Spectrum Resource Management of Cognitive Radio

As the most important technology of CR, the wireless spectrum resource management technology is the key to CR performance improvement. By introducing the concept of resource space to describe wireless spectrum resource management in the field of CR technology, a data system of wireless resource management is formed that covers wireless spectrum resource space, resource grid and available resource atlas. Besides, the corresponding lamination distributional management structure and the resource management database are constructed. The resources description system and the management structure will become the theoretical concept foundation and reference of the CR spectrum resources management technology.

A DYNAMIC SPECTRUM ACCESS NETWORK BASED ON COGNITIVE RADIO

Dynamic spectrum access technologies based on Cognitive Radio(CR) is under intensive research carried out by the wireless communication society and is expected to solve the problem of spectrum scarcity.However,most enabling technologies related to dynamic spectrum access are con-sidered individually.In this paper,we consider these key technologies jointly and introduce a new implementation scheme for a Dynamic Spectrum Access Network Based on Cognitive Radio(DSAN-BCR).We start with a flexible hardware platform for DSAN-BCR,as well as a flexible protocol structure that dominates the operation of DSAN-BCR.We then focus on the state of the art of key technologies such as spectrum sensing,spectrum resources management,dynamic spectrum access,and routing that are below the network layer in DSAN-BCR,as well as the development of technologies related to higher layers.Last but not the least,we analyze the challenges confronted by these men-tioned technologies in DSAN-BCR,and give the perspectives on the future development of these technologies.The DSAN-BCR introduced is expected to provide a system level guidance to alleviate the problem of spectrum scarcity.

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.

The Novel Spectrum Allocation Algorithm in Cognitive Radio System

In order to reduce the disturbance on an authorizing user and lower the competition between cognitive users, assure the normal communication of a cognitive radio system, reliability theory is applied to describe if a channel can be used by a cognitive user or not and the probability that the channel is continually used for a period. Three aspects including space, time domain and frequency domain are united for the research on the distribution of frequency spectrum. The simulation result shows that, in the space domain, time domain, frequency domain algorithm, the transmitted data volume and the total throughput of the system are superior to those in greedy algorithm and time domain—frequency domain algorithm, the novel algorithm is helpful to reduce the disturbance caused by a cognitive user to an authorizing user and lower the competition between cognitive users, this simulation result shows that the proposed algorithm is effective.

Joint power and spectrum allocation algorithm in cognitive radio networks

The spectrum sharing problem between primary and cognitive users is mainly investigated. Since the interference for primary users and the total power for cognitive users are constrained, based on the well-known water-filling theorem, a novel one-user water-filling algorithm is proposed, and then the corresponding simulation results are given to analyze the feasibility and validity. After that this algorithm is used to solve the communication utility optimization problem subject to the power constraints in cognitive radio network. First, through the gain to noise ratio for cognitive users, a subcarrier and power allocation algorithm based on the optimal frequency partition is proposed for two cognitive users. Then the spectrum sharing algorithm is extended to multiuser conditions such that the greedy and parallel algorithms are proposed for spectrum sharing. Theory and simulation analysis show that the subcarrier and power allocation algorithms can not only protect the primary users but also effectively solve the spectrum and power allocation problem for cognitive users.

Relay Selection and Power Allocation in Amplify-and-Forward Cognitive Radio Systems Based on Spectrum Sharing

In this paper, we consider a spectrum sharing scheme that is a joint optimization of relay selection and power allocation at the secondary transmitter, which aims to achieve the maximum possible throughput for the secondary user. This paper considers the scenario where the primary user is incapable of supporting its target signal-to-noise ratio (SNR). More especially, the secondary transmitter tries to assist the primary user with achieving its target SNR by cooperative amplify-and-forward (AF) relaying with two-phase. By exhaustive search for all candidate secondary transmitters, an optimal secondary transmitter can be selected, which not only can satisfy the primary user’s target SNR, but also maximize the secondary user’s throughput. The optimal secondary transmitter acts as a relay for the primary user by allocating a part of its power to amplify-and-forward the primary signal over the primary user’s licensed spectrum bands. At the same time, as a reward, the optimal secondary transmitter uses the remaining power to transmit its own signal over the remaining licensed spectrum bands. Thus, the secondary user obtains the spectrum access opportunities. Besides, there is no interference between the primary user and the secondary user. We study the joint optimization of relay selection and power allocation such that the secondary user’s throughput is maximized on the condition that it satisfies the primary user’s target SNR. From the simulation, it is shown that the joint optimization of relay selection and power allocation provides a significant throughput gain compared with random relay selection with optimal power allocation (OPA) and random relay selection with water-filling power allocation (WPA). Moreover, the simulation results also shown that our spectrum sharing scheme obtains the win-win solution for the primary system and the secondary system.

Optimization of QoS Parameters in Cognitive Radio Using Combination of Two Crossover Methods in Genetic Algorithm

Radio Cognitive (RC) is the new concept introduced to improve spectrum utilization in wireless communication and present important research field to resolve the spectrum scarcity problem. The powerful ability of CR to change and adapt its transmit parameters according to environmental sensed parameters, makes CR as the leading technology to manage spectrum allocation and respond to QoS provisioning. In this paper, we assume that the radio environment has been sensed and that the SU specifies QoS requirements of the wireless application. We use genetic algorithm (GA) and propose crossover method called Combined Single-Heuristic Crossover. The weighted sum multi-objective approach is used to combine performance objectives functions discussed in this paper and BER approximate formula is considered.

Unlicensed Spectrum Sharing Game Between LEO Satellites and Terrestrial Cognitive Radio Networks

By cognitive radio,the low Earth orbit（LEO） satellites may prefer to operate in the unlicensed spectrum which is open to all the users,and compete for the limited resources with terrestrial cognitive radio networks（CRNs）.The competition can be regarded as a game and analyzed with game theory.This particular unlicensed spectrum sharing problem is modeled here,and the special properties of ＂spatially-distinguished-interference＂ and the short period of the interactions between satellites and terrestrial CRNs are explored.Then,the problem is formulated as a ＂partially-blind＂ finitely repeated prisoner＇s dilemma by game theory.Finally,we begin with two promising spectrum sharing schemes,which can be used to enforce the frequency reuse among the remotely located terrestrial CRN players as well as to overcome the observation noise.By analysis and comparison,it is proposed that the novel refreshing-contrite-tit-for-tat（R-CTFT） is the optimal spectrum sharing scheme.Simulation results verify that it can be used to utilize the spectrum most efficiently.

Joint bandwidth allocation and power control with interference constraints in multi-hop cognitive radio networks

We investigate the bandwidth allocation and power control schemes in orthogonal frequency division multiplexing (OFDM) based multi-hop cognitive radio networks,and the color-sensitive graph coloring (CSGC) model is viewed as an efficient solution to the spectrum assignment problem. We extend the model by taking into account the power control strategy to avoid interference among secondary users and adapt dynamic topology. We formulate the optimization problem encompassing the channel allocation,power control with the interference constrained below a tolerable limit. The optimization objective with two different optimization strategies focuses on the routes rather than the links as in traditional approaches. A heuristic solution to this nondeterministic polynomial (NP)-hard problem is presented,which performs iterative channel allocation according to the lowest transmission power that guarantees the link connection and makes channel reuse as much as possible,and then the transmission power of each link is maximized to improve the channel capacity by gradually adding power level from the lowest transmission power until all co-channel links cannot satisfy the interference constraints. Numerical results show that our proposed strategies outperform the existing spectrum assignment algorithms in the performance of both the total network bandwidth and minimum route bandwidth of all routes,meanwhile,saving the transmission power.

Realizing Intelligent Spectrum Management for Integrated Satellite and Terrestrial Networks

Nowadays both satellite and terrestrial networks are expanding rapidly to meet the ever-increasing demands for higher throughput,lower latency,and wider coverage.However,spectrum scarcity places obstacles in the sustainable development.To accommodate the expanding network within a limited spectrum,spectrum sharing is deemed as a promising candidate.Particularly,cognitive radio(CR)has been proposed in the literature to allow satellite and terrestrial networks to share their spectrum dynamically.However,the existing CR-based schemes are found to be impractical and inefficient because they neglect the difficulty in obtaining the accurate and timely environment perception in satellite communications and only focus on link-level coexistence with limited interoperability.In this paper,we propose an intelligent spectrum management framework based on software defined network(SDN)and artificial intelligence(AI).Specifically,SDN transforms the heterogenous satellite and terrestrial networks into an integrated satellite and terrestrial network(ISTN)with reconfigurability and interoperability.AI is further used to make predictive environment perception and to configure the network for optimal resource allocation.Briefly,the proposed framework provides a new paradigm to integrate and exploit the spectrum of satellite and terrestrial networks.

HF Communications: Past, Present, and Future

High frequency(HF) communication, commonly covering frequency range between 3 and 30 MHz, is an important wireless communication paradigm to offer over-thehorizon or even global communications with ranges up to thousands of kilometers via skywave propagation with ionospheric refraction. It has widespread applications in fields such as emergency communications in disaster areas, remote communications with aircrafts or ships and non-light-of-the-sight military operations. This tutorial article overviews the history of HF communication, demystifies the recent advances, and provides a preview of the next few years, which the authors believe will see fruitful outputs towards wideband, intelligent and integrated HF communications. Specifically, we first present brief preliminaries on the unique features of HF communications to facilitate general readers in the communication community. Then, we provide a historical review to show the technical evolution on the three generations of HF communication systems. Further, we highlight the key challenges and research directions. We hope that this article will stimulate more interests in addressing the technical challenges on the research and development of future HF radio communication systems.

一种基于频谱感知的智能电网需求响应管理方法

将认知无线电技术引入智能电网需求响应管理(demand response management,DRM),是缓解频谱资源短缺和提高通信质量的重要方法。为确保低信噪比条件下的频谱感知性能,本文研究了一种基于实时定价的DRM方法,并给出了全局优化问题的模型,旨在实现智能电网社会效益的最大化。采用基于广义随机共振(generalized stochastic resonance-based energy detection,GSRED)算法的能量检测算法,以实现可靠的频谱感知。同时,分析了中断概率和感知时间对DRM控制性能的影响。仿真结果表明,设置合理的感测时间可以最大化智能电网的社会效益,并优化DRM控制性能,而不会过度增加DRM系统的复杂性和感知延迟。此外,在频谱感知过程中应用GSRED算法进行能量检测,能够降低中断概率,从而提升整体的控制性能。这些研究结果为智能电网中的认知无线电技术应用提供了有价值的参考。

Spectrum Sensing via Temporal Convolutional Network

In this paper,we investigate a spectrumsensing system in the presence of a satellite,where the satellite works as a sensing node.Considering the conventional energy detection method is sensitive to the noise uncertainty,thus,a temporal convolutional network(TCN)based spectrum-sensing method is designed to eliminate the effect of the noise uncertainty and improve the performance of spectrum sensing,relying on the offline training and the online detection stages.Specifically,in the offline training stage,spectrum data captured by the satellite is sent to the TCN deployed on the gateway for training purpose.Moreover,in the online detection stage,the well trained TCN is utilized to perform real-time spectrum sensing,which can upgrade spectrum-sensing performance by exploiting the temporal features.Additionally,simulation results demonstrate that the proposed method achieves a higher probability of detection than that of the conventional energy detection(ED),the convolutional neural network(CNN),and deep neural network(DNN).Furthermore,the proposed method outperforms the CNN and the DNN in terms of a lower computational complexity.

Opportunistic Usage of Maritime VHF Band—Deployment Challenges for a New Regulatory Framework

Similarly to other domains, maritime community requests for broadband services have been significantly increasing. Worldwide navigation footprint and the lack of practical alternatives to Satellite Communications (SATCOM) empower VHF band as the natural choice to support most of those demands. Nevertheless, the major challenge for an implementation of maritime broadband VHF services is unquestionably the spectrum availability and management. Eventually, the solution must include spectrum sharing, using a Cognitive Radio (CR) based approach, but unfortunately current regulatory framework and spectrum management regime are not appropriate for such concepts and emerging technologies. To overcome such constraints, it is necessary to address a whole field of regulatory and standardization issues in order to prepare an evolution towards a more flexible and dynamic approach to spectrum management and a transition that would ensure incumbents live operations and legacy systems. The required paradigm change encompasses a new policy definition, an enforcement mechanism implementation and a comprehensive transition plan. The presented analysis pretends to address the regulatory feasibility of a framework change, discusses its evolving process and points some challenges related with practical aspects associated to Quality of Service (QoS) enforcement metrics definition, centering the arguments in maritime VHF band.

Hierarchical Spectrum Sharing Networks

A spectrum heterogeneity analysis in the cognitive radio network is conducted in this paper. Subsequently,a spectrum-heterogeneity-based hierarchical spectrum sharing(HSS) network for cognitive radio is proposed.The corresponding method of classifying available spectrums and communication based on the proposed architecture is also presented. Based on the above network architecture,we propose a reference protocol architecture.Research on these protocol function blocks,such as spectrum sensing,spectrum manager,and so on,is conducted. Numerical results show that HSS can provide a considerable extension to available spectrums so that the spectral utility may be further improved.

面向天地融合网络的无线资源智能分配方法

为满足天地融合网络全时、全域通信需求,采用认知无线电技术可实现有限频谱资源的感知与高效利用,有效缓解同频干扰问题。文章提出了一种用于天地一体认知网络的信道选择和功率调整的无线资源智能分配方法,在保证主用户服务质量的前提下最大化系统数据速率。首先,将天地融合网络建模为异质图结构,通过用户距离估计信道状态信息,并且利用图卷积网络提取和分析关键环境特征。其次,采用深度强化学习探索底层拓扑环境信息,通过试错与奖惩机制不断优化资源分配策略。仿真结果验证了所提方法的收敛性,并且证明系统数据速率能够得到显著提升。

无人机测控系统中干扰感知与频谱资源管理技术

为了提高无人机测控系统在复杂电磁环境下的适应性、安全性和可靠性,解决多站多机同空域工作时面临的动态频谱资源管理问题,提出了一种基于干扰感知和频谱资源管理技术的无人机测控系统。该系统将通信侦察技术与认知无线电频谱感知技术相结合,为上/下行链路提供全频带干扰识别以及动态频谱资源管理。搭建了干扰感知和频谱资源管理系统无线测试平台,测试结果表明,当上/下行链路存在干扰信号且链路质量较差时,系统能自适应调整链路的频谱资源,可有效避开干扰信号对系统的影响,提升了无人机测控系统的生存能力和使用效能。

认知无线电技术在电磁频谱管理中的应用

作为一种重要的战略资源,无线电频谱资源对保障信息通畅、增强军事实力,以及保证国家长治久安有着重要意义。因此,必须对其进行规范有效地管理。同时,无线电技术已被广泛应用于各行各业以及生活的各个方面。比如,气象预测、军事以及人们的日常通信。无线电技术已成为生活中不可或缺的重要组成部分。但是,随着电磁频谱的需求逐渐扩大,供需矛盾日益凸显,高峰期供给不足,同时又存在电磁频谱闲置的现象。如何解决该问题,值得相关从业者进行深入思考。文章以认知无线电技术为突破口,论述认知无线电技术在电磁频谱管理中的应用,以期为相关行业提供参考。

认知无线电研究综述

认知无线电是一种可通过与其运行环境交互而改变其发射机参数的无线电,它可以提高频谱的利用率,特别是可让未授权用户使用授权用户的频谱,此外,它还为网络及终端提供了更高的灵活性。文中在描述认知无线电定义和概念模型的基础上,分析其特点和性能评估参数,结合已有研究,讨论了干扰温度、频谱汇聚共享、动态频谱管理等关键问题,并介绍了目前的研究进展。最后,针对应用需求,提出了几点研究设想。