A framework for statistical wireless spectrum occupancy modeling

In this paper, we propose a novel spectrum occupancy model designed to generate accurate temporal and frequency behavior of various wireless transmissions. Our proposed work builds upon existing concepts in open literature in order to develop a more accurate time-varying spectrum occupancy model. This model can be employed by wireless researchers for evaluating new wireless communication and networking algorithms and techniques designed to perform dynamic spectrum access (DSA). Using statistical characteristics extracted from actual radio frequency measurements, first- and second-order parameters are employed in a statistical spectrum occupancy model based on a combination of several different probability density functions (PDFs) defining various features of a specific spectrum band with several concurrent transmissions. To assess the accuracy of the model, the output characteristics of the proposed spectrum occupancy model are compared with realtime radio frequency measurements in the television and paging bands.     

Quantitative spectrum occupancy evaluation in China: based on a large scale concurrent spectrum measurement

Due to the usable frequency becomes more and more crowed,dynamic spectrum access (DSA) is a new hope to solve this problem.However,DSA in China requires a quantitative analysis of the current spectrum ...     

Efficient Mesh Division and Differential Information Coding Schemes in Broadcast Cognitive Pilot Channel

Driven by the inevitable trends of heterogeneous network convergence and cooperation technology innovations have been put forward to improve the spectrum usage efficiency, such as the Cognitive Radio (CR) technology. As one of the candidate solutions for heterogeneous network information delivery for dynamic spectrum sharing in CR environment, the Cognitive Pilot Channel (CPC) techniques are proposed, which greatly improve the efficiency and accuracy of network information delivery to User Equipments. Based on the assumption that the geographical region is divided into meshes and covered by different Radio Access Technologies (RATs), an Efficient Mesh Division (EMD) scheme is brought forward by taking into account both the error probability parameter in the Global Position System localization shift scenario and the information loss ratio parameter in the multi-RATs overlapped scenario. Besides, the impacts of these parameters to the EMD scheme are analyzed and evaluated by using the Analytic Hierarchy Process and Grey Relational Analysis algorithms. Furthermore, in order to improve the efficiency of network information coding among different meshes, the Differential Information Coding (DIC) scheme is put forward, which includes the Homogeneous Meshes Grouping scheme that is based on the frequency occupancy graph and the basic mesh selection strategy with the most popular commonality using image processing techniques. In virtue of the DIC scheme, the duplicate network information in homogeneous meshes is greatly reduced and information coding efficiency is improved by quantizing only the differential information against the basic mesh. Finally, contributions of the proposed EMD and DIC schemes reside in the improvements of the accuracy and efficiency of heterogeneous network information delivery via broadcast CPC channel, which are proved by numerous results.     

比特谱相关改进循环谱的单通道混合信号参数估计快速算法

为了应对复杂环境下非合作通信、电磁频谱监管等宽带接收中存在的先验信息缺失,针对单通道混合信号的参数盲估计问题,提出了一种基于比特谱相关算法改进循环谱估计的快速算法。针对信号集{BPSK、QPSK、OQPSK、8PSK、8QAM、16QAM、16APSK、32APSK、CPM},系统给出整套参数估计算法流程,通过高阶累积量以及功率中心方法确定信号个数以及载波频率,然后利用改进循环谱提取符号速率谱线,完成单通道混合信号参数估计。论文对改进算法的统计特性进行了理论推导。理论和实验表明:算法不需要同步码等先验信息,适用于高斯噪声信道和多径平坦衰落信道,同时降低了计算复杂度、存储空间、估计方差以及数据量的需求,当混合信号等功率混合时,且信噪比分别为-4 dB和-1 dB时,算法对载波频率和符号速率的估计正确率可以达到90%,验证了算法的有效性和可行性。      

Long-Term Spectrum Monitoring with Big Data Analysis and Machine Learning for Cloud-Based Radio Access Networks

Spectrum monitoring is important for efficient spectrum sharing and resource management in cloud-based radio access networks (C-RAN). In this paper we show how data obtained from long-term spectrum monitoring together with machine learning (ML) operating on big data (BD) can be used in a C-RAN scenario for spectrum management purposes. We propose an approach for spectrum occupancy forecasting which can be used to reduce the delay in making dynamic spectrum allocation decisions and improve the cognitive and management functionalities of cloud-based architectures such as C-RAN. The spectrum occupancy and usage activity in a predefined frequency band is based on the statistical processing of a large amount of collected data and the introduction of a frequency–time resources indicator as a measure of spectrum usage. Furthermore, we apply ML algorithms to predict spectrum usage and compare the predicted with actual measured data. Taking into consideration that the accuracy of the prediction depends on the volume of collected data and the time of prediction on the BD and ML approach, we propose the development of a cloud-based generic processing architecture to solve the “accuracy versus latency” trade-off problem. The proposed architecture is appropriate for deployment in cognitive C-RAN.     

卫星S频段下行链路频谱占用建模与预测

目前提出的频谱占用模型能够在时域上描述和重现基本的统计特征，如传统的地面移动通信的频谱占用/空闲周期长度可以用经典的广义帕累托(GP)分布、指数分布等分布来拟合。然而在某些复杂的如卫星链路频谱占用场景中，传统的参数估计分布无法给出良好的拟合。为此提出了用核密度估计(KDE)的方法来进行概率密度分布的拟合，在此基础上，分别采用差分整合移动平均自回归模型(ARIMA)和模糊神经网络对频谱占用模型的时间序列进行预测并进行对比。结论表明，核密度估计的使用可以更加准确地描述并再现卫星下行链路所使用S频段的占用时间序列的统计特征，而模糊神经网络的预测比ARIMA模型预测更加精确。     

基于时间序列分析的频谱异常自主检测和稳健估计方法

复杂电磁环境和无用频先验知识条件下有效检测电磁频谱异常使用信息,是无线电监测和电磁环境评估等领域的重要难题.本文基于时间序列分析理论,通过构建反映有限频谱占用度序列动态依存关系且包含频谱异常值的时序模型,实现对无线电频谱异常的自主检测和稳健估计.研究结果表明,该方法无需用频数据库或无线电监测历史数据支持,能够有效识别典型频谱异常类型、发生时间以及异常影响强度等信息;同时通过对频谱占用度时序模型的稳健估计,能够显著降低模型拟合误差,提高模型对外部干扰环境的适应性和鲁棒性.     

A Subcarriers Allocation Scheme for Cognitive Radio Systems Based on Multi-Carrier Modulation

Cognitive radio (CR) is a dynamic spectrum access technology as a solution to spectrum under-utilization problem in some licensed bands. Operating over an exceedingly wide spectrum, CR systems usually adopt multi-carrier modulation (MCM) to implement flexible channelization. Consequently, efficient channel allocation scheme becomes extremely important to an MCM based CR (MCM-CR) system. In this paper, a maximum likelihood detection model is developed to detect the presence and locations of licensed users (LUs) signals in the frequency domain. Performance of the detection model, including the optimal detection region, detection probability and false alarm probability, is analyzed. A one-order two-state Markovian chain model is proposed to predict channel status information. In particular, a novel subcarrier allocation scheme for MCM-CR systems is proposed, taking into account the confidence of channel estimation, quality of services (QoS) of rental users (RUs) and throughput. To validate the analytical results, simulations have been conducted to show effectiveness of the proposed scheme.     

一种基于局域中心频率的SAR图像舰船方位向速度估计方法

针对现有船速估计算法大多数只能估计出舰船距离向速度的问题,该文提出一种基于合成孔径雷达(SAR)图像局域中心频率的舰船方位向速度估计方法。首先分析了动目标在SAR图像局域多普勒中心频率的变化规律,并推导了利用中心频率变化率估计目标方位向速度的理论公式。然后给出了根据SAR图像局域方位向功率谱的概率密度函数,利用最大似然估计算法估计中心频率变化率的方法。同时,对所提方法的精度与适用性应用性进行分析。最后,通过仿真和实测数据,将该方法的估计结果与直接计算调频率获得的结果进行对比分析。结果表明,相对于调频率法,该方法具有更高的估计精度,验证了所提方法的有效性。     

基于二维幅值谱的正弦信号频率估计

信号中含有噪声或非整周期截断时发生的频谱泄漏是导致正弦信号频率估计精度不高的主要原因。针对这一问题,从扩展信号频谱表征方式出发,将经典幅值谱扩展至不受频谱泄漏制约、表现力更强、可读性更好的二维幅值谱。与经典幅值谱相比,二维幅值谱除包含信号的频率个数、幅值信息外还包含了易于获取的周期信息,且具有一定的抗噪性,在信噪比低至-10dB时仍有较好表现力。提出一种估计方法,先从二维幅值谱中估计出信号的周期T,然后根据信号采样频率、信号频率、以及信号周期T之间的定量关系完成信号的频率估计。实验结果证明了该方法的有效性。基于二雏幅值谱的正弦信号频率估计方法为正弦信号的频谱估计提供了一种新思路。     

基于边缘方向投影的图像块修复方法

压缩图像在传输中,信道干扰常导致图像块数据丢失.给出一种基于边缘方向投影的图像损失块修复方法.按照周围邻域未损失像素的边缘方向信息将损失块分类,并根据类别用结合频域和空域信息的凸集投影算法自适应地修复损失块.能较好地修复图像边缘和复杂纹理.与RIBMAP方法相比,此方法在修复质量和算法顽健性上均有提高.     

Spectrum inpainting: a new framework for spectrum status determination in large cognitive radio networks

In this paper, the problem of spectrum status determination is considered for large cognitive radio (CR) ad hoc networks. Spectrum sensing and spectrum decision are critical for cognitive radio network throughput and hence obtaining accurate knowledge of the spectrum status is vitally important to better spectrum usage decisions. The major challenge of this type of problem lies in the fact that for a network covering a large geographical area, only very limited measurements of spectrum occupancy during spectrum sensing may be obtained by the CR users for a certain location in any given time slot. This is due to both the hardware limitations as well as the tradeoff between spectrum sensing time and data throughput of the CR users. By representing the spectrum sensing results across the network as an image, spectrum status determination is formulated as an image recovery problem. The method of total variation inpainting is applied to solve the problem with low determination error. The proposed method takes advantage of the correlations in multiple dimensions and the numerical results demonstrate the effectiveness of the proposed scheme.     

Stochastic spectrum handoff protocols for partially observable cognitive radio networks

Recent studies have been conducted to indicate the ineffective usage of licensed bands due to static spectrum allocation. In order to improve spectrum utilization, cognitive radio (CR) is therefore suggested to dynamically exploit the opportunistic primary frequency spectrums. How to provide efficient spectrum handoff has been considered a crucial issue in the CR networks. Existing spectrum handoff algorithms assume that all the channels can be correctly sensed by the CR users in order to perform appropriate spectrum handoff process. However, this assumption is impractical since excessive time will be required for the CR user to sense the entire spectrum space. In this paper, the partially observable Markov decision process (POMDP) is applied to estimate the network information by partially sensing the frequency spectrums. A POMDP-based spectrum handoff (POSH) scheme is proposed to determine the optimal target channel for spectrum handoff according to the partially observable channel state information. Moreover, a POMDP-based multi-user spectrum handoff (M-POSH) protocol is proposed to exploit the POMDP policy into multi-user CR networks by distributing CR users to frequency spectrum bins opportunistically. By adopting the policies resulted from the POSH and M-POSH algorithms for target channel selection, minimal waiting time at each occurrence of spectrum handoff can be achieved which will be feasible for multimedia applications. Numerical results illustrate that the proposed spectrum handoff protocols can effectively minimize the required waiting time for spectrum handoff in the CR networks.     

Fast detection and DOA estimation of the unknown wideband signal sources

The direction of arrival (DOA) estimation analysis requires prior knowledge of frequency‐related information of the incident wideband signals, eg, center frequency and bandwidth, which are not available in many cases. This research is based on applications where DOA estimation of the wideband signal source is unknown, eg, in astronomy and unauthorized transmissions. Therefore, this paper has two major contributions. The first contribution is to identify the frequency spectrum of the wideband signals transmitted from an unknown source. The method use edge detection prestage to identify the frequency spectrum of the received signal. The second contribution is to estimate the DOA of the wideband signal at higher accuracy while keeping a minimum computational cost. The estimation of the DOAs was analyzed by measuring the orthogonal relationship between the signal and the noise subspaces of multiple frequency components of the sources. The introduced method utilizes subband as a reference frequency based on the extracted frequency‐related information rather than examining the complete incoming signal spectrum and exploits the spatial information of a few subbands. The introduced algorithm is implemented based on the well‐known method, test of orthogonality of projected subspaces (TOPS). Tests are conducted on a range of wideband signals with extreme values of signal‐to‐noise ratio (SNR). Considerable performance improvement is obtained.     

数据驱动的开放动态频谱接入系统构建方法

在认知无线电(CR)背景下,动态频谱接入已成为提高无线网络频谱利用率的重要途径。基于全球移动通信系统-铁路(GSM-R)系统中采集的细粒度频谱监测数据,提出一种数据驱动的深度学习方法,建模频谱模式,并建立一套动态频谱接入访问框架。采用一种深度频谱生成模型指导频谱分配;设计一种综合递归序列表征与场景特征嵌入的深度网络,建模和预测短时频谱占用情况,并由此提出一种动态信道接入策略。进一步,利用软件无线电(SDR)平台实现一套跳频系统,并将其与动态频谱接入策略进行集成。使用真实的历史频谱数据评估该系统的数据吞吐能力,测试结果表明,所提方法及构建的跳频系统能有效提高机会通信能力,高效利用频谱资源。该频谱接入框架及SDR系统实现具有较强的通用性,易于集成到不同场景和频段的系统中。     

基于二次统计CFAR处理的目标径向尺寸估计

利用雷达高分辨距离像(high resolution range profile,HRRP)实现对目标径向尺寸估计,可为目标分类识别提供重要特征判据.实际中常采用双向恒虚警(constant false alarm rate,CFAR)门限法进行目标信号支撑区和噪声区的鉴别,低信噪比条件下现有方法尺寸估计精度较低,且当...     

Method for Enhancing the Accuracy of Frequency Offset Estimation in OFDM Modulation Based Communication Systems

The paper proposes a method for enhancing the accuracy of estimation of frequency offset for OFDM modulation based communication systems. An increase accuracy of the proposed method is achieved at the expense of combined use of pilot and information symbols of OFDM signal. The main factors determining the accuracy of proposed method were identified. Mathematical simulation of the proposed method was performed for a multipath radio wave propagation channel. The proposed method efficiency was shown in comparison with existing methods for the estimation of frequency offset. Advantages and disadvantages of existing and proposed methods were described. The relationships of the root-mean-square error of frequency offset estimation as a function of the signal-to-noise ratio were built for the proposed and existing methods. The attainable high accuracy of frequency offset estimation by employing the proposed method makes its use expedient in control and measuring equipment. Conclusions have been made regarding the simulation results obtained and the expediency of proposed method applications.     

Robust Cognitive‐Radio‐Based OFDM Architecture with Adaptive Traffic Allocation in Time and Frequency

Cognitive radio (CR) has been proposed as an effective technology for flexible use of the radio spectrum. The interference between primary users and CR users, however, becomes a critical problem when they are using adjacent frequency channels with different transmission power levels. In this paper, a robust CR orthogonal frequency division multiplexing (OFDM) architecture, which can effectively suppress interference to nearby primary users and overcome adjacent channel interference (ACI) to the CR user, is proposed. This new approach is characterized by adaptive data repetition for subcarriers under heavy ACI, and adaptive time spreading for subcarriers near the borders of the CR user's spectrum. The data repetition scheme provides extra power gain against the ACI coming from primary users. Time spreading guarantees an acceptable interference level to nearby primary users. By computer simulation, we demonstrate that, under a CR environment, the proposed CR OFDM architecture outperforms conventional OFDM systems in terms of throughput and BER performance.     

基于稀疏迭代协方差估计的缺失数据谱分析及时域重建方法

应用于缺失数据恢复的迭代自适应方法(IAA)被证实可利用20%的有效数据估计信号参数,并能高精度恢复缺失数据,优于经典GAPES方法,但当缺失数据超过80%时其数据恢复性能迅速下降。该文基于稀疏迭代协方差估计提出一种新的缺失数据谱分析方法(M-SPICE)及针对该方法的缺失数据修正时域重建方法。该方法将加权缺失数据协方差拟合代价函数转换为凸优化问题,构造循环最小化器保证缺失数据参数估计的全局收敛特性,通过对缺失数据估计算子的更新实现了时域重建方法的修正,使其在有效数据功率谱欠估计的情况下获得更高的数据重建精度。仿真实验表明无论是数据块缺失还是任意缺失,该方法均能够利用更少的有效数据进行谱分析,并重建大比例缺失数据。     

Prediction based channel allocation performance for cognitive radio

The interdependency, in a cognitive radio (CR) network, of spectrum sensing, occupancy modelling, channel switching and secondary user (SU) performance, is investigated. Achievable SU data throughput and primary user (PU) disruption rate have been examined for both theoretical test data as well as data obtained from real-world spectrum measurements done in Pretoria, South Africa. A channel switching simulator was developed to investigate SU performance, where a hidden Markov model (HMM) was employed to model and predict PU behaviour, from which proactive channel allocations could be made. Results show that CR performance may be improved if PU behaviour is accurately modelled, since accurate prediction allows the SU to make proactive channel switching decisions. It is further shown that a trade-off may exist between achievable SU throughput and average PU disruption rate. When using the prediction model, significant performance improvements, particularly under heavy traffic density conditions, of up to double the SU throughput and half the PU disruption rate were observed. Results obtained from a measurement campaign were comparable with those obtained from theoretical occupancy data, with an average similarity score of 95% for prediction accuracy, 90% for SU throughput and 70% for PU disruption rate.