Achievable Tradeoff Research Articles

Deep Reinforcement Learning for Latency-Sensitive Communication With Adaptive Redundant Retransmissions

This paper studies packet repetition strategies over erasure channels with memory and a long feedback delay. The problem is initially formulated as a communications problem where a source wishes to transmit one message packet to a destination while minimizing both the delay and the number of transmissions. At each time instant, the sender is provided a delayed acknowledgement feedback about past attempts, and must decide whether to attempt a new transmission or not. This problem is then re-formulated as an episodic reinforcement learning problem, where an agent attempts to learn the optimal transmission policy, provided delayed feedback about past transmission attempts. The agent is helped by a channel estimator, which attempts to capture the channel memory and use that to predict probabilities of erasures in a future window. This channel estimator is also data-driven and learns the channel model without any <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> channel knowledge. The paper presents a lower bound on the achievable trade-off between delay and number of transmissions for any channel modeled as a Markov process. Experimental results show that the combination of the proposed channel estimator and the agent can noticeably outperform naive strategies for channels with memory, and achieves results close to the lower bound.

Rate and Detection-Error Exponent Tradeoff for Joint Communication and Sensing of Fixed Channel States

We study the information-theoretic limits of joint communication and sensing when the sensing task is modeled as the estimation of a discrete channel state fixed during the transmission of an entire codeword. This setting captures scenarios in which the time scale over which sensing happens is significantly slower than the time scale over which symbol transmission occurs. The tradeoff between communication and sensing then takes the form of a tradeoff region between the rate of reliable communication and the state detection-error exponent. We investigate such tradeoffs for both mono-static and bi-static scenarios, in which the sensing task is performed at the transmitter or receiver, respectively. In the mono-static case, we develop an exact characterization of the tradeoff in open-loop, when the sensing is not used to assist the communication. We also show the strict improvement brought by a closed-loop operation, in which the sensing informs the communication. In the bi-static case, we develop an achievable tradeoff region that highlights the fundamentally different nature of the bi-static scenario. Specifically, the rate of communication plays a key role in the characterization of the tradeoff and we show how joint strategies, which simultaneously estimate message and state, outperform successive strategies, which only estimate the state after decoding the transmitted message.

Optimal Integrated Emission Management through Variable Engine Calibration

In this work, the potential for improving the trade-off between fuel consumption and tailpipe NOx emissions through variable engine calibration (VEC) is demonstrated for both conventional and hybrid electric vehicles (HEV). First, a preoptimization procedure for the engine operation is proposed to address the challenge posed by the large number of engine control inputs. By excluding infeasible and suboptimal operation offline, an engine model is developed that can be evaluated efficiently during online optimization. Next, dynamic programming is used to find the optimal trade-off between fuel consumption and tailpipe NOx emissions for various vehicle configurations and driving missions. Simulation results show that for a conventional vehicle equipped with VEC and gear optimization run on the worldwide harmonized light vehicles test cycle (WLTC), the fuel consumption can be reduced by 5.4% at equivalent NOx emissions. At equivalent fuel consumption, the NOx emissions can be reduced by 80%. For an HEV, the introduction of VEC, in addition to the optimization of the torque split and the gear selection, drastically extended the achievable trade-off between fuel consumption and tailpipe NOx emissions in simulations. Most notably, the region with very low NOx emissions could only be reached with VEC.

Pareto gamuts

Manufactured parts are meticulously engineered to perform well with respect to several conflicting metrics, like weight, stress, and cost. The best achievable trade-offs reside on the Pareto front , which can be discovered via performance-driven optimization. The objectives that define this Pareto front often incorporate assumptions about the context in which a part will be used, including loading conditions, environmental influences, material properties, or regions that must be preserved to interface with a surrounding assembly. Existing multi-objective optimization tools are only equipped to study one context at a time, so engineers must run independent optimizations for each context of interest. However, engineered parts frequently appear in many contexts: wind turbines must perform well in many wind speeds, and a bracket might be optimized several times with its bolt-holes fixed in different locations on each run. In this paper, we formulate a framework for variable-context multi-objective optimization. We introduce the Pareto gamut , which captures Pareto fronts over a range of contexts. We develop a global/local optimization algorithm to discover the Pareto gamut directly, rather than discovering a single fixed-context "slice" at a time. To validate our method, we adapt existing multi-objective optimization benchmarks to contextual scenarios. We also demonstrate the practical utility of Pareto gamut exploration for several engineering design problems.

Local Data Debiasing for Fairness Based on Generative Adversarial Training

The widespread use of automated decision processes in many areas of our society raises serious ethical issues with respect to the fairness of the process and the possible resulting discrimination. To solve this issue, we propose a novel adversarial training approach called GANSan for learning a sanitizer whose objective is to prevent the possibility of any discrimination (i.e., direct and indirect) based on a sensitive attribute by removing the attribute itself as well as the existing correlations with the remaining attributes. Our method GANSan is partially inspired by the powerful framework of generative adversarial networks (in particular Cycle-GANs), which offers a flexible way to learn a distribution empirically or to translate between two different distributions. In contrast to prior work, one of the strengths of our approach is that the sanitization is performed in the same space as the original data by only modifying the other attributes as little as possible, thus preserving the interpretability of the sanitized data. Consequently, once the sanitizer is trained, it can be applied to new data locally by an individual on their profile before releasing it. Finally, experiments on real datasets demonstrate the effectiveness of the approach as well as the achievable trade-off between fairness and utility.

Analysis of the Matrix Event Graph Replicated Data Type

Matrix is a new kind of decentralized, topic-based publish-subscribe middleware for communication and data storage that is getting popular particularly as a basis for secure instant messaging. In comparison to traditional decentralized communication systems, Matrix replaces pure message passing with a replicated data structure. This data structure, which we extract and call the Matrix Event Graph (MEG), depicts the causal history of messages. We show that this MEG represents an interesting and important replicated data type for general decentralized applications that are based on causal histories of publish-subscribe events: we show that a MEG possesses strong properties with respect to consistency, byzantine attackers, and scalability. First, we show that the MEG provides Strong Eventual Consistency (SEC), and that it is available under partition, by proving that the MEG is a Conflict-Free Replicated Data Type for causal histories. While strong consistency is impossible here as shown by the famous CAP theorem, SEC is among the best known achievable trade-offs. Second, we discuss the implications of byzantine attackers on the data type's properties. We note that the MEG, as it does not strive for consensus, can cope with $n > f$ environments with $n$ total participants of which $f$ show byzantine faults. Furthermore, we analyze scalability: Using Markov chains we study the width of the MEG, defined as the number of forward extremities, over time and observe an almost optimal evolution. We conjecture that this property is inherent to the underlying spatially inhomogeneous random walk.

A Deep Study on Layered Multi-Relay Non-Orthogonal Amplify-Forward Networks

The paper introduces a cross-layer design for transmission of Successive Refinement (SR) source code interplayed with non-orthogonal layered code, deployed over a half-duplex multi-relay Non-orthogonal Amplify-Forward (NAF) network. Assuming the Channel State Information (CSI) is not available at the source node, firstly the achievable layered Diversity-Multiplexing Tradeoff (DMT) curve is derived. Then, by taking Distortion Exponent (DE) as the figure of merit, several achievable lower bounds are proved and the optimal expected distortion performance under high Signal to Noise Ratio (SNR) approximation is explicitly obtained. It is shown that the proposed coding can achieve the Multi-Input Single-Output (MISO) upper bound under certain regions of bandwidth ratios, by which the optimal performance in these regions can be explicitly characterized. Further the non-orthogonal layered coding scheme is extended to a multi-hop MIMO Decode-Forward (DF) relay network and set of DE lower bounds is derived. Simulation results confirm the effectiveness of the proposed scheme under general SNR values and compared with its counterparts.

Tradeoffs Between Weak-Noise Estimation Performance and Outage Exponents in Nonlinear Modulation

We focus on the problem of modulating a parameter onto a power-limited signal transmitted over a discrete-time Gaussian channel and estimating this parameter at the receiver. Considering the well-known threshold effect in the non-linear modulation systems, our approach is the following: instead of deriving upper and lower bounds on the total estimation error, which weighs both weak-noise errors and anomalous errors beyond the threshold, we separate the two kinds of errors. In particular, we derive upper and lower bounds on the best achievable tradeoff between the exponential decay rate of the weak-noise expected error cost and the exponential decay rate of the probability of the anomalous error event, also referred to as the outage event . This outage event is left to be defined as a part of the communication system design problem. Our achievability scheme, which is based on lattice codes, meets the lower bound at the high signal-to-noise limit and for a certain range of tradeoffs between the weak-noise error cost and the outage exponent.

Detection Rate Optimization for Swerling Targets in Gaussian Noise

In this paper, we consider a pulse radar and study the tradeoff between integration time and scan rate for diverse target scattering models. At the design stage, we optimize the available degrees of freedom (namely, pulse train length and detection threshold) so as to maximize the detection rate, defined as the average number of detections from the target per unit of time, subject to a constraint on the false alarm rate, which is the average number of false alarms from the monitored area per unit of time. This objective function allows to carefully balance the contrasting needs for a large probability of detection (achievable through a large dwell time) and a short scan time. Closed-form solutions are provided for Swerling's Cases 1 and 3 target fluctuation and for the Marcum nonfluctuating model, while, for the Swerling's Cases 2 and 4, the solution is found with the aid of computer simulation. A thorough performance analysis is given to show the achievable tradeoffs among the principal system parameters under the different target models.

Timely Two-Way Data Exchanging in Unilaterally Powered Fog Computing Systems

Fog computing is attracting more and more attention to the next-generation wireless communication systems. In this paper, we consider such a mobile edge scenario where a fog node and a mobile user exchange their data in a two-way manner and only the fog node has a constant power supply. In particular, the fog node performs energy transfer when it is not transmitting information and the mobile user has an energy harvesting capability. For this scenario, we investigate the timeliness of the two-way data exchange between the fog node and the mobile user in terms of age of information (AoI) and derive closed-form average AoI for both directions, as well as the achievable data rate of the mobile user. We also discuss the achievable tradeoff on the average downlink/uplink AoIs and the achievable tradeoff on downlink/uplink data rates, which explicitly present the performance limit of the system in terms of timeliness and efficiency.

Cache-Aided Content Delivery Over Erasure Broadcast Channels

A cache-aided broadcast network is studied, in which a server delivers contents to a group of receivers over a packet erasure broadcast channel. The receivers are divided into two sets with regards to their channel qualities: the weak and the strong receivers, where all the weak receivers have statistically worse channel qualities than all the strong receivers. The weak receivers, in order to compensate for the high erasure probability they encounter over the channel, are equipped with cache memories of equal size, while the receivers in the strong set have no caches. Data can be pre-delivered to the weak receivers’ caches over the off-peak traffic period before the receivers reveal their demands. Allowing arbitrary erasure probabilities for the weak and strong receivers, a joint caching and channel coding scheme, which divides each file into several subfiles, and applies a different caching and delivery scheme for each subfile, is proposed. It is shown that all the receivers, even those without any cache memories, benefit from the presence of caches across the network. An information theoretic tradeoff between the cache size and the achievable rate is formulated. It is shown that the proposed scheme improves upon the state-of-the-art in terms of the achievable tradeoff.

On the Tradeoff Region of Secure Exact-Repair Regenerating Codes

We consider the $(n,k,d,\ell )$ secure exact-repair regenerating code problem, which generalizes the $(n,k,d)$ exact-repair regenerating code problem with the additional constraint that the stored file needs to be kept information-theoretically secure against an eavesdropper, who can access the data transmitted to regenerate a total of $\ell $ different failed nodes. For all known results on this problem, the achievable tradeoff regions between the normalized storage capacity and repair bandwidth have a single corner point, achieved by a scheme proposed by Shah, Rashmi, and Kumar (the SRK point). Since the achievable tradeoff regions of the exact-repair regenerating code problem without any secrecy constraints are known to have multiple corner points in general, these existing results suggest a phase-change-like behavior, i.e., enforcing a secrecy constraint ( $\ell \geq 1$ ) immediately reduces the tradeoff region to one with a single corner point. In this paper, we first show that when the secrecy parameter $\ell $ is sufficiently large, the SRK point is indeed the only corner point of the tradeoff region. However, when $\ell $ is small, we show that the tradeoff region can in fact have multiple corner points. In particular, we establish a precise characterization of the tradeoff region for the (7, 6, 6, 1) problem, which has exactly two corner points. Thus, a smooth transition, instead of a phase-change-type of transition, should be expected as the secrecy constraint is gradually strengthened.

Long-distance WPT unconventional arrays synthesis

Two innovative array concepts are introduced for the design of long-distance Wireless Power Transfer (WPT) radiating systems. The achievable tradeoffs between complexity/cost mitigation and power focusing capabilities of unconventional WPT architectures with respect to state-of-the-art optimal WPT solutions are investigated. To this end, clustered or sparse WPT arrangements are introduced by formulating their syntheses either as excitation or as pattern matching problems then solved by ad-hoc versions of the Contiguous Partition Method and Compressive Sensing algorithms. Selected numerical examples are presented to assess the features and the potentialities of unconventional WPT designs also in comparison with traditional state-of-the-art optimal methods.

Vortex-free phase profiles for uniform patterning with computer-generated holography.

Computer-generated holography enables efficient light pattern generation through phase-only wavefront modulation. While perfect patterning usually requires control over both phase and amplitude, iterative Fourier transform algorithms (IFTA) can achieve phase-only approximations which maximize light efficiency at the cost of uniformity. The phase being unconstrained in the output plane, it can vary abruptly in some regions leading to destructive interferences. Among such structures phase vortices are the most common. Here we demonstrate theoretically, numerically and experimentally, a novel approach for eliminating phase vortices by spatially filtering the phase input to the IFTA, combining it with phase-based complex amplitude control at the spatial light modulator (SLM) plane to generate smooth shapes. The experimental implementation is achieved performing complex amplitude modulation with a phase-only SLM. This proposed experimental scheme offers a continuous and centered field of excitation. Lastly, we characterize achievable trade-offs between pattern uniformity, diffraction efficiency, and axial confinement.

Trellis-Assisted Constellation Subset Selection for PAPR Reduction of OFDM Signals

In this paper, we propose a new peak-to-average power ratio (PAPR) reduction technique, which is referred to as trellis-assisted constellation subset selection ( TACSS , or TAX for short), for orthogonal frequency-division multiplexing (OFDM) signals. We control the signal transition by judiciously selecting the appropriate constellation subset for PAPR reduction with the controlling bits that are subject to the trellis-based constraint imposed by a bank of memories. A novel constellation design suitable for the proposed system is also introduced. A major property offered by the proposed approach is that the controlling bits and the information bits are perfectly separable at the receiver, which leads to a simple implementation of its soft-in–soft-out (SISO) decoder for coded OFDM systems. Through the performance analysis of PAPR reduction capability, power amplifier (PA) efficiency, average mutual information (AMI), and bit error rate (BER), the achievable tradeoff relationship between the transmitter and receiver performances of the proposed system is addressed. Furthermore, performance comparisons of the proposed scheme with other major PAPR reduction schemes such as trellis shaping, discrete Fourier transform (DFT) precoding, and clipping and filtering demonstrate its practical advantage in a high-spectral-efficiency regime.

A Fundamental Tradeoff Between Total and Brown Power Consumption in Geographically Dispersed Data Centers

This letter aims at deriving a fundamental tradeoff between the total and brown power consumption associated with geographical dispersed data centers, where utilizing more green energy mostly happens at the cost of increasing the total power consumption. To this end, we define a new service efficiency parameter for data centers in satisfying the QoS requirements based on the queuing analysis. More importantly, we propose the idea of modeling geo-dispersed data centers with an information flow graph to capture a total-brown power consumption tradeoff region. Accordingly, we characterize the achievable tradeoff between total and brown power consumption.

Scheduling traffic for maximum switch lifetime in optical data center fabrics

Optical switching is a promising technology to scale the performance of data centers. We address two scenarios: hybrid data centers, in which an optical circuit switching network operates in parallel to an electronic packet switching network that interconnect the servers, and full optical data centers, in which all the traffic is switched in the optical domain. The traffic on the optical network is scheduled proactively by configuring the optical fabric according to a precomputed sequence of configurations. We consider that fast optical circuit switching is implemented through micro-mechanical devices (like MEMS) at each crosspoint, whose lifetime is limited by a maximum number of configuration cycles, due to the experienced mechanical fatigue. We consider the traffic scheduling problem on the optical network and address specifically the problem of minimizing the number of reconfigurations at each crosspoint, in order to maximize the MEMS lifetime, while maximizing the throughput. We propose a family of scheduling algorithms and discuss the achievable tradeoff between lifetime, throughput and computational complexity, throughout a set of extensive simulations and theoretical results.

Dynamic Pricing and Distributed Energy Management for Demand Response

The problem of dynamic pricing of electricity in a retail market is considered. A Stackelberg game is used to model interactions between a retailer and its customers; the retailer sets the day-ahead hourly price of electricity and consumers adjust real-time consumptions to maximize individual consumer surplus. For thermostatic demands, the optimal aggregated demand is shown to be an affine function of the day-ahead hourly price. A complete characterization of the trade-offs between consumer surplus and retail profit is obtained. The Pareto front of achievable trade-offs is shown to be concave, and each point on the Pareto front is achieved by an optimal day-ahead hourly price. Effects of integrating renewables and local storage are analyzed. It is shown that benefits of renewable integration all go to the retailer when the capacity of renewable is relatively small. As the capacity increases beyond a certain threshold, the benefit from renewable that goes to consumers increases.

Multi-Objective Framework for Dynamic Optimization of OFDMA Cellular Systems

Green cellular networking has become an important research area in recent years due to environmental and economical concerns. Switching off under-utilized BSs during off-peak traffic load conditions is a promising approach to reduce energy consumption in cellular networks. In practice, during initial cell planning, the BS locations and RAN parameters are optimized to meet the basic system design requirements like coverage, capacity, overlap, QoS etc. As these metrics are tightly coupled with each other due to co-channel interference, switching off certain BSs may affect the system requirements. Therefore, identifying a subset of large number of BSs which are to be put into sleep mode, is a challenging dynamic optimization problem. In this work, we develop a multiobjective framework for dynamic optimization framework for OFDMA based cellular systems. The objective is to identify the appropriate set of active sectors and RAN parameters that maximize coverage and area spectral efficiency while minimizing overlap and area power consumption without violating the QoS requirements for a given traffic demand density. The objective functions and constraints are obtained using appropriate analytical models which capture the traffic characteristics, propagation characteristics (pathloss, shadowing, and small scale fading) as well as load condition in neighbouring cells. A low complexity evolutionary algorithm is used for identifying the global Pareto optimal solutions at a faster convergence rate. The inter-relationships between the system objectives are studied and guidelines are provided to find an appropriate network configuration that provides the best achievable trade-offs. The results show that using the proposed framework, significant amount of energy saving can be achieved and with a low computational complexity while maintaining good trade-offs among the other objectives.

Optimization models for the joint Power-Delay minimization problem in green wireless access networks

In wireless access networks, one of the most recent challenges is reducing the power consumption of the network, while preserving the quality of service perceived by users. Hence, mobile operators are rethinking their network design by considering two objectives, namely, saving power and guaranteeing a satisfactory quality of service. Since these objectives are conflicting, a tradeoff becomes inevitable. We formulate a multi-objective optimization with aims of minimizing the network power consumption and transmission delay. Power saving is achieved by adjusting the operation mode of the network base stations from high transmit power levels to low transmit levels or even sleep mode. Minimizing the transmission delay is achieved by selecting the best user association with the network base stations. In this article, we cover two different technologies: IEEE 802.11 and LTE. Our formulation captures the specificity of each technology in terms of the power model and radio resource allocation. After exploring typical multi-objective approaches, we resort to a weighted sum mixed integer linear program. This enables us to efficiently tune the impact of the power and delay objectives.We provide extensive simulations for various preference settings that enable to assess the tradeoff between power and delay in IEEE 802.11 WLANs and LTE networks. We show that for a power minimization setting, a WLAN consumes up to 16% less power than legacy solutions. A thorough analysis of the optimization results reveals the impact of the network topology, particularly the inter-cell distance, on both objectives. For an LTE network, we assess the impact of urban, rural and realistic deployments on the achievable tradeoffs. The power savings mainly depend on user distribution and the power consumption of the sleep mode. Compared with legacy solutions, we obtained power savings of up to 22.3% in a realistic LTE networks. When adequately tuned, our optimization approach reduces the transmission delay by up to 6% in a WLAN and 8% in an LTE network.