Low Complexity Minimum Mean Square Error Channel Estimation for Adaptive Coding and Modulation Systems

Performance of the Adaptive Coding and Modulation(ACM) strongly depends on the retrieved Channel State Information(CSI),which can be obtained using the channel estimation techniques relying on pilot symbol transmission.Earlier analysis of methods of pilot-aided channel estimation for ACM systems were relatively little.In this paper,we investigate the performance of CSI prediction using the Minimum Mean Square Error(MMSE)channel estimator for an ACM system.To solve the two problems of MMSE:high computational operations and oversimplified assumption,we then propose the Low-Complexity schemes(LC-MMSE and Recursion LC-MMSE(R-LC-MMSE)).Computational complexity and Mean Square Error(MSE) are presented to evaluate the efficiency of the proposed algorithm.Both analysis and numerical results show that LC-MMSE performs close to the wellknown MMSE estimator with much lower complexity and R-LC-MMSE improves the application of MMSE estimation to specific circumstances.

Demodulation method combining virtual reference interferometry and minimum mean square error for fiber-optic Fabry–Perot sensors

We propose a cavity length demodulation method that combines virtual reference interferometry（VRI） and minimum mean square error（MMSE） algorithm for fiber-optic Fabry–Perot（F-P） sensors. In contrast to the conventional demodulating method that uses fast Fourier transform（FFT） for cavity length estimation,our method employs the VRI technique to obtain a raw cavity length, which is further refined by the MMSE algorithm. As an experimental demonstration, a fiber-optic F-P sensor based on a sapphire wafer is fabricated for temperature sensing. The VRI-MMSE method is employed to interrogate cavity lengths of the sensor under different temperatures ranging from 28°C to 1000°C. It eliminates the ＂mode jumping＂ problem in the FFT-MMSE method and obtains a precision of 4.8 nm, corresponding to a temperature resolution of 2.0°C over a range of 1000°C. The experimental results reveal that the proposed method provides a promising, high precision alternative for demodulating fiber-optic F-P sensors.

Codebook selected beamforming algorithm for multiuser MIMO systems

For reducing the inter-user interference in multi-user multiple-input multiple-output（MU-MIMO） wireless communication systems,e.g.,MIMO-orthogonal frequency division multiplexing（MIMO-OFDM） systems,it is often desirable to the complex preprocessing at the transmitter.This paper proposes a multi-user beamforming algorithm with sub-codebook selection.Based on the minimal leakage criterion,the codebook selection,limited feed-forward and minimum mean square error（MMSE） detection are combined in the proposed algorithm.This avoids the complex channel matrix decomposition and inversion.Consequently,the computational complexity at the transmitter is significantly reduced.Simulation results show that the proposed algorithm performs better than existing beamforming algorithms.

Joint Source and Relay Precoding/Beamforming Algorithm for OFDM Multi-user Cooperative Relay Systems

In order to reduce the feedback load of multi-user orthogonal frequency division multiplexing （ OFDM ） -based wireless systems, a practiral limited bits feedback precoding algorithm is proposed with direct source-destination link based on amplify-and- forward cooperative relay network under frequency selective fading channels. Using joint minimum mean square error（MMSE） filter, the receiving decoding matrix is designed for each user in the paper. Source precoding （beamforming） matrix is optimized with convex function of weight mean square error （MSE）. Relay precoding matrix is obtained under MSE decomposition and convex optimization. The precoding matrix index is fed back for clustered subcarrier of OFDM with limited feedback. Then using interpolation algorithm, all precoding matrices are achieved at base station （BS） and relay nodes. Simulations indicate the effectiveness of the proposed limited feedback joint precoding and beam_formlng design. The proposed method can improve bit error rate （BER） performance and obtain better sum-rate performance in contrast to existing algorithms. It displays the BER performance is close to that of the unquantified precoding feedback method.

Statistical-mechanical analysis of multiuser channel capacity with imperfect channel state information

In this paper, the effect of imperfect channel state information at the receiver, which is caused by noise and other interference, on the multi-access channel capacity is analysed through a statistical-mechanical approach. Replica analyses focus on analytically studying how the minimum mean square error （MMSE） channel estimation error appears in a multiuser channel capacity formula. And the relevant mathematical expressions are derived. At the same time, numerical simulation results are demonstrated to validate the Replica analyses. The simulation results show how the system parameters, such as channel estimation error, system load and signal-to-noise ratio, affect the channel capacity.

Joint Channel and Multi-User Detection Empowered with Machine Learning

The numbers of multimedia applications and their users increase with each passing day.Different multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network systems.In this article,a fuzzy logic empowered adaptive backpropagation neural network(FLeABPNN)algorithm is proposed for joint channel and multi-user detection(CMD).FLeABPNN has two stages.The first stage estimates the channel parameters,and the second performsmulti-user detection.The proposed approach capitalizes on a neuro-fuzzy hybrid systemthat combines the competencies of both fuzzy logic and neural networks.This study analyzes the results of using FLeABPNN based on a multiple-input andmultiple-output(MIMO)receiver with conventional partial oppositemutant particle swarmoptimization(POMPSO),total-OMPSO(TOMPSO),fuzzy logic empowered POMPSO(FL-POMPSO),and FL-TOMPSO-based MIMO receivers.The FLeABPNN-based receiver renders better results than other techniques in terms of minimum mean square error,minimum mean channel error,and bit error rate.

ON THE PERFORMANCE OF ANALOG MULTIUSER DETECTION IN ULTRA-WIDEBAND TRANSMITTED-REFERENCE SYSTEM

The detection performance is evaluated for our proposed analog multiuser receiver in Ultra-WideBand (UWB) transmitted-reference system. In the presence of dense multipath and multi-access signals,the performance analysis is difficult due to the complicated waveform of received impulse. We develop an approach to analyze the steady-state Signal-to-Interference-plus-Noise (SINR) of the detector output. The multipath-spread impulse is fitted to an exponentially decaying profile in the analysis. A closed-form expression of steady-state SINR is further deduced for the proposed Least Minimum Square (LMS) detector. The analysis is validated by simulations in Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) channel respectively. Based on the theoretical results,the multipath delay spread is employed to determine the optimal width of the integration window of the detector.

LOW COMPLEXITY LMMSE TURBO EQUALIZATION FOR COMBINED ERROR CONTROL CODED AND LINEARLY PRECODED OFDM

The turbo equalization approach is studied for Orthogonal Frequency Division Multiplexing （OFDM） system with combined error control coding and linear precoding. While previous literatures employed linear precodcr of small size for complexity reasons, this paper proposes to use a linear precoder of size larger than or equal to the maximum length of the equivalent discrete-time channel in order to achieve full frequency diversity and reduce complexities of the error control coder/decoder. Also a low complexity Linear Minimum Mean Square Error （LMMSE） turbo equalizer is derived for the receiver. Through simulation and performance analysis, it is shown that the performance of the proposed scheme over frequency selective fading channel reaches the matched filter bound; compared with the same coded OFDM without linear precoding, the proposed scheme shows an Signal-to-Noise Ratio （SNR） improvement of at least 6dB at a bit error rate of 10 6 over a multipath channel with exponential power delay profile. Convergence behavior of the proposed scheme with turbo equalization using various type of linear precoder/transformer, various interleaver size and error control coder of various constraint length is also investigated.

Asymptotic sum rate of MMSE receivers in multi-user distributed antenna systems

In this paper, the asymptotic sum rate of a multi-user distributed antenna system （DAS） is analyzed. To mitigate inter-user interference, minimum mean squared error （MMSE） receivers are utilized to cooperatively process received signals in the uplink. It shows that inter-user interference is efficiently mitigated and the uplink sum rate of a multi-user DAS is greatly improved by adopting MMSE receivers. For very large number of users and remote antennas, the asymptotic uplink sum rate of MMSE receivers is derived by using virtue of the random matrix theory, which can be The approximation is verified to be quite accurate by Monte Carlo simply calculated in an iterative way simulations.

OFDM系统中一种A-MMSE信道估计算法

针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统中最小均方误差(Minimum Mean Squared Error,MMSE)信道估计算法误码率(BER)高的问题,提出一种平均最小均方误差(Averaged-Minimum Mean Squared Error,A-MMSE)信道估计算法。该算法首先基于802.11n标准而构造了一种新的导频结构,收发两端分别进行降采样和过采样处理,利用已知训练序列和导频获得信道频域响应。仿真结果表明,所提出的A-MMSE信道估计算法与传统的MMSE算法相比,在BER为10^(-3)时,信噪比改善了约8dB。因而所提出的信道估计算法能明显改善系统的BER性能。

非均匀网络中半径可调的ARDV-Hop定位算法

针对无线传感网络中传统DV-Hop(Distance Vector Hop)定位算法节点分布不均匀导致定位误差较大的问题,提出了非均匀网络中半径可调的ARDV-Hop(Adjustable Radius DV-Hop in Non-uniform Networks)定位算法。该算法通过半径可调的方式对节点间的跳数进行细化,用细化后呈小数级的跳数代替传统的整数级跳数,并建立了数据能量消耗模型,优化了网络传输性能。ARDV-Hop算法还针对节点分布不均匀的区域提出跳距优化算法:在节点密度大的区域,采用余弦定理优化跳距;密度小的区域,采用最小均方误差(Least Mean Square,LMS)来修正跳距。仿真实验表明,在同等网络环境下,与传统DV-Hop算法、GDV-Hop算法和WOA-DV-Hop算法相比,ARDV-Hop算法能更有效地降低定位误差.

面向高速移动环境的二级信号检测算法

正交时间序列复用(OTSM)可以以更低的复杂度实现类似正交时频空间(OTFS)调制的传输性能,为未来需要低复杂度收发器的高速移动性通信系统提供一种有前景的解决方法。针对现有的基于时域的高斯-赛德尔(GS)迭代均衡效率不高的问题,提出二级信号检测算法。首先在时域进行低复杂度线性最小均方误差(LMMSE)检测,其次采用连续超松弛(SOR)迭代算法进一步消除残余符号干扰。为进一步提高收敛效率和检测性能,对SOR算法进行线性优化得到改进SOR(ISOR)算法。仿真实验结果表明,与SOR算法相比,ISOR算法在增加较低复杂度前提下可以提升检测性能并加快算法收敛。与GS迭代算法相比,ISOR算法采用16QAM调制且误码率为10-4时有1.61 dB的增益。

SC-FDMA系统的MMSE-FSE算法分析

单载波频分多址(Single-Carrier Frequency Division Multiple Access,SC-FDMA)系统均衡器的输入信号通常是按符号间隔进行采样的,其对抽样时间十分敏感。在短波波段,由于多径反射显著,当多径延时接近符号周期长度时,对抽样时间敏感的缺点会被放大。针对短波信道的特征,研究了SC-FDMA系统的分数间隔均衡器(Fractional Spaced Equalizer,FSE)模型,通过与符号间隔均衡器对比发现,虽然符号间隔均衡器可以补偿接收信号的频率响应,但其对短时延衰落信道的补偿效果较差;FSE对于抽样时间的选择不敏感,在多径信道下能够获得更好的性能。链路仿真结果表明,在短时衰落信道环境下,FSE的译码性能比符号间隔均衡器有最大1.5 dB的增益。

基于SDW-MMSE的广义特征值稳健波束形成方法

最大输出信噪比(Signal-to-noise ratio,SNR)准则下,广义特征值(Generalized eigenvalue,GEV)波束形成存在复系数难以控制的问题,在复杂的声学环境中容易导致输出信号严重失真。针对复系数估计问题,本文提出一种基于最小均方误差(Minimum mean square error,MMSE)的复系数估计方法,并通过引入语音失真权重因子(Speech distortion weight,SDW),调节降噪效果和语音失真之间的权重关系,进而提出了基于SDW-MMSE的广义特征值稳健波束形成方法。通过最大似然法估计目标信号和噪音信号的功率谱,进而求解主广义特征向量。进一步基于SDW-MMSE估计复系数,将复系数与主广义特征向量相结合,从而得到基于SDW-MMSE的广义特征值稳健波束形成滤波向量。仿真实验结果表明,本文提出的波束形成方法可有效消除相干噪声和非相干噪声,具有输出信噪比高、语音失真少等稳健性能。

一种基于ASLC的数字波束抗干扰改进算法研究

针对阵列雷达系统在自适应抗干扰处理过程中设备复杂度高、期望信号信噪比下降等问题,在自适应旁瓣对消(ASLC)理论基础上,研究了一种改进的数字抗干扰处理算法。该算法分析了辅助天线单元独立设计引起的空间资源占用等问题,通过在天线阵列中灵活选取数字单元形成辅助信号的方式,优化阵列资源的同时增加了主阵列与辅助单元收到的干扰信号相关性,提高了系统的干扰抑制比;根据最小均方误差准则对辅助信号进行预加权处理,削减辅助波束接收的期望信号能量,改善了由于辅助波束接收信号的自相关矩阵中含有期望信号引起的期望信号相消问题。通过系统测试,验证了该技术的有效性,实测结果表明,该算法在简化天线阵列设计的同时,干扰调零深度达到了51.6 dB,而期望信号信噪比仅损失0.65 dB,解决了传统ASLC算法效率下降的难题,具有广泛的工程应用前景。

STAR-RIS辅助ISAC系统波束赋形优化方法

本文提出了将通信感知一体化(Integrated Sensing and Communication,ISAC)系统与同时透射和反射可重构智能表面(Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces,STAR-RIS)相结合的系统模型,解决了传统可重构智能表面RIS不能实现全空间通信与感知的问题;同时考虑在STAR-RIS上安装低成本的专用传感器以实现ISAC系统在STAR-RIS上执行目标感知的一种新颖有源架构,解决了雷达感知的严重路径损耗问题.本文旨在联合优化ISAC基站处的波束赋形和STAR-RIS的无源波束赋形,以最大化通信用户的加权和速率(Weighted Sum Rate,WSR),同时保证感知性能的最低信噪比(signal-to-noise ratio,SNR).为了解决该复杂非凸优化问题,交替优化基站波束赋形及STAR-RIS无源波束赋形.针对所提的满足雷达感知SNR最低要求下最大化WSR问题,基站处波束赋形的优化子问题等价为加权最小均方误差(Weighted Minimum Mean Square Error,WMMSE)问题,STAR-RIS处无源波束赋形优化子问题等价为分式规划(Fractional Programming,FP)问题.进一步,分别将优化的非凸子问题转化为二次约束二次规划(Quadratic Constraint Quadratic Programming,QCQP),并使用半正定松弛(Semidefinite Relaxation,SDR)技术将它们分别转化为凸的半正定规划(Semidefinite Programming,SDP)子问题进行迭代求解.仿真结果验证了所采用新型STAR-RIS辅助ISAC方案的优点和所提算法在提高WSR性能上的有效性.

Performance Analysis of ZF and RZF in Low-Resolution ADC/DAC Massive MIMO Systems

Large number of antennas and higher bandwidth usage in massive multiple-input-multipleoutput(MIMO)systems create immense burden on receiver in terms of higher power consumption.The power consumption at the receiver radio frequency(RF)circuits can be significantly reduced by the application of analog-to-digital converter(ADC)of low resolution.In this paper we investigate bandwidth efficiency(BE)of massive MIMO with perfect channel state information(CSI)by applying low resolution ADCs with Rician fadings.We start our analysis by deriving the additive quantization noise model,which helps to understand the effects of ADC resolution on BE by keeping the power constraint at the receiver in radar.We also investigate deeply the effects of using higher bit rates and the number of BS antennas on bandwidth efficiency(BE)of the system.We emphasize that good bandwidth efficiency can be achieved by even using low resolution ADC by using regularized zero-forcing(RZF)combining algorithm.We also provide a generic analysis of energy efficiency(EE)with different options of bits by calculating the energy efficiencies(EE)using the achievable rates.We emphasize that satisfactory BE can be achieved by even using low-resolution ADC/DAC in massive MIMO.

Low-complexity signal detection for massive MIMO systems via trace iterative method

Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.

基于统计模型的麦克风阵列语音增强算法

提出一种基于统计模型的麦克风阵列语音增强算法。为了估计第一通道语音信号谱幅度平方,假设第一通道语音信号谱的实部和虚部相互独立,并服从方差相同的高斯分布。先利用贝叶斯公式计算第一通道语音信号谱幅度平方的后验概率,再利用数学期望的计算公式得到第一通道语音信号谱幅度平方的最小均方误差估计,最后用第一通道观测语音信号的谱角度来估计第一通道语音信号的谱角度。实验结果表明,所提出的算法优于三种传统的语音增强算法。

基于图注意力网络的无线信道功率资源优化分配

为更好地优化无线自组织网络中的节点发射功率,进一步提升网络总体吞吐量,文章提出一种基于图神经网络理论的发射功率分配算法。该算法以“展开加权最小均方误差”迭代算法为总体框架,在迭代结构中引入“图注意力网络”模型,通过无监督学习机制对特定参数进行训练,在保持良好优化性能的同时加快算法收敛。仿真结果表明,文章提出的功率优化分配算法可在达到优于同类算法性能的前提下,显著降低计算复杂度。