Blockchain-Based MCS Detection Framework of Abnormal Spectrum Usage for Satellite Spectrum Sharing Scenario

In this paper, the problem of abnormal spectrum usage between satellite spectrum sharing systems is investigated to support multi-satellite spectrum coexistence. Given the cost of monitoring, the mobility of low-orbit satellites, and the directional nature of their signals, traditional monitoring methods are no longer suitable, especially in the case of multiple power level. Mobile crowdsensing(MCS), as a new technology, can make full use of idle resources to complete a variety of perceptual tasks. However, traditional MCS heavily relies on a centralized server and is vulnerable to single point of failure attacks. Therefore, we replace the original centralized server with a blockchain-based distributed service provider to enable its security. Therefore, in this work, we propose a blockchain-based MCS framework, in which we explain in detail how this framework can achieve abnormal frequency behavior monitoring in an inter-satellite spectrum sharing system. Then, under certain false alarm probability, we propose an abnormal spectrum detection algorithm based on mixed hypothesis test to maximize detection probability in single power level and multiple power level scenarios, respectively. Finally, a Bad out of Good(BooG) detector is proposed to ease the computational pressure on the blockchain nodes. Simulation results show the effectiveness of the proposed framework.

Covert Communication with a Spectrum Sharing Relay in the Finite Blocklength Regime

In this paper,we investigate the feasibility and performance of the covert communication with a spectrum sharing relay in the finite blocklength regime.Specifically,the relay opportunistically forwards the source's messages to the primary receiver or conveys the covert messages to its own receiver via the sharing spectrum,while the warden attempts to detect the transmission.First,we derive a lower bound on the covertness constraint,and the analytical expressions of both the primary average effective covert throughput(AECT)and sum AECT are presented by considering the overall decoding error performance.Then,we formulate two optimization problems to maximize the primary and sum AECT respectively by optimizing the blocklength and the transmit power at the source and the relay.Our examinations show that there exists an optimal blocklength to maximize the primary and sum AECT.Besides,it is revealed that,to maximize the primary AECT,the optimal transmit power of each hop increases as its channel quality deteriorates.Furthermore,in the optimization for maximizing the sum AECT,the optimal transmit power at the source equals to zero when the channel quality from relay to the secondary receiver is not weaker than that from relay to the primary receiver.

Directed Acyclic Graph Blockchain for Secure Spectrum Sharing and Energy Trading in Power IoT

Peer-to-peer(P2P)spectrum sharing and energy trading are promising solutions to locally satisfy spectrum and energy demands in power Internet of Things(IoT).However,implementation of largescale P2P spectrum sharing and energy trading confronts security and privacy challenges.In this paper,we exploit consortium blockchain and Directed Acyclic Graph(DAG)to propose a new secure and distributed spectrum sharing and energy trading framework in power IoT,named spectrum-energy chain,where a set of local aggregators(LAGs)cooperatively confirm the identity of the power devices by utilizing consortium blockchain,so as to form a main chain.Then,the local power devices verify spectrum and energy micro-transactions simultaneously but asynchronously to form local spectrum tangle and local energy tangle,respectively.Moreover,an iterative double auction based micro transactions scheme is designed to solve the spectrum and energy pricing and the amount of shared spectrum and energy among power devices.Security analysis and numerical results illustrate that the developed spectrum-energy chain and the designed iterative double auction based microtransactions scheme are secure and efficient for spectrum sharing and energy trading in power IoT.

Spectrum Sensing Using Optimized Deep Learning Techniquesin Reconfigurable Embedded Systems

The exponential growth of Internet of Things(IoT)and 5G networks has resulted in maximum users,and the role of cognitive radio has become pivotal in handling the crowded users.In this scenario,cognitive radio techniques such as spectrum sensing,spectrum sharing and dynamic spectrum access will become essential components in Wireless IoT communication.IoT devices must learn adaptively to the environment and extract the spectrum knowledge and inferred spectrum knowledge by appropriately changing communication parameters such as modulation index,frequency bands,coding rate etc.,to accommodate the above characteristics.Implementing the above learning methods on the embedded chip leads to high latency,high power consumption and more chip area utilisation.To overcome the problems mentioned above,we present DEEP HOLE Radio sys-tems,the intelligent system enabling the spectrum knowledge extraction from the unprocessed samples by the optimized deep learning models directly from the Radio Frequency(RF)environment.DEEP HOLE Radio provides(i)an opti-mized deep learning framework with a good trade-off between latency,power and utilization.(ii)Complete Hardware-Software architecture where the SoC’s coupled with radio transceivers for maximum performance.The experimentation has been carried out using GNURADIO software interfaced with Zynq-7000 devices mounting on ESP8266 radio transceivers with inbuilt Omni direc-tional antennas.The whole spectrum of knowledge has been extracted using GNU radio.These extracted features are used to train the proposed optimized deep learning models,which run parallel on Zynq-SoC 7000,consuming less area,power,latency and less utilization area.The proposed framework has been evaluated and compared with the existing frameworks such as RFLearn,Long Term Short Memory(LSTM),Convolutional Neural Networks(CNN)and Deep Neural Networks(DNN).The outcome shows that the proposed framework has outperformed the existing framework regarding the area,power and time.More-over,the experimental results show that the proposed framework decreases the delay,power and area by 15%,20%25%concerning the existing RFlearn and other hardware constraint frameworks.

基于全频谱共享的三维轨迹和功率优化方法

当前蜂窝系统频谱资源极度短缺,免授权频谱因而被建议在蜂窝系统中使用。无人机(UAV)的飞行轨迹和功率控制对频谱利用效率有重大影响。然而,基于频谱共享的3维轨迹和功率优化方法却鲜少研究。为此,该文首先提出一种全频谱共享方法,即无人机通过控制上行蜂窝用户和设备到设备(D2D)用户的发射功率,在不影响WiFi设备正常传输的前提下使用免授权频谱;同时无人机也能够在不影响其他下行蜂窝用户的前提下使用授权频谱。然后基于提出的全频谱共享方法,该文构建了无人机电池能量约束下的3维飞行轨迹和发射功率的联合优化问题。为了求解提出的复杂多变量耦合的非凸优化问题,该文采用块坐标下降和连续凸逼近方法将原问题转化为3维轨迹优化和功率控制两个凸优化子问题并迭代求解。大量仿真结果证明提出的基于3维轨迹和功率优化的全频谱共享方法能够显著提高频谱利用效率。

IRS辅助的感知增强认知无线电网络资源分配方案

在过去的几十年里,大部分可用频谱已被用于高速通信服务。这导致了无线通信系统中的频谱资源稀缺问题。另一方面,大量可用频谱的利用率并不高。为提升通信系统的频谱利用率,学界提出了基于共享的认知无线电技术。认知无线电允许次用户与主用户共享频谱资源,对于提升无线通信网络的频谱效率十分重要,然而目前基于认知无线电的通信网络存在着低信噪比情况下感知性能低下、严苛干扰条件下性能提升受限等缺点。最近,智能反射面(Intelligent Reflecting Surface,IRS)被广泛应用在无线通信的各个领域,可以智能调控无线传输环境以满足频谱和能效的高要求。为了进一步提升认知无线电感知性能与频谱效率,提出了一种新颖的IRS辅助的感知增强频谱共享认知无线电网络。针对此网络,建立了次用户和速率最大化优化问题,联合优化次基站波束赋形向量、智能反射面相移矩阵、感知时间。因存在变量耦合,所构建的优化问题是非凸的,提出一种基于块坐标下降(Block Coordinate Descent,BCD)的迭代算法以交替优化波束形成和智能反射面相位以及感知时间。以逐次凸逼近(Successive Convex Approximation,SCA)求解非凸目标和约束函数,并利用半正定松弛(Semidefinite Relaxation,SDR)解决秩一条件约束,采用一维搜索来寻找最佳感知时间。通过仿真结果证明提出的方法能够快速收敛且对认知无线电网络的感知性能以及频谱效率有较大提升作用。

基于无线环境地图的星地智能频谱共享方法

卫星和地面组件共享同一频段可以有效缓解频谱稀缺问题,提升频谱利用率,星地频谱共享将在未来的无线系统中发挥至关重要的作用。相较于传统的七色复用星地频谱共享方案均匀地分配点波束带宽,通过波束预测能够基于点波束流量需求的变化合理分配带宽,而频谱预测能够有效降低碰撞概率,从而提升网络频谱效率。提出一种基于无线环境地图的星地智能频谱共享方法,该方法联合波束预测和频谱预测,构建无线环境地图并基于无线环境地图进行频谱分配。仿真结果表明,该方法可有效提升频谱利用率,降低网络阻塞率和用户等待时间。

基于多智能体深度强化学习的车联网频谱共享

针对高动态车联网环境中基站难以收集和管理瞬时信道状态信息的问题,提出了基于多智能体深度强化学习的车联网频谱分配算法.该算法以车辆通信延迟和可靠性约束条件下最大化网络吞吐量为目标,利用学习算法改进频谱和功率分配策略.首先通过改进DQN模型和Exp3策略训练隐式协作智能体.其次,利用迟滞性Q学习和并发体验重放轨迹解决多智能体并发学习引起的非平稳性问题.仿真结果表明,该算法有效载荷平均成功交付率可达95.89%,比随机基线算法提高了16.48%,可快速获取近似最优解,在降低车联网通信系统信令开销方面具有显著优势.

面向6G的星地融合网络频谱共享技术

卫星网络被公认为是下一代移动通信系统的重要组成部分,其与现有的地面蜂窝网络实现优势互补形成星地融合网络,可以为全球范围内的海量用户提供无处不在的宽带接入服务。而两网如何高效共享有限的频谱资源、缓解同频共存干扰成为当前的技术难点与热点问题之一。分析了星地融合网络频谱共享时存在的典型干扰类型,并对星地频谱共享场景中系统间干扰进行了仿真。介绍了星地频谱共享需要进一步研究的关键技术,并总结了星地融合网络频谱共享面临的挑战。

复杂电磁环境下LTE网络监测与分类研究

由于免许可证频段电磁环境复杂,因此LTE(Long Term Evolution)业务在该频段部署实现多业务共存的关键是对其进行监测和分类。为此,采用NS-3网络仿真模拟器建立了LTE和Wi-Fi室内业务共存模型,通过改变网络和场景的各类参数构建了监测模型,获得了文件传输FT(file transfers)、音频流VF(voice flows)和恒定比特流CBRS(constant bit rate streams)三种模式下的数据集。结果表明,采用FT数据集,在K最近邻、支持向量机、决策树和随机森林算法的分类准确率有明显提高;同时,VF与CBRS两种数据集在上述算法上准确率达到了80%左右。

面向星地一体化网络的频谱共享方法研究

星地一体化网络是B5G/6G网络发展的重要趋势,业界正积极推动星地一体化网络的发展。随着卫星业务广泛进入消费市场,L、S等频段的频谱短缺问题日益严重。星、地网络间频谱共享是解决该问题的有效方案之一,但会导致系统间严重干扰。为此,提出一种基于干扰感知的频率资源共享分组方法,该方法在约束地面网络干扰功率的前提下,最大化星地一体化网络的系统效用,并通过局部搜索算法求解非线性整数规划问题。仿真结果表明,所提方案能够有效降低来自地面网络的干扰,并提升频率资源利用率。

Mutual Information Maximization via Joint Power Allocation in Integrated Sensing and Communications System

In this paper, we focus on the power allocation of Integrated Sensing and Communication(ISAC) with orthogonal frequency division multiplexing(OFDM) waveform. In order to improve the spectrum utilization efficiency in ISAC, we propose a design scheme based on spectrum sharing, that is,to maximize the mutual information(MI) of radar sensing while ensuring certain communication rate and transmission power constraints. In the proposed scheme, three cases are considered for the scattering off the target due to the communication signals,as negligible signal, beneficial signal, and interference signal to radar sensing, respectively, thus requiring three power allocation schemes. However,the corresponding power allocation schemes are nonconvex and their closed-form solutions are unavailable as a consequence. Motivated by this, alternating optimization(AO), sequence convex programming(SCP) and Lagrange multiplier are individually combined for three suboptimal solutions corresponding with three power allocation schemes. By combining the three algorithms, we transform the non-convex problem which is difficult to deal with into a convex problem which is easy to solve and obtain the suboptimal solution of the corresponding optimization problem. Numerical results show that, compared with the allocation results of the existing algorithms, the proposed joint design algorithm significantly improves the radar performance.

基于HDSS构建800 MHz的5G和LTE共覆盖研究与实践

800 MHz频段5G是广覆盖很好选择,但是由于800 MHz频率资源短缺,传统的硬重耕方式舍旧换新,不能适应现网需要并且资源投入较大。而频谱动态共享的技术可以在现有4G网络上快速进行5G部署,实现4G和5G双网并存,并动态扩展演进。该文通过对HDSS的技术的15 MHz HDSS动态频谱共享(15 MHz的NR包含10 MHz的LTE)方案,对共享资源调度、干扰规避、功率配置等关键技术问题进行了分析验证,并对HDSS组网下的多种场景进行了测试,给出了实际配置建议,对实现绿色共享频率资源,4G和5G网络协同、5G的广覆盖等方面具有参考价值。

认知智能电网中基于非合作博弈的电视频谱资源共享研究

智能电网(Smart Grid,SG)中的需求响应管理(Demand Response Management,DRM)是SG的控制单元,用来平衡实时负荷及转移高峰时段负荷。通信对于DRM的准确性和最优性至关重要,在智能电网的邻域网中广泛采用无线网络作为通信网,但是用于无线通信的频谱资源目前未被充分利用。随着模拟广播电视向数字电视的过渡,在电视频谱中产生更多的电视空白(TV White Space,TVWS)频谱。基于此,通过及建立Underlay模式下的TVWS频谱共享分层网络模型,以提高频谱利用率,并提出基于非合作博弈功率控制算法。数值结果表明,提出的方法可以提高频谱的利用率,有益于发挥DRM的性能。

基于天基干扰绘图的星地一体化系统频谱共享研究

针对未来星地一体化系统中新入低轨道地球卫星(LEO)组件的用频需求和现任地面组件聚合干扰影响的问题,面向天基认知系统提出频谱感知和频谱数据库辅助的交织模式频谱共享方案。首先,针对低轨认知系统节点稀疏分布属性对频谱感知和干扰绘图带来的负面影响,提出“认知节点位置”概念。其次,研究低轨认知系统的双门限混合频谱感知和干扰绘图的机制,构建频谱数据库。最后,面向多状态虚拟网络模型,提出卫星组件上行频谱共享方案,实现LEO组件和地面组件之间的同频共存工作。仿真结果表明,所提双门限混合感知和干扰绘图能以较低密度的低轨认知节点实现高可靠性的频谱空间态势认知;所提基于频谱数据库和交织模式的LEO组件协作接入方案可以实现与地面组件的频谱共享,且不会显著增加LEO组件的端到端时延。

动态频谱共享技术在5G中的应用

移动通信是一个资源受限性系统,而频谱是非常关键的资源和要素。目前,移动通信已经进入5G时代,而5G的主力频谱是3.5 GHz,频率越高,覆盖能力就越弱,因此其很难实现城区的深度覆盖及农村的广覆盖,且实现的建设成本也会非常高。目前,中低频谱资源几乎被3/4G占据,且无法全部重耕这些优质的低频段资源。5G与3/4G共用中低频段,由于每张网络承载用户是一个动态变化的过程,若采用固定分配频谱的策略,则会出现网络忙闲不均的现象,从而不能完全发挥中低频谱的利用率。动态频谱共享技术可有效解决上述问题,提高频谱利用率。文中在介绍技术原理的前提下,对该技术实现方式以及部署方式进行了介绍,最后通过测试数据证明了该技术的有效性。

雷达信号处理对移动通信信息传输效率的影响研究

随着无线通信技术的飞速发展,高频率频谱资源日趋紧缺,而雷达系统作为主要的电磁频谱使用者,其信号处理特点常会对同频带及邻近频带内的移动通信系统产生干扰,从而限制了移动通信的信息传输效率。面对日益增长的频谱资源和需求,如何有效地提高频谱利用率成为亟待解决的问题。文中拟研究了雷达信号处理技术在抑制干扰,提升信息传输效能方面的应用,重点探讨了其协同工作机理。

面向6G无线通信系统的动态频谱管理技术研究

随着通信技术的快速发展,6G无线通信系统将面临更为严峻的频谱资源调配难题。文章通过深入研究6G无线通信系统的动态频谱管理技术,阐述动态频谱管理的基础理念,包括其种类、追求的目标、运作的原理以及优势。重点分析6G无线通信技术对无线电频谱资源的需求,包括提升频谱使用效率、在多元用户和多样化业务情境下的频谱分配与调度等。同时,深入探索针对无线电频谱的先进管理策略,如无线电频谱的感知与预测、借助机器学习和人工智能的智能管理技术以及面向6G通信技术的动态频谱分配技巧等。最后,剖析无线频道的共用与买卖规则,展望未来研究的趋势与方向。

Chirp-DS-BPSK雷达通信一体化波形设计

针对线性调频-二进制相移键控(Chirp-Binary Phase Shift Keying,Chirp-BPSK)雷达通信一体化信号存在模糊函数受通信数据严重影响的问题,分析了传统周期扩频、混沌序列扩频及Logistic映射混沌序列的自相关性能,设计了一种扩频信号与线性调频脉冲相结合的雷达通信一体化共用波形,即Chirp-DS-BPSK一体化信号。该一体化波形分别采用混沌序列和BPSK方式对通信数据进行直接序列扩频与调制,以线性调频信号作为载波,并采用脉冲成型滤波器来提高频谱利用率,降低脉压旁瓣。最后,通过仿真分析证明了所提波形具有良好的模糊函数特性和误码率性能。在扩频比为7的条件下,与传统的Chirp-BPSK波形相比,Chirp-DS-BPSK波形的脉压峰值旁瓣比降低了约2.69 dB,通信误码率下降约8.45 dB,表明Chirp-DS-BPSK比Chirp-BPSK更适合作为雷达通信一体化共用波形。

Improved spectrum sharing algorithm based on feedback control information in cognitive radio networks

In order to avoid the system performance deterioration caused by the wireless fading channel and imperfect channel estimation in cognitive radio networks, the spectrum sharing problem with the consideration of feedback control information from the primary user is analyzed. An improved spectrum sharing algorithm based on the combination of the feedback control information and the optimization algorithm is proposed. The relaxation method is used to achieve the approximate spectrum sharing model, and the spectrum sharing strategy that satisfies the individual outage probability constraints can be obtained iteratively with the observed outage probability. Simulation results show that the proposed spectrum sharing algorithm can achieve the spectrum sharing strategy that satisfies the outage probability constraints and reduce the average outage probability without causing maximum transmission rate reduction of the secondary user.