmit and receive nodes is most efficient for DoF allocation, which is contrary to existing unilateral IC models based on full-rank channel assumption. Further, we show that existing DoF models under the full-rank assumption are a special case of our generalized DoF model. The findings of this study pave the way for future research of many-antenna networks under general channel rank conditions. Efficient DoF utilization for MIMO networks: We observes that, in addition to the fact that channel is not full-rank, the strength of signals on different directions in the eigenspace is extremely uneven. This offers us new opportunities to efficiently utilize DoFs in a MIMO network. In this study, we introduce a novel concept called ``effective rank threshold''. Based on this threshold, DoFs are consumed only to cancel strong interferences in the eigenspace while weak interferences are treated as noise in throughput calculation. To better understand the benefits of this approach, we study a fundamental trade-off between network throughput and effective rank threshold for an MU-MIMO network. Our simulation results show that network throughput under optimal rank threshold is significantly higher than that under existing DoF IC models. In the second part, we offered real-time designs and implementations to solve many-antenna MIMO problems for 5G cellular systems. In addition to maximizing a specific optimization objective, we aim at offering a solution that can be implemented in sub-ms to meet requirements in 5G standards. The main contributions of this part are summarized as follows. Turbo-HB---A novel design and implementation for ultra-fast hybrid beamforming: We investigate the beamforming problem under hybrid beamforming (HB) architecture. A major practical challenge for HB is to obtain a solution in 500 $mu$s, which is an extremely stringent but necessary time requirement for its deployment in the field. To address this challenge, we present Turbo-HB---a novel beamforming design under the HB architecture that can obtain the beamforming matrices in about 500 $mu$s. The key ideas of Turbo-HB are two-fold. First, we develop low-complexity SVD by exploiting randomized SVD technique and leveraging channel sparsity at mmWave frequencies. Second, we accelerate the overall computation time through large-scale parallel computation on a commercial off-the-shelf (COTS) GPU platform, with special engineering efforts for matrix operations and minimized memory access. Experimental results show that Turbo-HB is able to obtain the beamforming matrices in 500 $mu$s for an MU-MIMO cellular system while achieving similar or better throughput performance by those state-of-the-art algorithms. mCore+---A sub-millisecond scheduler for 5G MU-MIMO systems: We study a scheduling problem in a 5G NR environment. In 5G NR, an MU-MIMO scheduler needs to allocate RBs and assign MCS for each user at each TTI. In particular, multiple users may be co-scheduled on the same RB under MU-MIMO. In addition, the real-time requirement for determining a scheduling solution is at most 1 ms. In this study, we present a novel scheduler mCore+ that can meet the sub-ms real-time requirement. mCore+ is designed through a multi-phase optimization, leveraging large-scale parallelism. In each phase, mCore+ either decomposes the optimization problem into a large number of independent sub-problems, or reduces the search space into a smaller but more promising subspace, or both. We implement mCore+ on a COTS GPU platform. Experimental results show that mCore+ can obtain a scheduling solution in $sim$500 $mu$s. Moreover, mCore+ can achieve better throughput performance than state-of-the-art algorithms. M3---A sub-millisecond scheduler for multi-cell MIMO networks under C-RAN architecture: We investigate a scheduling problem for a multi-cell environment. Under Cloud Radio Access Network (C-RAN) architecture, the signal processing can be performed cooperatively for multiple cells at a centralized baseband unit (BBU) pool. However, a new resource scheduler is needed to jointly determine RB allocation, MCS assignment, and beamforming matrices for all users under multiple cells. In addition, we aim at finding a scheduling solution within each TTI (i.e., at most 1 ms) to conform to the frame structure defined by 5G NR. To do this, we propose M3---a GPU-based real-time scheduler for a multi-cell MIMO system. M3 is developed through a novel multi-pipeline design that exploits large-scale parallelism. Under this design, one pipeline performs a sequence of operations for cell-edge users to explore joint transmission, and in parallel, the other pipeline is for cell-center users to explore MU-MIMO transmission. For validation, we implement M3 on a COTS GPU. We showed that M3 can find a scheduling solution within 1 ms for all tested cases, while it can significantly increase user throughput by leveraging joint transmission among neighboring cells. / Doctor of Philosophy / MIMO is widely considered to be a major breakthrough in modern wireless communications. MIMO comes in different forms. For conventional MIMO, the number of antennas at a base station (BS) or access point (AP) is typically small (< 8). Today, the number of antennas at a BS/AP is typically ranging from 8 to 64 when the carrier frequency is below 24 GHz. When the carrier frequency is above 24 GHz (e.g., mmWave), the number of antennas can be even larger (> 64). We call today's MIMO systems (typically with $ge$ 8 antennas at some nodes) as ``many-antenna'' MIMO systems, and this will be the focus of this dissertation. Although there exists a considerable amount of works on many-antenna MIMO techniques, most efforts focus on physical (PHY) layer for information-theoretic exploitation. There is a lack of investigation on how to efficiently and effectively utilize many-antenna MIMO from a networking perspective. The goal of this dissertation is to advance many-antenna MIMO techniques for networking research. We focus on the following two critical areas in the context of many-antenna MIMO networks: (i) degree-of-freedom (DoF)--based modeling and (ii) real-time optimization. In the first part, we investigate a novel DoF model under general channel rank conditions for many-antenna MIMO networks. The main contributions of this part are summarized as follows. New DoF models and theories under general channel rank conditions: In this study, we develop a novel DoF model under general channel rank conditions. We show that existing works claiming that unilateral DoF consumption is optimal no longer hold when channel rank is deficient (not full-rank). We find that for IC, shared DoF consumption at both Tx and Rx nodes is the most efficient scheme for DoF allocation. Efficient DoF utilization for MIMO networks: In this study, we proposed a new approach to efficiently utilize DoFs in a MIMO network. The DoFs used to cancel interference are conserved by exploiting the interference signal strength in the eigenspace. Our simulation results show that network throughput under our approach is significantly higher than that under existing DoF IC models. In the second part, we offer real-time designs and implementations to solve many-antenna MIMO problems for 5G cellular systems. The timing performance of these designs is tested in actual wall-clock time. A novel design and implementation for ultra-fast hybrid beamforming: We investigate a beamforming problem under the hybrid beamforming (HB) architecture. We propose Turbo-HB---a novel beamforming design under the HB architecture that can obtain the beamforming matrices in about 500 $mu$s. At the same time, Turbo-HB can achieve similar or better throughput performance by those state-of-the-art algorithms. A sub-millisecond scheduler for 5G multi-user (MU)-MIMO systems: We study a resource scheduling problem in 5G NR. We present a novel scheduler called mCore+ that can schedule time-frequency resources to MU-MIMO users and meet the 500 $mu$s real-time requirement in 5G NR. A sub-millisecond scheduler for multi-cell MIMO networks under C-RAN architecture: We investigate the scheduling problem for a multi-cell environment under a centralized architecture. We present M3---a GPU-based real-time scheduler that jointly determines a scheduling solution among multiple cells. M3 can find the scheduling solution within 1 ms.

Age of Information (AoI) is a performance metric that can be used to measure the freshness of information. Since its inception, it has captured the attention of the research community and is now an area of active research. By its definition, AoI measures the elapsed time period between the present time and the generation time of the information. AoI is fundamentally different from traditional metrics such as delay or latency as the latter only considers the transit time for a packet to traverse the network. Among the state-of-the-art in the literature, we identify two limitations that deserve further investigation. First, many existing efforts on AoI have been limited to information-theoretic exploration by considering extremely simple models and unrealistic assumptions, which are far from real-world communication systems. Second, among most existing work on scheduling algorithms to optimize AoI, there is a lack of research on guaranteeing AoI deadlines. The goal of this dissertation is to address these two limitations in the state-of-the-art. First, we design schedulers to minimize AoI under more practical settings, including varying sampling periods, varying sample sizes, cellular transmission models, dynamic channel conditions, etc. Second, we design schedulers to guarantee hard or soft AoI deadlines for each information source. More important, inspired by our results from guaranteeing AoI deadlines, we develop a general design framework that can be applied to construct high-performance schedulers for AoI-related problems. This dissertation is organized into three parts. In the first part, we study two problems on AoI minimization under general settings. (i) We consider general and heterogeneous sampling behaviors among source nodes, varying sample size, and a cellular-based transmission model. We develop a near-optimal low-complexity scheduler---code-named Juventas---to minimize AoI. (ii) We study the AoI minimization problem under a 5G network with dynamic channels. To meet the stringent real-time requirement for 5G, we develop a GPU-based near-optimal algorithm---code-named Kronos---and implement it on commercial off-the-shelf (COTS) GPUs. In the second part, we investigate three problems on guaranteeing AoI deadlines. (i) We study the problem to guarantee a hard AoI deadline for information from each source. We present a novel low-complexity procedure, called Fictitious Polynomial Mapping (FPM), and prove that FPM can find a feasible scheduler for any hard deadline vector when the system load is under ln 2. (ii) For soft AoI deadlines, i.e., occasional violations can be tolerated, we present a novel procedure called Unstable Tolerant Scheduler (UTS). UTS hinges upon the notions of Almost Uniform Schedulers (AUSs) and step-down rate vectors. We show that UTS has strong performance guarantees under different settings. (iii) We investigate a 5G scheduling problem to minimize the proportion of time when the AoI exceeds a soft deadline. We derive a property called uniform fairness and use it as a guideline to develop a 5G scheduler---Aequitas. To meet the real-time requirement in 5G, we implement Aequitas on a COTS GPU. In the third part, we present Eywa---a general design framework that can be applied to construct high-performance schedulers for AoI-related optimization and decision problems. The design of Eywa is inspired by the notions of AUS schedulers and step-down rate vectors when we develop UTS in the second part. To validate the efficacy of the proposed Eywa framework, we apply it to solve a number of problems, such as minimizing the sum of AoIs, minimizing bandwidth requirement under AoI constraints, and determining the existence of feasible schedulers to satisfy AoI constraints. We find that for each problem, Eywa can either offer a stronger performance guarantee than the state-of-the-art algorithms, or provide new/general results that are not available in the literature. / Doctor of Philosophy / Age of Information (AoI) is a performance metric that can be used to measure the freshness of information. It measures the elapsed time period between the present time and the generation time of the information. Through a literature review, we have identified two limitations: (i) many existing efforts on AoI have employed extremely simple models and unrealistic assumptions, and (ii) most existing work focuses on optimizing AoI, while overlooking AoI deadline requirements in some applications. The goal of this dissertation is to address these two limitations. For the first limitation, we study the problem to minimize the average AoI in general and practical settings, such as dynamic channels and 5G NR networks. For the second limitation, we design schedulers to guarantee hard or soft AoI deadlines for information from each source. Finally, we develop a general design framework that can be applied to construct high-performance schedulers for AoI-related problems.

In recent years, metasurfaces have shown promising abilities to control and manipulate electromagnetic (EM) waves through modified surface boundary conditions. These surfaces are electrically thin and comprise an array of spatially varying sub-wavelength scattering elements (or meta-atoms). Metasurfaces can transform an incident EM wave into an arbitrarily tailored transmitted or reflected wavefront through carefully engineering each meta-atom. Recent developments in metasurfaces have opened exciting new opportunities in antenna design, sensing, and communications systems. In particular, reconfigurable metasurfaces - wherein meta-atoms are embedded with active components - lead to the development of low-cost, lightweight, and compact systems capable of producing programmable radiation patterns and jointly performing multi-function communications, and enable advanced sensors for next-generation platforms. This research introduces reconfigurable metasurfaces and their various applications in designing simplified communications systems, wherein the RF aperture and transceiver are integrated within the metasurface. Finally, we will present our recent work on reconfigurable metasurfaces control, metasurface-enabled direct signal modulation, and deep learning-based metasurface design. / Doctor of Philosophy / Metasurfaces are a promising new technology to enhance the capacity and coverage of wireless communication networks by dynamically reconfiguring the wireless propagation environment. These low-profile artificial electromagnetic surfaces, consisting of subwavelength resonant elements, are capable of tailoring electromagnetic waves controllably. In this dissertation, we control the transmission or reflection properties of the surface using digital codes by embedding tunable elements within each subwavelength element. Furthermore, metasurface antennas are a promising candidate for reducing the cost and hardware footprint of wireless sensor systems, such as radar or imaging. Using a digital microcontroller, we program the metasurface to steer the antenna beam in the direction of interest, modulate the radio wave, or change the polarization of an incoming signal. In addition to dynamic beamforming capabilities, we program the metasurface to reduce the scattering of an incoming signal, thereby reducing its perturbations on the radio environment. Still, the design of metasurfaces for specific applications remains complex and technically challenging. Lastly, we present innovative deep learning techniques to simplify metasurface design.

In the context of wireless communications, low latency autonomous systems continue to grow in importance. Some applications of autonomous systems where low latency communication is essential are (i) vehicular network's safety performance depends on how recently the vehicles are updated on their neighboring vehicle's locations, (ii) updates from IoT devices need to be aggregated appropriately at the monitoring station before the information gets stale to extract temporal and spatial information from it, and (iii) sensors and controllers in a smart grid need to track the most recent state of the system to tune system parameters dynamically, etc. Each of the above-mentioned applications differs based on the connectivity between the source and the destination. First, vehicular networks involve a broadcast network where each of the vehicles broadcasts its packets to all the other vehicles. Secondly, in the case of UAV-assisted IoT networks, packets generated at multiple IoT devices are transmitted to a final destination via relays. Finally for the smart grid and generally for distributed systems, each source can have varying and unique destinations. Therefore in terms of connectivity, they can be categorized into one-to-all, all-to-one, and variable relationship between the number of sources and destinations. Additionally, some of the other major differences between the applications are the impact of mobility, the importance of a reduced AoI, centralized vs distributed manner of measuring AoI, etc. Thus the wide variety of application requirements makes it challenging to develop scheduling schemes that universally address minimizing the AoI. All these applications involve generating time-stamped status updates at a source which are then transmitted to their destination over a wireless medium. The timely reception of these updates at the destination decides the operating state of the system. This is because the fresher the information at the destination, the better its awareness of the system state for making better control decisions. This freshness of information is not the same as maximizing the throughput or minimizing the delay. While ideally throughput can be maximized by sending data as fast as possible, this may saturate the receiver resulting in queuing, contention, and other delays. On the other hand, these delays can be minimized by sending updates slowly, but this may cause high inter-arrival times. Therefore, a new metric called the Age of Information (AoI) has been proposed to measure the freshness of information that can account for many facets that influence data availability. In simple terms, AoI is measured at the destination as the time elapsed since the generation time of the most recently received update. Therefore AoI is able to incorporate both the delay and the inter-packet arrival time. This makes it a much better metric to measure end-to-end latency, and hence characterize the performance of such time-sensitive systems. These basic characteristics of AoI are explained in detail in Chapter 1. Overall, the main contribution of this dissertation is developing scheduling and resource allocation schemes targeted at improving the AoI of various autonomous systems having different types of connectivity, namely vehicular networks, UAV-assisted IoT networks, and smart grids, and then characterizing and quantifying the benefits of a reduced AoI from the application perspective. In the first contribution, we look into minimizing AoI for the case of broadcast networks having one-to-all connectivity between the source and destination devices by considering the case of vehicular networks. While vehicular networks have been studied in terms of AoI minimization, the impact of mobility and the benefit of a reduced AoI from the application perspective has not been investigated. The mobility of the vehicles is realistically modeled using the Simulation of Urban Mobility (SUMO) software to account for overtaking, lane changes, etc. We propose a safety metric that indicates the collision risk of a vehicle and do a simulation-based study on the ns3 simulator to study its relation to AoI. We see that the broadcast rate in a Dedicated Short Range Network (DSRC) that minimizes the system AoI also has the least collision risk, therefore signifying that reducing AoI improves the on-road safety of the vehicles. However, we also show that this relationship is not universally true and the mobility of the vehicles becomes a crucial aspect. Therefore, we propose a new metric called the Trackability-aware AoI (TAoI) which ensures that vehicles with unpredictable mobility broadcast at a faster rate while vehicles that are predicable are broadcasting at a reduced rate. The results obtained show that minimizing TAoI provides much better on-road safety as compared to plain AoI minimizing, which points to the importance of mobility in such applications. In the second contribution, we focus on networks with all-to-one connectivity where packets from multiple sources are transmitted to a single destination by taking an example of IoT networks. Here multiple IoT devices measure a physical phenomenon and transmit these measurements to a central base station (BS). However, under certain scenarios, the BS and IoT devices are unable to communicate directly and this necessitates the use of UAVs as relays. This creates a two-hop scenario that has not been studied for AoI minimization in UAV networks. In the first hop, the packets have to be sampled from the IoT devices to the UAV and then updated from the UAVs to the BS in the second hop. Such networks are called UAV-assisted IoT networks. We show that under ideal conditions with a generate-at-will traffic generation model and lossless wireless channels, the Maximal Age Difference (MAD) scheduler is the optimal AoI minimizing scheduler. When the ideal conditions are not applicable and more practical conditions are considered, a reinforcement learning (RL) based scheduler is desirable that can account for packet generation patterns and channel qualities. Therefore we propose to use a Deep-Q-Network (DQN)-based scheduler and it outperforms MAD and all other schedulers under general conditions. However, the DQN-based scheduler suffers from scalability issues in large networks. Therefore, another type of RL algorithm called Proximal Policy Optimization (PPO) is proposed to be used for larger networks. Additionally, the PPO-based scheduler can account for changes in the network conditions which the DQN-based scheduler was not able to do. This ensures the trained model can be deployed in environments that might be different than the trained environment. In the final contribution, AoI is studied in networks with varying connectivity between the source and destination devices. A typical example of such a distributed network is the smart grid where multiple devices exchange state information to ensure the grid operates in a stable state. To investigate AoI minimization and its impact on the smart grid, a co-simulation platform is designed where the 5G network is modeled in Python and the smart grid is modeled in PSCAD/MATLAB. In the first part of the study, the suitability of 5G in supporting smart grid operations is investigated. Based on the encouraging results that 5G can support a smart grid, we focus on the schedulers at the 5G RAN to minimize the AoI. It is seen that the AoI-based schedulers provide much better stability compared to traditional 5G schedulers like the proportional fairness and round-robin. However, the MAD scheduler which has been shown to be optimal for a variety of scenarios is no longer optimal as it cannot account for the connectivity among the devices. Additionally, distributed networks with heterogeneous sources will, in addition to the varying connectivity, have different sized packets requiring a different number of resource blocks (RB) to transmit, packet generation patterns, channel conditions, etc. This motivates an RL-based approach. Hence we propose a DQN-based scheduler that can take these factors into account and results show that the DQN-based scheduler outperforms all other schedulers in all considered conditions. / Doctor of Philosophy / Age of information (AoI) is an exciting new metric as it is able to characterize the freshness of information, where freshness means how representative the information is of the current system state. Therefore it is being actively investigated for a variety of autonomous systems that rely on having the most up-to-date information on the current state. Some examples are vehicular networks, UAV networks, and smart grids. Vehicular networks need the real-time location of their neighbor vehicles to make maneuver decisions, UAVs have to collect the most recent information from IoT devices for monitoring purposes, and devices in a smart grid need to ensure that they have the most recent information on the desired system state. From a communication point of view, each of these scenarios presents a different type of connectivity between the source and the destination. First, the vehicular network is a broadcast network where each vehicle broadcasts its packets to every other vehicle. Secondly, in the UAV network, multiple devices transmit their packets to a single destination via a relay. Finally, with the smart grid and the generally distributed networks, every source can have different and unique destinations. In these applications, AoI becomes a natural choice to measure the system performance as the fresher the information at the destination, the better its awareness of the system state which allows it to take better control decisions to reach the desired objective. Therefore in this dissertation, we use mathematical analysis and simulation-based approaches to investigate different scheduling and resource allocation policies to improve the AoI for the above-mentioned scenarios. We also show that the reduced AoI improves the system performance, i.e., better on-road safety for vehicular networks and better stability for smart grid applications. The results obtained in this dissertation show that when designing communication and networking protocols for time-sensitive applications requiring low latency, they have to be optimized to improve AoI. This is in contrast to most modern-day communication protocols that are targeted at improving the throughput or minimizing the delay.

Network optimization plays an important role in 5G/next-G networks, which requires knowledge of network parameters (e.g., channel state information). The majority of existing works assume that all network parameters are either given a prior or can be accurately estimated. However, in many practical scenarios, some parameters are uncertain at the time of allocating resources and can only be modeled by random variables. Further, we only have limited knowledge of those uncertain parameters. For instance, channel gains are not exactly known due to channel estimation errors, network delay, limited feedback, and a lack of cooperation (between networks). Therefore, a practical solution to network optimization must address such uncertainty inside wireless networks. There are three approaches to address such a network uncertainty: stochastic programming, worst-case optimization, and chance-constrained programming (CCP). Among the three, CCP has some unique benefits compared to the other two approaches. Stochastic programming explicitly requires full distribution knowledge, which is usually unavailable in practice. In comparison, CCP can work with various settings of available knowledge such as first and second order statistics, symmetric properties, or limited data samples. Therefore, CCP is more flexible to handle different network settings, which is important to address problems in 5G/next-G networks. Further, worst-case optimization assumes upper or lower bounds (i.e., worst cases) for the uncertain parameters and it is known to be conservative due to its focus on extreme cases. In contrast, CCP allows occasional and controllable violations for some constraints and thus offers much better performance in resource utilization compared to worst-case optimization. The only drawback of CCP is that it may lead to intractability due to its probabilistic formulation and limited knowledge of the underlying random variables. To date, CCP has not been well utilized in the wireless communication and networking community. The goal of this dissertation is to extend the state-of-the-art of CCP techniques and address a number of challenging network optimization problems. This dissertation is correspondingly organized into two parts. In the first part, we assume the uncertain parameters are only known by their mean and covariance (without distribution knowledge). We assume these statistics are rather stationary (i.e., time-invariant for a sufficiently long time) and thus can be accurately estimated. In this setting, we introduce a novel reformulation technique based on the mean and covariance to derive a solution. In the second part, we assume these statistics are time-varying and thus cannot be accurately estimated.In this setting, we employ limited data samples that are collected in a small time window and use them to derive a solution. For the first part, we investigate four research problems based on the mean and covariance of the uncertain parameters: - In the first problem, we study how to maximize spectrum efficiency in underlay coexistence.The interference from all secondary users to each primary user must be kept below a given threshold. However, there is much uncertainty about the channel gains between the primary users and the second users due to a lack of cooperation between them. We formulate probabilistic interference constraints using CCP for the primary users. For tractability, we introduce a novel and powerful reformulation technique called Exact Conic Reformulation (ECR). With limited knowledge of mean and covariance, ECR offers an equivalent reformulation for the intractable chance constraints with tractable deterministic constraints without relaxation errors. After reformulation, we employ linearization techniques to the mixed-integer non-linear problem to reduce the computation complexity. We show that our proposed approach can achieve near-optimal performance and stands as a performance benchmark for the underlay coexistence problem. - To find a solution for the same underlay coexistence problem that can be used in the real world, we need to find a solution in "real-time". The real-time requirement here refers to finding a solution in 125 us (the minimum time slot for small cells in 5G). Our proposed solution has three steps. First, it employs ECR to reformulate the original CCP into a deterministic optimization problem. Then it decomposes the problem and narrows down the search space into a smaller but promising one. By random sampling inside the promising search space and through local search, our proposed solution can meet the 125 us requirement in 5G while achieving 90% optimality on average. - We further apply CCP, predicated on the reformulation technique ECR, to two other problems. * We study the problem of power control in concurrent transmissions. Our objective is to maximize energy efficiency for all transmitter-receiver pairs with capacity requirements. This problem is challenging due to mutual interference among different transmitter-receiver pairs and the uncertain channel gain between any transmitter and receiver. We formulate a CCP and reformulate it into a deterministic problem using ECR. Then we employ Geometric Programming (GP) with a tight approximation to derive a near-optimal solution. * We study task offloading in Mobile Edge Computing (MEC) where the number of processing cycles of a task is unknown until completion. The goal is to minimize the energy consumption of the users while meeting probabilistic deadlines for the tasks. We formulate the probabilistic deadlines into chance constraints and then use ECR to reformulate them into deterministic constraints. We propose a solution that consists of periodic scheduling and schedule updates to choose the offloaded tasks and task-to-processor assignments at the base station. In the second part, we investigate two research problems based on limited data samples of the uncertain parameters: - We study MU-MIMO beamforming based on Channel State Information (CSI). The goal is to derive a beamforming solution---minimizing power consumption at the BS while meeting the probabilistic data rate requirements of the users---by using very limited CSI data samples. For our CCP formulation, we explore the idea of Wasserstein ambiguity set to quantify the distance between the true (but unknown) distribution and the empirical distribution based on the limited data samples. Our proposed solution---Data-Driven Beamforming (D^2BF)---reformulates the CCP into a non-convex deterministic optimization problem based on the properties of Wasserstein ambiguity set. Then D^2BF employs a novel convex approximation to the non-convex deterministic problem, which can be directly solved by commercial solvers. - For a solution to the MU-MIMO beamforming to be useful in the real world, it must meet the "real-time" requirement. Here, the real-time requirement refers to 1 ms, which is one transmission time interval (TTI) under 5G numerology 0. We present ReDBeam---a Real-time Data-driven Beamforming solution for the MU-MIMO beamforming problem (minimizing power consumption while offering probabilistic data rate guarantees to the users) with limited CSI data samples. RedBeam is a parallel algorithm and is purposefully designed to take advantage of the vast parallel processing capability offered by GPU. ReDBeam generates a large number of initial solutions from a promising search space and then refines each solution by a local search. We show that ReDBeam meets the 1 ms real-time requirement on a commercial GPU and is orders of magnitude faster than other state-of-the-art algorithms for the same problem. / Doctor of Philosophy / Network optimization plays an important role in 5G/next-G networks. In a wireless network optimization problem, we typically want to maximize or minimize an objective function under a set of performance or resource constraints. Knowledge of network parameters is typically required in these problems. The majority of existing works assume that all network parameters are either given a prior or can be accurately estimated. However, in many practical scenarios, some parameters are uncertain in nature and cannot be accurately estimated beforehand. This dissertation addresses uncertainty in wireless network optimizations using chance-constrained programming (CCP). CCP can work with limited knowledge of uncertain parameters such as statistics or data samples, instead of full distribution information. In a CCP formulation, violations of certain target performance or requirement thresholds are expressed as probabilistic constraints and the frequency of such violations is bounded through a risk parameter. By changing this risk level, CCP offers a unique trade-off between the guaranteed threshold violation probabilities and the achieved objective value. The only drawback of CCP is that it may lead to intractability due to its probabilistic formulation and limited knowledge of the underlying random variables. The goal of this dissertation is to extend the state-of-the-art of CCP techniques to address a number of challenging network optimization problems. This dissertation is organized into two parts. In the first part, the mean and covariance of the uncertain parameters are assumed to be stationary and thus can be accurately estimated. Our main contribution is a novel reformulation technique for CCP called Exact Conic Reformulation (ECR). Based on knowledge of mean and covariance, ECR is able to offer an equivalent reformulation for the intractable chance constraints with tractable deterministic constraints without relaxation errors. We apply CCP, predicated on ECR, to address three problems: (i) scheduling and power control in underlay coexistence; (ii) power control in concurrent transmissions, and (iii) task offloading in Mobile Edge Computing (MEC). For the first problem, we further address the "real-time" requirement in a solution and propose a solution that can meet the stringent timing requirement. In the second part, when the uncertain parameters are non-stationary and their statistics cannot be accurately estimated, we propose to employ limited data samples that are collected over a small window and use them to develop a solution. To demonstrate the efficacy of this approach, we investigate the MU-MIMO beamforming problem that minimizes the power consumption of the base station while providing probabilistic guarantees to users' data rates. We further address the timing requirement for such a solution in practice, and present a real-time data-driven beamforming solution for MU-MIMO.

The development of Unmanned Aerial Vehicles (UAVs) and fifth-generation (5G) wireless technology provides more possibilities for wireless networks. The application of UAVs is gradually evolving from individual UAVs performing tasks to UAV swarm performing tasks in concert. A UAV swarm network is when many drones work cooperatively in a swarm mode to achieve a particular goal. Due to the UAV swarm's easy deployment, self-organization, self-management, and high flexibility, it can provide robust and efficient wireless communications in some unique scenarios, such as emergency communications, hotspot region coverage, sensor networks, and vehicular networks. Therefore, UAV networks have attracted more and more attention from commercial and military; however, many problems need to be resolved before UAV cellular communications become a reality. One of the most challenging core components is the routing protocol design in the UAV swarm network. Due to the high mobility of UAVs, the position of each UAV changes dynamically, so problems such as high latency, high packet loss rate, and even loss of connection arise when UAVs are far apart. These problems dramatically reduce the transmission rate and data integrity for traditional routing protocols based on path discovery. This thesis focuses on developing, emulating, and optimizing a flooding-based routing protocol for UAV swarm using Random Linear Network Coding (RLNC) to improve the latency and bit rate and solve the packet loss problem without routing information and network topology. RLNC can reduce the number of packets demand in some hops. Due to this feature of RLNC, when relay transmitter UAVs or the destination receiver UAV receive sufficient encoded packets from any transmitter UAVs, the raw data can be decoded. For those relay transmitter UAVs in the UAV swarm network that already received some encoded packets in previous hops but not enough to decode the raw data, only need to receive the rest of the different encoded packets needed for decoding. Thus, flooding-based routing protocol significantly improves transmission efficiency in the UAV swarm network. / Master of Science / People are used to using fiber, 4G, and Wi-Fi in the city, but numerous people still live in areas without Internet access. Moreover, in some particular scenarios like large-scale activities, remote areas, and military operations, when the cellular network cannot provide enough bandwidth or good signal, UAV wireless network would be helpful and provide stable Internet access. Successful UAV test flights can last for several weeks, and researchers' interest in high-altitude long-endurance (HALE) UAVs are booming. HALE UAVs will create Wi-Fi or other network signals for remote areas, including polar regions, which will allow millions of people to enter the information society and connect to the Internet. The development of UAV and 5G provides more possibilities for wireless networks. UAV applications have evolved from individual UAV performing tasks to UAV swarm performing tasks. A UAV swarm network is where multiple drones work in tandem to achieve a particular goal. It can provide robust and efficient wireless communications in unique scenarios. As a result, UAVs are receiving attention from both commercial and military. However, there are still many problems that need to be resolved before the actual use of UAVs. One of the biggest challenges is routing protocol which is how UAVs communicate with each other and select routes. As the location of UAVs is constantly changing, this leads to delays, data loss, or complete loss of connectivity. Ultimately these issues can lead to slow transmission speed and lack of data integrity for traditional routing protocols based on path discovery. This thesis focuses on developing, emulating, and optimizing a flooding-based routing protocol for the UAV swarm. Specifically, this protocol uses RLNC, which can reduce the number of packets demand in some hops so that the latency and transmission speed will be improved, and the data loss problem will also be solved. Due to this feature of RLNC, when any receiver receives enough encoded packets from any transmitter, the original data can be decoded. Some receivers that already received some encoded packets in the previous transmission only need to receive the rest of the different encoded packets needed for decoding. Therefore, flooding-based routing protocol significantly improves transmission efficiency for UAV swarm networks.

With the rapid and ubiquitous deployment of mobile communications in recent years, cochannel interference has become a critical problem because of its impact on system capacity and quality of service. The conventional approach to minimizing interference is through better cell planning and design. Digital Signal Processing COSP) based interference rejection techniques provide an alternative approach to minimize interference and improve system capacity. Single channel adaptive interference rejection techniques have long been used for enhancing digitally modulated signals. However these techniques are not well suited for analog mobile phone system (AMPS) and narrowband AMPS (NAMPS) signals because of the large spectral overlap of the signals of interest with interfering signals and because of the lack of a well defined signal structure that can be used to separate the signals. Our research has created novel interference rejection techniques based on time-dependent filtering which exploit spectral correlation characteristics exhibited by AMPS and NAMPS signals. A mathematical analysis of the cyclostationary features of AMPS and NAMPS signals is presented to help explain and analyze these techniques. Their performance is investigated using both simulated and digitized data. The impact of these new techniques on AMPS system capacity is also studied. The adaptive algorithms and structures are refined to be robust in various channel environments and to be computationally efficient. / Ph. D.

Yes / Reconfigurable antennas play important roles in smart and adaptive systems and are the subject of many research studies. They offer several advantages such as multifunctional capabilities, minimized volume requirements, low front-end processing efforts with no need for a filtering element, good isolation, and sufficient out-ofband rejection; these make them well suited for use in wireless applications such as fourth generation (4G) and fifth generation (5G) mobile terminals. With the use of active materials such as microelectromechanical systems (MEMS), varactor or p-i-n (PIN) diodes, an antenna’s characteristics can be changed through altering the current flow on the antenna structure. If an antenna is to be reconfigurable into many different states, it needs to have an adequate number of active elements. However, a large number of high-quality active elements increases cost, and necessitates complex biasing networks and control circuitry. We review some recently proposed reconfigurable antenna designs suitable for use in wireless communications such as cognitiveratio (CR), multiple-input multiple-output (MIMO), ultra-wideband (UWB), and 4G/5G mobile terminals. Several examples of antennas with different reconfigurability functions are analyzed and their performances are compared. Characteristics and fundamental properties of reconfigurable antennas with single and multiple reconfigurability modes are investigated. / European Union’s Horizon 2020 research and innovation programme under grant agreement H2020-MSCA-ITN-2016 SECRET-722424.

This dissertation addresses a fundamental question of multi-agent coordination: what infor- mation should be sent to whom and when, with the limited resources available to each agent? Communication requirements for multi-agent systems can be rather high when an accurate picture of the environment and the state of other agents must be maintained. To reduce the impact of multi-agent coordination on networked systems, e.g., power and bandwidth, this dissertation introduces new concepts to enable Intelligent Knowledge Distribution (IKD), including Constrained-action POMDPs (CA-POMDP) and concurrent decentralized (CoDec) POMDPs for an agnostic plug-and-play capability for fully autonomous systems. Each agent runs a CoDec POMDP where all the decision making (motion planning, task allocation, asset monitoring, and communication) are separated into concurrent individual MDPs to reduce the combinatorial explosion of the action and state space while maintaining dependencies between the models. We also introduce the CA-POMDP with action-based constraints on partially observable Markov decision processes, rewards driven by the value of information, and probabilistic constraint satisfaction through discrete optimization and Markov chain Monte Carlo analysis. IKD is adapted real-time through machine learning of the actual environmental impacts on the behavior of the system, including collaboration strategies between autonomous agents, the true value of information between heterogeneous systems, observation probabilities and resource utilization. / Doctor of Philosophy / This dissertation addresses a fundamental question behind when multiple autonomous sys- tems, like drone swarms, in the field need to coordinate and share data: what information should be sent to whom and when, with the limited resources available to each agent? Intelligent Knowledge Distribution is a framework that answers these questions. Communication requirements for multi-agent systems can be rather high when an accurate picture of the environment and the state of other agents must be maintained. To reduce the impact of multi-agent coordination on networked systems, e.g., power and bandwidth, this dissertation introduces new concepts to enable Intelligent Knowledge Distribution (IKD), including Constrained-action POMDPs and concurrent decentralized (CoDec) POMDPs for an agnostic plug-and-play capability for fully autonomous systems. The IKD model was able to demonstrate its validity as a "plug-and-play" library that manages communications between agents that ensures the right information is being transmitted at the right time to the right agent to ensure mission success.