Centralizers in semisimple algebras, and descent spectrum in Banach algebras

AbstractWe prove that semisimple algebras containing some algebraic element whose centralizer is semiperfect are artinian. As a consequence, semisimple complex Banach algebras containing some element whose centralizer is algebraic are finite-dimensional. This answers affirmatively a question raised in Burgos et al. (2006) [4], and is applied to show that an element a in a semisimple complex Banach algebra A does not perturb the descent spectrum of every element commuting with a if and only if some of power of a lies in the socle of A. This becomes a Banach algebra version of a theorem in Burgos et al. (2006) [4], Kaashoek and Lay (1972) [9] for bounded linear operators on complex Banach spaces

Concurrent bandits and cognitive radio networks

We consider the problem of multiple users targeting the arms of a single multi-armed stochastic bandit. The motivation for this problem comes from cognitive radio networks, where selfish users need to coexist without any side communication between them, implicit cooperation or common control. Even the number of users may be unknown and can vary as users join or leave the network. We propose an algorithm that combines an $\epsilon$-greedy learning rule with a collision avoidance mechanism. We analyze its regret with respect to the system-wide optimum and show that sub-linear regret can be obtained in this setting. Experiments show dramatic improvement compared to other algorithms for this setting

Multi-Objective and Financial Portfolio Optimization of Carrier-Sense Multiple Access Protocols with Cooperative Diversity

8th International Workshop on Multiple Access Communications (MACOM2015), Helsinki, Finland.This paper presents a trade-off design and optimization of a class of wireless carrier-sense multiple access protocols where collision-free transmissions are assisted by the potential cooperative retransmissions of inactive terminals with a correct copy of the original transmission. Terminals are enabled with a decode-and-forward relaying protocol. The analysis is focused on asymmetrical settings, where terminals experience different channel and queuing statistics. This work is based on multi-objective and financial portfolio optimization tools. Each packet transmission is thus regarded not only as a network resource, but also as a financial asset with different values of return and risk (or variance of the return). The objective of this financial optimization is to find the transmission policy that simultaneously maximizes return and minimizes risk in the network. The work is focused on the characterization of the boundaries (envelope) of different types of trade-off performance regions: the conventional throughput region, sum-throughput vs. fairness, sum-throughput vs. power, and return vs. risk regions. Fairness is evaluated by means of the Gini-index, which is a metric commonly used in economics to measure income inequality. Transmit power is directly linked to the global transmission rate. The protocol is shown to outperform non-cooperative solutions under different network conditions that are here discussed

A joint scheduling and content caching scheme for energy harvesting access points with multicast

© 2017 IEEE. In this work, we investigate a system where users are served by an access point that is equipped with energy harvesting and caching mechanism. Focusing on the design of an efficient content delivery scheduling, we propose a joint scheduling and caching scheme. The scheduling problem is formulated as a Markov decision process and solved by an on-line learning algorithm. To deal with large state space, we apply the linear approximation method to the state-Action value functions, which significantly reduces the memory space for storing the function values. In addition, the preference learning is incorporated to speed up the convergence when dealing with the requests from users that have obvious content preferences. Simulation results confirm that the proposed scheme outperforms the baseline scheme in terms of convergence and system throughput, especially when the personal preference is concentrated to one or two contents

Task-Oriented Delay-Aware Multi-Tier Computing in Cell-free Massive MIMO Systems

Multi-tier computing can enhance the task computation by multi-tier computing nodes. In this paper, we propose a cell-free massive multiple-input multiple-output (MIMO) aided computing system by deploying multi-tier computing nodes to improve the computation performance. At first, we investigate the computational latency and the total energy consumption for task computation, regarded as total cost. Then, we formulate a total cost minimization problem to design the bandwidth allocation and task allocation, while considering realistic heterogenous delay requirements of the computational tasks. Due to the binary task allocation variable, the formulated optimization problem is non-convex. Therefore, we solve the bandwidth allocation and task allocation problem by decoupling the original optimization problem into bandwidth allocation and task allocation subproblems. As the bandwidth allocation problem is a convex optimization problem, we first determine the bandwidth allocation for given task allocation strategy, followed by conceiving the traditional convex optimization strategy to obtain the bandwidth allocation solution. Based on the asymptotic property of received signal-to-interference-plus-noise ratio (SINR) under the cell-free massive MIMO setting and bandwidth allocation solution, we formulate a dual problem to solve the task allocation subproblem by relaxing the binary constraint with Lagrange partial relaxation for heterogenous task delay requirements. At last, simulation results are provided to demonstrate that our proposed task offloading scheme performs better than the benchmark schemes, where the minimum-cost optimal offloading strategy for heterogeneous delay requirements of the computational tasks may be controlled by the asymptotic property of the received SINR in our proposed cell-free massive MIMO-aided multi-tier computing systems.This work was supported by the National Key Project under Grant 2020YFB1807700

Revolutionizing biological digital twins: Integrating internet of bio-nano things, convolutional neural networks, and federated learning.

Digital twins (DTs) are advancing biotechnology by providing digital models for drug discovery, digital health applications, and biological assets, including microorganisms. However, the hypothesis posits that implementing micro- and nanoscale DTs, especially for biological entities like bacteria, presents substantial challenges. These challenges stem from the complexities of data extraction, transmission, and computation, along with the necessity for a specialized Internet of Things (IoT) infrastructure. To address these challenges, this article proposes a novel framework that leverages bio-network technologies, including the Internet of Bio-Nano Things (IoBNT), and decentralized deep learning algorithms such as federated learning (FL) and convolutional neural networks (CNN). The methodology involves using CNNs for robust pattern recognition and FL to reduce bandwidth consumption while enhancing security. IoBNT devices are utilized for precise microscopic data acquisition and transmission, which ensures minimal error rates. The results demonstrate a multi-class classification accuracy of 98.7% across 33 bacteria categories, achieving over 99% bandwidth savings. Additionally, IoBNT integration reduces biological data transfer errors by up to 98%, even under worst-case conditions. This framework is further supported by an adaptable, user-friendly dashboard, expanding its applicability across pharmaceutical and biotechnology industries

Cyberattack detection in mobile cloud computing: A deep learning approach

© 2018 IEEE. With the rapid growth of mobile applications and cloud computing, mobile cloud computing has attracted great interest from both academia and industry. However, mobile cloud applications are facing security issues such as data integrity, users' confidentiality, and service availability. A preventive approach to such problems is to detect and isolate cyber threats before they can cause serious impacts to the mobile cloud computing system. In this paper, we propose a novel framework that leverages a deep learning approach to detect cyberattacks in mobile cloud environment. Through experimental results, we show that our proposed framework not only recognizes diverse cyberattacks, but also achieves a high accuracy (up to 97.11%) in detecting the attacks. Furthermore, we present the comparisons with current machine learning-based approaches to demonstrate the effectiveness of our proposed solution

Joint Trajectory and Beamforming Optimization for Secure UAV Transmission Aided by IRS

Unmanned aerial vehicle (UAV) communications are susceptible to eavesdropping, and intelligent reflecting surface (IRS) is capable of reconfiguring the propagation environment, thereby facilitating the security for UAV networks. In this paper, we aim to maximize the average secrecy rate for an IRS-assisted UAV network by jointly optimizing the UAV trajectory, the transmit beamforming, and the phase shift of IRS. The complex problem is decomposed into three subproblems and solved via an iterative algorithm. First, the closed-form solution to the active beamforming is derived. Then, the passive beamforming of fractional programming is converted into corresponding parametric sub-problems. Finally, the trajectory problem is reformulated as a convex one by utilizing the successive convex approximation. Simulation results are provided to validate the effectiveness of the proposed scheme

The tradeoff analysis in RF-powered backscatter cognitive radio networks

© 2016 IEEE. In this paper, we introduce a new model for RF-powered cognitive radio networks with the aim to improve the performance for secondary systems. In our proposed model, when the primary channel is busy, the secondary transmitter is able either to backscatter the primary signals to transmit data to the secondary receiver or to harvest RF energy from the channel. The harvested energy then will be used to transmit data to the receiver when the channel becomes idle. We first analyze the tradeoff between backscatter communication and harvest-then-transmit protocol in the network. To maximize the overall transmission rate of the secondary network, we formulate an optimization problem to find time ratio between taking backscatter and harvest-thentransmit modes. Through numerical results, we show that under the proposed model can achieve the overall transmission rate higher than using either the backscatter communication or the harvest-then-transmit protocol