基于低采样率模数转换器的延时复用频分多址无源光网络

基于正交频分复用技术的无源光网络中,光网络单元为了获得其所属小部分下行数据,需高采样率模数转换器将所有频宽的信号恢复才能分出其所需要数据.同时正交频分信号峰均比很高,传输中容易引起非线性效应.为此,本文提出一种基于低采样模数转换器的延时复用频分多址无源光网络.在光线路终端将数据序列交错排序并在时域映射为正交幅度调制信号;再通过离散傅里叶变换扩频技术,将信号转换为频域信号并映射到子载波上.通过预先发送和回传训练信号,估测包括延时采样和低采样接收在内的信道频响;再将频域信号利用估测信息在光线路终端做预处理,从而使信号传输中的失真得到有效预补偿.本文实验演示了含有多个光网络单元的系统,对于含有M个光网络单元的无源光网络,模数转换器的采样率可以降低到1/M Nyquist采样率,实验中模数转换器的采样率可以降低到1/32 Nyquist采样率;由于下行信号通过光线路终端预处理实现失真预补偿,光网络单元接收到的信号不需要均衡,不需要傅里叶变换和傅里叶逆变换,避免了与之对应的相关计算量,降低了光网络单元的计算复杂度;由于使用了扩频技术,信号波形具有更低的峰均比,从而降低了非线性对信号的影响,增加了功率预算.此外,随着光网络单元的增加,信号的误码率几乎没有增加,光网络单元个数增加到32时,向前纠错极限为10~(-3)的功率代价小于0.5 dB;系统对光网络单元采样时刻偏离具有一定容限;25 km光纤传输的功率代价大约0.5 dB.理论和实验均证明本方案能够简化光网络单元,降低无源光网络的成本;与传统的无源光网络相比具有明显优势.     

Optical access transmission with improved channel capacity using non-orthogonal frequency quadrature amplitude modulation

A new modulation scheme that improves the bandwidth efficiency of an optical access link is proposed in this paper. It is implemented using non-orthogonal frequency shift keying (FSK) and quadrature amplitude modulation (QAM) simultaneously. We call the proposed technique non-orthogonal frequency quadrature amplitude modulation (NOFQAM). Especially, non-orthogonal FSK based on digital signal processing (DSP) is proposed for the first time. DSP-aided non-orthogonal FSK allows us to select RF carrier frequency irrespective of the channel bandwidth to allocate FSK symbols, unlike the existing orthogonal FSK. The non-orthogonality is implemented using a sequential correlation, where a received NOFQAM signal is correlated with only one RF carrier at a time by using DSP. After the sequential correlation is completed, both the FSK and the QAM symbols are recovered successfully and merged to generate the NOFQAM symbols. For experimental verification, a 20-km optical access link, which can transmit a 64-NOFQAM signal sampled at 10 Gsample/s, is implemented. We observed no increase in occupied channel bandwidth and a power penalty <0.5 dB compared to the 16-QAM scheme. A bit error rate lower than 10^?11 was obtained for the frequency spacing considered herein, which corresponds to 3% of the used RF carrier (1.5 GHz) when there are 50 sampling points per 64-NOFQAM symbol.     

The impact of ADC nonlinearity in a mixed-signal compressive sensing system for frequency-domain sparse signals

Compressive sensing (CS) holds new promises for the digitization of wideband frequency-domain sparse signals at sub-Nyquist rate sampling without compromising the reconstruction quality. In this paper, the impact of ADC nonlinearity in a CS receiver for frequency-domain sparse signals is investigated. In a mixed-signal CS system, signals are randomized before sampling. The signal spectrum at each building block in the mixed-signal CS system is analyzed and compared to a conventional Nyquist-rate sampling system. It is shown that the signal randomization in a mixed-signal CS system is able to spread the spurious energy due to ADC nonlinearity along the signal bandwidth, rather than the concentration of harmonic distortion on a few frequencies as it is the case for a conventional ADC. As a result, this paper shows that a significant ADC SFDR (Spurious Free Dynamic Range) improvement is achieved in a CS receiver when processing sparse signals. Simulation results are reported which are in good agreement with the qualitative analysis.     

LDPC coded DFT/DCT-spread SC-FDMA for image communication: A compressed sensing approach

A compressed sensing (CS)-based detector is proposed for the low-density parity-check (LDPC) coded single carrier frequency division multiple access (SC-FDMA) scheme. The proposed CS-based detector can be employed at the receiver of LDPC-coded SC-FDMA systems for efficient image communications over vehicular channels. The proposed detector employs a suitable sparse recovery algorithm. We have considered both the discrete Fourier transform (DFT)-based and the discrete cosine transform (DCT)-based SC-FDMA for mitigating the channel-induced dispersion at a low peak to average power ratio (PAPR). Additionally, both the linear equalizer (LE) and the decision feedback equalizer (DFE)-based SC-FDMA have been considered for image communication. The performance of the proposed technique is investigated using a number of image quality metrics. The qualities of the received images are also compared visually. The complexity of the proposed detector and that of the benchmark detectors are quantified. Furthermore, the performance and the complexity of the proposed system using some of the sparse recovery techniques are investigated and compared. Our simulations demonstrate that LDPC coded SC-FDMA using the compressed sampling matching pursuit (CoSaMP)-based CS detector can significantly improve the performance of image communication over vehicular channels.     

Frequency-domain channel estimation and equalization for shallow-water acoustic communications

A new frequency-domain channel estimation and equalization (FDE) scheme, combined with a new group-wise phase correction scheme, is proposed for single-carrier (SC) underwater acoustic communications systems employing single transducer and multiple hydrophones. The proposed SC-FDE scheme employs a 2N-point Fast Fourier Transform (FFT) to estimate and equalize the channel in frequency domain, where N is the number of symbols in a data block. Both the frequency-domain channel estimation and equalization are designed by the linear minimum mean square error criterion. Initial channel estimation is performed by a pilot signal block and later updates are achieved using the detected data blocks. The proposed phase correction scheme utilizes a few pilot symbols in each data block to estimate the initial phase shift and then correct it for the block to combat the large phase rotation due to the instantaneous Doppler drifts in the acoustic channels. Time-varying instantaneous phase drifts are re-estimated and compensated adaptively by averaging the phase variation across a group of symbols. The proposed SC-FDE and phase correction method is applied to the AUVFest’07 experimental data measured off the coast of Panama City, Florida, USA, June 2007. With the Quadrature Phase Shift Keying (QPSK) modulation and a symbol rate of 4 ksps, the proposed scheme achieves an average uncoded bit error rate on the order of 1×10^?4 for fixed-to-fixed channels with the source–receiver range of 5.06 km. For the moving-to-fixed source–receiver channels where the source–receiver range is 1–3 km, the multipath delay spread is 5 ms, the average Doppler shifts are ±20 Hz, and the maximum instantaneous Doppler drifts from the mean is ±4 Hz, the proposed scheme achieves an average uncoded bit error rate on the order of 1×10^?3.     

An embedded pilot power based channel estimation and low-complexity feedback equalization scheme for OTFS system

Orthogonal Time Frequency Space modulation (OTFS) has evolved as an astounding modulation technique for high-speed communication in a doubly dispersive channel. In any wireless communication system, channel estimation and equalization are essential at the receiver to recover the transmitted data. To accomplish this for the emerging OTFS based systems, a modified embedded pilot-based channel estimation technique and low complexity feedback equalization algorithm for integer Doppler shifts in the delay-Doppler domain are proposed in this paper. Our channel estimation scheme exploits embedded-pilot arrangement, and the symbol equalization relies on the Interference calculation and its mitigation iteratively. To achieve this we contemplate a prudent arrangement of symbols in the OTFS frame in such a way that the Guard symbols prevent the interference between data symbols and the pilot symbol at the receiver. Two distinct lumps of received data of the same OTFS frame will be engaged in channel estimation and data detection. An analytical expression of the theoretical Cramer Rao Lower Bound (CRLB) is derived and plotted for the proposed channel estimation scheme. The attained simulation results for Bit-Error-Rate (BER) under the proposed scheme show a significant error rate improvement over the Minimum Mean Squared Error (MMSE) equalization algorithm. Further, a lower computational complexity is also achieved in comparison with modified MMSE detection and MP detection algorithms.     

Distributed spatial media-based modulation for relay networks with RF mirrors

A novel distributed spatial media-based modulation scheme is proposed in this paper by cleverly utilizing distributed spatial modulation (DSM) and media-based modulation (MBM) principles. This proposed scheme is referred to as distributed channel modulation (DCM) for relay networks. In this scheme, decode-and-forward relaying protocol is adopted, and the channel states are exploited for transmitting extra information bits by using a number of radio frequency (RF) mirrors that are placed near each relay. To provide a fair comparison with the conventional state-of-the-art schemes, the symbol error rate (SER) performance of DSM scheme is evaluated. Besides, a low complexity detection technique known as iterative maximum ratio combining (i-MRC) is used in order to reduce the receiver complexity of the proposed scheme. Simulation results demonstrate that the proposed DCM scheme significantly outperforms DSM scheme for the same average rate. It is also shown that there is a negligible degradation in the SER performance of the proposed DCM scheme when i-MRC detection is used as compared to the performance with maximum likelihood (ML) detection. Furthermore, a significant reduction in the receiver complexity is achieved by using i-MRC detection technique in contrast to the results with ML detector. It has been also revealed that the proposed DCM scheme shows a performance drop of about 3 dB when the availability of an imperfect channel state information (CSI) is assumed with the presence of channel estimation errors (CEEs). Finally, simulation results have confirmed the analytical findings.     

Low-complexity soft-output signal detector based on AI-SSOR preconditioned conjugate gradient method over massive MIMO correlated channel

Massive multiple-input multiple-output (MIMO) techniques with a large number of antenna elements at base station (BS) have been proved as an alternative to provide potential opportunity to increase the spectrum and energy efficiency. However, in the system, there generally exists a spatial correlation effect due to insufficient antenna elements spacing and/or the lack of rich scattering at BS. The minimum mean square error (MMSE) method performs signal detection at the expense of large-scale matrix inversion operation. Thus, the conjugate gradient (CG) method has received a lot of attentions to realize the MMSE detection efficiently. Unfortunately, this efficiency can be compromised due to the ill-conditioned equalization matrix of MMSE method over the correlated channel environments. Moreover, the hard output signal detection exhibits a sharply degradation in performance for higher-order quadrature amplitude modulation (QAM). Therefore, the modern communication systems use the soft-output information, i.e., log-likelihood ratio (LLR) along with the forward error-correcting code (FEC) to achieve satisfactory performance. The LLR computation along with a higher-order QAM remains challenging due to the exhaustive search of symbol in the modulation constellation. In this paper, a low-complexity soft-output signal detector based on approximate inverse symmetric successive over-relaxation preconditioned conjugate gradient (AI-SSOR-CG-SOD) method is proposed to realize MMSE method detection for uplink multiuser massive MIMO correlated channel. In the proposed method, a new preconditioner, an AI-SSOR, which is based on the Neumann series approximation of the inverse of the conventional SSOR preconditioner is firstly developed to handle ill-conditioned matrix, and then incorporated with CG method to improve the convergence rate and performance. According to the characteristic of the Gray-coding that adjacent symbols in the constellation set have only one different bit, the constellation set is divided multiple times based on the bits of the inphase and the quadrature components of the symbol, which reduces the complexity of the LLR computation of the transmitted bits by avoiding the exhaustive search process. Simulation results show that the AI-SSOR preconditioner is robust against spatial correlation effect, and the proposed detector converges at 3 iterations. Simulation results also show that the proposed detector achieves a better trade-off between the complexity and the performance compared to other existing detectors.     

Shifted LT Code Security Scheme for Partial Information Encryption

The existing physical layer security technology based on fountain codes needs to ensure that the legal channel is superior to the eavesdropping channel; when the quality of the legal channel and the eavesdropping channel are close, the information security cannot be guaranteed. Aiming at this problem, this paper proposes a shifted Luby transform (SLT) code security scheme for partial information encryption, which is mainly divided into two stages, partial information encryption transfer and degree distribution adjustment. The main idea is that the source randomly extracts part of the information symbols, and performs XOR encryption with the random sequence containing the main channel noise sent by the legitimate receiver. Afterward, the degree distribution is adjusted using the number of transfer information symbols received by the legitimate receiver to improve the average degree of the encoded codewords. Since the eavesdropper can only obtain fewer information symbols in the initial stage, it is difficult to decode the generated coded symbols after the degree distribution adjustment, thereby ensuring the safe transmission of information. The experimental results show that, compared with other LT anti-eavesdropping schemes, even if the legitimate channel is not dominant, the proposed scheme still has better security performance and less decoding overhead.     

Bandwidth-efficient frequency-domain equalization for single carrier multiple-input multiple-output underwater acoustic communications

This paper proposes a single carrier (SC) receiver scheme with bandwidth-efficient frequency-domain equalization (FDE) for underwater acoustic (UWA) communications employing multiple transducers and multiple hydrophones. Different from the FDE methods that perform FDE on a whole data block, the proposed algorithm implements an overlapped-window FDE by partitioning a large block into small subblocks. A decision-directed channel estimation scheme is incorporated with the overlapped-window FDE to track channel variations and improve the error performance. The proposed algorithm significantly increases the length of each block and keeps the same number of training symbols per block, hence achieving better data efficiency without performance degradation. The proposed scheme is tested by the undersea data collected in the Rescheduled Acoustic Communications Experiment (RACE) in March 2008. Without coding, the 2-by-12 MIMO overlapped-window FDE reduces the average bit error rate (BER) over traditional SC-FDE schemes by 74.4% and 84.6% for the 400 m and 1000 m range systems, respectively, at the same data efficiency. If the same BER performance is required, the proposed algorithm has only 8.4% transmission overhead, comparing to over 20% overhead in other existing UWA OFDM and SC-FDE systems. The improved data efficiency and/or error performance of the proposed FDE scheme is achieved by slightly increased computational complexity over traditional SC-FDE schemes.     

基于指数再生窗Gabor框架的窄脉冲欠Nyquist采样与重构

基于Gabor框架的窄脉冲信号采样及重构效果已经得到验证, 其解决了有限新息率(finite rate of innovation, FRI)采样方法无法在波形未知的情况下重构出脉冲波形的问题.但是目前的Gabor框架采样系统的窗函数构造复杂且难以物理实现.本文将指数再生窗函数引入Gabor框架, 将窗函数序列调制部分简化为一阶巴特沃斯模拟滤波器, 构造了Gabor系数重构所需要的压缩感知(compressed sensing, CS)测量矩阵.为了使得测量矩阵满足信号精确重构所需的约束等距特性(restricted isometry property, RIP), 根据高阶指数样条函数能量聚集特性, 选择了最优的窗函数支撑宽度, 推导了信号重构所需的约束条件, 还对其鲁棒性进行了分析.本文通过仿真实验对上述分析进行了有效验证, 该系统可应用于测试仪器、状态监测、雷达及通信领域等多种背景下的窄脉冲信号采样与重构.     

双扩展水声信道的时延多普勒域稀疏估计

利用双扩展水声信道在时延-多普勒域存在的稀疏结构,将信道估计转化为压缩感知框架下的稀疏恢复问题可改善估计性能。但是,稀疏恢复经典方法如l_1范数、近似l₀范数无法适应水声信道时延-多普勒域稀疏度的动态变化,而匹配追踪(Matching pursuit,MP)、正交匹配追踪(Orthogonal Matching Pursuit,OMP)等贪婪类算法则存在着易进入局部最优解、二维搜索导致运算复杂度高等问题。提出在时延-多普勒域稀疏恢复的目标函数中引入非均匀范数约束(Non-uniform Norm Constraint,NNC),即在时延-多普勒域信道响应中根据每个时延-多普勒域位置的幅值分别分配为l₀或l₁范数约束,因而可通过不同范数约束组合的方式适应不同的时延-多普勒域稀疏度;同时,通过对非均匀范数代价函数进行梯度下降迭代求解并将梯度解投影至解空间推导了非均匀范数稀疏恢复的迭代求解方法,从而实现双扩展水声信道时延-多普勒估计。数值仿真和实验数据处理表明该算法相对经典方法有较明显的性能改善。通过仿真、海上水声通信实验结果可获取结论,利用时延-多普勒域稀疏特性的信道估计方法结合均衡器可有效提高双扩展信道条件下的水声通信性能。     

Adaptive Block-Based Compressed Video Sensing Based on Saliency Detection and Side Information

The setting of the measurement number for each block is very important for a block-based compressed sensing system. However, in practical applications, we only have the initial measurement results of the original signal on the sampling side instead of the original signal itself, therefore, we cannot directly allocate the appropriate measurement number for each block without the sparsity of the original signal. To solve this problem, we propose an adaptive block-based compressed video sensing scheme based on saliency detection and side information. According to the Johnson–Lindenstrauss lemma, we can use the initial measurement results to perform saliency detection and then obtain the saliency value for each block. Meanwhile, a side information frame which is an estimate of the current frame is generated on the reconstruction side by the proposed probability fusion model, and the significant coefficient proportion of each block is estimated through the side information frame. Both the saliency value and significant coefficient proportion can reflect the sparsity of the block. Finally, these two estimates of block sparsity are fused, so that we can simultaneously use intra-frame and inter-frame correlation for block sparsity estimation. Then the measurement number of each block can be allocated according to the fusion sparsity. Besides, we propose a global recovery model based on weighting, which can reduce the block effect of reconstructed frames. The experimental results show that, compared with existing schemes, the proposed scheme can achieve a significant improvement in peak signal-to-noise ratio (PSNR) at the same sampling rate.     

On the possibility of separating signals of pulsed ionization detectors from amplifier noise by using self-coincidences

A method for separating ionization detector pulses from amplifier noise is proposed. Two amplifiers are used. As usual, one amplifies the signal from the detector anode, while the other amplifies the signal from the cathode. Summing the signals, we obtain the total noise of both amplifiers during a pulse. Knowing the noise distribution law for each channel, we divide the noise sum integral by pairs of the noise integrals in the channels with an estimate of their probability. Subtracting the noise integral from the channel signal integral, we obtain possible values for the detector pulses with an indicator of their probabilities. A regime for continuous signal analysis without use of the coincidence scheme is also proposed.     

A measurement-domain adaptive beamforming approach for ultrasound instrument based on distributed compressed sensing: Initial development

High efficient acquisition of the sensor array signals and accurate reconstruction of the backscattering medium are important issues in ultrasound imaging instrument. This paper presents a novel measurement-domain adaptive beamforming approach (MABF) based on distributed compressed sensing (DCS) which seeks to simultaneously measure signals that are each individually sparse in some domain(s) and also mutually correlated with much few measurements under the Nyquist rate. Instead of sampling conventional backscattering signals at the Nyquist rate, few linear projections of the returned signal with random vectors are taken as measurements, which can reduce the amount of samples per channel greatly and makes the real-time transmission of sensor array data possible. Then high resolution ultrasound image is reconstructed from the few measurements of DCS directly by the proposed MABF algorithm without recovering the raw sensor signals with complex convex optimization algorithm. The simulated results show the effectiveness of the proposed method.     

一种基于选择性测量的自适应压缩感知方法

针对低信噪比条件下现有压缩感知系统重构性能严重恶化的问题,提出了一种基于选择性测量的自适应压缩感知结构.首先推导并分析了经过压缩测量的噪声的统计特性及其对重构性能的影响;然后基于输出能量最小化准则,设计了一种压缩域投影滤波联合噪声检测的自适应感知器,感知获得噪声子空间的位置信息;进一步利用该信息构造选择性压缩测量矩阵,智能选择测量信号,同时"屏蔽"噪声分量,极大提高了压缩测量值的信噪比.仿真结果表明,相对于现有压缩感知结构,选择性测量的压缩感知结构明显改善了含噪稀疏信号的重构性能,可更好地应用于吸波材料的前端特性分析、认知无线电的频谱感知等领域.     

混沌与湍流大气中的光通信

目前, 光无线通信的质量主要受到大气信道环境的制约, 大气信道中混沌介质与湍流的强烈扰动使得通信质量很差, 甚至通信中断. 提出了一种面到点的光无线通信机理: 利用面阵各单元的光信号在混沌介质中传输通道的空间非相干性, 通过桶探测器收集通过混沌介质的光信号的能量和, 平均各传输通道的交叉干扰, 降低混沌介质对光无线通信的影响; 利用随机噪声与随机编码的空间非相干性, 经过二阶相关运算, 构建新的信号传输方程, 减弱大气湍流及背景光对信号解码的干扰, 使得接收端并不需要窄带光学滤波器. 数值仿真和演示实验表明, 该光无线通信机理在混沌与湍流大气中的误码率为10^-4-10^-2, 能够实现复杂大气环境中的光通信, 在军事、抢险救援等方面具有重要应用价值.     

基于高阶累计量的大气光通信自适应信号处理

针对强湍流信道下信号衰落的特点,分析了对数正态分布模型与K分布模型的适用范围.基于K分布模型建立大气光通信接收信号模型,并给出了自适应最优门限检测方法.采用四阶和六阶累计量对强湍流信道参量进行估计,采用二阶累计量对其它高斯噪音进行估计,得到K分布参量及高斯噪音统计量的预测值,实现自适应门限更新.基于Monte Calro算法进行仿真,给出了门限更新算法对通信系统误码率的影响,同时分析了信号采样率对估计参量偏差的影响.计算表明,在强湍流情况下,大气光通信系统的误码率性能得到极大的改善,优于基于MLSD检测的接收机.     

联合重叠压缩感知法消除水声通信系统限幅噪声

针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)水声通信系统的高峰值平均功率比(Peakto-Average Ratio,PAPR)问题,在发射端采用了压扩变换法和限幅法联合的方法降低PAPR。由于限幅法会产生带内干扰和带外噪声,降低通信系统的误码性能,因此利用限幅噪声的稀疏性,在接收端采用压缩感知(Compressed Sensing,CS)法对限幅噪声进行估计和恢复。限幅噪声的估计受信道估计准确性的影响,为提高限幅噪声估计的准确度,提出了重叠压缩感知算法,在恢复限幅噪声的过程中利用了压缩感知信道估计法估计所得的信道信息和发射数据对限幅噪声进行估计,有效降低了限幅法对系统误码性能的影响。仿真和水池实验验证了该算法的有效性。     

Source Symbol Purging-Based Distributed Conditional Arithmetic Coding

A distributed arithmetic coding algorithm based on source symbol purging and using the context model is proposed to solve the asymmetric Slepian–Wolf problem. The proposed scheme is to make better use of both the correlation between adjacent symbols in the source sequence and the correlation between the corresponding symbols of the source and the side information sequences to improve the coding performance of the source. Since the encoder purges a part of symbols from the source sequence, a shorter codeword length can be obtained. Those purged symbols are still used as the context of the subsequent symbols to be encoded. An improved calculation method for the posterior probability is also proposed based on the purging feature, such that the decoder can utilize the correlation within the source sequence to improve the decoding performance. In addition, this scheme achieves better error performance at the decoder by adding a forbidden symbol in the encoding process. The simulation results show that the encoding complexity and the minimum code rate required for lossless decoding are lower than that of the traditional distributed arithmetic coding. When the internal correlation strength of the source is strong, compared with other DSC schemes, the proposed scheme exhibits a better decoding performance under the same code rate.