Fast-Fading Channel Environment Estimation Using Linear Minimum Mean Squares Error-Support Vector Regression

Wireless Personal Communications - In this article, we propose a Linear Minimum Mean Squares Error-Support Vector Machine regression (LMMSE-SVR) approach which is applied to Long Term Evolution...     

一种改进型MMSE语音增强方法

本文提出了一种改进型语音短时谱最小均方误差(MMSE)估计的增强方法。通过在每一帧及帧内每一频点对无音的概率(SAP)进行估计,得到Ephraim和MalahMMSE估计算法的改进形式。对增强后的语音客观和主观测试表明:在低信噪比条件下,相对于传统的谱减法和MMSE估计方法,这种改进的方法能更好的抑制背景噪声和残留的“音乐噪声”。     

LTE上行链路中基于探测参考信号的信噪比估计

信噪比是衡量信道质量的一个重要参数,该文主要研究LTE(Long Term Evolution)系统中基于探测参考信号(Sounding Reference Signal, SRS)的信噪比估计方法。针对DASS(Difference of Adjacent Subcarrier Signal)算法在高信噪比下噪声估计误差较大的这一缺点,该文提出一种适用于SRS的改进DASS方法。该方法通过重新定义子载波的差分方式,减小了噪声估计的误差,并且由于对连续的3个SRS频点,仅需要估计一次噪声,使得该文方法的复杂度仅为原DASS方法的1/3。仿真结果表明,所提方法的估计性能优于其余的方法,特别是在低时延和中等时延信道下,高信噪比时的估计精度提高了约10倍。     

CDMA系统中信道估计最小均方误差的研究

CDMA系统中信道估计的精确度一直是人们所关心的问题。本文在研究了理想维纳滤波器的基础上，给出了这个信道估计均方误差的下界，并从信息论的角度探讨了均方误差与信道参数的熵以及互信息量之间的关系，从而最终得到了信道估计中最大的互信息量。     

一种基于最小均方误差准则的联合多小区信道估计算法

信道估计的准确程度直接影响到联合检测算法的性能。传统的信道估计算法将其他小区用户的信号均作为白噪声来处理,因此影响了信道估计的准确性。该文提出基于最小均方误差准则的联合多小区信道估计算法,由于改变了信道估计矩阵的结构,将邻小区强干扰用户也作为可以检测的用户信号来处理,降低了信道估计中噪声功率。该算法不仅适用于多小区联合检测,也可用于单小区联合检测。与传统的Steiner信道估计算法相比,新算法在存在邻小区同频干扰的情况下,能够很大程度提高信道估计的精度,进而大幅提升TD-SCDMA系统的性能。     

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

Performance of the Adaptive Cod- ing and Modulation （ACM） strongly depends on the retrieved Channel State Information （CSI）, which can be obtained using the chan- nel estimation techniques relying on pilot sy- mbol 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 compu- tational operations and oversimplified assum- ption, 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 algo- rithm. Both analysis and numerical results sh- ow that LC-MMSE performs close to the well- known MMSE estimator with much lower com- plexity and R-LC-MMSE improves the appli- cation of MMSE estimation to specific cir- cumstances.     

Best Minimum Summation Scheduler for Long Term Evolution

Resource scheduling in Long Term Evolution (LTE) is an open and rising issue. It has an enormous impact on the entire system performance. Due to the nature of LTE system, the scheduler has to be designed carefully. It has to overcome many challenges such as limited processing time and the high dynamic behavior. This paper proposes a novel scheduling policy for the MAC layer in LTE called the Best Minimum Summation (BMS). The main aim of this scheduling policy is to achieve high performance with low complexity. Three sub-schedulers have been developed. Each one of these schedulers deals with scheduling table in different dimension. The first one operates on the scheduling table through the user dimension (BMS.UE); while the second one operates on the scheduling table through the resource block dimension (BMS.RB). The third scheduler operates on the scheduling table correlating both of these dimensions (BMS.2D). All of the proposed solutions were intensively evaluated in a system level simulator. Three performance metrics were used which are throughput, error rate and fairness. The results have shown that the ability of the BMS.UE scheduler to outperform other existing schedulers of LTE.     

Monaural Speech Separation Based on Gain Adapted Minimum Mean Square Error Estimation

We present a new model-based monaural speech separation technique for separating two speech signals from a single recording of their mixture. This work is an attempt to solve a fundamental limitation in current model-based monaural speech separation techniques in which it is assumed that the data used in the training and test phases of the separation model have the same energy level. To overcome this limitation, a gain adapted minimum mean square error estimator is derived which estimates sources under different signal-to-signal ratios. Specifically, the speakers’ gains are incorporated as unknown parameters into the separation model and then the estimator is derived in terms of the source distributions and the signal-to-signal ratio. Experimental results show that the proposed system improves the separation performance significantly when compared with a similar model without gain adaptation as well as a maximum likelihood estimator with gain estimation.     

雷达高度表最小均方误差拟合高度估计算法研究

针对采用波门分裂法和半功率点法的传统雷达高度表,受噪声和地面各散射单元散射起伏的影响较大,进而影响高度估计的精度问题。文中提出了采用最小均方误差拟合高度估计算法,该算法基于地面回波解析模型,充分利用地面回波能量进行最小均方误差拟合高度估计,从而提高高度估计精度。仿真结果表明,在不同高度上,最小均方误差的精度均优于波门分裂法和半功率点法,降低了噪声和地面散射起伏对高度估计精度的影响。     

The Stability of Adaptive Minimum Mean Square Error Equalizers Using Delayed Adjustment

The necessary and sufficient conditions for the stability and convergence of adaptive minimum mean square error equalizers are developed. The analysis includes the effect of delays in the adjustment path, as occur, for instance, when the decoder in a data transmission system delays its output decisions.     

Joint Maximum-Likelihood CFO and Channel Estimation for OFDMA Uplink Using Importance Sampling

In this paper, the optimal carrier frequency offset (CFO) and channel estimator for an orthogonal frequency-division multiple-access (OFDMA) uplink based on a joint maximum-likelihood (ML) criterion is derived. Direct implementation of the resultant estimation scheme is challenging due to the need for a multidimensional exhaustive search in multi-CFO estimation. To solve this problem, an optimization theorem is exploited, and a computationally efficient method using an importance sampling technique is proposed. Without the need to provide an initial estimate, the proposed estimator guarantees the generation of the global optimal solution. Simulation results clearly verify the effectiveness of the proposed estimation scheme.      

上行信道估计在TD-LTE系统中的研究

LTE作为一个很有前景的通信标准,与现有通信标准相比,具有更高的传输速率和更好的传输质量,而上行信道估计是LTE系统中不可缺少的一个环节。在对现有LTE系统的两种信道检测算法分析的基础上,提出了一种兼具高准确度和低复杂度的改进算法,在使用原有算法对导频符号进行信道估计之前,先对其进行时域降噪处理,在插值后能够提高系统的估计准确性。仿真结果表明,改进后的算法与原算法相比,在不增加计算复杂度的前提下,能够较大程度地提高系统的估计性能,更适合第4代移动通信的要求。     

3GPP的长期演进计划

本文首先介绍了3GPP LTE的演进过程,随后详细说明了其在容量、系统性能、网络部署、RAT共存和交互、体系结构及移植、无线资源管理、复杂度等方面的演进需求,最后阐述了LTE的无线空中接口技术和系统结构的演进.     

上行3D-MIMO中利用结构稀疏低秩特性的信道估计算法

3维多输入多输出(3D-MIMO)系统能有效提升频谱效率,提高系统容量。但用户数和天线数的剧增,无法保证所有用户的导频都正交,给3D-MIMO信道估计带来估计精度下降和复杂度增加等问题。该文分析了上行3D-MIMO系统信道的结构稀疏特性和低秩特性,并基于这些特性提出一种信道估计算法,给出了算法的收敛性和复杂度。仿真结果表明估计算法能准确地恢复3D-MIMO的信道系数,并有较低的复杂度。     

Joint Channel and DOA Estimation for Multicarrier CDMA Uplink Transmissions

The use of antenna arrays in wireless communications makes it possible to estimate the directions of arrival (DOAs) of impinging waveforms. The latter can be exploited to enhance channel estimation accuracy or as an input for advanced mobile positioning systems. In this paper, we consider the uplink of a multicarrier code-division multiple-access (MC-CDMA) network in which the base station is endowed with multiple receiving antennas arranged in a uniform linear array. Transmission takes place over a multipath channel, and the goal is the joint estimation of the channel responses and DOAs of the uplink signals. In doing so, we follow a maximum-likelihood (ML) approach and assume that users transmit orthogonal training sequences to facilitate the task of separating the signals. This way, all unknown parameters are estimated independently of each other with affordable complexity. Theoretical analysis and computer simulations are used to assess the performance of the proposed scheme and make comparisons with existing alternatives.     

Decision Aided Uplink Compressive Channel Estimation for Massive MIMO Systems

Thank to the observation that in massive multi-input multi-output systems, the channels associated with different base station antennas may share common sparse support, the significant path delays can be accurately captured by only few pilots, leading to a reduction of pilot overhead. However, when the number of pilots is small, the path gains can not be accurately estimated and this limits the system performance. To solve this problem, in this paper we propose a decision aided compressive sensing based channel estimation scheme, which utilizes the decoded data to refine the channel estimation. This scheme can effectively improve the channel estimation without increasing the length of pilot sequence, which is confirmed by both analyses and simulation results.     

TD-SCDMA的HSPA＋和长期演进

为了进一步改进和增强现有3G技术和应对WiMAX的竞争，3GPP提出了新的演进方案HSPA＋和LTE计划。本文首先介绍了TD—SCDMA向未来的演进和发展趋势，然后重点阐述了其中长期的演进方案HSPA＋和LTE，深入分析了HSPA＋和LTE的关键技术，给出了演进过程中的对策。     

An Improvement of UMP‐BP Decoding Algorithm Using the Minimum Mean Square Error Linear Estimator

In this paper, we propose the modified uniformly most powerful (UMP) belief‐propagation (BP)‐based decoding algorithm which utilizes multiplicative and additive factors to diminish the errors introduced by the approximation of the soft values given by a previously proposed UMP BP‐based algorithm. This modified UMP BP‐based algorithm shows better performance than that of the normalized UMP BP‐based algorithm, i.e., it has an error performance closer to BP than that of the normalized UMP BP‐based algorithm on the additive white Gaussian noise channel for low density parity check codes. Also, this algorithm has the same complexity in its implementation as the normalized UMP BP‐based algorithm.