TRAFFIC CHANNEL SIR ESTIMATION BASED ON REVERSE PILOT CHANNEL IN cdma2000

Signal-to-Interference Ratio(SIR) is a very important metric of communication link quality. For wireless cellular systems, several control mechanisms, such as power control mechanisms, rate control mechanisms, and allocation of radio resource, are based on SIR estimation.In previous researches, most of researchers concentrated on WCDMA systems, in which pilot symbol is time-multiplexed with data symbol; the method developed in this case is not feasible for cdma2000 systems where pilot symbol is code-multiplexed with data symbol. This paper first develops the SIR estimators based on the reverse pilot channel and then derives the approximate analytic expression for its Mean Squared Error (MSE) function, the accuracy of which is validated through simulation. It is shown that the MSE of the new SIR estimator is significantly smaller than that of other widely used SIR estimators, especially in low SIR case. Finally, the estimate quality of the proposed method is further improved by long-termly averaging the sample interference.

Fractal theory based dynamic mesh grouping scheme for efficient cognitive pilot channel design

As one of the key characteristics in cognitive wireless networks(CWNs),network environment awareness techniques have received much attention recently.Instead of traditional spectrum sensing technology that suffers from problems of miss detection,false alarm,hidden node,and inefficiency,cognitive pilot channel(CPC) technology has been proposed as one of the candidate solutions for an efficient and accurate network information delivery scheme to user equipment(UE).The aim of the CPC technology is to provide the necessary information for the reconfiguration of the UE using the public signaling channel.To ensure the efficient information delivery,the whole geographical area covered by the CPC is divided into square meshes of the same size.Moreover,two typical network information transmission modes for CPC deployment are also proposed:broadcast CPC mode and on-demand CPC mode.To further improve the efficiency of network information delivery,an efficient dynamic mesh grouping scheme has been designed which is based on the fractal theory for the broadcast CPC mode,where adaptive rectangular sized meshes are used to approximately cover the whole area.Compared to the traditional fixed size mesh division strategy,results show that the proposed dynamic mesh grouping scheme significantly reduces the number of meshes by grouping similar meshes together,and the average delay of receiving CPC information on the UE side is therefore reduced.

Pilot Placement for Time-Varying MIMO OFDM Channels with Virtual Subcarriers

The rapid time-variation of a fading multipath environment can impair the performance of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO OFDM). This paper proposes a pilot placement method for MIMO OFDM systems under time-varying channels with the guard band. The time-varying channel is described by complex exponential basis expansion model (BEM). We discuss the least square (LS) channel estimation to obtain the minimum mean square error (MSE) and derive the pilot allocation that can satisfy the minimum MSE with regard to guard band in time-varying channels. It is shown that optimal pilot clusters can distribute non-uniformly in frequency domain and minimize the MSE. We generalize our scheme over G OFDM symbols and compare it with comb pilots. It is demonstrated that the proposed approach is more effective than previous work. Simulation results validate our theoretical analysis.

The Tight Bound for the Number of Pilots in Channel Estimation for OFDM Systems

Coherent detection in OFDM systems requires accurate channel state information (CSI) at the receiver. Channel estimation based on pilot-symbol-assisted transmissions provides a reliable way to obtain CSI. Use of pilot symbols for channel estimation, introduces overhead and it is desirable to keep the number of pilot symbols as minimum as possible. This paper introduces a new tight bound for the number of pilots in channel estimation using adaptive scheme in OFDM systems. We calculate the minimum number of necessary pilots using two approaches. The first approach for the number of pilots is obtained based on Doppler frequency shift estimation and the second approach is acquired based on channel length estimation using second order statistics of received signal. Finally we obtain the tight bound for the number of pilots using attained values.

Pilot Based Channel Estimation in Broadband Power Line Communication Networks

In this paper pilot based channel estimation is being considered for broadband power line communication (BPLC) networks witch used orthogonal frequency division multiplexing (OFDM) in order to transmit high rate data. To estimate channel in time or frequency some pilot must be used. Number of these pilots and deployment of them is very important for proper estimation in different channel with varying time and frequency. Carrier sense multiple access (CSMA) and hybrid multiple access protocol are taken into consideration in MAC sub-layer. Multilayered perceptions neural network with backpropagation (BP) learning channel estimator algorithm with different pilot deployment compare to classic algorithm in for channel estimating. Simulation results show the proposed neural network estimation decreases bit error rate and therefore network throughput increases.

Pilot and Channel Estimation for WiMAX System

Orthogonal Frequency Division Multiplexing(OFDM)is very suitable for high data rate transmission in wide band wireless channel for the excellent capability to mitigate the frequency selective fading and Inter-Symbol Interference(ISI).In WiMAX system,Orthogonal Frequency Division Multiple Access(OFDMA)is adopted as the basic multiple access scheme,and different pilot patterns has been defined for both uplink and downlink channels.Pilot pattern should be changed,especially when Multiple Input Multiple Output(MIMO)technique is combined with OFDMA,in order to support multiple antennas.There are five pilot patterns in the WiMAX-MIMO-OFDMA system,namely:Downlink-Partially Used Sub-Channel(DL-PUSC),Downlink-Fully Used Sub-Channel(DL-FUSC),Downlink-Optional Fully Used Sub-Channel(DL-OFUSC),Uplink-Partially Used Sub-Channel(UL-PUSC)and Uplink-Optional Partially Used Sub-Channel(UL-OPUSC).Moreover,by analyzing the simulation results of time domain Least Square(LS),frequency domain LS,and Fast Fourier Transform(FFT)-based channel estimation algorithms,the best pilot pattern can be found.Based on the simulation comparison of several channel estimation methods in the WiMAX-MIMO-OFDMA system presented in this article,the best channel estimation for each pilot pattern is concluded.

Hybrid pilots assisted channel estimation algorithm for MIMO-OFDM systems

A hybrid pilots assisted channel estimation algorithm for multiple input multiple output（MIMO） orthogonal frequency division multiplexing（OFDM） systems under low signal-to-noise ratio（SNR） and arbitrary Doppler spread scenarios is proposed.Motivated by the dissatisfactory performance of the optimal pilots（OPs） designed under static channels over multiple OFDM symbols imposed by fast fading channels,the proposed scheme first assumes that the virtual pilot tones superimposed at data locations over specific subcarriers are transmitted from all antennas,then the virtual received pilot signals at the corresponding locations can be obtained by making full use of the time and frequency domain correlations of the frequency responses of the time varying dispersive fading channels and the received signals at pilot subcarriers,finally the channel parameters are derived from the combination of the real and virtual received pilot signals over one OFDM symbol based on least square（LS） criterion.Simulation results illustrate that the proposed method is insensitive to Doppler spread and can effectively ameliorate the mean square error（MSE） floor inherent to the previous method,meanwhile its performance outmatches that of OPs at low SNR region under static channels.

Optimal Spacing Design for Pilots in OFDM Systems over Multipath Fading Channels

In wireless orthogonal frequency division multiplexing (OFDM) systems, the time-varying channel is often estimated by algorithms based on pilot symbols. Such an estimator, however, requires statistical prior knowledge that is not easily obtained. Therefore, the pilot tones have to be close enough to fulfill the sampling theorem. In this case the statistical knowledge of the channel is not required to reconstruct correctly the channel impulse response (CIR). This paper explores the optimal placement and number of pilot symbols, we investigate optimal training sequences in OFDM systems and we analyze the number of pilot symbols required to fulfill the sampling theorem. Using a general model for a multipath slowly fading channel, the approach is based on the LS as a criterion of channel estimation while the channel interpolation is done using the piecewise-constant interpolation compromising between complexity and performance. Simulation results demonstrate the good performance of our approach.

Analysis and Comparison of Time Replica and Time Linear Interpolation for Pilot Aided Channel Estimation in OFDM Systems

This paper analyzes and compares two time interpolators, i.e., time replica and time linear interpolator, for pilot aided channel estimation in orthogonal frequency division multiplexing (OFDM) systems. The mean square error (MSE) of two interpolators is theoretically derived for the general case. The equally spaced pilot arrangement is proposed as a special platform for these two time interpolators. Based on this proposed platform, the MSE of two time interpolators at the virtual pilot tones is derived analytically;moreover, the MSE of per channel estimator at the entire OFDM symbol based on per time interpolator is also derived. The effectiveness of the theoretical analysis is demonstrated by numerical simulation in both the time-invariant frequency-selective channel and the time varying frequency-selective channel.

OFDM叠加导频联合信道估计和检测方法

针对现有正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统信道估计和迭代检测算法中频谱效率低和鲁棒性差等问题,提出了一种基于酉近似消息传递和叠加导频的信道估计与联合检测方法。首先,在软调制/解调中叠加导频对正交幅度调制的星座点进行预处理,检测时将叠加的导频作为频域符号的先验分布,利用置信传播算法进行调制和解调,实现检测模型的简化。然后,应用因子图-消息传递算法对OFDM传输系统和信道进行建模和全局优化,引入酉变换加强信道估计算法的鲁棒性。最后,建立OFDM仿真环境对现有方法进行仿真分析。仿真结果表明,相对于现有的独立导频类算法,所提算法能够以相同复杂度显著提升OFDM系统的频谱效率和鲁棒性。

无蜂窝大规模MIMO系统信道估计中基于加权图的叠加导频分配

针对无蜂窝大规模多输入多输出(cell-free massive multiple-input multiple-output, CF-mMIMO)系统信道估计中导频污染问题,提出了基于加权图的叠加导频(superimposed pilot, SP)分配方案。首先分析导频污染对SP信道估计的影响,引入一种全新度量表示用户间潜在导频污染程度;其次根据接入点与用户间大尺度衰落系数完成用户加权干扰图构建,将系统吞吐量最大化问题转化为有容量最大k切割问题求解;最后采取顶点交换局部搜索算法实现SP次优分配。仿真结果表明:本文方案能够有效地改善SP信道估计误差以及系统吞吐量。

基于Pilot的OFDM系统定时和频率同步算法

为改善系统性能,提出一种将近似最大似然法与导频相结合的方法。首先对近似最大似然法的定时函数,设定门限,以此确定一个粗时偏估计范围,再利用导频的定时函数,按一定规则,在该范围内搜索出精确的时偏,同时FFT之前记录下的对应于该时刻的频偏即为估计出的系统频偏。仿真表明,该算法能改进多径环境下的同步性能,尤其是在首径非能量最大径的环境下,提高系统的性能。

面向宽带航空数据链复杂多变应用场景的信道估计方法

针对复杂多变航空信道场景导致宽带航空数据链信道参数难以精确估计的问题,以及小体积、低功耗等航空通信工程应用需求,提出了一种几何距离插值联合自适应滤波的高性能低复杂度信道估计方法。首先,根据宽带航空数据链传输信道的特点,求取导频位置的信道频域响应;其次,在最佳匹配滤波设计原则的基础上,提出了一种自适应滤波方法;最后,在现有的插值信道估计方法的基础上,提出了一种高效的几何距离相关算法。从而,避免了压缩感知、深度学习等信道估计方法需要的信道信息统计和矩阵求逆等复杂运算,并且具有较高的估计精度和较强的信道场景自适应能力。4种航空信道场景下的实验结果表明,为使系统误码率达到10-6,该方法相对现有方法所需比特信噪比可以降低4.0 dB,在一定程度上提高了航空飞行安全水平。

基于端到端系统的导频插入和信道估计设计

自深度学习技术驱动的端到端通信系统解决方案被引入后,通信系统的架构模式已经发生了巨大的变革。虽然该技术在AWGN信道上表现良好,但在无线信道环境变化情况下,其表现并不尽如人意。因此,当前普遍采用的策略是引入导频信号,将导频按照头部位置插入或固定间隔插入的方式插入数据流中,同时在端到端系统之外增设信道估计模块以补充信道信息。然而,这种做法实际上与端到端一体化的理念相矛盾。提出了一种综合的端到端系统,利用深度神经网络进行导频插入位置选择并设计接收机,使系统能够有效收集信道信息,进而顺利恢复传输数据。与传统的导频插入方案相比,该系统具备明显的性能优势,误码率降低1~2 dB。另外,针对各类信道环境,系统内部的导频插入算法都可以提供具有针对性且高效的导频插入方案,因此对通信环境具有极高的适应性。

一种基于小数倍多普勒信道的OTFS信道估计方法

针对现有正交时频空(OTFS)调制系统的信道估计中存在的高峰均比和小数倍多普勒信道下估计困难及复杂度高的问题,提出了一种基于序列导频的匹配滤波(SMF)信道估计方法。该算法首先将序列导频与数据联合成帧,依靠序列的自相关性获取路径数、时延和整数倍多普勒;然后通过互相关匹配滤波估计小数倍的多普勒抽头和信道增益,从而得到信道状态信息;最后根据小数倍信道整数采样的特征,更新信道增益和信道初始相位。仿真结果表明,该方法相比基于嵌入式脉冲导频的信道估计,改善了峰均比,并提高了信道估计性能。相比于传统的序列导频,该方法可以估计得到小数倍多普勒抽头,估计的信道状态信息更准确。该信道估计方法更具有普遍性。

6G物理层原生AI技术

针对6G系统的空口AI化演进,需要对研究的路线、所受的限制条件、基础问题、技术趋势,以及期待的突破与改变做出客观和明确的判断,以便在不同层面和维度上获得理论与工程上的增益与突破,具体包括:引入AI的6G一体化、系统化构建,形成基于AI的新传输方案与新空口设计,考虑面向AI的生命周期管理与解决方案。在物理层原生AI技术方面,具体分析了零开销叠加导频设计、信道与无线环境语义通信,以及两项重点6G AI潜在技术方向。本研究可对未来6G系统AI化的技术推进和标准化提供有益参考。

基于叠加导频的雷达与通信一体化信号处理算法研究

MIMO-OFDM技术日益成熟,MIMO系统为雷达通信一体化系统提供了硬件支持,OFDM调制为通信感知一体化系统提供了合适的调制方法;传统通信系统的接收方只接收通信信息而浪费了携带的信道信息,后者正是雷达系统所需的信息,通感一体化系统也提高了信息利用率;在通信感知一体化系统的存在诸多不同形式中,采用了叠加导频的系统,验证了叠加导频相较于传统导频策略的优越性,并研究了基于叠加导频的雷达系统的性能;直接使用香农公式计算通信雷达融合信道的信道容量,并研究两部分功率比对于系统互信息的影响。

Channel Estimation and Data Transmission in Massive MIMO TDD Systems

This paper mainly elaborates the studies of channel estimation and downlink data transmission in Massive MIMO. As there are different types of interference in single-cell and multi-cell systems, this paper establishes different models for them separately. In terms of uplink training, for getting channel state information, we introduce LS and MMSE channel estimation algorithms and make a comparison between them. At the same time, the problem of pilot contamination is solved by cell classification and pilot identification. Next, this paper defines mathematical models for downlink data transmission. We use pre-coding methods (including Zero-forcing and Maximal Ratio Combining schemes) and optimize power distribution to improve channel capacity and transmission rate. Furthermore, this paper provides numerical results to show the simulation performance in both single-cell and multi-cell systems and extends to prospects in the future.

Channel Estimation with an Interpolation Trained Deep Neural Network

This paper proposes a deep learning-based channel estimation method for orthogonal frequency-division multiplexing (OFDM) systems. The existing OFDM receiver has low estimation accuracy when estimating channel state information (CSI) with fewer pilots. To tackle the problem, in this paper, a deep learning model is first trained by the interpolated channel frequency responses (CFRs) and then used to denoise the CFR estimated by least square (LS) estimation. The proposed deep neural network (DNN) can also be trained in a short time because it only learns the CFR and the network structure is simple. According to the simulation results, the performance of the DNN estimator can be compared with the minimum mean-square error (MMSE) estimator. Furthermore, the DNN approach is more robust than conventional methods when fewer pilots are used. In summary, deep learning is a promising tool for channel estimation in wireless communications.