信噪比优化算法文档分类及特征权重公式改进研究

为了提高文档分类的效率,增强信息检索的有效性,提出了一种改良的基于信噪比的文本分类方法,改进了特征权重公式,优化了文本分类。在改良后的信噪比算法中,考虑了禁用词的剔除和近义词、同义词的归类合并,解决了传统信噪比对汉语应用存在的二个问题;在改进的权重公式里,引入了特征权重在同一文档不同部位的贡献率,据此特征词的检索准确率提高。

分组空时块码系统中的排序最大信噪比检测算法

提出并比较了应用于分组空时块码(G-STBC)系统中两种不同信道模型的排序最大信噪比(Max SNR)检测算法。该系统在发端对发射天线分组,每组进行独立的空时块码编码;在收端应用排序最大信噪比算法进行检测。由于空时块码提供了额外的时域约束,所以可以利用这个约束对信道模型进行变换,得到一个等效信道模型,然后再使用排序Max SNR检测;当然也可以直接利用原有的信道模型,即非等效信道模型进行检测。仿真结果表明:当接收天线数不少于发射天线数的一半时,采用等效信道模型下的排序Max SNR算法,系统的误码曲线就能保持陡降性;并且在收发天线数相同时,其性能总是优于非等效信道模型。

高速列车非平稳振动信号盲源分离方法及应用

高速列车具有若干时变激励源,传统的时频分析方法只能对观测的混合振动的总体强度分布、时频域结构加以分析,不能分离出与各振源对应的信号分量从而明晰振源状态与故障特征。盲源分离是一种可行的分析方法,但由于高速列车振动信号具有时变振源数目、时变信号长度、受车速调制的变频非平稳等特征,传统的盲源分离方法不适用。为了提高高速列车非平稳信号的盲源分离效果,基于自适应滤波理论提出全局最优信噪比盲源分离新方法,并对其可分离性的判别依据进行论证。新方法的有效性经仿真计算和实测数据分析得到验证。研究表明:新方法对高速列车时变非平稳信号的盲源分离效果优于传统的基于非线性函数的盲源分离方法和基于高阶累积量的盲源分离方法。

一种提高激光告警器性能的自适应方法

激光告警是激光对抗的重要前提和基本手段,如何在复杂背景噪声下识别来袭激光信号,进而准确报警是激光告警面临的首要问题。该文分析了激光告警设备检测概率与信噪比的关系,从关系式可以看出,为了提高激光告警设备的检测概率,需要增加激光信号的信噪比。提出了一种最大信噪比的自适应算法,进行了算法分析,并利用矩阵变换作出了算法简化,从而保证了系统信噪比的最佳化。最后对最佳权向量的求解这个问题,给出了适用于工程化的最佳权向量求解的简单迭代算法。

MU-MIMO用户调度和预编码跨层联合优化

针对多用户多输入多输出(Multi-User Multiple Input Multiple Output,MU-MIMO)系统中用户调度和预编码传统算法性能不佳和分层设计导致系统复杂度高的问题,利用最大信漏噪比(Signal to Leakage plus Noise Ratio,SLNR)算法的优越性,提出一种基于SLNR算法的用户调度和预编码的跨层联合优化策略,利用迭代搜索最优用户组,并在预编码中加入功率分配。仿真结果表明,新策略比传统算法能够提升系统吞吐量,并改善误码率性能,同时使系统具有较低的复杂度。

A novel blind deconvolution algorithm using single frequency bin

Former frequency-domain blind devolution algorithms need to consider a large number of frequency bins and recover the sources in different orders and with different amplitudes in each frequency bin,so they suffer from permutation and amplitude indeterminacy troubles. Based on sliding discrete Fourier transform,the presented deconvolution algorithm can directly recover time-domain sources from frequency-domain convolutive model using single frequency bin. It only needs to execute blind sepa-ration of instantaneous mixture once there are no permutation and amplitude indeterminacy troubles. Compared with former algorithms,the algorithm greatly reduces the computation cost as only one frequency bin is considered. Its good and robust per-formance is demonstrated by simulations when the signal-to-noise-ratio is high.

Robust Digital Non-Linear Self-Interference Cancellation in Full Duplex Radios with Maximum Correntropy Criterion

Full duplex radio increases the frequency efficiency but its performance is limited by the self-interference （SI）. We first analyze the multiple noises in the full duplex radio system and model such noises as an α - stable distribution. Then we formulate a novel non-Gaussian SI problem. Under the maximum correntropy criterion （MCC）, a robust digital non-linear self-interference cancellation algorithm is proposed for the SI channel estimation. A gradient descent based algorithm is derived to search the optimal solution. Simulation results show that the proposed algorithm can achieve a smaller estimation error and a higher pseudo signal to interference plus noise ratio （PSINR） than the well-known least mean square （LMS） algorithm and least square （LS） algorithm.

Blind Estimation of Long Code DSSS Signal Based On Subspace Tracking

Aiming at solving the blind estimation problem of dispreading spectrum sequence under low SNR, a spread-spectrum estimation algorithm based subspace tracking is studied in this paper. This method avoids the direct eigen decomposition, using the sliding window technique to obtain the code synchronization, then use segmentation subspace tracking method estimate spreading sequence and splice in a certain order to achieve pseudo-code blind estimation. The results show that the algorithm can complete the accurate estimation of PN code sequence in low SNR conditions, reduce the amount of data storage and be easy hardware implementation

Adaptive Decoding Algorithm Based on Multiplicity of Candidate Sequences for Block Turbo Codes

It is known that Block Turbo Codes （BTC） can be nearly optimally decoded by Chase-II algorithm, in which the Least Reliable Bits （LRBs） are chosen empirically to keep the size of the test patterns （sequences） relatively small and to reduce the decoding complexity. While there are also other adaptive techniques, where the decoder＇s LRBs adapt to the external parameter of the decoder like SNR （Signal Noise Ratio） level, a novel adaptive algorithm for BTC based on the statistics of an internal variable of the decoder itself is proposed in this paper. Different from the previous reported results, it collects the statistics of the multiplicity of the candidate sequences, i.e., the number of the same candidate sequences with the same minimum squared Euclidean distance resulted from the decoding of test sequences. It is shown by Monte Carlo simulations that the proposed adaptive algorithm has only about 0.02dB coding loss but the average complexity of the proposed algorithm is about 42% less compared with Pyndiah＇s iterative decoding algorithm using the fixed LRBs parameter.

An SNR-Adaptive Relaying Algorithm for Multi-Antenna Cooperative Networks

Multiple-input multiple-output(MIMO) and cooperative communications have been attracted great attention for the improvements of communication capacity, power consumption, and transmission coverage. The conventional fixed relaying protocols, amplify-and-forward(AF) and decode-and-forward(DF), have their own advantages and disadvantages, i.e. AF performs better than DF for low signal-to-noise ratio(SNR) region, while the reverse is true for high SNR region. Therefore, this paper proposes an SNR-adaptive forward(SAF) relaying scheme obtaining the advantages of both AF and DF. Furthermore, the proposed SAF does not need to switch between AF and DF when SNR changes. The main idea is to adaptively derive the soft information at the cooperative relay nodes based on the information of the received signal and the SNR. Besides, based on the theoretical analysis and the simulation results, it is affirmed that the proposed SAF achieves superior performance than both AF and DF for all SNRs. Moreover, the performance gain would be improved with the increasing number of parallel cooperative relay nodes.

蜂窝系统中分布式空域基站协作的仿真研究

对分布式空域基站协作进行了分析仿真,重点对两小区场景和三小区场景采用最大化层虚拟信干噪比算法的预编码技术进行了仿真验证,并与采用最大化虚拟信干噪比算法的预编码技术进行了比对。仿真结果表明通过分布式的基站协作预编码算法,可以有效地抑制小区间干扰,提高系统的性能;结果还表明层虚拟信干噪比算法比虚拟信干噪比算法有更好的性能,增加天线数量也可以提高系统的性能。

Performance of CO-OFDM system with RS-Turbo concatenated code

In this paper,the RS-Turbo concatenated code is applied to coherent optical orthogonal frequency division multiplexing(CO-OFDM) system.RS(186,166,8) and Turbo code with code rate of 1/2 are employed for RS-Turbo concatenated code.Two decoding algorithms,which are Max-Log-MAP algorithm and Log-MAP algorithm,are adopted for Turbo decoding,and the iteration Berlekamp-Massey(BM) algorithm is adopted for RS decoding.The simulation results show that the bit error rate(BER) performance of CO-OFDM system with RS-Turbo concatenated code is significantly improved at high optical signal to noise ratio(OSNR),and the iteration number is reduced compared with that of the Turbo coded system.Furthermore,when the Max-Log-MAP algorithm is adopted for Turbo decoding,the transmission distance of CO-OFDM system with RS-Turbo concatenated code can reach about 400 km without error,while that of the Turbo coded system can only reach about 240 km when BER is lower than 10^(-4) order of magnitude.