Fundamental Tradeoff Research Articles

Privacy and correctness trade-offs for information-theoretically secure quantum homomorphic encryption

Quantum homomorphic encryption, which allows computation by a server directly on encrypted data, is a fundamental primitive out of which more complex quantum cryptography protocols can be built. For such constructions to be possible, quantum homomorphic encryption must satisfy two privacy properties: data privacy which ensures that the input data is private from the server, and circuit privacy which ensures that the ciphertext after the computation does not reveal any additional information about the circuit used to perform it, beyond the output of the computation itself. While circuit privacy is well-studied in classical cryptography and many homomorphic encryption schemes can be equipped with it, its quantum analogue has received little attention. Here we establish a definition of circuit privacy for quantum homomorphic encryption with information-theoretic security. Furthermore, we reduce quantum oblivious transfer to quantum homomorphic encryption. By using this reduction, our work unravels fundamental trade-offs between circuit privacy, data privacy and correctness for a broad family of quantum homomorphic encryption protocols, including schemes that allow only the computation of Clifford circuits.

Integrating animal behaviour into research on multiple environmental stressors: a conceptual framework.

While a large body of research has focused on the physiological effects of multiple environmental stressors, how behavioural and life-history plasticity mediate multiple-stressor effects remains underexplored. Behavioural plasticity can not only drive organism-level responses to stressors directly but can also mediate physiological responses. Here, we provide a conceptual framework incorporating four fundamental trade-offs that explicitly link animal behaviour to life-history-based pathways for energy allocation, shaping the impact of multiple stressors on fitness. We first address how small-scale behavioural changes can either mediate or drive conflicts between the effects of multiple stressors and alternative physiological responses. We then discuss how animal behaviour gives rise to three additional understudied and interrelated trade-offs: balancing the benefits and risks of obtaining the energy needed to cope with stressors, allocation of energy between life-history traits and stressor responses, and larger-scale escape from stressors in space or time via large-scale movement or dormancy. Finally, we outline how these trade-offs interactively affect fitness and qualitative ecological outcomes resulting from multiple stressors. Our framework suggests that explicitly considering animal behaviour should enrich our mechanistic understanding of stressor effects, help explain extensive context dependence observed in these effects, and highlight promising avenues for future empirical and theoretical research.

Two-Timescale Design for Uplink Systems: How Many IRS Beamforming Patterns Are Needed?

In this paper, we develop a new dynamic intelligent reflecting surface (IRS) beamforming (BF) framework for an IRS aided energy-constrained Internet-of-Things system, where multiple IRS BF patterns are employed to assist uplink transmission. To alleviate the channel estimation overhead incurred by the IRS, a novel two-timescale scheme is proposed to maximize the ergodic sum-rate. Specifically, multiple IRS BF patterns are first optimized based on the statistical channel state information (CSI). Under the obtained IRS BF patterns, the transmit power and time allocation for devices in each channel coherence interval are optimized based on the instantaneous CSI. By characterizing the lower bound of the objective value, we further analytically quantify the performance degradation incurred by using less IRS BF patterns to shed light on the fundamental performance-overhead tradeoff. Simulation results validate the effectiveness of our proposed design and demonstrate that only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$33\%$</tex-math></inline-formula> of the total IRS BF patterns are needed to maintain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$95\%$</tex-math></inline-formula> of the maximum achieved ergodic sum-rate.

Channel Coding for 6G Extreme Connectivity—Requirements, Capabilities, and Fundamental Tradeoffs

Channel Coding for 6G Extreme Connectivity—Requirements, Capabilities, and Fundamental Tradeoffs

Ultra-Reliable and Low-Latency Multiple-Antenna Communications in the High SNR Regime

Emerging time-sensitive applications such as automated driving, factory automation, and telesurgery have stimulated an increased interest in Ultra-Reliable and Low-Latency Communications (URLLC). Meanwhile, Multiple-Input Multiple-Output (MIMO) techniques have played a vital role in radio access networks since the 2000s, due to their spatial multiplexing and diversity gains over fading channels. Thus, it is of interest to consider the fundamental performance limits of URLLC operating over MIMO channels. In this letter, we consider this problem in the asymptotic regime of high Signal-to-Noise Ratio (SNR) for short packets involving finite-blocklength codes. In particular, we show that a fundamental tradeoff exists among service capability, latency, and error probability in the high SNR regime. We present a piecewise linear gain conservation equation to characterize this cross-layer tradeoff, which is also validated by extensive numerical results.

Evaluation of Quantum Key Distribution for Secure Satellite-integrated IoT Networks

There has been a dramatic increase in the number of Internet of Things (IoT) devices and their applications. Furthermore, there is a growing impetus to integrate IoT networks on a global scale, using satellites to expand the range of IoT connectivity into geographically remote areas. Ensuring the security of satellite backhaul for IoT networks is thus of paramount importance. The steady advance of quantum computing in recent years threatens to nullify classical cryptographic approaches based on assumptions of computational hardness, motivating the need for post-quantum cryptography. Quantum computing algorithms have been developed that, once a quantum computer of sufficient scale is realised, will be able to break classical cryptosystems efficiently (at polynomial-time complexity). A promising method of securing information against this threat at the physical layer has emerged in the form of quantum key distribution (QKD). QKD exploits the fundamental physical properties of light to guarantee information-theoretic security. Research into the application and standardisation of QKD to secure satellite backhaul, however, is still in its infancy. This paper presents a brief overview of the theoretical basis for QKD, whilst also providing a survey of contemporary QKD protocols. It evaluates the ability of these protocols to secure satellite backhaul in the context of a typical satellite-IoT network architecture. Furthermore, it highlights the vulnerabilities, as well as the technical challenges associated with this endeavour. Finally, it proposes directions for future research and development into protocols and standardisation for the satellite-integrated IoT domain. Several challenges must be overcome before QKD can evolve into a global-scale solution for securing satellite-IoT. Secret key generation rate remains very low in practical demonstrations of trusted-relay QKD satellite architectures. Further research is needed to overcome or mitigate the fundamental rate-distance trade-off before satellite QKD can be considered practicable in an IoT application. Alternatives that do not rely on trusted nodes are contingent on nascent technologies such as quantum repeaters and quantum memory. Whilst in theory QKD provides perfect information-theoretic security, it remains vulnerable to attacks that exploit imperfections in real-world equipment. Further effort is needed to develop QKD protocols that can safeguard against the aforementioned challenges.

Trade-off between microbial ecophysiological features regulated by soil fertility governs plant residue decomposition

Soil fertility influences microbial-driven plant residue decomposition, thus further affecting soil organic carbon (C) formation. Both ecological and physiological features of microbial communities change with soil fertilities, however, the mechanism of how soil fertility influences microbial ecophysiological features (both the taxonomic and functional profiles) driving plant residue decomposition is unclear. Here, four series of microcosms were set up, inoculated with two distinct soils, each with low and high fertility levels as a result of contrasting long-term fertilization regimes, and incubated with 13C-labeled rice straw amendments. DNA stable-isotope probing (DNA-SIP) based amplicon sequencing and shotgun metagenomic sequencing were conducted. We found that both bacterial community composition (including the relative abundance, α- and β-diversities, and taxonomic composition) and carbohydrate metabolic efficiency were significantly changed under different soil fertilities. High fertile soils gave rise to greater increases in bacterial populations and straw decomposition than low fertile soils, yet DNA-SIP-based shotgun metagenomic sequencing showed that the bacterial average carbohydrate metabolic efficiency, characterized by the average carbohydrate-active enzyme (CAZyme) abundance within each bacterial phylum was significantly lower. Linear regressions between bacterial ribosomal RNA operon (rrn) copy number and bacterial carbohydrate metabolic efficiency under contrasting soil fertilities suggested that high fertile soils select for fast-but-inefficient bacteria to degrade rice straw while low fertile soils select for slow-but-efficient ones. Collectively, the data demonstrate a fundamental trade-off between the ecological and physiological attributes of straw-degrading microbial communities. The trade-off is regulated by soil fertility and in turn governs plant residue decomposition. This work provides novel insights for better understanding the ecological and physiological roles of microbes in organic matter decomposition and global terrestrial C cycling, especially in the context of crop straw degradation under contrasting soil fertilities.

Ventilated acoustic meta-barrier based on layered Helmholtz resonators

The performance of a multilayer sound attenuating metamaterial with ventilation capacity based on arrays of wall-embedded Helmholtz resonators is investigated analytically, numerically and experimentally. Each cell of the barrier front wall is a Euclidian polygon, such as square or hexagon. One thickness layer of the barrier cell contains a parallel array of 4 (for square cell) or 6 (for hexagon) Helmholtz resonators connected via the axial ventilation duct element. Multiple layers of resonators can be connected in sequence with the extension of the ventilation channel. The sound attenuation performance of the barriers is investigated first using the lumped parameter theory and transfer matrix method (TMM), and numerically using the finite element (FE) simulations solving the Helmholtz equations in frequency domain. These results indicate that the sound attenuation in audible range (300–2000 Hz) can be meaningfully improved using several layers with a unique peak resonance frequency assigned to each corresponding layer, with each additional layer improving the total attenuation. However, it is acknowledged that the previously studied fundamental trade-offs between the total barrier thickness, ventilation capacity, operational frequency range and integral sound attenuation do apply to the proposed design as well. Nevertheless, it is suggested that the simplicity of both structural design and theoretical methods of performance estimations make the suggested barrier a good candidate for applications with a well known desired attenuation spectrum. The impedance tube experiments validate the sound blocking performance and show a reasonable agreement with the numerical predictions.

Characterizing the performance of heat rectifiers

A physical system connected to two thermal reservoirs at different temperatures is said to act as a heat rectifier when it is able to bias the heat current in a given direction, similarly to an electronic diode. We propose to quantify the performance of a heat rectifier by mapping out the tradeoff between heat currents and rectification. By optimizing over the system's parameters, we obtain Pareto fronts, which can be efficiently computed using general coefficients of performance. This approach naturally highlights the fundamental tradeoff between heat rectification and conduction, and allows for a meaningful comparison between different devices for heat rectification. We illustrate the practical relevance of these ideas on three minimal models for spin-boson nanoscale rectifiers, i.e., systems consisting of one or two interacting qubits coupled to bosonic reservoirs biased in temperature. Our results demonstrate the superiority of two strongly interacting qubits for heat rectification.

Overcoming the field-of-view to diameter trade-off in microendoscopy via computational optrode-array microscopy.

High-resolution microscopy of deep tissue with large field-of-view (FOV) is critical for elucidating organization of cellular structures in plant biology. Microscopy with an implanted probe offers an effective solution. However, there exists a fundamental trade-off between the FOV and probe diameter arising from aberrations inherent in conventional imaging optics (typically, FOV < 30% of diameter). Here, we demonstrate the use of microfabricated non-imaging probes (optrodes) that when combined with a trained machine-learning algorithm is able to achieve FOV of 1x to 5x the probe diameter. Further increase in FOV is achieved by using multiple optrodes in parallel. With a 1 × 2 optrode array, we demonstrate imaging of fluorescent beads (including 30 FPS video), stained plant stem sections and stained living stems. Our demonstration lays the foundation for fast, high-resolution microscopy with large FOV in deep tissue via microfabricated non-imaging probes and advanced machine learning.

Highly angle-sensitive and efficient optical metasurfaces with broken mirror symmetry.

Optical metasurfaces have great potential to overcome the functional limitations of conventional optical devices. In addition to polarization- or wavelength-multiplexed metasurfaces, angle-multiplexed metasurfaces can provide new degrees of freedom, enabling previously unrealized complex functionality in diverse applications such as LiDAR, augmented reality glasses, and imaging. However, there have been fundamental trade-offs in transmission efficiency and angular sensitivity for practically important paraxial rays. In this paper, we overcome this limitation by breaking mirror symmetries of single-layer metasurface structures. Based on an effective medium theory, we intuitively explain which material parameters affect the sensitivity and efficiency and prove that high sensitivity and high efficiency can be achieved simultaneously by breaking the mirror symmetry. Based on this, we propose optimized metasurfaces for two applications: an angle-multiplexed beam-steering device with up to 93% relative efficiency and an angle-multiplexed metalens array that can break the fundamental resolution-density trade-off of microlens arrays with high efficiency. The proposed angle-selective designs could pave the way for the development of new classes of compact optical devices with novel functions.

Effective bet-hedging through growth rate dependent stability

Microbes in the wild face highly variable and unpredictable environments and are naturally selected for their average growth rate across environments. Apart from using sensory regulatory systems to adapt in a targeted manner to changing environments, microbes employ bet-hedging strategies where cells in an isogenic population switch stochastically between alternative phenotypes. Yet, bet-hedging suffers from a fundamental trade-off: Increasing the phenotype-switching rate increases the rate at which maladapted cells explore alternative phenotypes but also increases the rate at which cells switch out of a well-adapted state. Consequently, it is currently believed that bet-hedging strategies are effective only when the number of possible phenotypes is limited and when environments last for sufficiently many generations. However, recent experimental results show that gene expression noise generally decreases with growth rate, suggesting that phenotype-switching rates may systematically decrease with growth rate. Such growth rate dependent stability (GRDS) causes cells to be more explorative when maladapted and more phenotypically stable when well-adapted, and we show that GRDS can almost completely overcome the trade-off that limits bet-hedging, allowing for effective adaptation even when environments are diverse and change rapidly. We further show that even a small decrease in switching rates of faster-growing phenotypes can substantially increase long-term fitness of bet-hedging strategies. Together, our results suggest that stochastic strategies may play an even bigger role for microbial adaptation than hitherto appreciated.

Minimum Repair Bandwidth LDPC Codes for Distributed Storage Systems

In distributed storage systems (DSS), an optimal code design must meet the requirements of efficient local data regeneration in addition to reliable data retention. Recently, low-density parity-check (LDPC) codes have been proposed as a promising candidate that can secure high data rates as well as low repair bandwidth while maintaining low complexity in data reconstruction. The main objective of this study is to optimize the repair bandwidth characteristics of LDPC code families for a DSS application while meeting the data reliability requirements. First, a data access scenario in which nodes contact other available nodes randomly to download data is examined. Later, a minimum-bandwidth protocol is considered in which nodes make their selections based on the degree numbers of check nodes. Through formulating optimization problems for both protocols, a fundamental trade-off between the decoding threshold and the repair bandwidth is established for a given code rate. Finally, conclusions are confirmed by numerical results showing that irregular constructions have a large potential for establishing optimized LDPC code families for DSS applications.

The Degrees-of-Freedom in Monostatic ISAC Channels: NLoS Exploitation vs. Reduction

The degrees of freedom (DoFs) attained in monostatic integrated sensing and communications (ISAC) are analyzed. Specifically, monostatic sensing aims for extracting target-orientation information from the line of sight (LoS) channel between the transmitter and the target, since the Non-LoS (NLoS) paths only contain clutter or interference. By contrast, in wireless communications, typically, both the LoS and NLoS paths are exploited for achieving diversity or multiplexing gains. Hence, we shed light on the NLoS exploitation vs. reduction tradeoffs in a monostatic ISAC scenario. In particular, we optimize the transmit power of each signal path to maximize the communication rate, while guaranteeing the sensing performance for the target. The non-convex problem formulated is firstly solved in closed form for a single-NLoS-link scenario, then we harness the popular successive convex approximation (SCA) method for a general multiple-NLoS-link scenario. Our simulation results characterize the fundamental performance tradeoffs between sensing and communication, demonstrating that the available DoFs in the ISAC channel should be efficiently exploited in a way that is distinctly different from that of communication-only scenarios.

IRS Aided MEC Systems With Binary Offloading: A Unified Framework for Dynamic IRS Beamforming

In this paper, we develop a unified dynamic intelligent reflecting surface (IRS) beamforming framework to boost the sum computation rate of an IRS-aided mobile edge computing (MEC) system, where each device follows a binary offloading policy. Specifically, the task of each device has to be either executed locally or offloaded to MEC servers as a whole with the aid of given number of IRS beamforming vectors available. By flexibly controlling the number of IRS reconfiguration times, the system can achieve a balance between the performance and associated signalling overhead. We aim to maximize the sum computation rate by jointly optimizing the computational mode selection for each device, offloading time allocation, and IRS beamforming vectors across time. Since the resulting optimization problem is non-convex and NP-hard, there are generally no standard methods to solve it optimally. To tackle this problem, we first propose a penalty-based successive convex approximation algorithm, where all the associated variables in the inner-layer iterations are optimized simultaneously and the obtained solution is guaranteed to be locally optimal. Then, we further derive the offloading activation condition for each device by deeply exploiting the intrinsic structure of the original optimization problem. According to the offloading activation condition, a low-complexity algorithm based on the successive refinement method is proposed to obtain high-quality solutions, which is more appealing for practical systems with a large number of devices and IRS elements. Moreover, the optimal condition for the proposed low-complexity algorithm is revealed. Numerical results demonstrate the effectiveness of our proposed algorithms and also unveil the fundamental performance-cost tradeoff of the proposed dynamic IRS beamforming framework.

Optimized Power Control for Over-the-Air Federated Averaging With Data Privacy Guarantee

This paper investigates the privacy-preserving power control problem for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">over-the-air federated edge learning</i> (Air-FEEL), which features “one-shot” aggregation and significantly reduced communication latency. In Air-FEEL, the inevitable random perturbation encountered in the aggregation process due to the corruption by channel fading and noise poses a fundamental trade-off between the privacy and accuracy, as larger perturbation may harm the accuracy but benefit the privacy, and vice versa. Therefore, power control in Air-FEEL, as the main approach to regulate the said random perturbation, needs to be judiciously designed for balancing the accuracy-privacy trade-off. This, however, is a largely uncharted area. To bridge the research gap, we first analyze the convergence behavior (in terms of the optimality gap) and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">differential privacy</i> (DP) performance of Air-FEEL with respect to the power control policy at different iterations. Then, to achieve the maximized training accuracy under given DP guarantee requirement, we minimize the optimality gap by jointly optimizing the power control at edge devices and the denoising factors at the edge server, subject to a set of power constraints at individual edge devices and the DP constraint. Experimental results validate that the proposed power control policy for Air-FEEL achieves a better privacy-accuracy trade-off compared with the benchmarks.

Meeting Coflow Deadlines in Data Center Networks With Policy-Based Selective Completion

Recently, the abstraction of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coflow</i> is introduced to capture the collective data transmission patterns among modern distributed data-parallel applications. During processing, coflows generally act as barriers; accordingly, time-sensitive applications prefer their coflows to complete within deadlines, and deadline-aware coflow scheduling becomes very crucial. Regarding these data-parallel applications, we notice that many of them, including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">large-scale query systems</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distributed iterative training</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">erasure codes enabled storage</i> , are able to tolerate loss-bounded incomplete inputs by design. This tolerance indeed brings a flexible design space for the schedule of their coflows: when getting overloaded, the network can trade coflow completeness for the timeliness, and balance the completeness of different coflows on demand. Unfortunately, existing coflow schedulers neglect this tolerance, resulting in inflexible and inefficient bandwidth allocations. In this paper, we explore this fundamental trade-off and design POCO, a POlicy-based COflow scheduler, along with a transport layer enhancement scheme, to achieve customizable selective coflow completion for emerging time-sensitive distributed applications. Internally, POCO employs a suite of novel designs along with admission controls to make <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">flexible</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">work-conserving</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">performance-guaranteed</i> rate allocation to online coflow requests very efficiently. Extensive trace-based simulations indicate that POCO is highly flexible and achieves optimal coflow schedules respecting the requirements specified by applications.

Heterogeneity Shifts the Storage-Computation Tradeoff in Secure Multi-Cloud Systems

This paper considers the design of heterogeneous multi-cloud systems for big data storage and computing in the presence of cloud collusion and failures. A fundamental concept of such a system is the secrecy capacity, which represents the maximum amount of information that can be stored for each unit of storage space under the requirements of secure distributed computing. A capacity-achieving code is designed for matrix multiplication, a computing subroutine widely used in machine learning applications. The code allows fast parallel decoding and unequal data allocation in the clouds. Such a flexibility leads naturally to the idea of optimizing data allocation to minimize the computing time. Given any feasible storage budget, the optimal solution is derived, characterizing explicitly the fundamental tradeoff between storage and computing. Furthermore, it is shown via majorization theory that the whole tradeoff curve improves if the cloud computing rates are more even. Experiments on Amazon EC2 clusters are conducted, corroborating our theoretical observations and the negligibility of decoding overhead.

Beamforming Design for Integrated Sensing and Wireless Power Transfer Systems

This letter proposes a new concept of integrated sensing and wireless power transfer (ISWPT), where radar sensing and wireless power transfer functions are integrated into one hardware platform. ISWPT provides several benefits from the integrating operation such as system size, hardware cost, power consumption, and spectrum saving, which is envisioned to facilitate future 6G wireless networks. As the initial study, we aim to characterizing the fundamental trade-off between radar sensing and wireless power transfer, by optimizing transmit beamforming vectors. We first propose a semi-definite relaxation-based approach to solve the corresponding optimization problem globally optimal, and then provide a low-complexity sub-optimal solution. Finally, numerical results verify the effectiveness of our proposed solutions, and also show the trade-off between radar sensing and wireless power transfer.

Metagrating meets the geometry-based efficiency limit for AR waveguide in-couplers.

Recently, augmented reality (AR) displays have attracted considerable attention due to the highly immersive and realistic viewer experience they can provide. One key challenge of AR displays is the fundamental trade-off between the extent of the field-of-view (FOV) and the size of the eyebox, set by the conservation of etendue sets this trade-off. Exit-pupil expansion (EPE) is one possible solution to this problem. However, it comes at the cost of distributing light over a larger area, decreasing the overall system's brightness. In this work, we show that the geometry of the waveguide and the in-coupler sets a fundamental limit on how efficient the combiner can be for a given FOV. This limit can be used as a tool for waveguide designers to benchmark the in-coupling efficiency of their in-coupler gratings. We design a metasurface-based grating (metagrating) and a commonly used SRG as in-couplers using the derived limit to guide optimization. We then compare the diffractive efficiencies of the two types of in-couplers to the theoretical efficiency limit. For our chosen waveguide geometry, the metagrating's 28% efficiency surpasses the SRG's 20% efficiency and nearly matches the geometry-based limit of 29% due to the superior angular response control of metasurfaces compared to SRGs. This work provides new insight into the efficiency limit of waveguide-based combiners and paves a novel path toward implementing metasurfaces in efficient waveguide AR displays.