Abstract

We establish a connection between non-Markovianity and negative entropy production rate for various classes of quantum operations. We analyse several aspects of unital and thermal operations in connection with resource theories of purity and thermodynamics. We fully characterize Lindblad operators corresponding to unital operations. We also characterize the Lindblad dynamics for a large class of thermal operations. We next generalize the definition of the entropy production rate for the non-equilibrium case to connect it with the rate of change of free energy of the system, and establish complementary relations between non-Markovianity and maximum loss of free energy. We naturally conclude that non-Markovianity in terms of divisibility breaking is a necessary resource for the backflow of other resources like purity or free energy under the corresponding allowed operations.

Abstract

This paper proposes the use of copula theory for cooperative spectrum sensing (CSS) of orthogonal frequency-division multiplexing (OFDM) based primary user (PU). A distributed detection model is assumed where secondary users (SUs) employ autocorrelation detectors (ADs) for the detection of a PU. In the presence of a PU, it is assumed that the observations across different SUs and subsequently the decision statistics are dependent. For the fusion of these dependent statistics, different copulas such as -copula, Gaussian, Clayton and Gumbel are employed. In the presence of dependence among decision statistics, significant improvement in detection performance is observed while using copula theory instead of the traditional assumption of independence. Simulation results are presented to show the superiority of copula-based spectrum sensing.

Abstract

Cross-tier interference management is one of the major challenges in heterogeneous cellular networks (HetNets). Though the network throughput increases due to a better area spectral efficiency of a HetNet, there is possibility that high interference will make few link capacities close to zero when users regard interference as noise (IAN). In this letter, successive interference cancellation (SIC) is used to cancel the cross-tier interference in a reverse time division duplexing (RTDD) scheme. We demonstrate that by opportunistic use of SIC, a minimum guarantee on the sum link capacity can be ensured for an RTDD HetNet. This minimum sum link capacity is later on proved to be the maximum that can be achieved by orthogonal resource allocation schemes. Through system-level simulations for random allocation, it is shown that the proposed scheme is better than using SIC and IAN alone. To further improve the overall system capacity, an optimization problem for selecting co-channel users is formulated, and the Hungarian algorithm is employed to solve it.

Abstract

This paper analyzes the performance of low earth orbit (LEO) satellites based internet-of-things (IoT) network where each IoT node makes use of multiple satellites to communicate with the ground station (GS). In this work, we consider fixed and variable gain amplify-and-forward (AF) relaying protocol at each satellite where the received signal from each IoT node is amplified before transmitting to the terrestrial GS for data processing. To analyze the performance of this novel LEO satellites based direct access architecture, the closed-form expressions for outage probability are derived considering two combining schemes at the GS:(i) selection combining;(ii) maximal ratio combining. Further, to gain more insights for diversity order and coding gain, asymptotic outage probability analysis at high SNR for both schemes is also performed. Finally, simulation results are presented to validate the analytical results derived and also to develop several interesting insights into the system performance.

Abstract

This paper develops efficient channel estimation techniques for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems under practical hardware limitations, including an arbitrary array geometry and a hybrid hardware structure. Taking on an angle-based approach, this work adopts a generalized array manifold separation approach via Jacobi-Anger approximation, which transforms a non-ideal, non-uniform array manifold into a virtual array domain with a desired uniform geometric structure to facilitate super-resolution angle estimation and channel acquisition. Accordingly, structure-based optimization techniques are developed to effectively estimate both the channel covariance and the instantaneous channel state information (CSI) within a short sensing time. The different time-varying scales of channel path angles versus path gains are capitalized to design a two-step CSI estimation scheme that can quickly sense fading channels. Theoretical results are provided on the fundamental limits of the proposed technique in terms of sample efficiency. For computational efficiency, a fast iterative algorithm is developed via the alternating direction method of multipliers. Other related issues such as spurious-peak cancellation in non-uniform linear arrays and extensions to higher-dimensional cases are also discussed. Simulations testify the effectiveness of the proposed approaches in hybrid mmWave massive MIMO systems with arbitrary arrays.

Abstract

Coding theoretic techniques have been proposed for synchronous Gradient Descent (GD) on multiple servers to mitigate stragglers. These techniques provide the flexibility that the job is complete when any k out of n servers finish their assigned tasks. The task size on each server is found based on the values of k and n. However, it is assumed that all the n jobs are started when the job is requested. In contrast, we assume a tiered system, where we start with n1 ≥ k tasks, and on completion of c tasks, we start n2 – n1 more tasks. The aim is that as long as k servers can execute their tasks, the job gets completed. This paper exploits the flexibility that not all servers are started at the request time to obtain the achievable task sizes on each server. The task sizes are in general lower than starting all n2 tasks at the request times thus helping achieve lower task sizes which helps to reduce both the job completion time and the total server utilization.

Abstract

Distributed databases often suffer unequal distribution of data among storage nodes, which is known asdata skew'. Data skew arises from a number of causes such as removal of existing storage nodes and addition of new empty nodes to the database. Data skew leads to performance degradations and necessitatesrebalancing'at regular intervals to reduce the amount of skew. We define an -balanced distributed database as a distributed database in which the storage across the nodes has uniform size, and each bit of the data is replicated in distinct storage nodes. We consider the problem of designing such balanced databases along with associated rebalancing schemes which maintain the -balanced property under node removal and addition operations. We present a class of -balanced databases (parameterized by the number of storage nodes) which have the property of structural invariance, ie, the databases designed for different number of storage nodes have the same structure. For this class of -balanced databases, we present rebalancing schemes which use coded transmissions between storage nodes, and characterize their communication loads under node addition and removal. We show that the communication cost incurred to rebalance our distributed database for node addition and removal is optimal, ie, it achieves the minimum possible cost among all possible balanced distributed databases and rebalancing schemes.

Abstract

In this paper, we address the novel, highly challenging problem of estimating the layout of a complex urban driving scenario. Given a single color image captured from a driving platform, we aim to predict the bird’s eye view layout of the road and other traffic participants. The estimated layout should reason beyond what is visible in the image, and compensate for the loss of 3D information due to projection. We dub this problem amodal scene layout estimation, which involves hallucinating scene layout for even parts of the world that are occluded in the image. To this end, we present MonoLayout, a deep neural network for realtime amodal scene layout estimation from a single image. We represent scene layout as a multi-channel semantic occupancy grid, and leverage adversarial feature learning to “hallucinate" plausible completions for occluded image parts. We extend several state-of-the-art approaches for road-layout estimation and vehicle occupancy estimation in bird’s eye view to the amodal setup and thoroughly evaluate against them. By leveraging temporal sensor fusion to generate training labels, we significantly outperform current art over a number of datasets.

Abstract

This paper presents a new system to obtain dense object reconstructions along with 6-DoF poses from a single image. Geared towards high fidelity reconstruction, several recent approaches leverage implicit surface representations and deep neural networks to estimate a 3D mesh of an object, given a single image. However, all such approaches recover only the shape of an object; the reconstruction is often in a canonical frame, unsuitable for downstream robotics tasks. To this end, we leverage recent advances in differentiable rendering (in particular, rasterization) to close the loop with 3D reconstruction in camera frame. We demonstrate that our approach—dubbed reconstruct, rasterize and backprop (RRB)—achieves significantly lower pose estimation errors compared to prior art, and is able to recover dense object shapes and poses from imagery. We further extend our results to an (offline) setup, where we demonstrate a dense monocular object-centric egomotion estimation system.

Abstract

This paper introduces the Omnidirectional Tractable Three Module Robot for traversing inside complex pipe networks. The robot consists of three omnidirectional modules fixed 120 apart circumferentially which can rotate about their axis allowing holonomic motion of the robot. Holonomic motion enables the robot to overcome motion singularity when negotiating T-junctions and further allows the robot to arrive in a preferred orientation while taking turns inside a pipe. The singularity region while negotiating T-junctions is analyzed to formulate the geometry of the region. The design and motion capabilities are validated by conducting simulations in MSC ADAMS on a simplified lumped-model of the robot