CN103974284B - A kind of broader frequency spectrum cognitive method based on partial reconfiguration - Google Patents
A kind of broader frequency spectrum cognitive method based on partial reconfiguration Download PDFInfo
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Abstract
本发明提供了一种基于部分重构的宽带频谱感知方法,将部分重构的思想应用于基于最小范数求解的内点法。本发明根据采样值,经过多次迭代运算,逐步调整,区别于传统的不进行调整的重构方法。使用的迭代次数不是固定的,随着重构后子信道的能量而改变。如果能量大,则降低迭代次数;能量小则增加迭代次数。本发明可以运用在稀疏度未知,甚至是稀疏度变化的宽带频谱场景中,而且在保证检测精度的情况下降低了运算时间,提高了检测的实时性。
The invention provides a wideband spectrum sensing method based on partial reconstruction, which applies the idea of partial reconstruction to the minimum Interior point method for norm solution. According to the sampling value, the present invention gradually adjusts through multiple iterative calculations, which is different from the traditional reconstruction method without adjustment. The number of iterations used is not fixed and varies with the energy of the reconstructed subchannels. If the energy is large, reduce the number of iterations; if the energy is small, increase the number of iterations. The present invention can be applied in the broadband spectrum scene where the degree of sparsity is unknown, or even the degree of sparsity changes, and reduces the calculation time while ensuring the detection accuracy, and improves the real-time performance of detection.
Description
技术领域technical field
本发明涉及无线通信领域,具体是一种基于部分重构的宽带频谱感知方法。The invention relates to the field of wireless communication, in particular to a wideband spectrum sensing method based on partial reconstruction.
背景技术Background technique
认知无线电(CR,Cognitive Radio)中,提供可靠通信和高效利用无线电频谱这两个目标决定了快速而准确地进行频谱感知的重要性。但在对宽带频谱进行感知时,所需的高采样速率及海量采样数据处理能力都对频谱感知的硬件提出了严峻的挑战。在现实中,宽带频谱上主用户占用的子频带数量远远小于总数,满足稀疏性。于是人们自然地联想到了压缩采样技术(CS,Compressed Sampling,也称作压缩感知,Compressive Sensing)。它表明只要信号是可压缩的或在某个变换域是稀疏的,就可用一个与变换基不相关的观测矩阵将变换所得到的高维信号投影到一个低维空间上,然后通过优化算法就可以从这些少量的投影中以高概率重构出原信号,最后根据重构后的信号进行处理,做出检测判决。压缩采样技术可以大大降低设备对采样率的要求,其中重构是压缩采样研究中关键的一步,如果无法快速而有效地根据采样值重构出原信号的话,那么压缩采样理论对于奈奎斯特采样定律的优越性也凸显不出来了。In cognitive radio (CR, Cognitive Radio), the two goals of providing reliable communication and efficient use of radio spectrum determine the importance of fast and accurate spectrum sensing. However, when sensing wideband spectrum, the required high sampling rate and massive sampling data processing capability pose severe challenges to the spectrum sensing hardware. In reality, the number of sub-bands occupied by primary users on the broadband spectrum is far less than the total number, which satisfies sparsity. So people naturally think of Compressed Sampling (CS, Compressed Sampling, also known as Compressed Sensing, Compressive Sensing). It shows that as long as the signal is compressible or sparse in a certain transformation domain, an observation matrix unrelated to the transformation base can be used to project the transformed high-dimensional signal onto a low-dimensional space, and then optimize the The original signal can be reconstructed with a high probability from these small number of projections, and finally processed according to the reconstructed signal to make a detection decision. Compressed sampling technology can greatly reduce the sampling rate requirements of equipment. Reconstruction is a key step in the research of compressed sampling. If the original signal cannot be reconstructed quickly and effectively according to the sampling value, then compressed sampling theory is very important for Nyquist. The superiority of the sampling law is also not highlighted.
目前,压缩采样的研究重点在于低速观测序列的获得和信号波形的高概率、高精度重构。其中常用重构算法主要分成三大类:基于l1范数最小的凸优化算法、基于l0范数最小贪婪算法以及组合算法。这些方法有的重构精度高,有的适用于数据量大的场合,但是共同的缺点有二:计算量巨大并且压缩比受原信号稀疏度的影响。此外,现有基于压缩采样的认知无线电宽带频谱感知技术均假设宽带频谱稀疏度是已知的。但是在CR场景中,由于主用户(PU,Primary User)与认知用户(或次级用户,SU,Second User)之间无法直接通信或信息交互,且主用户的频谱占用情况是动态变化的,所以宽带频谱的稀疏性这一先验信息并不容易得到。已有文献提出了稀疏度估计算法:先用小部分采样值快速估计实际稀疏度,根据估计值调整总的采样数后再进行运算。该方法不需要稀疏度的先验知识,但要进行额外采样。另一种保守的方法是根据认知用户长时间的统计观测来获得稀疏度的上界,以此确定压缩比。然而,这往往会造成采样数目过多,进一步增大之后信号重构的计算量。At present, the research focus of compressed sampling is on the acquisition of low-speed observation sequences and the high-probability and high-precision reconstruction of signal waveforms. Among them, the commonly used reconstruction algorithms are mainly divided into three categories: the convex optimization algorithm based on the minimum l 1 norm, the minimum greedy algorithm based on the l 0 norm, and the combination algorithm. Some of these methods have high reconstruction accuracy, and some are suitable for occasions with a large amount of data, but they have two common shortcomings: the calculation is huge and the compression ratio is affected by the sparsity of the original signal. In addition, the existing broadband spectrum sensing technologies based on compressed sampling for cognitive radio all assume that the wideband spectrum sparsity is known. However, in the CR scenario, since the primary user (PU, Primary User) and the cognitive user (or secondary user, SU, Second User) cannot directly communicate or exchange information, and the spectrum occupancy of the primary user is dynamically changing , so the prior information of the sparsity of the broadband spectrum is not easy to obtain. Algorithms for sparsity estimation have been proposed in the literature: first use a small number of sampling values to quickly estimate the actual sparsity, and then adjust the total number of samples according to the estimated value before performing the operation. The method does not require prior knowledge of sparsity, but additional sampling is required. Another conservative method is to obtain the upper bound of sparsity based on the long-term statistical observation of cognitive users, so as to determine the compression ratio. However, this often results in an excessive number of samples, which further increases the amount of computation for subsequent signal reconstruction.
另一方面,完全重构原信号在许多处理应用中并不是必须的。很多时候,我们只是希望从观测序列里提取有用信息或滤除后续处理中不感兴趣的信息。此时,试图通过观测序列重构出所有信号之后再来解决信号提取和滤波问题显然不是最好的选择。例如,在认知无线电系统中,认知用户只关心信道占用与否,而不关心信道的具体情况,因此,并不总是需要精确重构出原始信号。无论是最小化l1范数的重构算法或是贪婪算法,它们均经过多次迭代,逐步调整,最终得到最优解,是一个逼近的过程。基追踪降噪法(BPDN,BasisPursuit De-noising)是经典的基于l1范数最小的重构受噪声污染信号的算法。已有学者对该方法进行改进,但是着重于提高BPDN算法的精度或者是拓展其在其它稀疏噪声(如脉冲噪声)坏境下的性能。如何在满足精度要求的前提下减少计算量,还需要进一步研究。On the other hand, complete reconstruction of the original signal is not necessary in many processing applications. Many times, we just want to extract useful information from observation sequences or filter out uninteresting information in subsequent processing. At this time, it is obviously not the best choice to solve the signal extraction and filtering problems after trying to reconstruct all the signals through the observation sequence. For example, in a cognitive radio system, cognitive users only care about whether the channel is occupied or not, but not the specific conditions of the channel. Therefore, it is not always necessary to reconstruct the original signal accurately. Whether it is the reconstruction algorithm that minimizes the l 1 norm or the greedy algorithm, they all go through multiple iterations and gradually adjust to finally get the optimal solution, which is an approximation process. Basis Pursuit De-noising (BPDN, BasisPursuit De-noising) is a classic algorithm based on the minimum l 1 norm to reconstruct the signal contaminated by noise. Scholars have improved this method, but focused on improving the accuracy of the BPDN algorithm or expanding its performance in other sparse noise (such as impulse noise) environments. How to reduce the amount of calculation under the premise of meeting the accuracy requirements requires further research.
发明内容Contents of the invention
本发明为了解决现有宽带频谱感知技术中计算量巨大并且压缩比受原信号稀疏度影响的问题,提供了一种基于部分重构的宽带频谱感知方法,可以运用在稀疏度未知,甚至是稀疏度变化的宽带频谱场景中,而且在保证检测精度的情况下降低了运算时间,提高了检测的实时性。In order to solve the problem that the calculation amount is huge and the compression ratio is affected by the sparsity of the original signal in the existing wideband spectrum sensing technology, the present invention provides a wideband spectrum sensing method based on partial reconstruction, which can be used when the sparsity is unknown or even sparse In the wide-band spectrum scene with varying degrees, and while ensuring the detection accuracy, the calculation time is reduced and the real-time performance of the detection is improved.
本发明包括以下步骤:The present invention comprises the following steps:
1)认知用户对频谱进行采样并将低速采样序列传输给融合中心;1) The cognitive user samples the spectrum and transmits the low-speed sampling sequence to the fusion center;
2)融合中心运用内点法对原信号进行完全重构,记录迭代次数为T,更新迭代次数为T=T-1;2) The fusion center uses the interior point method to completely reconstruct the original signal, the number of recording iterations is T, and the number of updating iterations is T=T-1;
3)计算各个子信道的能量,最大值记为Jmax,最小的记为Jmin,定义门限值3) Calculate the energy of each sub-channel, the maximum value is recorded as J max , the minimum value is recorded as J min , and the threshold value is defined
Gmma=0.5(Jmax-Jmin); Gmma =0.5(Jmax- Jmin );
4)运用内点法进行部分重构;4) Use interior point method for partial reconstruction;
5)计算重构后各个信道的能量值,记录大于门限值Gmma的信道个数及子信号的编号,回馈给认知用户;5) Calculate the energy value of each channel after reconstruction, record the number of channels greater than the threshold Gmma and the serial number of the sub-signal, and feed it back to the cognitive user;
6)根据子信道的能量情况更新迭代次数,若存在子信道的能量大于2Gmma,或存在两个子信道能量值之差小于1.5Gmma,则在下一个检测周期,T=T+1,否则T=T-1;6) Update the number of iterations according to the energy of the sub-channel, if the energy of the sub-channel is greater than 2Gmma, or the difference between the energy values of the two sub-channels is less than 1.5Gmma, then in the next detection cycle, T=T+1, otherwise T=T -1;
7)重复步骤4)至步骤6);7) Repeat step 4) to step 6);
所述的运用内点法对原信号进行完全重构包括以下步骤:The complete reconstruction of the original signal using the interior point method includes the following steps:
1)输入误差容忍度ε和最大迭代次数Tmax;1) Input error tolerance ε and maximum number of iterations T max ;
2)根据目标频段频谱压缩检测模型y=Φx,初始化数据,设当前迭代次数T=0,x=0,u=1∈RN,定义一个比较大的数,记为λ,要求λ≥||2ΦTy||∞,其中Φ为M×N维观测矩阵,y是观测得到的序列,u为收敛误差;2) According to the target frequency band spectrum compression detection model y=Φx, initialize the data, set the current iteration number T=0, x=0, u=1∈R N , define a relatively large number, denoted as λ, and require λ≥| |2Φ T y|| ∞ , where Φ is the M×N dimensional observation matrix, y is the observed sequence, and u is the convergence error;
3)令T=T+1,通过计算搜索方向(Δx,Δu),其中H是海森矩阵,g是当前(x,u)的梯度;3) Let T=T+1, pass Calculate the search direction (Δx, Δu), where H is the Hessian matrix, and g is the gradient of the current (x,u);
4)计算步长s,s为向量{λ/|2ΦT(Φx-y)|}中的最小元素;4) Calculation step s, s is the smallest element in the vector {λ/|2Φ T (Φx-y)|};
5)更新(x,u)=(x,u)+s(Δx,Δu);5) Update (x, u) = (x, u) + s(Δx, Δu);
6)计算收敛误差u;6) Calculate the convergence error u;
7)重复步骤3)至步骤6)直到满足误差容忍度ε,即min||x||1s.t.||y-Φx||<ε或达到最大迭代数Tmax时停止。7) Repeat step 3) to step 6) until the error tolerance ε is satisfied, that is, min||x|| 1 st||y-Φx||<ε or stop when the maximum iteration number T max is reached.
所述的运用内点法进行部分重构包括以下步骤:The partial reconstruction using the interior point method includes the following steps:
1)令T=T+1,通过计算搜索方向(Δx,Δu),其中H是海森矩阵,g是当前(x,u)的梯度;1) Let T=T+1, pass Calculate the search direction (Δx, Δu), where H is the Hessian matrix, and g is the gradient of the current (x,u);
2)计算步长s,s为向量{λ/|2ΦT(Φx-y)|}中的最小元素;2) Calculation step size s, s is the smallest element in the vector {λ/|2Φ T (Φ x -y)|};
3)更新(x,u)=(x,u)+s(Δx,Δu);3) Update (x, u) = (x, u) + s(Δx, Δu);
4)计算收敛误差u;4) Calculate the convergence error u;
5)重复步骤1)至步骤4),迭代运行到更新后的迭代次数T时强制停止。5) Repeat step 1) to step 4), and iteratively stops when it reaches the updated number of iterations T.
本发明有益效果在于:将部分重构的思想应用于基于最小l1范数求解的内点法,提出了一种基于部分重构的频谱感知方法。本发明根据采样值,经过多次迭代运算,逐步调整,区别于传统的不进行调整的重构方法。使用的迭代次数不是固定的,随着重构后子信道的能量而改变。如果能量大,则降低迭代次数;能量小则增加迭代次数。本发明可以运用在稀疏度未知,甚至是稀疏度变化的宽带频谱场景中,而且在保证检测精度的情况下降低了运算时间,提高了检测的实时性。The beneficial effect of the invention is that: the idea of partial reconstruction is applied to the interior point method based on the minimum l 1 norm solution, and a spectrum sensing method based on partial reconstruction is proposed. According to the sampling value, the present invention gradually adjusts through multiple iterative calculations, which is different from the traditional reconstruction method without adjustment. The number of iterations used is not fixed and varies with the energy of the reconstructed subchannels. If the energy is large, reduce the number of iterations; if the energy is small, increase the number of iterations. The present invention can be applied in the broadband spectrum scene where the degree of sparsity is unknown, or even the degree of sparsity changes, and reduces the calculation time while ensuring the detection accuracy, and improves the real-time performance of detection.
附图说明Description of drawings
图1(a)为一个CR宽带频谱感知场景原始观测序列图形。Figure 1(a) is a graph of the original observation sequence of a CR broadband spectrum sensing scene.
图1(b)为一个CR宽带频谱感知场景运用内点法迭代25次完全重构后的序列图形。Figure 1(b) is a fully reconstructed sequence graph of a CR broadband spectrum sensing scene using the interior point method for 25 iterations.
图1(c)为一个CR宽带频谱感知场景迭代12次部分重构图形。Figure 1(c) is a partially reconstructed graph of a CR wideband spectrum sensing scene iterated 12 times.
图1(d)为一个CR宽带频谱感知场景迭代6次部分重构图形。Figure 1(d) is a partially reconstructed graph of a CR wideband spectrum sensing scene iterated 6 times.
图2为本发明运用于图1场景中迭代次数随检测周期的变化。Fig. 2 is the change of the number of iterations with the detection cycle when the present invention is applied to the scene in Fig. 1 .
图3为在100个检测周期中本发明同完全重构再检测所用时间的比较。Figure 3 is a comparison of the time taken by the present invention and a fully reconstructed retest over 100 test cycles.
具体实施方式detailed description
下面结合附图对本发明作进一步说明。The present invention will be further described below in conjunction with accompanying drawing.
本发明的核心思想在于:将压缩采样技术与认知无线电中针对宽带频谱的感知相结合。采用部分重构的方法对基于最小l1范数求解的内点法进行改进。令其迭代次数随着重构后子信道的能量而改变。本发明不仅可以应用于稀疏度恒定的宽带频谱环境中,在稀疏度时变的情况下也可以取得良好的检测效果。在保证精度的前提下,本发明可以降低运算时间,提高检测实时性。The core idea of the present invention is to combine the compressed sampling technology with the perception of wideband spectrum in cognitive radio. The interior point method based on the minimum l 1 norm solution is improved by partial reconstruction. Let the number of iterations change with the energy of the reconstructed sub-channel. The present invention can not only be applied in the broadband spectrum environment with constant sparsity, but also can achieve good detection effect in the case of time-varying sparsity. On the premise of ensuring the precision, the invention can reduce the operation time and improve the real-time performance of detection.
在认知无线网络中,频谱感知的任务是在保护主用户的前提下实现动态频谱接入,认知用户需要在每个采样周期内进行检测来判断该频段内是否存在主用户。为了减少感知节点的数据采集和存储,每个认知用户都采用模拟/信息转换器对宽带模拟信号直接进行信息获取。本发明讨论在不完全重构原信号的情况下,用部分重构后子信道的能量与门限值比较,由此对两种假设进行检验,做出判决。目标频段频谱压缩检测模型可以表示为:y=Φx。In cognitive wireless networks, the task of spectrum sensing is to realize dynamic spectrum access under the premise of protecting the primary user. Cognitive users need to detect in each sampling period to determine whether there is a primary user in the frequency band. In order to reduce the data acquisition and storage of sensing nodes, each cognitive user uses an analog/information converter to directly obtain information from broadband analog signals. The present invention discusses that in the case of not completely reconstructing the original signal, the energy of the partially reconstructed sub-channel is compared with the threshold value, thereby testing two hypotheses and making a decision. The spectrum compression detection model of the target frequency band can be expressed as: y=Φx.
对应于主用户不存在和存在两种情况,两种假设分别如下:Corresponding to the two cases where the primary user does not exist and exists, the two assumptions are as follows:
H0:x=ωH 0 :x=ω
H1:x=ω+sH 1 : x=ω+s
其中s=[s(0),...,s(N-1)]T与ω=[ω(0),...,ω(N-1)]T分别是某个信道内随机稀疏信号与噪声的抽样值序列,且s与ω之间相互独立。M为抽样点数,Φ为M×N维观测矩阵。Where s=[s(0),...,s(N-1)] T and ω=[ω(0),...,ω(N-1)] T are random sparse The sequence of sampled values of signal and noise, and s and ω are independent of each other. M is the number of sampling points, and Φ is an M×N-dimensional observation matrix.
当Φ满足有限等距约束限制时,s可以被重构。其求解的模型为:When Φ satisfies the finite isometric constraints, s can be reconstructed. The model it solves is:
min||x||1s.t.||y-Φx||<εmin||x|| 1 st||y-Φx||<ε
或写作无约束问题:or write the unconstrained problem:
min||y-Φx||+λ||x||1 min||y-Φx||+λ||x|| 1
对上述两个表达式求解的方法统称为基追踪降噪法(BPDN)。参数ε或λ的作用是用来控制稀疏性与允许误差之间的平衡,力求保证信号误差最小的同时尽可能使得信号的表示最稀疏。同其它的重构算法相比,BPDN算法对高斯白噪声的去噪效果比较好。但是,BPDN法的复杂度非常高。其要求采样个数M满足M>cK,c≈log2(N/K+1),时间复杂度为O(N3),K为稀疏度。本发明应用于稀疏度未知的场景,M取上界。The methods for solving the above two expressions are collectively called basis pursuit denoising method (BPDN). The role of the parameter ε or λ is to control the balance between sparsity and allowable error, and strive to ensure that the signal error is minimized while making the signal representation as sparse as possible. Compared with other reconstruction algorithms, BPDN algorithm has a better denoising effect on Gaussian white noise. However, the complexity of the BPDN method is very high. It requires the number of samples M to satisfy M>cK, c≈log 2 (N/K+1), the time complexity is O(N 3 ), and K is the degree of sparsity. The present invention is applied to the scene where the sparsity is unknown, and M takes the upper bound.
由上述描述可知BPDN其实质是一个最优化原理。注意到同样是带约束的最优化问题,线性规划(LP,Linear Program)的标准形式为:It can be seen from the above description that BPDN is essentially an optimization principle. Note that it is also an optimization problem with constraints. The standard form of linear programming (LP, Linear Program) is:
mincTx s.t.Ax=bminc T x stAx = b
其中,变量x∈Rm,cTx是目标函数,Ax=b是等式约束。显然,表达式min||x1s.t.||y-Φx||<ε和表达式mincTx s.t.Ax=b可以相互转换。因此解线性规划的方法可以用来解决BPDN问题。Among them, the variable x∈R m , c T x is the objective function, and Ax=b is the equality constraint. Obviously, the expression min||x 1 st||y-Φx||<ε and the expression minc T x stAx=b can be transformed into each other. Therefore, the method of solving linear programming can be used to solve the BPDN problem.
单纯形法是解决线性规划的通用方法。其理论依据是,若线性规划问题的可行域存在(约束条件之间不存在矛盾),则可行域是向量空间RN中的凸集。顶点所对应的可行解成为基本可行解。线性规划问题的最优值如果存在,则最优解必在该凸集的某顶点处达到。单纯形法的思路是:先找出一个基本可行解,计算该顶点处的目标函数值,看是否是最优解;若不是,比较周围相邻顶点的目标函数值,转到更小的那个顶点上,并重复这一过程。在单纯形法中,仅搜索相邻顶点,每步迭代的计算量很小,但是需要访问很多顶点才能找到最优解。在最坏情况下可能需要访问所有非最优的顶点。为了减少迭代步数,众学者提出了内点法,其替代的方法是在可行域内部沿着“最短路”移动,即下一次迭代时沿着使目标函数值取得最小值的最快方向。The simplex method is a general method for solving linear programming. The theoretical basis is that if the feasible region of the linear programming problem exists (there is no contradiction between the constraints), then the feasible region is a convex set in the vector space R N. The feasible solution corresponding to the vertex becomes the basic feasible solution. If the optimal value of the linear programming problem exists, the optimal solution must be reached at a vertex of the convex set. The idea of the simplex method is: first find a basic feasible solution, calculate the objective function value at the vertex to see if it is the optimal solution; if not, compare the objective function values of the surrounding adjacent vertices, and switch to the smaller one vertex, and repeat the process. In the simplex method, only adjacent vertices are searched, and the calculation amount of each iteration is small, but many vertices need to be visited to find the optimal solution. In the worst case all non-optimal vertices may need to be visited. In order to reduce the number of iteration steps, many scholars have proposed the interior point method. The alternative method is to move along the "shortest path" inside the feasible region, that is, to move along the fastest direction that minimizes the objective function value in the next iteration.
根据本发明的方法,首先运用内点法,根据采样序列对原信道的信号进行完全重构,具体实施步骤有:According to the method of the present invention, the interior point method is first used to completely reconstruct the signal of the original channel according to the sampling sequence, and the specific implementation steps include:
输入:误差容忍度ε,最大迭代次数Tmax;Input: error tolerance ε, maximum number of iterations T max ;
初始化:当前迭代次数T=0,x=0,u=1∈RN,定义一个比较大的数,记为λ,要求λ≥||2ΦTy||∞;Initialization: the current number of iterations T=0, x=0, u=1∈R N , define a relatively large number, denoted as λ, and require λ≥||2Φ T y|| ∞ ;
重构运算:Refactoring operation:
令T=T+1;Let T=T+1;
通过pass
计算搜索方向(Δx,Δu),其中H是海森矩阵,g是当前(x,u)的梯度;Calculate the search direction (Δx, Δu), where H is the Hessian matrix, and g is the gradient of the current (x,u);
计算步长s,s为向量{λ/|2ΦT(Φx-y)|}中的最小元素;Calculate the step size s, s is the smallest element in the vector {λ/|2Φ T (Φx-y)|};
更新:(x,u)=(x,u)+s(Δx,Δu);Update: (x,u)=(x,u)+s(Δx,Δu);
计算收敛误差u,Compute the convergence error u,
重复重构运算直到满足误差要求或达到最大迭代数则停止并输出结果,同时记录下所用迭代次数T。Repeat the reconstruction operation until the error requirement is met or the maximum number of iterations is reached, then stop and output the result, and record the number of iterations T used.
内点法在每步迭代中为了找到最好的移动方向通常需要考虑所有的可行方向。换言之,相较于单纯形法,内点法以每次迭代中较大的计算量换取迭代次数的减少。而实际在频谱感知中,通过在迭代过程中得到的可行解就能正确检测出主用户是否存。说明书附图1简单假设了一个CR宽带频谱感知场景,包含50个互不交叠的子信道,每个信道用10个数表示,选择3个子信道被占用且频谱幅值衡为1。信噪比为10dB。图1(a)是原始观测序列,即y,图1(b)是运用内点法完全重构后的序列,迭代次数为25,图1(c)和图1(d)都是部分重构的结果。其中图1(c)图迭代次数为12,图1(d)为6。显然,完全迭代后得到的重构信号比较准确,随着迭代次数的减少,重构的效果也越来越差。但这并不意味着会影响检测结果的准确性。实际上,在后两张图,通过肉眼仍可以辨别出哪些信道被占用。即使是部分重构,被主用户占用的子信道其能量也远远大于空信道的能量。The interior point method usually needs to consider all feasible directions in order to find the best moving direction at each iteration. In other words, compared with the simplex method, the interior point method trades a larger amount of computation in each iteration for a reduction in the number of iterations. In practice, in spectrum sensing, the existence of the primary user can be correctly detected through the feasible solution obtained in the iterative process. Figure 1 of the manual simply assumes a CR wideband spectrum sensing scenario, including 50 non-overlapping sub-channels, each channel is represented by 10 numbers, and 3 sub-channels are selected to be occupied and the spectrum amplitude balance is 1. The signal-to-noise ratio is 10dB. Figure 1(a) is the original observation sequence, that is, y, Figure 1(b) is the completely reconstructed sequence using the interior point method, and the number of iterations is 25, Figure 1(c) and Figure 1(d) are partial reconstructions result of the structure. The number of iterations in Figure 1(c) is 12, and that in Figure 1(d) is 6. Obviously, the reconstruction signal obtained after full iteration is more accurate, and the reconstruction effect becomes worse and worse as the number of iterations decreases. But this does not mean that the accuracy of the test results will be affected. In fact, in the last two pictures, it is still possible to tell which channels are occupied by the naked eye. Even with partial reconstruction, the energy of the sub-channel occupied by the primary user is far greater than that of the empty channel.
更新T=T-1,并计算各个子信道的能量,选择最大的记为Jmax,最小的记为Jmin。定义门限值Gmma=0.5(Jmax-Jmin),该门限值将在之后的检测中被运用。记录大于门限值Gmma的信道个数及子信号的编号,回馈给认知用户;认为这些信道中存在主用户,认知用户不能占用;Update T=T-1, and calculate the energy of each sub-channel, select the largest one as J max , and the smallest one as J min . A threshold value Gmma=0.5(J max -J min ) is defined, and this threshold value will be used in subsequent detections. Record the number of channels and sub-signal numbers greater than the threshold value Gmma, and feed back to cognitive users; think that there are primary users in these channels, and cognitive users cannot occupy them;
在第二个检测周期内,认知用户再次对频谱进行检测并将投影后的结果传输给融合中心。中心运用内点法进行部分重构但是当迭代次数到达更新后的T时强制停止。计算重构后各个信道的能量值,与Gmma比较并作出判断;In the second detection cycle, the cognitive user detects the spectrum again and transmits the projected result to the fusion center. The center uses the interior point method for partial reconstruction but is forced to stop when the number of iterations reaches the updated T. Calculate the energy value of each channel after reconstruction, compare it with Gmma and make a judgment;
依然是在该周期,分析子信道的能量。若存在子信道的能量大于2Gmma,则更新T=T+1;若存在两个子信道能量值之差小于1.5Gmma,同样T=T+1;否则T=T-1。Still in this period, the energy of the sub-channels is analyzed. If there is a sub-channel whose energy is greater than 2Gmma, update T=T+1; if there is a difference between two sub-channel energy values less than 1.5Gmma, also T=T+1; otherwise, T=T-1.
之后的检测周期操作与第二个检测周期一样,总结来说依照下述步骤进行。The operation of the subsequent detection cycle is the same as that of the second detection cycle, and in summary, proceed according to the following steps.
1、根据上一个检测周期更新的迭代次数进行部分重构;1. Perform partial reconstruction according to the number of iterations updated in the previous detection cycle;
2、根据部分重构后的结果计算各个子信道能量;2. Calculate the energy of each sub-channel according to the partially reconstructed results;
3、与门限值比较并作出判决;3. Compare with the threshold value and make a judgment;
4、更新迭代次数。4. Update the number of iterations.
根据对本发明的说明,和说明书附图2、3,本领域的技术人员应该不难看出,本发明可以被应用于认知无线电的宽带频谱检测中。根据信道环境和精度要求,自适应调整迭代次数。本发明不仅可以运用在稀疏度未知,甚至是稀疏度变化的宽带频谱场景中,而且在保证检测精度的情况下降低运算时间,提高检测的实时性。具有广泛的适用范围。According to the description of the present invention and the accompanying drawings 2 and 3 of the description, those skilled in the art should not be difficult to see that the present invention can be applied to wideband spectrum detection of cognitive radio. Adaptively adjust the number of iterations according to the channel environment and accuracy requirements. The present invention can not only be applied in the broadband spectrum scene where the degree of sparsity is unknown or even changes, but also reduce the calculation time and improve the real-time performance of detection under the condition of ensuring the detection accuracy. Has a wide range of application.
本发明具体应用途径很多,以上所述仅是本发明的优选实施方式,应当指出,对于本技术领域的普通技术人员来说,在不脱离本发明原理的前提下,还可以作出若干改进,这些改进也应视为本发明的保护范围。本发明申请书中未作详细描述的内容属于本领域专业技术人员公知的现有技术。There are many specific application approaches of the present invention, and the above description is only a preferred embodiment of the present invention. It should be pointed out that for those of ordinary skill in the art, some improvements can also be made without departing from the principles of the present invention. Improvements should also be regarded as the protection scope of the present invention. The contents not described in detail in the application of the present invention belong to the prior art known to those skilled in the art.
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