Fundamental Tradeoff Research Articles

Understanding fundamental trade-offs in nanomechanical resonant sensors

Nanomechanical resonators are used as high performance detectors in a variety of applications such as mass spectrometry and atomic force microscopy. Initial emphasis in nanomechanical resonant sensors based on tracking resonance frequency deviations was on increasing the sensitivity to the level of a single molecule, atom, and beyond. On the other hand, there are applications where the speed of detection is crucial, prompting recent works that emphasize sensing schemes with improved time resolution. Here, we first develop a general modeling framework and a comprehensive theory encompassing all resonance frequency tracking schemes currently in use. We then explore the fundamental trade-offs between accuracy and speed in three resonant sensor architectures, namely, the feedback-free open-loop approach, positive-feedback based self-sustaining oscillator, and negative-feedback based frequency-locked loop scheme. We comparatively analyze them in a unified manner, clarify some misconceptions and confusion that seem to exist in the literature, and unravel their speed vs accuracy characteristics.

LossPass: Absorbing Microbursts by Packet Eviction for Data Center Networks

A bursty traffic pattern, called the microburst, is a key hurdle to achieve low latency for user-facing applications because it causes excessive packet losses in shallow buffered switches. Explicit Congestion Notification (ECN) can absorb microbursts by reserving buffer headroom, but the existence of headroom results in a fundamental trade-off between latency and throughput. To this end, we present LossPass, a buffer sharing mechanism that absorbs microbursts as many as possible while maintaining line-rate throughput. Specifically, LossPass evicts buffered large flow packets to make free buffer space on demand for arriving small flow packets. Our solution is inexpensive to implement on hardware. We implement a LossPass prototype and evaluate its performance through extensive testbed experiments and large-scale simulations. Our evaluation results show that LossPass reduces the FCT of small flows while maintaining line-rate throughput. For example, in testbed experiments, LossPass outperforms ECN by up to <inline-formula><tex-math notation="LaTeX">$3.20\times$</tex-math></inline-formula> in the 99th percentile FCT of small flows.

Fundamental Limits of Non-Orthogonal Multiple Access (NOMA) for the Massive Gaussian Broadcast Channel in Finite Block-Length.

Superposition coding (SC) has been known to be capacity-achieving for the Gaussian memoryless broadcast channel for more than 30 years. However, SC regained interest in the context of non-orthogonal multiple access (NOMA) in 5G. From an information theory point of view, SC is capacity-achieving in the broadcast Gaussian channel, even when the number of users tends to infinity. However, using SC has two drawbacks: the decoder complexity increases drastically with the number of simultaneous receivers, and the latency is unbounded since SC is optimal only in the asymptotic regime. To evaluate these effects quantitatively in terms of fundamental limits, we introduce a finite time transmission constraint imposed at the base station, and we evaluate fundamental trade-offs between the maximal number of superposed users, the coding block-length and the block error probability. The energy efficiency loss due to these constraints is evaluated analytically and by simulation. Orthogonal sharing appears to outperform SC for hard delay constraints (equivalent to short block-length) and in low spectral efficiency regime (below one bit per channel use). These results are obtained by the association of stochastic geometry and finite block-length information theory.

Critical policy frontiers: The drugs-development-peacebuilding trilemma

Recent years have seen the emergence of a policy consensus around the need for fundamental reforms of global drug policies. This is reflected in the call for ‘development-oriented drug policies’ that align and integrate drug policies with development and peacebuilding objectives, as captured in the Sustainable Development Goals (SDGs). These calls have been important in acknowledging the damage caused by the war on drugs and in drawing attention to how drugs are inextricably linked to wider development and peacebuilding challenges. Yet there is surprisingly limited academic research that looks critically at the drugs-development-peace nexus and which asks whether the goals of a ‘drug-free world’, ‘sustainable development’ and ‘the promotion of peace’ are commensurate with one another, can be pursued simultaneously, or are indeed achievable. This article studies these policy fields and policy-making processes from the geographical margins of the state – frontiers and borderland regions – because they offer a privileged vantage point for studying the contested nature of policymaking in relation to the drugs-development-peace nexus. We set out a historical political economy framework to critically assess the assumptions underlying the integrationist agenda, as well as the evidence base to support it. By developing the notion of a policy trilemma we are critical of the dominant policy narrative that ‘all good things come together’, showing instead the fundamental tensions and trade-offs between these policy fields. In exploring the interactions between these policy fields, we aim to advance discussion and debate on how to engage with the tensions and trade-offs that this integrationist agenda reveals, but which have to date been largely ignored.

Massively parallel cantilever-free atomic force microscopy

Resolution and field-of-view often represent a fundamental tradeoff in microscopy. Atomic force microscopy (AFM), in which a cantilevered probe deflects under the influence of local forces as it scans across a substrate, is a key example of this tradeoff with high resolution imaging being largely limited to small areas. Despite the tremendous impact of AFM in fields including materials science, biology, and surface science, the limitation in imaging area has remained a key barrier to studying samples with intricate hierarchical structure. Here, we show that massively parallel AFM with >1000 probes is possible through the combination of a cantilever-free probe architecture and a scalable optical method for detecting probe–sample contact. Specifically, optically reflective conical probes on a comparatively compliant film are found to comprise a distributed optical lever that translates probe motion into an optical signal that provides sub-10 nm vertical precision. The scalability of this approach makes it well suited for imaging applications that require high resolution over large areas.

Stochastic Adaptive Optimization With Dithers

Optimization methods are essential and have been used extensively in a broad spectrum of applications. Most existing literature on optimization algorithms does not consider systems that involve unknown system parameters. This article studies a class of stochastic adaptive optimization problems in which identification of unknown parameters and search for the optimal solutions must be performed simultaneously. Due to a fundamental conflict between parameter identifiability and optimality in such problems, we introduce a method of adding stochastic dither signals into the system, which provide sufficient excitation for estimating the unknown parameters, leading to convergent adaptive optimization algorithms. Joint identification and optimization algorithms are developed and their simultaneous convergence properties of parameter estimation and optimization variable updates are proved. Under both noise-free and noisy observations, the corresponding convergence rates are established. The main results of this article reveal certain fundamental relationships and tradeoff among updating step sizes, dither magnitudes, parameter estimation errors, optimization accuracy, and convergence rates. Simulation case studies are used to illustrate the adaptive optimization algorithms and their main properties.

5G as a wireless power grid

5G has been designed for blazing fast and low-latency communications. To do so, mm-wave frequencies were adopted and allowed unprecedently high radiated power densities by the FCC. Unknowingly, the architects of 5G have, thereby, created a wireless power grid capable of powering devices at ranges far exceeding the capabilities of any existing technologies. However, this potential could only be realized if a fundamental trade-off in wireless energy harvesting could be circumvented. Here, we propose a solution that breaks the usual paradigm, imprisoned in the trade-off between rectenna angular coverage and turn-on sensitivity. The concept relies on the implementation of a Rotman lens between the antennas and the rectifiers. The printed, flexible mm-wave lens allows robust and bending-resilient operation over more than 20 GHz of gain and angular bandwidths. Antenna sub-arrays, rectifiers and DC combiners are then added to the structure to demonstrate its combination of large angular coverage and turn-on sensitivity—in both planar and bent conditions—and a harvesting ability up to a distance of 2.83 m in its current configuration and exceeding 180 m using state-of-the-art rectifiers enabling the harvesting of several μW of DC power (around 6 μW at 180 m with 75 dBm EIRP).

Foraging trade-offs, flagellar arrangements, and flow architecture of planktonic protists

Unicellular flagellated protists are a key element in aquatic microbial food webs. They all use flagella to swim and to generate feeding currents to encounter prey and enhance nutrient uptake. At the same time, the beating flagella create flow disturbances that attract flow-sensing predators. Protists have highly diverse flagellar arrangements in terms of number of flagella and their position, beat pattern, and kinematics, but it is unclear how the various arrangements optimize the fundamental trade-off between resource acquisition and predation risk. Here we describe the near-cell flow fields produced by 15 species and demonstrate consistent relationships between flagellar arrangement and swimming speed and between flagellar arrangement and flow architecture, and a trade-off between resource acquisition and predation risk. The flow fields fall in categories that are qualitatively described by simple point force models that include the drag force of the moving cell body and the propulsive forces of the flagella. The trade-off between resource acquisition and predation risk varies characteristically between flow architectures: Flagellates with multiple flagella have higher predation risk relative to their clearance rate compared to species with only one active flagellum, with the exception of the highly successful dinoflagellates that have simultaneously achieved high clearance rates and stealth behavior due to a unique flagellar arrangement. Microbial communities are shaped by trade-offs and environmental constraints, and a mechanistic explanation of foraging trade-offs is a vital part of understanding the eukaryotic communities that form the basis of pelagic food webs.

On the Fundamental Limits of Fog-RAN Cache-Aided Networks With Downlink and Sidelink Communications

Maddah-Ali and Niesen (MAN) in 2014 showed that coded caching in single bottleneck-link broadcast networks allows serving an arbitrarily large number of cache-equipped users with a total link load (bits per unit time) that does not scale with the number of users. Since then, the general topic of coded caching has generated enormous interest both from the information theoretic and (network) coding theoretic viewpoint, and from the viewpoint of applications. Building on the MAN work, this paper considers a particular network topology referred to as cache-aided Fog Radio Access Network (Fog-RAN), that includes a Macro-cell Base Station (MBS) co-located with the content server, several cache-equipped Small-cell Base Stations (SBSs), and many users without caches. Some users are served directly by the MBS broadcast downlink, while other users are served by the SBSs. The SBSs can also exchange data via rounds of direct communication via a side channel, referred to as “sidelink”. For this novel Fog-RAN model, the fundamental tradeoff among (a) the amount of cache memory at the SBSs, (b) the load on the downlink (from MBS to directly served users and SBSs), and (c) the aggregate load on the sidelink is studied, under the standard worst-case demand scenario. We propose a converse bound whose key novelty is to jointly bound the downlink load an the sidelink load. For the achievability, by leveraging the network topology, we propose two classes of memory-loads point, where the SBS sidelink load is minimum and the MBS downlink load is minimum, respectively. By memory-sharing between these two classes of memory-loads points, some exact or order optimality results are obtained. Several existing models (e.g., Device-to-Device coded caching, single bottleneck-link coded caching with shared caches, single bottleneck-link caching coded caching with cache-less users) are recovered as special cases of this network model and by-product results of independent interest are given. Finally, the role of topology-aware versus topology-agnostic caching is discussed.

Fundamental Limits of Biometric Identification System Under Noisy Enrollment

The fundamental limits of biometric identification systems under a strong secrecy criterion are investigated. In the previous studies of this scenario, the fundamental trade-off among secrecy, template, privacy- and secrecy-leakages has been revealed in the case where there is only one user, while the case of multiple users has not been discussed yet. In this study, we consider the system with exponentially many users, and we characterize the capacity region of the rate tuples including the user (identification) rate under the strong secrecy criterion. In the achievability proof, we derive a new method to incorporate multiple users by extending the random binning for one user's case. The obtained result shows that the characterization of the capacity region does not vary regardless of the weak or strong secrecy criterion in terms of secrecy-leakage.

Class Action Democracy

A persistent concern with class actions is that the attorney will take advantage of the class members, conducting the litigation in a way that lines their own pockets at the class members’ expense. Holding an agent like an attorney accountable is always a problem, but it is especially dire in class actions because the class cannot meaningfully participate in the litigation. If the attorney misbehaves, class members cannot, for example, vote to replace them. This lack of effective democratic procedures in class actions rationalizes a range of doctrines and proposals that restrict them. Given this democratic deficit it makes sense to treat class actions as something of a last resort. I question this democratic critique of class actions. Despite the intuitive appeal and legal importance of democratic procedures, they would actually do little to benefit class members. Drawing on the economics of communication I show that there is a fundamental trade-off between democratic control and expertise. Taking this trade-off into account, even idealized democratic procedures would not make class members better off: if class members could vote to replace their attorney they would generally prefer not doing so, instead delegating matters to their attorney. These observations defang the democratic critique of class actions, therefore removing the most promising justification for restricting them and treating them as a disfavored procedure. That being said, courts should carefully scrutinize settlement and fee arrangements to make certain that the interests of the attorney and the class remain roughly, albeit imperfectly, aligned.

Characterizing Performance Limits in Payment Channel Networks

With their instant transaction confirmation and high scalability, payment channel networks (PCNs), running off-chain and in parallel with blockchain systems, have recently attracted a substantial amount of research attention. It has been shown that there exists a significant gap between the theoretically optimal performance and the performance achievable given the stringent privacy requirements in practice. However, it remains unclear what the fundamental performance limits and key factors involved are, which turns out to be a challenging problem due to the unique characteristics in PCNs. In this paper, we, for the first time, develop a mathematical model capturing the PCN performance, and examine the impact from a number of factors including channel capacity and transactions. We are articularly interested in obtaining the gap between the theoretically optimal performance and the performance achievable in practice, which characterizes the design space in PCNs for scheduling transactions. Specifically, we derive how different transactions and channel capacities affect the PCN performance and the performance gap. Our analytical characterization of PCNs offers an in-depth understanding on their fundamental trade-off, and provides important insights on the design of PCNs.

Chase or Wait: Dynamic UAV Deployment to Learn and Catch Time-Varying User Activities

Unmanned aerial vehicle (UAV) technology is a promising solution for rapidly providing wireless communication services to ground users, where a UAV has limited service coverage and needs to fly through users at different locations for serving them locally. The existing UAV deployment studies largely assume the users’ demands do not change during UAV deployment. When the users’ demands dynamically change over time, the key challenge is how to adapt the UAV deployment strategy to the partial and even outdated observations on the users’ activities given the UAV's flying speed limit. In this paper, we study dynamic UAV deployment to learn and adapt to the time-varying user activities, where the activity pattern of a user (if out of the UAV service coverage) is hidden from the UAV and follows a time-slotted Markov chain that switches between active and idle states. We formulate the learning-and-adaption based UAV deployment problem as a partially observable Markov decision process (POMDP) to maximize the total discounted hit rate of active users, where the UAV decides for itself whether to chase an active user in a distant location (with delayed reward) or to wait for the idle user in the current location to return to the active state (with smaller service probability) over time. We show there is a fundamental delay-reward tradeoff, and prove that the UAV will optimally follow a threshold-based policy by waiting at an idle user for a time threshold before moving to another user. We also show the UAV is more likely to move if the temporal correlation of each user's idling pattern is stronger or the travel distance between users is shorter. Furthermore, we extend to a more general scenario where the UAV does not even know the parameters of each user's temporal activity distribution, and apply Q-learning to develop another threshold-based deployment policy for a multi-user scenario.

Request Scheduling in Quantum Networks

Quantumnetworking is emerging as a new research area to explore the opportunities of interconnecting quantum systems through end-to-end entanglement of qubits at geographical distance via quantum repeaters. A promising architecture has been proposed in the literature that decouples entanglement between adjacent quantum nodes/repeaters from establishing end-to-end paths by adopting a time slotted approach. Within this model, we destructure further end-to-end path establishment into two subproblems: path selection and scheduling. The former is set to determine the best repeaters to connect two end nodes, provided that all their local entanglements have succeeded. On the other hand, scheduling is concerned with deciding, which pairs of end nodes are served in the current time slot, while the others remain queued for later time slots. Unlike path selection, scheduling has not been investigated so far in the literature, particularly in presence of quantum noise, which makes both problems even more challenging. In this article, we propose to address it via a general framework of heuristic algorithms, for which we propose three illustrative instances with the objective of keeping the application delay small while achieving a good system utilization, in terms of high entanglement rate and fidelity of remotely entangled qubits. The system proposed is evaluated extensively via event-driven quantum network simulations, with noisy repeaters, in different node topologies under a Poisson arrival of requests from quantum applications. The results show the existence of a fundamental tradeoff between system- and application-level metrics, such as fairness versus entanglement and fidelity, which lays the foundations for further studies in this thriving research area.

Privacy-Preserving Identification Systems With Noisy Enrollment

In this paper, we study fundamental trade-offs in privacy-preserving biometric identification systems with noisy enrollment. The proposed identification systems include helper data, secret keys, and private keys. Helper data are stored in a public database and used for identification. Secret keys are either stored in a secure database or provided to the user, and can be used in a next step, e.g. for authentication. Private keys are provided by users, and are also used for identification. In this paper, we impose a noisy enrollment channel and an arbitrarily small privacy and secrecy leakage rate. We characterize the optimal trade-off among the identification, secret key, private key, and helper data rates. Depending on how secret keys are produced, we study two cases of the proposed privacy-preserving identification systems, where the secret keys are generated and chosen respectively. By introducing private keys, it is shown that the identification system achieves close to zero privacy leakage rate in both generated and chosen secret key settings. The results also show that the identification rate and the secret key rate can be enlarged by increasing the private key rate. This work provides a framework for analyzing privacy-preserving identification systems and an insight on the design of optimal systems.

Trade-off for Heterogeneous Distributed Storage Systems between Storage and Repair Cost

We consider heterogeneous distributed storage systems (DSSs) having flexible reconstruction degree, where each node in the system has nonuniform repair bandwidth and nonuniform storage capacity. In particular, a data collector can reconstruct the file using some $$k$$ nodes in the system and, for a node failure, the system can be repaired by some set of active nodes. Using min-cut bound, we investigate the fundamental trade-off between storage and repair costs for our model of the heterogeneous DSS. Further, the problem is formulated as bi-objective optimization linear programing problem for various heterogeneous DSSs. For some DSSs, it is shown that the calculated min-cut bound is tight.

Rate-Power Trade-Off in Simultaneous Lightwave Information and Power Transfer Systems

In this letter, we study the fundamental trade-off that exists between the power harvested and data rate supported by a simultaneous lightwave information and power transmission (SLIPT) receiver. By selecting the DC operating point of the photovoltaic cell using a power converter, the receiver can be tuned to optimize collected power or data rate. Utilizing an electrical model of a photovoltaic cell, an expression for the bandwidth and a lower bound on the rate is derived as function of its DC terminal voltage. Using this dependency, a novel adaptive-SLIPT receiver is developed where the rate-power trade-off is quantified for the first time. Finally, we propose and study an optimization problem to find the optimum operating voltage of the photovoltaic cell to obtain a desired trade-off between the data rate and harvested power for a given battery charge state and incident irradiance.

Performance Analysis of Integrated Electro-Optic Phase Modulators Based on Emerging Materials

Electro-optic modulators are utilized ubiquitously ranging from applications in data communication to photonic neural networks. While tremendous progress has been made over the years, efficient phase-shifting modulators are challenged with fundamental tradeoffs, such as voltage-length, index change-losses or energy-bandwidth, and no single solution available checks all boxes. While voltage-driven phase modulators, such as based on lithium niobate, offer low loss and high speed operation, their footprint of 10’s of cm-scale is prohibitively large, especially for density-critical applications, for example in photonic neural networks. Ignoring modulators for quantum applications, where insertion loss is critical, in this work we distinguish between current versus voltage-driven modulators. We focus on the former, since current-based schemes of emerging thin electro-optical materials have shown unity-strong index modulation suitable for heterogeneous integration into foundry waveguides. Here, we provide an in-depth ab-initio analysis of obtainable modulator performance based on heterogeneously integrating low-dimensional materials, i.e., graphene, thin films of indium tin oxide, and transition metal dichalcogenide monolayers into a plurality of optical waveguide designs atop silicon photonics. Using the fundamental modulator tradeoff of energy-bandwidth-product as a design-quality quantifier, we show that a small modal cross section, such as given by plasmonic modes, enables high-performance operation, physically realized by arguments on charge-distribution and low electrical resistance. An in-depth design understanding of phase-modulator performance, beyond doped-junctions in silicon, offers opportunities for micrometer-compact yet energy-bandwidth-ratio constrained modulators with timely opportunities to hardware-accelerate applications beyond data communication towards photonic machine intelligence, for instance; where both performance and integration-density are critical.

Real-Time Magnetic Resonance Imaging.

Real-time magnetic resonance imaging (RT-MRI) allows for imaging dynamic processes as they occur, without relying on any repetition or synchronization. This is made possible by modern MRI technology such as fast-switching gradients and parallel imaging. It is compatible with many (but not all) MRI sequences, including spoiled gradient echo, balanced steady-state free precession, and single-shot rapid acquisition with relaxation enhancement. RT-MRI has earned an important role in both diagnostic imaging and image guidance of invasive procedures. Its unique diagnostic value is prominent in areas of the body that undergo substantial and often irregular motion, such as the heart, gastrointestinal system, upper airway vocal tract, and joints. Its value in interventional procedure guidance is prominent for procedures that require multiple forms of soft-tissue contrast, as well as flow information. In this review, we discuss the history of RT-MRI, fundamental tradeoffs, enabling technology, established applications, and current trends. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 1.

Wireless Distributed Edge Learning: How Many Edge Devices Do We Need?

We consider distributed machine learning at the wireless edge, where a parameter server builds a global model with the help of multiple wireless edge devices that perform computations on local dataset partitions. Edge devices transmit the result of their computations (updates of current global model) to the server using a fixed rate and orthogonal multiple access over an error prone wireless channel. In case of a transmission error, the undelivered packet is retransmitted until successfully decoded at the receiver. Leveraging on the fundamental tradeoff between computation and communication in distributed systems, our aim is to derive how many edge devices are needed to minimize the average completion time while guaranteeing convergence. We provide upper and lower bounds for the average completion and we find a necessary condition for adding edge devices in two asymptotic regimes, namely the large dataset and the high accuracy regime. Conducted experiments on real datasets and numerical results confirm our analysis and substantiate our claim that the number of edge devices should be carefully selected for timely distributed edge learning.