IDMA系统中基于序列叠加信道估计的功率分配方法

本文在序列叠加信道估计研究的基础上,以交织多址接入系统为模型,利用导频训练序列与信息序列不相关的特性在接收端估计出信道状态信息,并采用最大化有效信噪比的方法给出了训练序列与信息序列的最优功率分配策略.此外,本文讨论了不同统计特性的训练序列对信道估计性能的影响以及估计误差对信道容量的影响,通过仿真研究验证了相关的理论分析,并说明了本文功率分配算法较其它分配策略更有利于提高系统性能.     

基于频域波形合成的盲空间分集均衡器设计

针对频率选择性衰落信道下的多天线盲接收问题,本文提出了一种新的"合成器-均衡器"盲空间分集均衡器结构.基于频域波形合成、均衡器与软符号信息的联合处理框架,首先设计了频域BLOCK-SUMPLE迭代算法,对多路信号频域合成权值进行联合估计,实现频率选择性衰落信道下的波形合成.在此基础上,利用均衡器输出软符号信息重构合成参考,对权值相位予以修正,实现对等效合成信道的优化补偿.与传统盲空间分集均衡算法相比,上述算法实现无需训练序列辅助,并且优化了信号分集合并性能,降低了均衡器的设计难度.仿真结果表明,采用新型结构可有效改善衰落信道下的盲接收效果,逼近最佳空间分集均衡器性能.     

多径信道下MIMO系统发送天线数估计

为了解决认知无线电或信号截获中多径信道下MIMO系统发送天线数估计问题,首先分析了现有模型在多径信道下失效的原因,将MIMO多径信道模型等效变换出一种虚拟信道矩阵,从而建立多径信道下MIMO系统发送天线数估计模型;然后,利用随机矩阵理论中协方差矩阵最小特征值分布的相关研究结果,证明了时不变瑞利信道的协方差矩阵最小特征值收敛于第二类Tracy-Widom分布,分析了该特点对发送天线数估计的影响,并提出一种改进的RMT估计算法来估计多径信道下MIMO系统发送天线数.最后对改进算法进行了仿真验证,结果表明在低信噪比和小数据条件下,改进算法的估计性能相比RMT算法有较大提升.     

LTE系统中一种改进的天线端口数检测算法

在长期演进( LTE)系统中，发送天线端口数通过循环冗余校验掩码的方式隐含于物理广播信道( PBCH)中，终端在解读PBCH信道获取主信息块( MIB)的同时，需要正确检测PBCH采用的发送天线端口数。传统的检测方法尝试分别用1、2、4等3种发射天线数进行PBCH译码，直至正确译码为止，译码复杂度非常高。基于此，提出一种改进的天线端口数估计算法，提取各天线端口传输的小区参考信号序列做相关，通过相关值与相关系数门限值的比较直接快速获取基站的实际天线端口数。仿真结果表明：该算法相较于传统检测算法和功率检测算法在时间复杂度上分别提升了4个数量级和3倍；在信噪比高于0 dB时，所提检测算法较功率检测算法有4~6 dB的增益。     

MIMO SC-FDE系统的时域信道估计新算法

研究了循环正交训练序列的设计,并提出了适用于多入多出单载波频域均衡系统的时域信道估计新算法。首先选取Chu序列作为基准序列,并引入循环前缀与循环后缀构成循环正交序列,从而在接收端通过相关运算得到多天线信道的时域冲激响应估计。进一步,将长训练序列替换为多组连续重复且总长度相等的循环正交短训练序列,并利用时域相关和时间分集达到与原有长序列法相同的性能。仿真结果表明,所提时域信道估计算法不仅能够抑制天线间的干扰,而且有效地降低了噪声的方差,即使在低信噪比下也能获得较好的误比特率性能。     

基于隐含ZCZ训练序列的MIMO信道估计性能改进

 本文研究了采用隐含训练序列无直流偏移影响的MIMO频率选择性信道的估计问题.通过选用具有平衡特性的二进制ZCZ序列作为训练序列,可以在不增加任何复杂度的情况下直接消除直流偏移量的影响.由于每根天线的发射数据经过预处理后再算术叠加到训练序列上,从而消除了传统隐含估计方法中未知数据对信道估计性能的影响.本文推导了采用新方法的MIMO信道估计误差方差表达式,并从时域的角度给出了分析.理论分析和仿真结果表明,本文方法性能优于已有的隐含信道估计方法.     

基于叠加训练的非合作多用户/MIMO信道估计

提出一种基于叠加训练的单载波非合作多用户/MIMO系统的迭代信道估计与检测方案。首先利用变换域方法构造具有零周期互相关特性的训练序列,从而消除多天线间的相互干扰,实现基于一阶统计量的信道估计。然后采用联合符号检测的迭代信道估计方法,利用检测序列作为额外的“训练序列”来降低信息序列自身干扰。与现有的叠加训练信道估计方案比较,新方案中训练序列构造更加灵活,在低信噪比下信道估计均方误差和误码率性能更优,且复杂度更低,仿真结果表明了该方案的有效性。     

一种新的半盲联合信道数据估计算法

提出一种新的半盲联合信道估计与数据检测器,通过将递归-EM信道估计算法嵌入Turbo均衡器中,根据接收信号进行逐符号信道估计,不但能够适应时变信道,而且有效提高了联合信道数据估计收敛速度。仿真表明本算法对初始信道响应估计精度要求很低,从而大大降低了对训练序列长度的要求,使用很短的训练序列就能够达到较好性能,有效提高了频谱利用率。     

基于叠加训练序列和低复杂度频域Turbo均衡的时变水声信道估计和均衡

针对时变水声信道估计和均衡问题,该文提出基于叠加训练序列(ST)和低复杂度频域Turbo均衡(LTE)的时变水声信道估计和均衡(ST-LTE)算法。基于叠加训练序列方案,将训练序列和符号线性叠加,使得训练序列和符号信道信息一致;基于最小二乘算法,进行信道估计。基于频域训练序列干扰消除技术,在频域消除训练序列对符号的干扰;基于频域线性最小均方误差(LMMSE)均衡算法,通过先验、后验、外均值和方差的计算,实现低复杂度信道均衡(符号估计);基于Turbo均衡算法,软重构叠加训练序列和更新信道估计,进行均衡器和译码器的信息交换,利用编码冗余信息,大幅度提升信道均衡性能。进行仿真、水池静态通信试验(通信频率12 kHz,带宽6 kHz,采样频率96 kHz,符号传输速率4.8 ksym/s,训练序列和符号的功率比为0.25:1)和胶州湾运动通信试验(通信频率12 kHz,带宽6 kHz,采样频率96 kHz,符号传输速率3 ksym/s,训练序列和符号的功率比为0.25:1),仿真和试验结果验证了所提算法的有效性。     

基于叠加训练序列和低复杂度频域Turbo均衡的时变水声信道估计和均衡

针对时变水声信道估计和均衡问题,该文提出基于叠加训练序列(ST)和低复杂度频域Turbo均衡(LTE)的时变水声信道估计和均衡(ST-LTE)算法.基于叠加训练序列方案,将训练序列和符号线性叠加,使得训练序列和符号信道信息一致;基于最小二乘算法,进行信道估计.基于频域训练序列干扰消除技术,在频域消除训练序列对符号的干扰;基于频域线性最小均方误差(LMMSE)均衡算法,通过先验、后验、外均值和方差的计算,实现低复杂度信道均衡(符号估计);基于Turbo均衡算法,软重构叠加训练序列和更新信道估计,进行均衡器和译码器的信息交换,利用编码冗余信息,大幅度提升信道均衡性能.进行仿真、水池静态通信试验(通信频率12 kHz,带宽6 kHz,采样频率96 kHz,符号传输速率4.8 ksym/s,训练序列和符号的功率比为0.25:1)和胶州湾运动通信试验(通信频率12 kHz,带宽6 kHz,采样频率96 kHz,符号传输速率3 ksym/s,训练序列和符号的功率比为0.25:1),仿真和试验结果验证了所提算法的有效性.     

On the capacity of frequency- selective channels in training-based transmission schemes

Communication systems transmitting over frequency-selective channels generally employ an equalizer to recover the transmitted sequence corrupted by intersymbol interference (ISI). Most practical systems use a training sequence to learn the channel impulse response and thereby design the equalizer. An important issue is determining the optimal amount of training: too little training and the channel is not learned properly, too much training and there is not enough time available to transmit data before the channel changes and must be learned anew. We use an information-theoretic approach to find the optimal parameters in training-based transmission schemes for channels described by a block-fading model. The optimal length of the training interval is found by maximizing a lower bound on the training-based channel capacity. When the transmitter is capable of providing two distinct transmission power levels (one for training and one for data transmission), the optimal length of the training interval is shown to be equal to the length of the channel. Further, we show that at high SNR, training-based schemes achieve the capacity of block-fading frequency selective channels, whereas at low SNR, they are highly suboptimal.     

Power allocation of data dependent superimposed training

Data Dependent Superimposed Training （DDST） scheme outperforms the traditional superimposed training by fully canceling the effects of unknown data in channel estimator. In DDST, however, the channel estimation accuracy and the data detection or channel equalization performance are affected significantly by the amount of power allocated to data and superimposed training sequence, which is the motivation of this research. In general, for DDST, there is a tradeoff between the channel estimation accuracy and the data detection reliability, i.e., the more accurate the channel estimation, the more reliable the data detection; on the other hand, the more accurate the channel estimation, the more demanding on the power consumption of training sequence, which in turn leads to the less reliable data detection. In this paper, the relationship between the Signal-to-Noise Ratio （SNR） of the data detector and the training sequence power is analyzed. The optimal power allocation of the training sequence is derived based on the criterion of maximizing SNR of the detector. Analysis and simulation results show that for a fixed transmit power, the SNR and the Symbol Error Rate （SER） of detector vary nonlinearly with the increasing of training sequence power, and there exists an optimal power ratio which accords with the derived optimal power ratio, among the data and training sequence.     

Doubly-Selective Channel Estimation Using Data-Dependent Superimposed Training and Exponential Basis Models

Channel estimation for single-user frequency- selective time-varying channels is considered using superimposed training. The time-varying channel is assumed to be well- approximated by a complex exponential basis expansion model (CE-BEM). A periodic (non-random) training sequence is arithmetically added (superimposed) at low power to the information sequence at the transmitter before modulation and transmission. In existing first-order statistics-based channel estimators, the information sequence acts as interference resulting in a poor signal-to-noise ratio (SNR). In this paper a data-dependent superimposed training sequence is used to cancel out the effects of the unknown information sequence at the receiver on channel estimation. A performance analysis is presented. We also consider the issue of superimposed training power allocation. Several illustrative computer simulation examples are presented.     

分布式双向中继选择算法及用户功率分配

针对放大转发的瑞利双向中继信道的节点选择问题,提出了基于部分信道信息的分布式双向中继选择算法。算法通过计算双向链路的接收信噪比,推导出满足目标接收信噪比的转发阈值,各中继节点根据该阈值决定是否参与转发,从而实现分布式选择。此外,考虑用户总功率受限的情况,在分布式中继选择基础上提出了优化功率分配策略,使双向信道的接收信噪比更加接近。仿真结果表明,分布式中继选择算法与最优多中继算法的系统传输速率相似,计算复杂度大大降低,尤其是在中继数目增大的情况下更加明显。优化功率分配策略能进一步提高系统能量效率,在相同性能下可节省7%左右的功率。     

The impact of both a priori information and channel estimation errors on the MAP equalizer performance

To combat the effects of intersymbol interference (ISI), the optimal equalizer to be used is based on maximum a posteriori (MAP) detection. In this paper, we consider the case where the MAP equalizer is fed with a priori information on the transmitted data and propose to study analytically their impact on the MAP equalizer performance. We assume that the channel is not perfectly estimated and show that the use of both the a priori information and the channel estimate is equivalent to a shift in terms of signal-to-noise ratio (SNR) for which we provide an analytical expression. Simulation results show that the analytical expression approximates well the equalizer behavior.     

Adaptive Interference Suppression for the Downlink of a Direct Sequence CDMA System with Long Spreading Sequences

A simple approach for adaptive interference suppression for the downlink (base-to-mobile link) of a direct sequence (DS) based cellular communication system is presented. The base station transmits the sum of the signals destined for the different mobiles, typically attempting to avoid intra-cell interference by employing orthogonal spreading sequences for different mobiles. However, the signal reaching any given mobile passes through a dispersive channel, thus destroying the orthogonality. In this paper, we propose an adaptive linear equalizer at the mobile that reduces interference by approximately restoring orthogonality. The adaptive equalizer uses the pilot's spreading sequence (which observes the same channel as the spreading sequence for the desired mobile) as training. Simulation results for the linear Minimum Mean Squared Error (MMSE) equalizer are presented, demonstrating substantial performance gains over the RAKE receiver. Long spreading sequences (which vary from symbol to symbol) are employed, so that the equalizer adapts not to the time-varying spreading sequences, but to the slowly varying downlink channel. Since the inter-cell interference from any other base station also has the structure of many superposed signals passing through a single channel, the adaptive equalizer can also suppress inter-cell interference, with the tradeoff between suppression of intra- and inter-cell interference and noise enhancement depending on their impact on the Mean Squared Error (MSE).     

How much training is needed in multiple-antenna wireless links?

Multiple-antenna wireless communication links promise very high data rates with low error probabilities, especially when the wireless channel response is known at the receiver. In practice, knowledge of the channel is often obtained by sending known training symbols to the receiver. We show how training affects the capacity of a fading channel-too little training and the channel is improperly learned, too much training and there is no time left for data transmission before the channel changes. We compute a lower bound on the capacity of a channel that is learned by training, and maximize the bound as a function of the received signal-to-noise ratio (SNR), fading coherence time, and number of transmitter antennas. When the training and data powers are allowed to vary, we show that the optimal number of training symbols is equal to the number of transmit antennas-this number is also the smallest training interval length that guarantees meaningful estimates of the channel matrix. When the training and data powers are instead required to be equal, the optimal number of symbols may be larger than the number of antennas. We show that training-based schemes can be optimal at high SNR, but suboptimal at low SNR.     

A Novel Interpolation Equalization Technique for Multicarrier Modulation/Demodulation Systems

This paper presents a novel multitap interpolation equalization technique for filter bank-based multicarrier modulation/demodulation systems. The proposed technique is based on the equalization of the channel fractional delay in each subchannel in time synchronization with the constituent receiver side decimator. The proposed synchronization is achieved by combining a subset of the polyphase components of the analysis filter output signal after having passed through a bank of interpolation equalizers. The resulting multitap interpolation equalization permits a trade-off between various equalization parameters, such as the number of used polyphase components, the length of the equalizer, and the interchannel interference terms, making it possible to achieve a high signal-to-noise ratio (SNR) involving moderate equalization cost or a moderate SNR involving very low equalization cost. Simulation results for the standard carrier serving area loop show that the proposed equalization technique gives rise to 15 dB improvement in SNR compared to the output combiner equalization technique and can achieve an SNR close to the matched-filter bound for the channel by employing a reasonable equalizer length. Compared to the output combiner equalization technique, the proposed equalization technique involves around three times less the storage requirement at the same computational cost or around three times less the computational cost at the same storage requirement for equalizer training. Two suboptimal solutions are also proposed to simplify the equalizer training at only a minor loss in SNR.