Fundamental Tradeoff Research Articles

Proactive Traffic Offloading in Dynamic Integrated Multisatellite Terrestrial Networks

The integration between the satellite network and the terrestrial network will play a key role in the upcoming sixth-generation (6G) of mobile cellular networks thanks to the wide coverage and bandwidth offered by satellite networks. To leverage this integration, we propose a proactive traffic offloading scheme in an integrated multi-satellite terrestrial network (IMSTN) that considers the future networks’ heterogeneity and predicts their variability. Our proposed offloading scheme hinges on traffic prediction to answer the stringent requirements of data-rate, latency and reliability imposed by heterogeneous and coexisting services and traffic namely enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable low latency communication (URLLC). However, the fulfilment of these requirements during offloading in dynamic IMSTN comes at the expense of significant energy consumption and introduces inherently supplementary latency. Therefore, our offloading scheme aims to balance the fundamental trade-offs first between energy consumption and the achievable data-rate and second between energy consumption and latency while meeting the respective needs of the present traffic. Our findings prove the importance of the cooperation between the multi-satellite network and the terrestrial network conditioned by traffic prediction to enhance the performance of IMTSN in terms of latency and energy consumption.

Space squeezing optics: Performance limits and implementation at microwave frequencies

Optical systems often largely consist of empty space as diffraction effects that occur through free-space propagation can be crucial for their function. Contracting these voids offers a path to the miniaturization of a wide range of optical devices. Recently, a new optical element—coined “spaceplate”—has been proposed, which is capable of emulating the effects of diffraction over a specified propagation distance using a thinner non-local metamaterial [Reshef et al., Nat. Commun. 12, 3512 (2021)]. The compression factor of such an element is given by the ratio of the length of free-space that is replaced to the thickness of the spaceplate itself. In this work, we test a prototype spaceplate in the microwave spectral region (20–23 GHz)—the first such demonstration designed to operate in ambient air. Our device consists of a Fabry–Pérot cavity formed from two reflective metasurfaces with a compression factor that can be tuned by varying the size of perforations within each layer. Using a pair of directive horn antennas, we measure a space compression factor of up to ∼6 over a numerical aperture (NA) of 0.34 and a fractional bandwidth of 6%. We also investigate the fundamental trade-offs that exist between the compression factor, transmission efficiency, NA, and bandwidth of this single resonator spaceplate design and highlight that it can reach arbitrarily high compression factors by restricting its NA and bandwidth.

Using Deep Reinforcement Learning to Coordinate Multi-Modal Journey Planning with Limited Transportation Capacity


 
 
 Multi-modal journey planning for large numbers of simultaneous travellers is a challenging problem, particularly in the presence of limited transportation capacity. Fundamental trade-offs exist between balancing the goals and preferences of each traveller and the optimization of the use of available capacity. Addressing these trade-offs requires careful coordination of travellers’ individual plans. This paper assesses the viability of Deep Reinforcement Learning (DRL) applied to simulated mobility as a means of learning coordinated plans. Specifically, the paper addresses the problem of travel to large-scale events, such as concerts and sports events, where all attendees have as their goal to arrive on time. Multi-agent DRL is used to learn coordinated plans aimed at maximizing just-in-time arrival while taking into account the limited capacity of the infrastructure. Generated plans take account of different transportation modes’ availability and requirements (e.g., parking) as well as constraints such as attendees’ ownership of vehicles. The results are compared with those of a naive decision-making algorithm based on estimated travel time. The results show that the learned plans make intuitive use of the available modes and improve average travel time and lateness, supporting the use of DRL in association with a microscopic mobility simulator for journey planning.
 
 

Exceptional Precision of a Nonlinear Optical Sensor at a Square-Root Singularity.

Exceptional points (EPs)-spectral singularities of non-Hermitian linear systems-have recently attracted interest for sensing. While initial proposals and experiments focused on enhanced sensitivities neglecting noise, subsequent studies revealed issues with EP sensors in noisy environments. Here we propose a single-mode Kerr-nonlinear resonator for exceptional sensing in noisy environments. Based on the resonator's dynamic hysteresis, we define a signal that displays a square-root singularity reminiscent of an EP. However, our sensor has crucial fundamental and practical advantages over EP sensors: the signal-to-noise ratio increases with the measurement speed, the square-root singularity is easily detected through intensity measurements, and both sensing precision and information content of the signal are enhanced around the singularity. Our sensor also overcomes the fundamental trade-off between precision and averaging time characterizing all linear sensors. All these unconventional features open up new opportunities for fast and precise sensing using hysteretic resonators.

Trade-Offs among Sensing, Reporting, and Transmission in Cooperative CRNs.

Cooperative spectrum sensing (CSS) has been verified as an effective approach to improve the sensing performances of cognitive radio networks (CRNs). Compared with existing works that commonly consider fusion with fixed inputs and neglect the duration of the reporting period in the design, we novelly investigate a fundamental trade-off among three periods of CSS: sensing, reporting, and transmission periods, and evaluate the impact of the fusion rule with a varying number of local sensing results. To be specific, the sensing time could be traded for additional mini-slots to report more local sensing results for fusion, or it could be traded for longer transmission time. In the CRNs with a given durations of sensing/reporting/transmission periods, we, respectively, formulate the throughput and collision probability and optimize the throughput under the collision constraint. The theoretical results show that, in the specific value intervals of the sensing parameters, the collision constraint provides an upper bound of the number of mini-slots in the reporting period or a lower bound of the sensing duration. We provide the approach to the maximum throughput in some cases.Finally, numerical results are presented to validate theoretical results.

Optimization of Energy Efficiency in UAV-Enabled Cognitive IoT With Short Packet Communication

The unmanned aerial vehicle (UAV) assisted communication can significantly improve the coverage and spectrum efficiency of the Internet of Things (IoT). One main feature of IoT is short packet communication (SPC), in which the data transmission uses finite block-length codewords. The transmission rate and the packet error rate will affect the effective throughput of the IoT. In this paper, we investigate the impact of SPC on the performance of UAV-enabled spectrum sharing network. Specifically, the optimization of energy efficiency (EE) in the UAV communication system is considered since the battery of the UAV is usually limited. We design the packet error rate, the sensing duration, the normalized sensing threshold and the UAV’s transmit power to maximize the EE under the constraint that the primary user is sufficiently protected. Since the system parameters are intertwined with each other, we employ a successive optimization algorithm to solve the optimization problem by dividing it into three subproblems. Then, an efficient iterative algorithm is proposed to obtain the optimal parameters. Simulation results reveal that the proposed algorithm can converge to global optimal value and has better EE performance than other benchmark schemes. And there is a fundamental tradeoff between the throughput and the EE.

Conditions for indexability of restless bandits and an algorithm to compute Whittle index

AbstractRestless bandits are a class of sequential resource allocation problems concerned with allocating one or more resources among several alternative processes where the evolution of the processes depends on the resources allocated to them. Such models capture the fundamental trade-offs between exploration and exploitation. In 1988, Whittle developed an index heuristic for restless bandit problems which has emerged as a popular solution approach because of its simplicity and strong empirical performance. The Whittle index heuristic is applicable if the model satisfies a technical condition known as indexability. In this paper, we present two general sufficient conditions for indexability and identify simpler-to-verify refinements of these conditions. We then revisit a previously proposed algorithm called the adaptive greedy algorithm which is known to compute the Whittle index for a sub-class of restless bandits. We show that a generalization of the adaptive greedy algorithm computes the Whittle index for all indexable restless bandits. We present an efficient implementation of this algorithm which can compute the Whittle index of a restless bandit withKstates in$\mathcal{O}\!\left(K^3\right)$computations. Finally, we present a detailed numerical study which affirms the strong performance of the Whittle index heuristic.

Dynamic Stochastic Matching Under Limited Time

In “Dynamic Stochastic Matching Under Limited Time,” Aouad and Sarıtaç analyze the design of matching policies in dynamic markets such as carpooling platforms and kidney exchange schemes. A crucial distinction with previous literature is that the agents’ arrivals and departures are fully dynamic. The demand and supply side are constantly replenished; each market participant remains available for potential matches during a limited period of time. Specifically, the authors formulate a general dynamic matching model over edge-weighted graphs, where the agents' arrivals and abandonments are stochastic and heterogeneous. The platform controls how long each agent waits and whom s/he is matched with. These decisions are subject to a fundamental tradeoff between increasing market thickness and mitigating the risk of abandonments from certain participants. The authors’ main contribution is to devise simple matching algorithms with strong performance guarantees for a broad class of networks. In contrast, they show that widely used batching algorithms have an arbitrary bad performance on certain graph-theoretic structures. Their analysis involves novel techniques including linear programming benchmarks, value function approximations, and proxies for continuous-time Markov chains, which may be of broader interest. Extensive simulations on real-world taxi demand data demonstrate that the newly developed algorithms can significantly improve cost efficiency against batching algorithms.

On the Degrees of Freedom Region for Simultaneous Imaging & Uplink Communication

In this paper, we take the first step towards quantifying the fundamental performance trade-offs between imaging and communication supported simultaneously using the same network resources. We analyze an uplink system configuration with a full-duplex base station (BS) illuminating an imaging scene while receiving data from a communication user. Our main contributions are two-fold. First, we propose a unified signal space analysis framework based on the degrees of freedom metric to characterize the trade-offs between the two operations in the high signal-to-noise ratio regime. Second, we propose a dual-function joint processing scheme, decode-and-image, that allows the BS to simultaneously form an image of the scene while decoding the uplink user’s data. Our analysis and proposed scheme highlight the benefits of exploiting the uplink signals for imaging, at the cost of increased cooperation between the BS and uplink user. Moreover, our proposed scheme outperforms traditional schemes that enable dual-function operation via spatial or temporal isolation of imaging and communication signals.

Channel Estimation and Data Detection Analysis of Massive MIMO With 1-Bit ADCs

We present an analytical framework for the channel estimation and the data detection in massive multiple-input multiple-output uplink systems with 1-bit analog-to-digital converters (ADCs) and i.i.d. Rayleigh fading. First, we provide closed-form expressions of the mean squared error (MSE) of the channel estimation considering the state-of-the-art linear minimum MSE estimator and the class of scaled least-squares estimators. For the data detection, we provide closed-form expressions of the expected value and the variance of the estimated symbols when maximum ratio combining is adopted, which can be exploited to efficiently implement minimum distance detection and, potentially, to design the set of transmit symbols. Our analytical findings explicitly depend on key system parameters such as the signal-to-noise ratio (SNR), the number of user equipments, and the pilot length, thus enabling a precise characterization of the performance of the channel estimation and the data detection with 1-bit ADCs. The proposed analysis highlights a fundamental SNR trade-off, according to which operating at the right noise level significantly enhances the system performance.

Trade-Offs in Sensor Systems Design: A Tutorial

In this tutorial, we discuss trade-offs in sensor system design. With examples from engineered systems in our laboratories, we address design considerations at the different system levels, from the choice of a particular sensor to the signal conditioning circuits, the choice of data converter, and data communication physical interfaces. Fundamental concepts from communication and information theory, as well as practical considerations applied to specific case studies, are presented for sensor readout systems. In particular, we address both fundamental trade-offs such as choice of signal representation for signal conditioning and conversion, as well as practical and technological trade-offs such as place of quantization (signal data conversion) application demands and integration technologies. The tutorial deals with what is commonly referred to as the Analog Front End (AFE) up to the point where the physical signal is represented in digital symbols. Although the discussion focuses on all the processing steps of the AFE, a more system-level approach is further explored, including stand-alone system approaches and system communication. Major system trade-offs during system design are discussed, providing important methodology insights towards the design of sensory systems operating within demanding constraints.

An Approximation Algorithm for Joint Caching and Recommendations in Cache Networks

Streaming platforms, like Netflix and YouTube, strive to offer high streaming quality (SQ), in terms of bitrate, delays, etc., to their users. Meanwhile, a significant share of content consumption of these platforms is heavily influenced by recommendations. In this setting, the user's overall experience is a product of both the user's interest in a recommended content, i.e., the recommendation quality (RQ), and the SQ of this content. However, network decisions (like caching) that affect the SQ are usually made without considering the recommender's actions. Likewise, recommendations are chosen independently of the potential delivery quality. In this paper, we define a metric of streaming experience (MoSE) that captures the fundamental tradeoff between the SQ and RQ. We aim to jointly optimize caching and recommendations in a generic network of caches, with the objective of maximizing this metric. This is in line with the recent trend for content providers to simultaneously act as Content Delivery Network owners, implying that the same entity may handle both caching and recommendation decisions. We formulate this joint optimization problem and prove that it can be approximated up to a constant factor. To the best of our knowledge, this is the first polynomial algorithm to achieve a constant approximation ratio for the joint problem. Moreover, our numerical experiments show important performance gains of our algorithm over baseline schemes and existing algorithms in the literature.

EntrapNet: A Blockchain-Based Verification Protocol for Trustless Computing

In this paper, we propose a blockchain-based computing verification protocol, called EntrapNet, for distributed shared computing networks, an emerging underlying network for many internet of things (IoT) applications. EntrapNet borrows the idea from the practice of entrapment in criminal law to reduce the possibility of receiving incorrect computing results from trustless service providers who have offered the computing resources. Furthermore, we mathematically optimize EntrapNet to deal with the fundamental tradeoff of a network: security and efficiency. We present an asymptotic optimal solution to this optimization. It will be seen that EntrapNet can be performed as an independent and low-cost layer atop of any trustless network that requires outsourced computing, thus making secure computing affordable and practical.

Modeling to inform economy‐wide pandemic policy: Bringing epidemiologists and economists together

Modeling to inform economy‐wide pandemic policy: Bringing epidemiologists and economists together

Aerial Base Station Placement: A Tutorial Introduction

The deployment of aerial base stations (ABSs) mounted onboard unmanned aerial vehicles is emerging as a promising technology to provide connectivity in areas where terrestrial infrastructure is insufficient or absent. This may occur, for example, in remote areas, large events, emergency situations, or areas affected by natural disasters such as wildfires or tsunamis. To successfully materialize this goal, it is required that ABSs are placed at locations in 3D space that ensure a high quality of service to the ground terminals. This article provides a tutorial introduction to this ABS placement problem where the fundamental challenges and trade-offs are first investigated by means of a toy application example. Next, the different approaches in the literature to address the aforementioned challenges in both 2D or 3D space are introduced, and a discussion on adaptive placement is provided. The article is concluded by discussing future research directions.

Introduction to Fourier Ptychography: Part I

Abstract:Fourier ptychography is an emerging computational microscopy technique that can generate gigapixel-scale images of biological samples. With only the addition of a low-cost LED array to a standard digital microscope and a reconstruction algorithm, Fourier ptychography overcomes the fundamental trade-off between a microscope's resolution and field-of-view without any moving parts. This article is the first in a three-part series that aims to introduce the fundamentals of the technology to the broader microscopy community and beyond, using intuitive explanations.

Thermodynamic Constraints on Quantum Information Gain and Error Correction: A Triple Trade-Off

Quantum error correction (QEC) is a procedure by which the quantum state of a system is protected against a known type of noise, by preemptively adding redundancy to that state. Such a procedure is commonly used in quantum computing when thermal noise is present. Interestingly, thermal noise has also been known to play a central role in quantum thermodynamics (QTD). This fact hints at the applicability of certain QTD statements in the QEC of thermal noise, which has been discussed previously in the context of Maxwell's demon. In this article, we view QEC as a quantum heat engine with a feedback controller (i.e., a demon). We derive an upper bound on the measurement heat dissipated during the error-identification stage in terms of the Groenewold information gain, thereby providing the latter with a physical meaning also when it is negative. Further, we derive the second law of thermodynamics in the context of this QEC engine, operating with general quantum measurements. Finally, we show that, under a set of physically motivated assumptions, this leads to a fundamental triple trade-off relation, which implies a trade-off between the maximum achievable fidelity of QEC and the super-Carnot efficiency that heat engines with feedback controllers have been known to possess. A similar trade-off relation occurs for the thermodynamic efficiency of the QEC engine and the efficacy of the quantum measurement used for error identification.

A simple and effective way to evaluate the accuracy of digital image correlation combined with scanning electron microscopy (SEM-DIC)

Digital image correlation (DIC) is a powerful technique enabling mapping of local strain fields with the spatial resolution down to a few nanometers when combined with scanning electron microscopy. However, the accuracy of strain determination is frequently unknown due to a large number of experimental parameters influencing it. Here we present a simple and time-effective method for quantitative evaluation of uncertainties in strain values obtained by DIC. By imposing a fictive hydrostatic strain a high absolute accuracy of mean strain determination is demonstrated with deviations from the target value below 0.03%. At the same time, the precision decreases strongly with the reduction of the subset and step sizes reflecting a fundamental tradeoff between the spatial resolution and trueness of local strain values.

Towards Secure Big Data Analysis via Fully Homomorphic Encryption Algorithms.

Privacy-preserving techniques allow private information to be used without compromising privacy. Most encryption algorithms, such as the Advanced Encryption Standard (AES) algorithm, cannot perform computational operations on encrypted data without first applying the decryption process. Homomorphic encryption algorithms provide innovative solutions to support computations on encrypted data while preserving the content of private information. However, these algorithms have some limitations, such as computational cost as well as the need for modifications for each case study. In this paper, we present a comprehensive overview of various homomorphic encryption tools for Big Data analysis and their applications. We also discuss a security framework for Big Data analysis while preserving privacy using homomorphic encryption algorithms. We highlight the fundamental features and tradeoffs that should be considered when choosing the right approach for Big Data applications in practice. We then present a comparison of popular current homomorphic encryption tools with respect to these identified characteristics. We examine the implementation results of various homomorphic encryption toolkits and compare their performances. Finally, we highlight some important issues and research opportunities. We aim to anticipate how homomorphic encryption technology will be useful for secure Big Data processing, especially to improve the utility and performance of privacy-preserving machine learning.

Optimum Data Transmission Allocation in Multimodal Communication of Underwater Sensor Networks

Multimodal communication (MC) plays a crucial role in the Underwater Sensor Networks (UWSNs). The appropriate resource allocation of MC can significantly optimize the UWSNs performance. This letter investigates the optimum data transmission allocation in the underwater MC. Considering the fundamental MC tradeoff, the MC energy efficiency model is established and formulated as a mixed integer nonlinear programming problem with the provable convex optimization properties. Then this letter designs two schemes for the various data amounts to optimize the optical-acoustic transmission ratio and an optimized data collection algorithm to save the MC energy. Simulation results validate the effectiveness of this letter.