An Efficient CSI Feedback Scheme for Dual-Polarized MIMO Systems Using Layered Multi-Paths Information

Massive MIMO is one of tile enabling technologies tbr beyond 4G and 5G systems due to its ability to provide beamforming gain and reduce interference Dual-polarized antenna is widely adopted to accommodate a large number of antenna elements in limited space. However, current CSI（channel state information） feedback schemes developed in LTE for conventional MIMO systems are not efficient enough for massive MIMO systems since the overhead increases almost linearly with the number of antenna. Moreover, the codebook for massive MIMO will be huge and difficult to design with the LTE methodology. This paper proposes a novel CSI feedback scheme named layered Multi-paths Information based CSI Feedback （LMPIF）, which can achieve higher spectrum efficiency for dual-polarized antenna system with low feedback overhead. The MIMO channel is decomposed into long term components （multipath directions and amplitudes） and short term components （multipath phases）. The relationship between the two components and the optimal precoder is derived in closed form. To reduce the overhead, different granularities in feedback time have been applied for the long term components and short term components Link and system level simulation results prove that LMPIF can improve performance considerably with low CSI feedback overhead.

基于CSI商联合AOA的增强型指纹定位方法

针对当前指纹定位技术需要基于多个接入点(AP)被动定位,导致单AP室内场景使用受限的问题,提出一种信道状态信息(CSI)商联合到达角(AOA)的室内指纹定位方法:指出技术关键是利用单链路上的多维信号参数来构建指纹模型;利用多进多出(MIMO)系统空间分集,构建CSI商以获得更稳健的CSI指纹信号,并结合多信号分类(MUSIC)算法原理设计一种多载波AOA指纹表示方法,该方法与原始CSI指纹相比具有区分性;然后为了解决单AP的AOA指纹对称性问题,将CSI商与AOA指纹相结合,得到新的指纹,通过机器学习的方法进行目标位置匹配。实验结果表明,本方法在空教室和实验室环境下的定位准确率分别达到98.96%和97.08%,平均定位误差分别为0.46 m和0.68 m,具有较高的定位性能。

CSI Feedback-based CS for Underwater Acoustic Adaptive Modulation OFDM System with Channel Prediction

In this paper, we investigate the performance of adaptive modulation （AM） orthogonal frequency division multiplexing （OFDM） system in underwater acoustic （UWA） communications. The aim is to solve the problem of large feedback overhead for channel state information （CSI） in every subcarrier. A novel CSI feedback scheme is proposed based on the theory of compressed sensing （CS）. We propose a feedback from the receiver that only feedback the sparse channel parameters. Additionally, prediction of the channel state is proposed every several symbols to realize the AM in practice. We describe a linear channel prediction algorithm which is used in adaptive transmission. This system has been tested in the real underwater acoustic channel. The linear channel prediction makes the AM transmission techniques more feasible for acoustic channel communications. The simulation and experiment show that significant improvements can be obtained both in bit error rate （BER） and throughput in the AM scheme compared with the fixed Quadrature Phase Shift Keying （QPSK） modulation scheme. Moreover, the performance with standard CS outperforms the Discrete Cosine Transform （DCT） method.

Improved spectrum sharing algorithm based on feedback control information in cognitive radio networks

In order to avoid the system performance deterioration caused by the wireless fading channel and imperfect channel estimation in cognitive radio networks, the spectrum sharing problem with the consideration of feedback control information from the primary user is analyzed. An improved spectrum sharing algorithm based on the combination of the feedback control information and the optimization algorithm is proposed. The relaxation method is used to achieve the approximate spectrum sharing model, and the spectrum sharing strategy that satisfies the individual outage probability constraints can be obtained iteratively with the observed outage probability. Simulation results show that the proposed spectrum sharing algorithm can achieve the spectrum sharing strategy that satisfies the outage probability constraints and reduce the average outage probability without causing maximum transmission rate reduction of the secondary user.

Leader-following coordination of multi-agent systems with information feedback

Based on the strategy of information feedback from followers to the leader, flocking control of a group of agents with a leader is studied. The leader tracks a pre-defined trajectory and at the same time the leader uses the feedback information from followers to the leader to modify its motion. The advantage of this control scheme is that it reduces the tracking errors and improves the robustness of the team cohesion to followers＇ faults. The results of simulation are provided to illustrate that information feedback can improve the performance of the system.

Microscopic simulation of multi-lane traffic under dynamic tolling and information feedback

To investigate drivers' lane-changing behavior under different information feedback strategies,a microscopic traffic simulation based on the cellular automaton model was made on the typical freeway with a regular lane and a high-occupancy one. A new dynamic tolling scheme in terms of the real-time traffic condition on the high-occupancy lane was further designed to enhance the whole freeway's flow throughput. The results show that the mean velocity feedback strategy is generally more efficient than the travel time feedback strategy in correctly guiding drivers' lane choice behavior. Specifically,the toll level,lane-changing rate and freeway's throughput and congestion coefficient induced by the travel time feedback strategy oscillate with larger amplitude and longer period. In addition,the dynamic tolling scheme can make the high-occupancy lane less congested and maximize the freeway's throughput when the regular-lane inflow rate is larger than 0.45.

AI Enlightens Wireless Communication:Analyses,Solutions and Opportunities on CSI Feedback

In this paper,we give a systematic description of the 1st Wireless Communication Artificial Intelligence(AI)Competition(WAIC)which is hosted by IMT-2020(5G)Promotion Group 5G+AI Work Group.Firstly,the framework of full channel state information(F-CSI)feedback problem and its corresponding channel dataset are provided.Then the enhancing schemes for DL-based F-CSI feedback including i)channel data analysis and preprocessing,ii)neural network design and iii)quantization enhancement are elaborated.The final competition results composed of different enhancing schemes are presented.Based on the valuable experience of 1stWAIC,we also list some challenges and potential study areas for the design of AI-based wireless communication systems.

Information perception and feedback mechanism and key techniques of multi-modality human-robot interaction for service robots

With the increasing of the elderly population and the growing hearth care cost, the role of service robots in aiding the disabled and the elderly is becoming important. Many researchers in the world have paid much attention to heaRthcare robots and rehabilitation robots. To get natural and harmonious communication between the user and a service robot, the information perception/feedback ability, and interaction ability for service robots become more important in many key issues.

CSI Intelligent Feedback for Massive MIMO Systems in V2I Scenarios

With the rapid development of the Internet of vehicles(IoV),vehicle to everything(V2X)has strict requirements for ultra-reliable and low latency communications(URLLC),and massive multiinput multi-output(MIMO)channel state information(CSI)feedback can effectively support URLLC communication in 5G vehicle to infrastructure(V2I)scenarios.Existing research applies deep learning(DL)to CSI feedback,but most of its algorithms are based on low-speed outdoor or indoor environments and assume that the feedback link is perfect.However,the actual channel still has the influence of additive noise and nonlinear effects,especially in the high-speed V2I scene,the channel characteristics are more complex and time-varying.In response to the above problems,this paper proposes a CSI intelligent feedback network model for V2I scenarios,named residual mixnet(RM-Net).The network learns the channel characteristics in the V2I scenario at the vehicle user(User Equipment,UE),compresses the CSI and sends it to the channel;the roadside base station(Base Station,BS)receives the data and learns the compressed data characteristics,and then restore the original CSI.The system simulation results show that the RM-Net training speed is fast,requires fewer training samples,and its performance is significantly better than the existing DL-based CSI feedback algorithm.It can learn channel characteristics in high-speed mobile V2I scenarios and overcome the influence of additive noise.At the same time,the network still has good performance under high compression ratio and low signal-to-noise ratio(SNR).

Fully Connected Feedforward Neural Networks Based CSI Feedback Algorithm

In modern wireless communication systems,the accurate acquisition of channel state information(CSI)is critical to the performance of beamforming,non-orthogonal multiple access(NOMA),etc.However,with the application of massive MIMO in 5G,the number of antennas increases by hundreds or even thousands times,which leads to excessive feedback overhead and poses a huge challenge to the conventional channel state information feedback scheme.In this paper,by using deep learning technology,we develop a system framework for CSI feedback based on fully connected feedforward neural networks(FCFNN),named CF-FCFNN.Through learning the training set composed of CSI,CF-FCFNN is able to recover the original CSI from the compressed CSI more accurately compared with the existing method based on deep learning without increasing the algorithm complexity.

RESEARCH ON THE MODEL OF SPEECH RECOGNITION AND UNDERSTANDING BY USING HIERARCHICAL INFORMATION FEEDBACK

In this paper according to the process of cognitive of human being to speech is put forward a model of speech recognition and understanding in a noisy environment. For speech recognition, two level modular Extended Associative Memory Neural Networks (EAMNN) are adopted. The learning speed is 9 times faster than that of the conventional BP net. It has high self-adaptability, robustness, fault toleration and associative memory ability to the noisy speech signals. To speech understanding, the structure of hierarchical analysis and examining faults which is a combination of statistic inference and syntactic rules is adopted, to pick up the candidates of the speech recognition and to predict the next word by the statistic inference base; and the syntactic rule base reduces effectively the recognition errors and candidates of acoustic level; then by comparing and rectifying errors through information feedback and guiding the succeeding speech process, the recognition of the sentence is realized.

Deep learning for joint channel estimation and feedback in massive MIMO systems

The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.

Dynamics of Quantum Fisher Information in Homodyne-Mediated Feedback Control

We investigate the quantum Fisher information(QFI) dynamics of a dissipative two-level system in homodynemediated quantum feedback control. The analytical results demonstrate that the maximum values and stable values of the QFI can be greatly enhanced via feedback control. The quantum feedback plays a more evident role in the improvement of classical Fisher information. The classical part can reach a high stable value, while the quantum part eventually decays to zero whatever the feedback parameter is.

A novel zero-payload downlink CSI feedback scheme for closed-loop beamforming system

A novel downlink channel state information(CSI)feedback scheme is proposed for the closed-loopbeamforming system.In the proposed scheme,mobile terminal(MT)superposes the uplink pilot on thereceived downlink pilot,forms the hybrid pilot(HP),and then transmits the HP to base station(BS)viathe uplink pilot channel.Because downlink CSI can be recovered from HP at BS side without consumingextra uplink bandwidth,the proposed scheme can achieve zero-payload CSI feedback,effectively solvingthe traditional bottleneck problems,i.e.,the heavy burden for transmitting CSI.Moreover,both MT'scomplexity and feedback delays can be reduced since the downlink channel needs not to be estimated atMT any more.Simulations verify that the proposed scheme can achieve the better MSE performance forthe uplink channel estimation than the traditional scheme,and the cost for the zero-payload CSI feedbackis some acceptable loss of feedback precision.

A Novel Scheme for Separate Training of Deep Learning-Based CSI Feedback Autoencoders

In this paper, we introduce a novel scheme for the separate training of deep learning-based autoencoders used for Channel State Information (CSI) feedback. Our distinct training approach caters to multiple users and base stations, enabling independent and individualized local training. This ensures the more secure processing of data and algorithms, different from the commonly adopted joint training method. To maintain comparable performance with joint training, we present two distinct training methods: separate training decoder and separate training encoder. It’s noteworthy that conducting separate training for the encoder can pose additional challenges, due to its responsibility in acquiring a compressed representation of underlying data features. This complexity makes accommodating multiple pre-trained decoders for just one encoder a demanding task. To overcome this, we design an adaptation layer architecture that effectively minimizes performance losses. Moreover, the flexible training strategy empowers users and base stations to seamlessly incorporate distinct encoder and decoder structures into the system, significantly amplifying the system’s scalability. .

Method of Designing Generators of Pseudorandom Sequences for Information Protection Based on Shift Register with Non-Linear Feedback Function

This paper proposes an efficient, high-tech method of construction of pseudorandom binary sequences generators with a repetition period 2n?for n-bit shift register with a nonlinear feedback function. The developed method is illustrated by constructing a nonlinear function feedback shift register. It is proved that the offered method requires the realization of a memory size proportional to n2?that allows making successful use of suitable generators for practical use on the shift register of the longer word.

利用统计CSI的DMA辅助无线携能通信传输方法

提出了一种利用统计信道状态信息(CSI)的动态超表面天线(DMA)辅助无线携能通信传输方法。考虑一个由DMA阵列代替传统天线阵列的基站、一个信息解码用户,以及一个能量收集用户构成的无线携能通信系统,将信道建模为莱斯空间相关信道,提出了平均收集能量和发送功率约束条件下的可达遍历速率最大化问题。首先,利用随机矩阵理论将优化问题转化为一个确定性的优化问题;然后,通过交替优化方法,设计了可达速率最大化和仅利用统计CSI的基站发送协方差矩阵和DMA权值矩阵。仿真表明,所提方法能达到通信速率和收集能量之间的最优折中。

一种融合5G CSI和地磁的集成学习定位方法

针对深度学习算法在多传感器融合定位中容易出现的局部收敛、异质融合性能不佳等问题,本文提出了一种集成双向长短期记忆网络(BiLSTM)和注意力机制的多输入卷积神经网络(CNN)的室内定位算法。该算法首先对5G信道状态信息(CSI)和地磁数据分别进行预处理;然后各自基于独立的分支网络进行离线训练,同时提取指纹数据的空间特征和时序特征,追加注意力机制层;最后在全连接层实现了异质传感器数据的融合定位。在会议室和教学楼大厅的试验结果表明,平均定位误差分别为0.95和1.84 m,相比误差反向传播网络(BPNN)分别提高了48.9%和42.7%,定位精度和系统稳定性均大幅提升。

RSSI和PC-CSI加权融合的指纹定位方法

针对基于RSSI和CSI的指纹定位技术易受环境干扰、定位精度较低的问题,提出了一种基于RSSI指纹和相位修正信道状态信息(phase correct based channel state information,PC-CSI)指纹的加权融合指纹定位技术。基于PC-CSI的指纹定位在传统基于CSI幅值的指纹定位基础上增加相位信息对定位结果进行修正,之后对RSSI指纹和PC-CSI指纹的定位结果加权重定位。实验结果表明,提出的加权融合指纹定位算法与基于CSI的主动定位算法相比,平均定位误差(mean position error,MPE)降低了36.2%,能满足室内定位需求。

基于SE-CsiNet的大规模MIMO信道状态信息反馈与重建算法

本文研究了大规模多输入多输出(MIMO)系统FDD模式下行链路信道状态信息(CSI)反馈与重建问题,针对传统基于压缩感知的CSI反馈与重建算法存在的计算复杂度高、对信道稀疏性要求严格等问题,以及现有的基于深度学习的CSI反馈与重建算法存在的性能不足问题,提出了一种基于联合注意力机制神经网络的大规模MIMO信道状态信息反馈与重建算法,基于自编码器神经网络架构提出了一种全新的SE-CsiNet神经网络模型,在译码器网络当中引入联合压缩-激活(SE)注意力机制算法,有效提高神经网络的特征提取能力。结果表明,所提出的算法相较于传统CSI反馈与重建算法及现有的深度学习算法性能更为优异。