一种基于授权信道特性的认知无线电频谱检测算法

针对认知无线电系统中频谱检测的频率直接影响系统容量以及与授权用户产生冲突的概率问题,分析了授权用户频谱使用的特性, 对授权用户行为进行统计建模, 提出一种自适应频谱检测算法. 引入控制因子, 在保证认知无线电系统稳定性的约束下, 自适应调整频谱感知的频率从而提高频谱利用率并减小系统冲突概率和检测开销, 进而降低了系统的能量消耗. 仿真结果表明, 该算法在保证不对授权用户产生干扰和一定的系统稳定性条件下, 有效地提高了系统的容量,并且具有良好的实用性和灵活性. 关键词： 认知无线电 自适应频谱检测 绿色通信 最大似然     

基于二进制粒子群算法的认知无线电决策引擎

提出了基于粒子群算法的认知无线电决策引擎,并提出了一种种群自适应粒子群算法,利用粒子群算法调整优化无线电参数,运用多载波系统对算法性能进行了仿真分析.实验结果表明基于二进制粒子群算法的认知决策引擎在收敛速度、收敛精度和算法稳定性上都要明显优于经典遗传算法,基于种群自适应粒子群算法的决策引擎则能进一步提高算法初期性能,满足认知无线电实时性要求. 关键词： 认知无线电 粒子群算法 遗传算法 认知决策引擎     

混沌免疫多目标算法求解认知引擎参数优化问题

合理的认知引擎参数设置可以提高频谱的使用性能. 通过分析认知无线网络中的认知引擎参数配置, 给出了其数学模型, 并将其转化为一个多目标优化问题, 进而提出一种基于混沌免疫多目标优化的求解方法. 算法使用Logistic混沌映射初始化种群, 并在每一代将混沌特性用于最优解集的搜索; 设计了适合此问题的免疫克隆算子和抗体群更新算子, 保证了Pateto最优解集分布的多样性和均匀性. 最后, 在多载波环境下对算法进行了仿真实验. 结果表明, 算法可以根据信道条件和用户服务的动态变化, 自适应调整各个子载波的发射功率和调制方式, 可以求出更多满足偏好需求的解, 满足认知引擎参数优化要求.     

混沌量子克隆优化求解认知无线网络决策引擎

通过分析认知无线网络引擎决策, 给出了其数学模型, 并将其转化为一个多目标优化问题, 进而提出一种基于混沌量子克隆的优化求解算法, 并证明了该算法以概率1收敛. 算法采用量子编码, 利用Logistic映射初始化抗体种群, 设计了一种基于混沌扰动的量子变异方案. 最后, 在多载波环境下对算法进行了仿真实验. 结果表明, 与QGA-CE(基于量子遗传算法的认知引擎)算法相比, 本文算法收敛速度较快, 具有较高的目标函数值, 可以对无线参数优化调整, 满足认知引擎的实时性要求.     

HLS-II移动端数据查询系统开发

合肥光源（HLS-II）是以真空紫外和软X射线为主的专用同步辐射光源,作为用户装置,对运行性能有很高的要求。为提高数据查询的实时性与便捷性,满足工作人员及时掌握装置运行状态的需求,基于Web技术开发了HLS-II移动端数据查询系统。该系统在EPICS环境下进行开发,以IOC作为实时数据源,以HBase数据库作为历史数据源,以Phoebus Alarms作为报警数据源,以MySQL数据库存储用户管理信息。整个系统采用前后端分离的模式进行设计,系统后端采用Spring Boot框架和Node.js环境进行开发;系统前端以Vue.js框架开发,使用lib-flexible弹性布局方案和postcss-pxtorem插件,以适配不同种类的移动设备。测试表明,HLS-II移动端数据查询系统信息更新流畅,操作直观方便,达到了设计要求。     

基于量子遗传算法的认知无线电决策引擎研究

提出了基于量子遗传算法的认知无线电决策引擎，设计了待优化的多目标函数，利用量子遗传算法调整优化无线电参数，运用多载波系统对算法性能进行了仿真分析.实验结果表明该方法在收敛速度、收敛精度和算法稳定性上都明显优于经典遗传算法，在种群规模较小时仍然能获得很好性能，适合于实际实现.不同权重设置模式下仿真结果表明该方法能够在多个目标函数间进行权衡，参数调整结果与当前对目标函数的偏好一致.     

基于信任度的双门限协作频谱感知算法

提出了一种兼顾认知无线电系统可靠性和低负载的基于信任度的双门限协作频谱检测算法.系统首先使满足双门限要求的认知节点参与协作感知,当满足双门限要求的认知节点数目不足时,增加满足信任度参数要求的认知节点参与协作感知.融合中心存储了认知节点的检测记录,并以此为局部检测结果设置融合权重.理论分析和仿真结果表明,该算法所需传输的感知参数减少了,占用的信道带宽降低.同时,由于不可靠用户的减少,算法的检测性能进一步提高了.此外,算法通过调整参数nt使系统适应于不同类型的无线业务,具有一定的灵活性.     

基于软件定义量子通信的自由空间量子通信信道参数自适应调整策略

自由空间量子通信会受到雾霾、沙尘、降雨等自然环境的干扰.为提升环境干扰下量子通信的性能,本文提出了基于软件定义量子通信(software defined quantum communication, SDQC)的自由空间量子通信信道参数自适应调整策略.该策略通过对环境状态实时监测,根据预置在应用层的程序,对量子初始状态及单量子态存在时间等相关参数进行自适应调整,提高自然环境背景干扰下自由空间量子通信系统的保真度.仿真结果表明,在退极化、自发幅度衰变及相位阻尼三种噪声信道参数取值不同时, SDQC系统参数的最佳取值也不同.系统根据环境变化及业务需求,自适应地选择量子初始状态及单量子态存在时间,使量子保真度在通信过程中始终保持在峰值,有效提升了量子通信系统的适应能力及综合免疫力.     

认知无线电网络中基于抢占式排队论的频谱切换模型

针对认知无线电网络中认知用户广义传输时间的优化问题, 提出了一种基于抢占式续传优先权M/G/m排队理论的频谱切换模型. 在该排队模型中, 为了最小化认知用户广义传输时间, 采用混合排队-并列式服务的排队方式. 在此基础上, 深入分析多个认知用户、多个授权信道、多次频谱切换条件下认知用户信道使用情况, 从而推导出广义传输时间表达式. 最后探讨了该模型下自适应频谱切换策略. 仿真结果表明, 相比于已有的频谱切换模型, 该模型不仅能够更加完整地描述认知用户频谱切换行为, 而且使得认知用户传输时延更小, 广义传输时间更短. 此外, 认知无线电网络允许的认知用户服务强度增加, 能够容纳的认知用户数量增多. 因此, 该模型提升了认知用户频谱切换的性能, 更好地实现了认知用户与授权用户的频谱共享.     

无线认知网络中拓扑控制方法研究

作为下一代通信网络,无线认知网络已成为当前的研究热点。由于节点的移动性,无线网络拓扑结构动态变化,拓扑控制一直是无线网络的难点问题。通过借鉴移动自组织网络(MANETS)中的拓扑控制方法,提出了无线认知网络中基于博弈论和认知功能相结合的拓扑控制方法。无线认知节点能够通过主动决策调节自身节点位置,在保证网络连通性的基础上实现网络覆盖面积最大。仿真实验结果验证了方法的有效性和收敛性。     

Benefits brought by cognitive radio for the next generation cellular networks: A perspective from industry

With the explosion of network traffic in the future IMT-advanced system, the revenue for mobile operators is not increasing anywhere near as fast as the network traffic. This means that operators must innovate, bring costs down, and leverage their networks as much as possible, given already significant investments made. Cognitive radio will solve such economic challenges on deployment and maintenance cost with two aspects. One is related to flexible spectrum usage with the used frequency range, coverage and the backbone network, such as TV white space usage. The other is that cognitive radio improves the next generation cellular network from channel adaptive to be environment aware, as in Self-Optimized Networks (SON). Cognitive radio will make the mobile communication paradigm become more and more personal with higher spectrum utilization efficiency in multiple dimensions than in the past. In this paper, we mainly focus on the benefit brought by cognitive radio for the next generation cellular networks, such as Long Term Evolution advanced or 802.16 m, and how to achieve this on application solutions and techniques. We present our initial results on these key techniques. We expect this paper to ignite the further enhanced topics on cognitive radio in IMT-advanced research and standard activities.     

Spectrum sensing in cognitive radio networks and metacognition for dynamic spectrum sharing between radar and communication system: A review

The massive growth in mobile users and wireless technologies has resulted in increased data traffic and created demand for additional radio spectrum. This growing demand for radio spectrum has resulted in spectrum congestion and mandated the need for coexistence between radar and interfering communication emitters. To address the aforementioned issues, it is critical to review existing policies and evaluate new technologies that can utilize spectrum in an efficient and intelligent manner. Cognitive radio and cognitive radar are two promising technologies that exploit spectrum using dynamic spectrum access techniques. Additionally, introducing the bio-inspired concept ‘metacognition’ in a cognitive process has shown to increase the effectiveness and robustness of the cognitive radio and cognitive radar system. Metacognition is a high-order thinking agent that monitors and regulates the cognition process through a feedback and control process called the perception–action cycle. Extensive research has been done in the field of spectrum sensing in cognitive radio and spectral coexistence between radar and communication systems. This paper provides a detailed classification of spectrum sensing schemes and explains how dynamic spectrum access strategies share the spectrum between radar and communication systems. In addition to this, the fundamentals of cognitive radio, its architecture, spectrum management framework, and metacognition concept in radar are discussed. Furthermore, this paper presents various research issues, challenges, and future research directions associated with spectrum sensing in cognitive radar and dynamic spectrum access strategies in cognitive radar.     

Random neural network based novel decision making framework for optimized and autonomous power control in LTE uplink system

This paper presents a novel decision making framework for cognitive radio networks. The traditional continuous process of sensing, analysis, reasoning, and adaptation in a cognitive cycle has been divided into two levels. In the first level, the process of sensing and adaptation runs over the radio transmission hardware during run-time. In the second level, the process of analysis and reasoning runs in the background in offline mode. This arrangement offloads the convergence time and complexity problem of reasoning process during run-time. For implementation of the first level, a random neural network (RNN) based controller trained on an open loop case based database on the cloud has been designed. For the second level, a genetic algorithm (GA) based reasoning and an RNN based learning has been developed. The proposed framework is used to address the uplink power control problem of long-term evolution (LTE) system. The performance of RNN is compared with artificial neural network (ANN) and state-of-the-art fractional power control (FPC) scheme in terms of essential cognitive engine (CE) design requirements, capacity, and coverage optimization (CCO). The simulation results have shown that RNN based CE can achieve comparable results with faster adaptation, even subject to severe environment changes without the need of retraining.     

Distributed dynamic spectrum access through multi-agent deep recurrent Q-learning in cognitive radio network

This paper addresses the problem of distributed dynamic spectrum access in a cognitive radio (CR) environment utilizing deep recurrent reinforcement learning. Specifically, the network consists of multiple primary users (PU) transmitting intermittently in their respective channels, while the secondary users (SU) attempt to access the channels when PUs are not transmitting. The problem is challenging considering the decentralized nature of CR network where each SU attempts to access a vacant channel, without coordination with other SUs, which result in collision and throughput loss. To address this issue, a multi-agent environment is considered where each of the SUs perform independent reinforcement learning to learn the appropriate policy to transmit opportunistically so as to minimize collisions with other users. In this article, we propose two long short-term memory (LSTM) based deep recurrent Q-network (DRQN) architectures for exploiting the temporal correlation in the transmissions by various nodes in the network. Furthermore, we investigate the effect of the architecture on success rate with varying number of users in the network and partial channel observations. Simulation results are compared with other existing reinforcement learning based techniques to establish the superiority of the proposed method.     

Deep transfer reinforcement learning for resource allocation in hybrid multiple access systems

This paper proposes a resource allocation scheme for hybrid multiple access involving both orthogonal multiple access and non-orthogonal multiple access (NOMA) techniques. The proposed resource allocation scheme employs multi-agent deep reinforcement learning (MA-DRL) to maximize the sum-rate for all users. More specifically, the MA-DRL-based scheme jointly allocates subcarrier and power resources for users by utilizing deep Q networks and multi-agent deep deterministic policy gradient networks. Meanwhile, an adaptive learning determiner mechanism is introduced into our allocation scheme to achieve better sum-rate performance. However, the above deep reinforcement learning adopted by our scheme cannot optimize parameters quickly in the new communication model. In order to better adapt to the new environment and make the resource allocation strategy more robust, we propose a transfer learning scheme based on deep reinforcement learning (T-DRL). The T-DRL-based scheme allows us to transfer the subcarrier allocation network and the power allocation network collectively or independently. Simulation results show that the proposed MA-DRL-based resource allocation scheme can achieve better sum-rate performance. Furthermore, the T-DRL-based scheme can effectively improve the convergence speed of the deep resource allocation network.     

Resource optimization for UAV-assisted mobile edge computing system based on deep reinforcement learning

Computational efficiency is a direction worth considering in moving edge computing (MEC) systems. However, the computational efficiency of UAV-assisted MEC systems is rarely studied. In this paper, we maximize the computational efficiency of the MEC network by optimizing offloading decisions, UAV flight paths, and allocating users’ charging and offloading time reasonably. The method of deep reinforcement learning is used to optimize the resources of UAV-assisted MEC system in complex urban environment, and the user’s computation-intensive tasks are offloaded to the UAV-mounted MEC server, so that the overloaded tasks in the whole system can be alleviated. We study and design a framework algorithm that can quickly adapt to task offload decision making and resource allocation under changing wireless channel conditions in complex urban environments. The optimal offloading decisions from state space to action space is generated through deep reinforcement learning, and then the user’s own charging time and offloading time are rationally allocated to maximize the weighted sum computation rate. Finally, combined with the radio map to optimize the UAC trajectory to improve the overall weighted sum computation rate of the system. Simulation results show that the proposed DRL+TO framework algorithm can significantly improve the weighted sum computation rate of the whole MEC system and save time. It can be seen that the MEC system resource optimization scheme proposed in this paper is feasible and has better performance than other benchmark schemes.     

An adaptive step-size spectrum auction mechanism for two-tier heterogeneous networks

Recently, with the rapid growth of demands for wireless communications, dynamic spectrum allocation is one of the key technologies in cognitive radio networks to resolve the realistic problem of low utilization efficiency of spectrum. It mainly focuses on how the spectrum owner dynamically allocates idle spectrum to secondary users who have no licensed spectrum for communications. In this paper, a dynamic spectrum allocation model based on auction theory in a two-tier heterogeneous network is proposed, in which the primary users (PUs) are the sellers, the central processor (CP) auctioneer is the coordinator, and femtocell base station (FBS) as the buyer bids for the idle spectrum and act as a wireless access point that provides communication services for secondary users (SUs). Its basic process is as follows: the auctioneer gradually raises the spectrum price from the reserved price; each bidder decides whether participates in the purchase or not. It is characterized by distributed execution and low complexity which can reduce unnecessary information exchange between primary users or secondary users. Meanwhile it can enhance the utilization of spectrum and improve the efficiency of the auction by generate the incentive mechanism.     

Wireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems

In this paper, the optimization of network performance to support the deployment of federated learning (FL) is investigated. In particular, in the considered model, each user owns a machine learning (ML) model by training through its own dataset, and then transmits its ML parameters to a base station (BS) which aggregates the ML parameters to obtain a global ML model and transmits it to each user. Due to limited radio frequency (RF) resources, the number of users that participate in FL is restricted. Meanwhile, each user uploading and downloading the FL parameters may increase communication costs thus reducing the number of participating users. To this end, we propose to introduce visible light communication (VLC) as a supplement to RF and use compression methods to reduce the resources needed to transmit FL parameters over wireless links so as to further improve the communication efficiency and simultaneously optimize wireless network through user selection and resource allocation. This user selection and bandwidth allocation problem is formulated as an optimization problem whose goal is to minimize the training loss of FL. We first use a model compression method to reduce the size of FL model parameters that are transmitted over wireless links. Then, the optimization problem is separated into two subproblems. The first subproblem is a user selection problem with a given bandwidth allocation, which is solved by a traversal algorithm. The second subproblem is a bandwidth allocation problem with a given user selection, which is solved by a numerical method. The ultimate user selection and bandwidth allocation are obtained by iteratively compressing the model and solving these two subproblems. Simulation results show that the proposed FL algorithm can improve the accuracy of object recognition by up to 16.7% and improve the number of selected users by up to 68.7%, compared to a conventional FL algorithm using only RF.