基于竞争双深度Q网络的动态频谱接入

针对多信道动态频谱接入问题，建立了存在感知错误与接入碰撞的复杂信道场景，提出了一种结合双深度Q网络和竞争Q网络的竞争双深度Q网络学习框架。双深度Q网络将动作的选择和评估分别用不同值函数实现，解决了值函数的过估计问题，而竞争Q网络解决了神经网络结构优化问题。该方案保证每个次要用户根据感知和回报结果做出频谱接入决策。仿真结果表明，在同时存在感知错误和次要用户冲突的多信道情况下，竞争双深度Q网络相比于同类方法具有较好的损失预测模型，其回报更稳定且提高了4%。     

基于深度强化学习的能量采集认知无线电动态频谱接入

为解决认知无线电(Cognitive Radio, CR)中频谱和能量短缺的问题，提出一种基于深度Q网络(Deep Q-Network, DQN)的动态频谱接入算法。次级用户(Secondary User, SU)通过基站射频信号采集能量，并在频谱感知后实现信道的自主接入。模型通过DQN训练，并使用奖励机制和训练算法优化，SU能够根据环境信息作出合适的接入策略。仿真结果表明，提出的深度强化学习(Deep Reinforcement Learning, DRL)模型性能优于无学习模型，提高了频谱感知准确率及用户吞吐量，对比结果证明了模型的适用性及合理的虚警率可以提升模型的学习性能。     

基于优先记忆库结合竞争深度Q网络的动态功率控制

针对认知无线电多用户的动态功率控制策略问题,提出了一种基于优先记忆库(Prioritized Experience Replay,PER)结合竞争深度Q网络（Dueling Deep Q Network,Dueling DQN）的功率控制方法。在不知道主用户的控制策略及发射功率情况下,次用户以下垫式接入到主用户信道进行传输任务。微基站收集到的接收信号强度信息作为环境状态信息输入到竞争深度Q网络中,经过训练和学习后,得到次用户的动态功率控制策略。实验结果表明,次用户经过深度强化学习后能够找到最优的功率控制策略,并且在环境参数发生变化时也能快速调整自身的行为和控制策略,提高了频谱利用率和网络能效。     

主用户活跃性对新的认知无线电系统容量的影响

在机会频谱接入认知无线电系统中,认知用户只有在通过感知确定信道空闲时才可以接入授权信道,因此频谱感知对于系统性能影响非常重要。本文提出了基于新帧结构的四元频谱感知模型,考虑主用户活跃性对认知网络吞吐量的影响,采用可同时最大化感知和数据传输时间的新帧结构模型,不需要考虑感知和吞吐量的均衡。理论分析新模型下感知时间,主用户活跃性,目标检测概率,主用户接收信噪比对系统吞吐量的影响,并与传统模型进行对比。     

Q学习算法在机会频谱接入信道选择中的应用

针对“先听后传”的机会频谱接入中认知用户的信道选择问题,本文提出了一种基于Q学习的信道选择算法。在非理想感知的条件下,通过建立认知用户的信道选择模型并设计恰当的奖励函数,使智能体能够与未知环境不断交互和学习,进而选择长期累积回报最大的信道接入。在学习过程中,本文引入了Boltzmann实验策略,运用模拟退火思想实现了资源探索与资源利用之间的折衷。仿真结果表明,所提算法能够在未知环境先验知识条件下可以快速选择性能较好的信道接入,有效提高认知用户的接入吞吐量和系统的平均容量。      

一种非理想感知条件下的双门限机会频谱接入策略

为了有效提高认知网络中次用户的吞吐率性能,提出了一种应用于非理想感知条件下的双门限机会频谱接入策略。该策略能在次用户对信道占用情况感知非理想的条件下,综合考虑次用户的信道质量和非准确的信道占用信息,使次用户在信道感知为空闲和占用时分别以不同的信道质量门限选择接入信道,从而最大程度地利用信道质量较好的传输机会,提高次用户的吞吐率性能。结合本策略,建立了以次用户的有效吞吐率最大化为目标、以对主用户的干扰程度为约束条件的优化问题,并在次用户对信道感知存在误差的条件下给出了最优门限的设计方法。仿真结果表明,所提出的双门限机会频谱接入策略能够在非理想感知条件下显著提高次用户的有效吞吐率。     

认知网络中基于用户带宽需求的频谱接入策略

针对认知网络中的频谱接入问题,基于多类用户不同带宽需求提出了一种新的频谱接入策略,即基于用户带宽的动态信道接入策略(BRDSA)。首次提出考虑拥有不同带宽需求的主用户和次用户利用频谱聚合技术进行频谱接入,而频谱聚合技术能够提高次用户对离散频谱的利用率。根据相关网络模型,利用排队理论和马尔科夫模型对整个系统状态进行了分析。仿真结果表明这种动态频谱接入策略极大地提高了频谱利用率。     

基于能量检测与信道质量评估的动态信道接入算法

在认知无线电通信中，动态的信道接入技术能够有效地解决频谱资源短缺和信道利用率低下的问题，它允许次级用户动态接入空闲信道，以进行数据的传输。针对多信道的认知无线电网络，本文提出一种基于能量检测与信道评估的动态信道接入方法，通过权衡感知精度与信道空闲时间，得出使信道质量最优的感知参数，在每一个信道中采用能量检测法，进行周期性检测，感知每一个信道中主用户的接入情况，判断可用信道集合，提高次级用户的动态接入效率。理论分析和结果表明，该方法有效降低了主次用户之间的接入冲突，增加了系统的可靠性和信道的利用率。     

感知不确定下卫星认知无线网络多频谱接入优化策略

针对卫星认知无线网络频谱感知不确定性较大导致传统频谱接入机制效率降低的问题,该文提出一种基于动态多频谱感知的信道接入优化策略。认知LEO卫星根据频谱检测概率与授权用户干扰门限之间的关系,实时调整不同频谱感知结果下的信道接入概率。在此基础上以系统吞吐量最大化为目标,设计了一种基于频谱检测概率和虚警概率联合优化的判决门限选取策略,并推导了最佳感知频谱数量。仿真结果表明,认知用户能够在不大于授权用户最大干扰门限的前提下,根据授权信道空闲状态动态选择最佳频谱感知策略,且在检测信号信噪比较低时以更加积极的方式接入授权频谱,降低了频谱感知不确定性对信道接入效率的影响,提高了认知系统吞吐量。     

等比缩减的机会频谱接入

重复使用一定的TV频段是机会频谱接入的一个典型应用。在认知无线电机会频谱接入中,为了最大化信道的利用,授权用户与认知用户相互协作共同利用信道。文章将授权用户对信道的占用过程模拟为连续时间马尔可夫过程。认知用户在限制其对授权用户的干扰影响条件下,利用等比缩减的策略对信道进行感知,提出一个最优频谱接入策略,提高频谱的利用率。     

Deep reinforcement learning-based resource reservation algorithm for emergency Internet-of-things slice

Based on the requirements of ultra-low latency services for emergency Internet-of-things (EIoT) applications,a multi-slice network architecture for ultra-low latency emergency IoT was designed,and a general methodology framework based on resource reservation,sharing and isolation for multiple slices was proposed.In the proposed framework,real-time and automatic inter-slice resource demand prediction and allocation were realized based on deep reinforcement learning (DRL),while intra-slice user resource allocation was modeled as a shape-based 2-dimension packing problem and solved with a heuristic numerical algorithm,so that intra-slice resource customization was achieved.Simulation results show that the resource reservation-based method enable EIoT slices to explicitly reserve resources,provide a better security isolation level,and DRL could guarantee accuracy and real-time updates of resource reservations.Compared with four existing algorithms,dueling deep Q-network (DQN) performes better than the benchmarks.     

一种利用优先经验回放深度Q-Learning的频谱接入算法

针对认知无线传感器网络中频谱接入算法的频谱利用率不高、重要经验利用率不足、收敛速度慢等问题,提出了一种采用优先经验回放双深度Q-Learning的动态频谱接入算法。该算法的次用户对经验库进行抽样时,采用基于优先级抽样的方式,以打破样本相关性并充分利用重要的经验样本,并采用一种非排序批量删除方式删除经验库的无用经验样本,以降低能量开销。仿真结果表明,该算法与采用双深度Q-Learning的频谱接入算法相比提高了收敛速度;与传统随机频谱接入算法相比,其阻塞概率降低了6%~10%,吞吐量提高了18%~20%,提高了系统的性能。     

Collaborative Spectrum Sensing for Illegal Drone Detection: A Deep Learning-Based Image Classification Perspective

Drones,also known as mini-unmanned aerial vehicles(UAVs),are enjoying great popularity in recent years due to their advantages of low cost,easy to pilot and small size,which also makes them hard to detect.They can provide real time situational awareness information by live videos or high definition pictures and pose serious threats to public security.In this article,we combine collaborative spectrum sensing with deep learning to effectively detect potential illegal drones with states of high uncertainty.First,we formulate the detection of potential illegal drones under illegitimate access and rogue power emission as a quaternary hypothesis test problem.Then,we propose an algorithm of image classification based on convolutional neural network which converts the cooperative spectrum sensing data at a sensing slot into one image.Furthermore,to exploit more information and improve the detection performance,we develop a trajectory classification algorithm which converts theflight process of the drones in consecutive multiple sensing slots into trajectory images.In addition,simulations are provided to verify the proposed methods’performance under various parameter configurations.     

认知无线电中频谱感知技术

固定频谱分配政策导致频谱使用率低下,为克服这一缺点,人们提出使用认知无线电技术实现动态频谱接入,从而有效利用频谱空穴。频谱感知是认知无线电的关键技术。文中介绍了目前已提出的频谱感知技术:发射机检测(包括匹配滤波器检测、能量检测和周期平稳特征检测)、合作检测、接收机检测及基于干扰温度的检测,仿真分析了各种方法的优缺点及有待解决的问题。为满足多用户环境和实时性要求,仍需探讨新的频谱感知方法,提高检测性能。     

基于循环平稳特征的频谱感知算法概述

传统的静态频谱资源分配分配政策导致频谱利用率低下,为解决这一问题,人们提出利用认知无线电技术实现动态频谱接入.频谱感知是认知无线电的关键技术,循环平稳特征检测算法是3种常见频谱检测算法之一,但是该算法存在各种不足.首先简要介绍认知无线电的背景和概念,然后详细介绍了循环平稳特征检测算法,以及目前提出的各种基于循环平稳特征检测的增强算法,分析了各自的原理及其优缺点.     

多信道频谱感知与频谱接入的优化与折衷

基于贝叶斯推理的多信道频谱感知方法和思想,文章通过多个认知用户随机地选择部分信道进行协作感知并利用特殊设计的贝叶斯推理法则来快速有效地获取所有信道的活动状态。贝叶斯推理的多信道频谱感知方法也是多分辨率频谱感知的基础,具有重要的应用价值。文章还通过分析多用户多信道条件下频谱感知和频谱接入之间复杂而微妙的内在联系,对其进行适当地优化与折衷,以提高认知系统的性能和效率。     

Spectrum redistribution for cognitive radios using discriminatory spectrum double auction

With the reformation of spectrum policy and the development of cognitive radio, secondary users will be allowed to access spectrums licensed to primary users. Spectrum auctions can facilitate this secondary spectrum access in a market‐driven way. To design an efficient auction framework, we first study the supply and demand pressures and the competitive equilibrium of the secondary spectrum market, considering the spectrum reusability. In well‐designed auctions, competition among participants should lead to the competitive equilibrium according to the traditional economic point of view. Then, a discriminatory price spectrum double auction framework is proposed for this market. In this framework, rational participants compete with each other by using bidding prices, and their profits are guaranteed to be non‐negative. A near‐optimal heuristic algorithm is also proposed to solve the auction clearing problem of the proposed framework efficiently. Experimental results verify the efficiency of the proposed auction clearing algorithm and demonstrate that competition among secondary users and primary users can lead to the competitive equilibrium during auction iterations using the proposed auction framework. Copyright © 2011 John Wiley & Sons, Ltd.     

Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios

Spectrum sensing is a key technology for cognitive radios.We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification.We normalize the received signal power to overcome the effects of noise power uncertainty.We train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new signals.We also use transfer learning strategies to improve the performance for real-world signals.Extensive experiments are conducted to evaluate the performance of this method.The simulation results show that the proposed method performs better than two traditional spectrum sensing methods,i.e.,maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based method.In addition,the experimental results of the new untrained signal types show that our method can adapt to the detection of these new signals.Furthermore,the real-world signal detection experiment results show that the detection performance can be further improved by transfer learning.Finally,experiments under colored noise show that our proposed method has superior detection performance under colored noise,while the traditional methods have a significant performance degradation,which further validate the superiority of our method.