Energy-Efficiency Maximization for a WPT-D2D Pair in a MISO-NOMA Downlink Network

The combination of non-orthogonal multiple access (NOMA) and wireless power transfer (WPT) is a promising solution to enhance the energy efficiency of Device-to-Device (D2D) enabled wireless communication networks. In this paper, we focus on maximizing the energy efficiency of a WPT-D2D pair in a multiple-input single-output (MISO)-NOMA downlink network, by alternatively optimizing the beamforming vectors of the base station (BS) and the time switching coefficient of the WPT assisted D2D transmitter. The formulated energy efficiency maximization problem is non-convex due to the highly coupled variables. To efficiently address the non-convex problem, we first divide it into two subproblems. Afterwards, an alternating algorithm based on the Dinkelbach method and quadratic transform is proposed to solve the two subproblems iteratively. To verify the proposed alternating algorithm's accuracy, partial exhaustive search algorithm is proposed as a benchmark. We also utilize a deep reinforcement learning (DRL) method to solve the non-convex problem and compare it with the proposed algorithm. To demonstrate the respective superiority of the proposed algorithm and DRL-based method, simulations are performed for two scenarios of perfect and imperfect channel state information (CSI). Simulation results are provided to compare NOMA and orthogonal multiple access (OMA), which demonstrate the superior performance of energy efficiency of the NOMA scheme

A BAC-NOMA Design for 6 G umMTC With Hybrid SIC: Convex Optimization or Learning-Based?

This paper presents a new backscattering communication (BackCom)-assisted non-orthogonal multiple access (BAC-NOMA) transmission scheme for device-to-device (D2D) communications. This scheme facilitates energy and spectrum cooperation between BackCom devices and cellular downlink users in 6 G ultra-massive machine -type communications (umMTC) scenarios. Given its quasi-uplink nature, the hybrid successive interference cancellation (SIC) is applied to further improve performance. The data rate of BackCom devices with high quality of service (QoS) requirements is maximized by jointly optimizing backscatter coefficients and the beamforming vector. The use of hybrid SIC and BackCom yields two non-concave sub-problems involving transcendental functions. To address this problem, this paper designs and compares convex optimization-based and unsupervised deep learning-based algorithms. In the convex optimization, the closed-form backscatter coefficients of the first sub-problem are obtained, and then semi-definite relaxation (SDR) is utilized to design the beamforming vector. On the other hand, the second sub-problem is approximated by using a combination of sequential convex approximation (SCA) and SDR. For unsupervised deep learning-based optimization, a loss function is properly designed to satisfy constraints. Computer simulations show the following instructive results: i) the superiority of the hybrid SIC strategy; ii) the distinct sensitivities and efficacies of these two algorithms in response to varying parameters; iii) the superior robustness of the unsupervised deep learning-based optimization

The Global Landscape of Neural Networks: An Overview

One of the major concerns for neural network training is that the non-convexity of the associated loss functions may cause bad landscape. The recent success of neural networks suggests that their loss landscape is not too bad, but what specific results do we know about the landscape? In this article, we review recent findings and results on the global landscape of neural networks. First, we point out that wide neural nets may have sub-optimal local minima under certain assumptions. Second, we discuss a few rigorous results on the geometric properties of wide networks such as "no bad basin", and some modifications that eliminate sub-optimal local minima and/or decreasing paths to infinity. Third, we discuss visualization and empirical explorations of the landscape for practical neural nets. Finally, we briefly discuss some convergence results and their relation to landscape results.Comment: 16 pages. 8 figure

GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning

In this work, we first formulate the problem of robotic water scooping using goal-conditioned reinforcement learning. This task is particularly challenging due to the complex dynamics of fluids and the need to achieve multi-modal goals. The policy is required to successfully reach both position goals and water amount goals, which leads to a large convoluted goal state space. To overcome these challenges, we introduce Goal Sampling Adaptation for Scooping (GOATS), a curriculum reinforcement learning method that can learn an effective and generalizable policy for robot scooping tasks. Specifically, we use a goal-factorized reward formulation and interpolate position goal distributions and amount goal distributions to create curriculum throughout the learning process. As a result, our proposed method can outperform the baselines in simulation and achieves 5.46% and 8.71% amount errors on bowl scooping and bucket scooping tasks, respectively, under 1000 variations of initial water states in the tank and a large goal state space. Besides being effective in simulation environments, our method can efficiently adapt to noisy real-robot water-scooping scenarios with diverse physical configurations and unseen settings, demonstrating superior efficacy and generalizability. The videos of this work are available on our project page: https://sites.google.com/view/goatscooping

Evidence for Majorana bound state in an iron-based superconductor

The search for Majorana bound state (MBS) has recently emerged as one of the most active research areas in condensed matter physics, fueled by the prospect of using its non-Abelian statistics for robust quantum computation. A highly sought-after platform for MBS is two-dimensional topological superconductors, where MBS is predicted to exist as a zero-energy mode in the core of a vortex. A clear observation of MBS, however, is often hindered by the presence of additional low-lying bound states inside the vortex core. By using scanning tunneling microscope on the newly discovered superconducting Dirac surface state of iron-based superconductor FeTe1-xSex (x = 0.45, superconducting transition temperature Tc = 14.5 K), we clearly observe a sharp and non-split zero-bias peak inside a vortex core. Systematic studies of its evolution under different magnetic fields, temperatures, and tunneling barriers strongly suggest that this is the case of tunneling to a nearly pure MBS, separated from non-topological bound states which is moved away from the zero energy due to the high ratio between the superconducting gap and the Fermi energy in this material. This observation offers a new, robust platform for realizing and manipulating MBSs at a relatively high temperature.Comment: 27 pages, 11 figures, supplementary information include

Nearly quantized conductance plateau of vortex zero mode in an iron-based superconductor

Majorana zero-modes (MZMs) are spatially-localized zero-energy fractional quasiparticles with non-Abelian braiding statistics that hold a great promise for topological quantum computing. Due to its particle-antiparticle equivalence, an MZM exhibits robust resonant Andreev reflection and 2e2/h quantized conductance at low temperature. By utilizing variable-tunnel-coupled scanning tunneling spectroscopy, we study tunneling conductance of vortex bound states on FeTe0.55Se0.45 superconductors. We report observations of conductance plateaus as a function of tunnel coupling for zero-energy vortex bound states with values close to or even reaching the 2e2/h quantum conductance. In contrast, no such plateau behaviors were observed on either finite energy Caroli-de Genne-Matricon bound states or in the continuum of electronic states outside the superconducting gap. This unique behavior of the zero-mode conductance reaching a plateau strongly supports the existence of MZMs in this iron-based superconductor, which serves as a promising single-material platform for Majorana braiding at a relatively high temperature