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Recently, the use of uncrewed aerial vehicles (UAVs) in joint sensing and communication applications has received a lot of attention. However, integrating UAVs in current cellular systems presents major challenges related to trajectory optimization and interference management among others. This paper considers a multi-cell network including a UAV, which senses and forwards the sensory data from different events to the central base station. Particularly, the current manuscript covers how to design the UAV's ({i} ) 3D trajectory, (ii) power allocation, and (iii) sensing scheduling such that (a) a set of events are sensed, (b) interference to neighboring cells is kept at bay, and (c) the amount of energy required by the UAV is minimized. The resulting nonconvex optimization problem is tackled through a combination of ({i} ) low-complexity binary optimization, (ii) successive convex approximation, and (iii) the Lagrangian method. Simulation results over a range of various key parameters have shown the merits of our approach, which consumes 33%-200% less energy compared to different benchmarks.

The use of uncrewed aerial vehicles (UAVs) in cell-free networks is poised to unleash a number of new opportunities to further improve wireless networks. However, cell-free UAV networks present major challenges related to the wireless nature of access and fronthaul links. This manuscript studies the uplink of cell-free systems where users connect to UAVs, the latter devices forwarding the information to a processing point through imperfect wireless fronthaul links. Three multiple access alternatives are considered for the fronthaul, namely frequency division multiples access, spatial division multiple access, and combinations thereof. Deterministic equivalent expressions for the spectral efficiency under these fronthaul schemes and minimum mean-square error reception are derived. Then, the optimization subproblems of (a) the 3D deployment of the UAVs, (b) the user transmit powers, and (c) the UAV transmit powers, are investigated. The joint optimization of these subproblems yields superior performance, with the 3D deployment being the main source of improvement.

We present a new framework based on quantization theory to design cellular networks optimized for both legacy ground users and uncrewed aerial vehicle (UAV) corridors, dedicated aerial highways for safe UAV flights. Our framework leverages antenna tilts and transmit power at each base station to enhance coverage and quality of service among users. We develop a comprehensive mathematical analysis and optimization algorithms for multiple system-level performance metrics, including received signal strength and signal-to-interference-plus-noise ratio. Realistic antenna radiation patterns and propagation channel models are considered, alongside a generic 3D user distribution that allows for performance prioritization on the ground, along UAV corridors, or a desired tradeoff between the two. We demonstrate the efficacy of the proposed framework through case studies, showcasing the non-trivial combinations of antenna tilts and power levels that improve coverage and signal quality along UAV corridors while incurring only a marginal impact on the ground user performance compared to scenarios without UAVs.

Uncrewed Aerial Vehicles (UAVs) provide a compelling solution for data collection in Internet of Things (IoT) networks due to their mobility and adaptability. However, the line-of-sight dominance in their channels may result in severe interference to ground users during UAV operations. To address this, we present an optimization framework that concurrently optimizes UAV trajectories and transmit powers. Our approach efficiently results in the collection of data from a variety of IoT sensors while (a) minimizing the UAVs flying time and (b) mitigating interference with terrestrial networks. Given the complex nature of such an optimization problem, this paper leverages reinforcement learning, specifically the twin delayed deep deterministic policy gradient algorithm, where a distributed learning algorithm is presented. Experimental results validate the efficacy of our proposed approach, demonstrating its capability to significantly enhance data collection in IoT networks while minimizing UAV flight time and interference with ground user links.

Densely distributed sensor networks can revolutionize environmental observations by providing real-time data with an unprecedented spatiotemporal resolution. However, field deployments often pose unique challenges in terms of power provisions and wireless connectivity. We present a framework for wirelessly connected distributed sensor arrays for near-surface temperature and/or deformation monitoring. Our research focuses on a novel time division duplex implementation of the LoRa protocol, enabling battery powered base stations and avoiding collisions within the network. In order to minimize transmissions and improve battery life throughout the network, we propose a dedicated delta encoding algorithm that utilizes the spatial and temporal similarity in the acquired data sets. We implemented the developed technologies in a AA battery powered hardware platform that can be used as a wireless data logger or base station, and we conducted an assessment of the power consumption. Without data compression, the projected battery life for a data logger is 4.74 years, and a wireless base stations can last several weeks or months depending on the amount of network traffic. The delta encoding algorithm can further improve this battery life with a factor of up to 3.50. Our results demonstrate the viability of the proposed methods for low-power environmental wireless sensor networks.

Recently, cell-free (CF) architectures, in which every user can potentially communicate with every base station, have received a lot of attention. This paper considers the uplink of fully and partially centralized CF networks where unmanned aerial vehicles serve as flying base stations (FBSs). A subset of FBSs participates in the reception of each user and a subset of users is received by each FBS. Deterministic equivalent expressions, exact asymptotically in the subset sizes and approximate for finite dimensions thereof, are derived for the spectral efficiency under Rician fading. Capitalizing on these expressions, the FBS deployment problem is investigated for different receiver architectures. The nonconvex deployment problem, tackled through a combination of gradient-based and Gibbs sampling algorithms, results in a superior performance with respect to a square grid deployment; this superiority extends to the minimum and aggregate spectral efficiency for both fully and partially centralized cell-free networks.

5G Multicast Broadcast Services (MBS) are viewed as a promising 5G New Radio (NR) application, as standardization begins in 3GPP Release 17. With MBS, one next generation Node B (gNB) delivers data to multiple user equipments (UE) simultaneously, thus improving spectrum efficiency. Millimeter wave (mmWave) beamforming further enhances system performance by focusing signals in a dedicated direction. However, despite the advantages, we identify three issues of multicast with beamforming techniques. First, link directionality causes the gNB to transmit data over the beams sequentially, resulting in a combinatorial resource allocation problem. Confronting this beam scheduling issue, we develop an optimization algorithm that obtains an optimal solution in polynomial time. Second, UEs may falsely report their valuations over beam resources to gain more utility. Under this scenario, the gNB cannot allocate the resources to those in need due to the lack of accurate UE information. Therefore, we propose a Vickrey-Clarke-Groves (VCG) auction-based mechanism to incentivize the UEs to reveal their valuations over resources truthfully. This mechanism guarantees solution efficiency and maximizes social welfare. Third, as 3GPP standards allow for different priorities for different multicast flows, and video content providers distinguish between ordinary and premium UEs, we take UE priority into account. In this regard, we extend the valuation-based mechanism to a multi-priority one. Finally, the mathematical analysis validates some desirable properties of the proposed scheme, such as incentive-compatibility. Simulation results also justify our superior performance in the 5G MBS system compared with other resource allocation schemes.

For a Chinook salmon restoration project in the lower Yuba River in California, we designed and deployed a wireless sensor network to monitor river temperatures at micro-habitat scales. The study required that temperatures be measured along a 3 km study reach, across the channel, and into off-channel areas. To capture diel and seasonal fluctuations, sensors were sampled quarter-hourly for the full duration of the six-month juvenile salmon winter residency. This sampling duration required that nodes minimize power-use. We adopted event-based software on MSP430 micro-controllers with 433 MHz radio and minimized the networking duty-cycle. To address link failures, we included network storage. As the network lacked real-time clocks, data were timestamped at the destination. This, coupled with the storage, yielded timestamp inaccuracies, which we re-aligned using a novel algorithm. We collected over six months of temperature data from 35 sensors across seven nodes. Of the packets collected, we identified 21% as being incorrectly timestamped and were able to re-align 41% of these incorrectly timestamped packets. We collected temperature data through major floods, and the network uploaded data until the flood destroyed the sensors. The network met an important need in ecological sampling with ultra-low power (multi-year battery life) and low-throughput.

In instances of online kernel learning where little prior information is available and centralized learning is unfeasible, past research has shown that distributed and online multi-kernel learning provides sub-linear regret as long as every pair of nodes in the network can communicate (i.e., the communications network is a complete graph). In addition, to manage the communication load, which is often a performance bottleneck, communications between nodes can be quantized. This letter expands on these results to non-fully connected graphs, which is often the case in wireless sensor networks. To address this challenge, we propose a gossip algorithm and provide a proof that it achieves sub-linear regret. Experiments with real datasets confirm our findings.

The problem of super-resolution is concerned with the reconstruction of temporally/spatially localized events (or spikes) from samples of their convolution with a low-pass filter. Distinct from prior works which exploit sparsity in appropriate domains in order to solve the resulting ill-posed problem, this paper explores the role of binary priors in super-resolution, where the spike (or source) amplitudes are assumed to be binary-valued. Our study is inspired by the problem of neural spike deconvolution, but also applies to other applications such as symbol detection in hybrid millimeter wave communication systems. This paper makes several theoretical and algorithmic contributions to enable binary super-resolution with very few measurements. Our results show that binary constraints offer much stronger identifiability guarantees than sparsity, allowing us to operate in "extreme compression" regimes, where the number of measurements can be significantly smaller than the sparsity level of the spikes. To ensure exact recovery in this "extreme compression" regime, it becomes necessary to design algorithms that exactly enforce binary constraints without relaxation. In order to overcome the ensuing computational challenges, we consider a first order auto-regressive filter (which appears in neural spike deconvolution), and exploit its special structure. This results in a novel formulation of the super-resolution binary spike recovery in terms of binary search in one dimension. We perform numerical experiments that validate our theory and also show the benefits of binary constraints in neural spike deconvolution from real calcium imaging datasets.

The goal of this short document is to explain why recent developments in the Internet's infrastructure are problematic. As context, we note that the Internet was originally designed to provide a simple universal service - global end-to-end packet delivery - on which a wide variety of end-user applications could be built. The early Internet supported this packet-delivery service via an interconnected collection of commercial Internet Service Providers (ISPs) that we will refer to collectively as the “public Internet.” The Internet has fulfilled its packet-delivery mission far beyond all expectations and is now the dominant global communications infrastructure. By providing a level playing field on which new applications could be deployed, the Internet has enabled a degree of innovation that no one could have foreseen. To improve performance for some common applications, “enhancements” such as caching (as in content-delivery networks) have been gradually added to the Internet. The resulting performance improvements are so significant that such enhancements are now effectively necessary to meet current content delivery demands. Despite these tangible benefits, this document argues that the way these enhancements are currently deployed seriously undermines the sustainability of the public Internet and could lead to an Internet infrastructure that reaches fewer people and is largely concentrated among only a few large-scale providers. We wrote this document because we fear that these developments are now decidedly tipping the Internet's playing field towards those who can deploy these enhancements at massive scale, which in turn will limit the degree to which the future Internet can support unfettered innovation. This document begins by explaining our concerns but goes on to articulate how this unfortunate fate can be avoided. To provide more depth for those who seek it, we provide a separate addendum with further detail.

Peering between two networks may be either settlement-free or paid. In order to qualify for settlement-free peering, large Internet Service Providers (ISPs) require that peers meet certain requirements. However, the academic literature has not yet shown the relationship between these settlement-free peering requirements and the value to each interconnecting network. We develop two models to analyze the value to each network from the most common and important requirements in the United States. Large ISPs in the U.S. often require potential settlement-free peers to interconnect at a minimum of 6-8 locations. We find that there is a substantial benefit from this requirement to the ISP, but little incremental benefit from a larger number of interconnection points. Large ISPs often require that the ratio of incoming traffic to outgoing traffic remain below approximately 2:1. In the case of two interconnecting ISPs, we find that this requirement ensures a roughly equal exchange of value. We also show that it is rational for an ISP to agree to settlement-free peering if the content provider agrees to interconnect at a specified minimum number of interconnection points and to deliver a specified minimum proportion of traffic locally, but a limit on the traffic ratio is irrational.

This paper presents flexible storage codes, a class of error-correcting codes that can recover information from a flexible number of storage nodes. As a result, one can make better use of the available storage nodes in the presence of unpredictable node failures and reduce the data access latency. Assume a storage system encodes kℓ information symbols over a finite field F into n nodes, each of size ℓ symbols. The code is parameterized by a set of tuples {(Rj, ℓj) : 1 ≤ j ≤ a}, satisfying ℓ1 < ℓ2 < < ℓa = ℓ and R1 > R2 > > Ra, such that the information symbols can be reconstructed from any Rj nodes, each node accessing ℓj symbols, for any 1 ≤ j ≤ a. In other words, the code allows a flexible number of nodes for decoding to accommodate the variance in the data access time of the nodes. Code constructions are presented for different storage scenarios, including LRC (locally recoverable) codes, PMDS (partial MDS) codes, and MSR (minimum storage regenerating) codes. We analyze the latency of accessing information and perform simulations on Amazon clusters to show the efficiency of the presented codes.

The distributed matrix multiplication problem with an unknown number of stragglers is considered, where the goal is to efficiently and flexibly obtain the product of two massive matrices by distributing the computation across N servers. There are up to N - R stragglers but the exact number is not known a priori. Motivated by reducing the computation load of each server, a flexible solution is proposed to fully utilize the computation capability of available servers. The computing task for each server is separated into several subtasks, constructed based on Entangled Polynomial codes by Yu et al. The final results can be obtained from either a larger number of servers with a smaller amount of computation completed per server or a smaller number of servers with a larger amount of computation completed per server. The required finite field size of the proposed solution is less than 2N. Moreover, the optimal design parameters such as the partitioning of the input matrices are discussed. Our constructions can also be generalized to other settings such as batch distributed matrix multiplication and secure distributed matrix multiplication.

We study a heterogeneous wireless sensor network (WSN) where N heterogeneous access points (APs) gather data from densely deployed sensors and transmit their sensed information to M heterogeneous fusion centers (FCs) via multi-hop wireless communication. The heterogeneous optimal deployment of APs and FCs is modeled as an optimization problem with total wireless communication power consumption of the network as its objective function. We consider both static WSNs, where APs and FCs retain their deployed position, and mobile WSNs where APs and FCs can move from their initial deployment to their optimal locations. Based on the derived necessary conditions for the optimal deployment in static WSNs, we propose an iterative algorithm to deploy APs and FCs. In addition, we study the necessary conditions of the optimal movement-efficient deployment in mobile WSNs with constrained movement energy and present iterative algorithms to find such deployments, accordingly. Simulation results show that our proposed deployment algorithms outperform the existing methods in the literature, and achieve a lower total wireless communication power in both static and mobile WSNs.

A butterfly-accelerated volume integral equation (VIE) solver is proposed for fast and accurate electromagnetic (EM) analysis of scattering from heterogeneous objects. The proposed solver leverages the hierarchical off-diagonal butterfly (HOD-BF) scheme to construct the system matrix and obtain its approximate inverse, used as a preconditioner. Complexity analysis and numerical experiments validate the O(N log 2 N) construction cost of the HOD-BF-compressed system matrix and O(N 1.5 log N) inversion cost for the preconditioner, where N is the number of unknowns in the high-frequency EM scattering problem. For many practical scenarios, the proposed VIE solver requires less memory and computational time to construct the system matrix and obtain its approximate inverse compared to a H matrix-accelerated VIE solver. The accuracy and efficiency of the proposed solver have been demonstrated via its application to the EM analysis of large-scale canonical and real-world structures comprising of broad permittivity values and involving millions of unknowns.

In this paper, we investigate dynamic resource scheduling (i.e., joint user, subchannel, and power scheduling) for downlink multi-channel non-orthogonal multiple access (MC-NOMA) systems over time-varying fading channels. Specifically, we address the weighted average sum rate maximization problem with quality-of-service (QoS) constraints. In particular, to facilitate fast resource scheduling, we focus on developing a very low-complexity algorithm. To this end, by leveraging Lagrangian duality and the stochastic optimization theory, we first develop an opportunistic MC-NOMA scheduling algorithm whereby the original problem is decomposed into a series of subproblems, one for each time slot. Accordingly, resource scheduling works in an online manner by solving one subproblem per time slot, making it more applicable to practical systems. Then, we further develop a heuristic joint subchannel assignment and power allocation (Joint-SAPA) algorithm with very low computational complexity, called Joint-SAPA-LCC, that solves each subproblem. Finally, through simulation, we show that our Joint-SAPA-LCC algorithm provides good performance comparable to the existing Joint-SAPA algorithms despite requiring much lower computational complexity. We also demonstrate that our opportunistic MC-NOMA scheduling algorithm in which the Joint-SAPA-LCC algorithm is embedded works well while satisfying given QoS requirements.

In this paper, we propose a general framework to provide a desirable trade-off between inference accuracy and privacy protection in the inference as service scenario (IAS). Instead of sending data directly to the server, the user will preprocess the data through a privacy-preserving mapping, which will increase privacy protection but reduce inference accuracy. To properly address the trade-off between privacy protection and inference accuracy, we formulate an optimization problem to find the privacy-preserving mapping. Even though the problem is non-convex in general, we characterize nice structures of the problem and develop an iterative algorithm to find the desired privacy-preserving mapping, with convergence analysis provided under certain assumptions. From numerical examples, we observe that the proposed method has better performance than gradient ascent method in the convergence speed, solution quality and algorithm stability.

The arrival of direct electron detectors (DEDs) with high frame rates in the field of scanning transmission electron microscopy (TEM) has enabled many experimental techniques that require collection of a full diffraction pattern at each scan position, a field which is subsumed under the name four-dimensional scanning transmission electron microscopy (4D-STEM). DED frame rates approaching 100 kHz require data transmission rates and data storage capabilities that exceed those of the commonly available computing infrastructures. Current commercial DEDs allow the user to make compromises in pixel bit depth, detector binning, or windowing to reduce the per-frame file size and allow higher frame rates. This change in detector specifications requires decisions to be made before data acquisition that may reduce or lose information that could have been advantageous during data analysis.

Scientific workflows in High Performance Computing ( HPC ) environments are processing large amounts of data. The storage hierarchy on HPC systems is getting deeper, driven by new technologies (NVRAMs, SSDs, etc.) There is a need for new programming abstractions that allow users to seamlessly manage data at the workflow level on multi-tiered storage systems, and provide optimal workflow performance and use of storage resources. In previous work, we introduced a software architecture Managing Data on Tiered Storage for Scientific Workflows (MaDaTS ) that used a Virtual Data Space ( VDS ) abstraction to hide the complexities of the underlying storage system while allowing users to control data management strategies. In this article, we detail the data-centric programming abstractions that allow users to manage a workflow around its data on the storage layer. The programming abstractions simplify data management for scientific workflows on multi-tiered storage systems, without affecting workflow performance or storage capacity. We measure the overheads and effectiveness introduced by the programming abstractions of MaDaTS. Our results show that these abstractions can optimally use the storage capacity in lesser capacity storage tiers, and simplify data management without adding any performance overheads.

The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we present several linear receivers based on the Bussgang decomposition that show significant performance gains over conventional linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based detector, namely OBMNet, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method.

This study explores how fallout from the changing public health policy around COVID-19 has changed how researchers access and process their science experiments. Using a combination of techniques from statistical analysis and machine learning, we conduct a retrospective analysis of historical network data for a period around the stay-At-home orders that took place in March 2020. Our analysis takes data from the entire ESnet infrastructure to explore DOE high-performance computing (HPC) resources at OLCF, ALCF, and NERSC, as well as User sites such as PNNL and JLAB. We look at detecting and quantifying changes in site activity using a combination of t-Distributed Stochastic Neighbor Embedding (t-SNE) and decision tree analysis. Our findings bring insights into the working patterns and impact on data volume movements, particularly during late-night hours and weekends.

This paper investigates a symbiotic unmanned aerial vehicle (UAV)-assisted intelligent reflecting surface (IRS) radio system, where the UAV is leveraged to help the IRS reflect its own signals to the base station, and meanwhile enhance the UAV transmission by passive beamforming at the IRS. First, we consider the weighted sum bit error rate (BER) minimization problem among all IRSs by jointly optimizing the UAV trajectory, IRS phase shift matrix, and IRS scheduling, subject to the minimum primary rate requirements. To tackle this complicated problem, a relaxation-based algorithm is proposed. We prove that the converged relaxation scheduling variables are binary, which means that no reconstruct strategy is needed, and thus the UAV rate constraints are automatically satisfied. Second, we consider the fairness BER optimization problem. We find that the relaxation-based method cannot solve this fairness BER problem since the minimum primary rate requirements may not be satisfied by the binary reconstruction operation. To address this issue, we first transform the binary constraints into a series of equivalent equality constraints. Then, a penalty-based algorithm is proposed to obtain a suboptimal solution. Numerical results are provided to evaluate the performance of the proposed designs under different setups, as compared with benchmarks.

In this work, we thoroughly analyze the rate-region provided by the asynchronous transmission in multiple access channels (MACs). We derive the corresponding capacity-regions, applicable to a wide range of pulse shaping methods. We analytically prove that asynchronous transmission enlarges the capacity-region of MACs. Although successive interference cancellation (SIC) can achieve the optimal sum-rate for the conventional uplink non-orthogonal multiple access (NOMA) methods, it is unable to achieve the boundary of the capacity-region for the asynchronous transmission. We demonstrate that for the asynchronous transmission, the optimal SIC decoding order to achieve the maximum sum-rate is based on the users' channel strengths. This optimal ordering is in contrast to the conventional uplink NOMA, where various decoding orders can result in the maximum sum-rate. Furthermore, we provide practical transceiver designs to approach the capacity-region. The memory induced by asynchronous transmission enables the use of the trellis-based detection methods which improves the performance. In addition, we propose a transceiver design, based on channel diagonalization to exploit the frequency-selectivity introduced by timing offsets. The proposed transceiver design, joint with the turbo principle, enables us to achieve a rate pair that is not achievable by the synchronous transmission.

The deployment of large-scale antenna arrays for cellular base stations (BSs), called massive MIMO, has been a key enabler for meeting the ever increasing capacity requirement for 5G communication systems and beyond. Despite their promising performance, fully digital massive MIMO systems require a large number of hardware components including radio frequency chains, power amplifiers, digital-to-analog converters (DACs), and so on, resulting in a huge increase in terms of the total power consumption and hardware costs for cellular BSs. Toward both spectrally-efficient and energy-efficient massive MIMO deployment, a number of hardware limited architectures have been proposed, including hybrid analog-digital structures, constant-envelope transmission, and the use of low-resolution DACs. In this article, we overview the recent advances in improving the error rate performance of massive MIMO systems with 1-bit DACs through precoding at the symbol level. This line of research goes beyond traditional interference suppression or cancellation techniques by managing interference on a symbol-by-symbol basis. This provides unique opportunities for interference-aware precoding tailored for practical massive MIMO systems. In this article, we first explain the concept of constructive interference (CI) and elaborate on how CI can benefit the 1-bit signal design by exploiting the traditionally undesired multi-user interference as well as the interference from imperfect hardware components. We then overview several solutions for 1-bit signal design to illustrate the gains achievable by exploiting CI. Finally, we identify some challenges and future research directions for 1-bit massive MIMO systems that have yet to be explored.

The goal of coded distributed computation is to efficiently distribute a computation task, such as matrix multiplication, N -linear computation, or multivariate polynomial evaluation, across S servers through a coding scheme, such that the response from any R servers ( R is called the recovery threshold) is sufficient for the user to recover the desired computed value. Current state-of-art approaches are based on either exclusively matrix-partitioning (Entangled Polynomial (EP) Codes for matrix multiplication), or exclusively batch processing (Lagrange Coded Computing (LCC) for N -linear computations or multivariate polynomial evaluations). We present three related classes of codes, based on the idea of Cross-Subspace Alignment (CSA) which was introduced originally in the context of secure and private information retrieval. CSA codes are characterized by a Cauchy-Vandermonde matrix structure that facilitates interference alignment along Vandermonde terms, while the desired computations remain resolvable along the Cauchy terms. These codes are shown to unify, generalize and improve upon the state-of-art codes for distributed computing. First we introduce CSA codes for matrix multiplication, which yield LCC codes as a special case, and are shown to outperform LCC codes in general in download-limited settings. While matrix-partitioning approaches (EP codes) for distributed matrix multiplication have the advantage of flexible server computation latency, batch processing approaches (CSA, LCC) have significant advantages in communication costs as well as encoding and decoding complexity per matrix multiplication. In order to combine the benefits of these approaches, we introduce Generalized CSA (GCSA) codes for matrix multiplication that bridge the extremes of matrix-partitioning and batch processing approaches and demonstrate synergistic gains due to cross subspace alignment. Finally, we introduce N -CSA codes for N -linear distributed batch computations and multivariate batch polynomial evaluations. N -CSA codes include LCC codes as a special case, and are in general capable of outperforming LCC codes in download-constrained settings by upto a factor of N. Generalizations of N -CSA codes to include X -secure data and B -byzantine servers are also provided.

In this article, we propose a new protection scheme for backbone networks to guarantee high service availability. The presented scheme does not require any reconfiguration immediately after the failure (i.e., it is proactive). At the same time, it does not require any reserved backup network resources either. To achieve these seemingly contradictory goals, we utilize the recent advancements in Machine Learning (ML) to implement a network intelligence that periodically re-allocates the unused capacity as protection bandwidth to meet the service availability requirements of each connection. Our goal is achieved by two components (1) predicting the traffic for the next period on each link, and (2) intelligently selecting the best fit dedicated protection scheme for the next period depending on the estimated unused (spare) bandwidth and the previous service availability violations. Note that re-allocating protection bandwidth affects neither the operational connections nor the current best practice of operators to over-provision network bandwidth to support elephant flows. Finally, we provide a case study on the real traffic from Energy Sciences Network (ESnet), a high-speed, international scientific backbone network. The key benefit of our framework is that adaptively utilizing the over-provisioned bandwidth for spare capacity is sufficient to improve the availability from three-nines to five-nines (in ESnet for the 30 examined connections). The drawback is negligible bandwidth limitations; the user perceives a minor and very temporal bandwidth limitation in less than 0.1% of the time.

We propose a new class of oscillators by engineering the dispersion of two-coupled periodic waveguides to exhibit a degenerate band edge (DBE). The DBE is an exceptional point of degeneracy (EPD) of order four, i.e., representing the coalescence of four eigenmodes of a waveguide system without loss and gain. We present a distributed DBE oscillator realized in periodic coupled transmission lines with a unique mode selection scheme that leads to a stable single-frequency oscillation, even in the presence of load variation. The DBE oscillator potentially leads to a boost of the efficiency and performance of radio frequency (RF) sources, due to the unique features associated with the EPD concept. This class of oscillators is promising for improving discrete-distributed coherent sources and can be extended to radiating structures to achieve a new class of active integrated antenna arrays.

Intelligent reflecting surfaces (IRSs) have emerged as a revolutionary solution to enhance wireless communications by changing propagation environment in a cost-effective and hardware-efficient fashion. In addition, symbol-level precoding (SLP) has attracted considerable attention recently due to its advantages in converting multiuser interference (MUI) into useful signal energy. Therefore, it is of interest to investigate the employment of IRS in symbol-level precoding systems to exploit MUI in a more effective way by manipulating the multiuser channels. In this article, we focus on joint symbol-level precoding and reflecting designs in IRS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both power minimization and quality-of-service (QoS) balancing problems are considered. In order to solve the joint optimization problems, we develop an efficient iterative algorithm to decompose them into separate symbol-level precoding and block-level reflecting design problems. An efficient gradient-projection-based algorithm is utilized to design the symbol-level precoding and a Riemannian conjugate gradient (RCG)-based algorithm is employed to solve the reflecting design problem. Simulation results demonstrate the significant performance improvement introduced by the IRS and illustrate the effectiveness of our proposed algorithms.

The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for reducing cost and power consumption for massive Multiple-Input-Multiple-Output (MIMO) systems. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and makes the channel estimation and data detection tasks much more challenging. In this paper, we show how Support Vector Machine (SVM), a well-known supervised-learning technique in machine learning, can be exploited to provide efficient and robust channel estimation and data detection in massive MIMO systems with one-bit ADCs. First, the problem of channel estimation for uncorrelated channels is formulated as a conventional SVM problem. The objective function of this SVM problem is then modified for estimating spatially correlated channels. Next, a two-stage detection algorithm is proposed where SVM is further exploited in the first stage. The performance of the proposed data detection method is very close to that of Maximum-Likelihood (ML) data detection when the channel is perfectly known. We also propose an SVM-based joint Channel Estimation and Data Detection (CE-DD) method, which makes use of both the to-be-decoded data vectors and the pilot data vectors to improve the estimation and detection performance. Finally, an extension of the proposed methods to OFDM systems with frequency-selective fading channels is presented. Simulation results show that the proposed methods are efficient and robust, and also outperform existing ones.

We study a heterogeneous two-tier wireless sensor network in which $N$ heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to $M$ heterogeneous fusion centers (FCs). This heterogeneous node deployment problem is modeled as an optimization problem with the total power consumption of the network as its cost function. The necessary conditions of the optimal AP and FC node deployment are explored in this paper. We provide a variation of Voronoi Diagram as the optimal cell partition for this network and show that each AP should be placed between its connected FC and the geometric center of its cell partition. In addition, we propose a heterogeneous two-tier Lloyd algorithm to optimize the node deployment. Furthermore, we study the sensor deployment when the communication range is limited for sensors and APs. Simulation results show that our proposed algorithms outperform the existing clustering methods like Minimum Energy Routing, Agglomerative Clustering, Divisive Clustering, Particle Swarm Optimization, Relay Node placement in Double-tiered Wireless Sensor Networks, and Improved Relay Node Placement, on average.

Kernel-basedlearning has well-documented merits in various machine learning tasks. Most of the kernel-based learning approaches rely on a pre-selected kernel, the choice of which presumes task-specific prior information. In addition, most existing frameworks assume that data are collected centrally at batch. Such a setting may not be feasible especially for large-scale data sets that are collected sequentially over a network. To cope with these challenges, the present work develops an online multi-kernel learning scheme to infer the intended nonlinear function 'on the fly' from data samples that are collected in distributed locations. To address communication efficiency among distributed nodes, we study the effects of quantization and develop a distributed and quantized online multiple kernel learning algorithm. We provide regret analysis that indicates our algorithm is capable of achieving sublinear regret. Numerical tests on real datasets show the effectiveness of our algorithm.