Beam Training and Tracking in mmWave Communication:A Survey

Communicating on millimeter wave(mmWave)bands is ushering in a new epoch of mobile communication which provides the availability of 10 Gbps high data rate transmission.However,mmWave links are easily prone to short transmission range communication because of the serious free space path loss and the blockage by obstacles.To overcome these challenges,highly directional beams are exploited to achieve robust links by hybrid beamforming.Accurately aligning the transmitter and receiver beams,i.e.beam training,is vitally important to high data rate transmission.However,it may cause huge overhead which has negative effects on initial access,handover,and tracking.Besides,the mobility patterns of users are complicated and dynamic,which may cause tracking error and large tracking latency.An efficient beam tracking method has a positive effect on sustaining robust links.This article provides an overview of the beam training and tracking technologies on mmWave bands and reveals the insights for future research in the 6th Generation(6G)mobile network.Especially,some open research problems are proposed to realize fast,accurate,and robust beam training and tracking.We hope that this survey provides guidelines for the researchers in the area of mmWave communications.

High Energy Efficiency Dynamic Connected Hybrid Precoding for mm Wave Massive MIMO Systems

This paper considers a high energy efficiency dynamic connected(HEDC)structure,which promotes the practicability and reduces the power consumption of hybrid precoding system by lowresolution phase shifters(PSs).Based on the proposed structure,a new hybrid precoding algorithm is presented to optimize the energy efficiency,namely,HP-HEDC algorithm.Firstly,via a new defined effective optimal precoding matrix,the problem of optimizing the analog switch precoding matrix is formulated as a sparse representation problem.Thus,the optimal analog switch precoding matrix can be readily obtained by the branch-and-bound method.Then,the digital precoding matrix optimization problem is modeled as a dictionary update problem and solved by the method of optimal direction(MOD).Finally,the diagonal entries of the analog PS precoding matrix are optimized by exhaustive search independently since PS and antenna is one-to-one.Simulation results show that the HEDC structure enjoys low power consumption and satisfactory spectral efficiency.The proposed algorithm presents at least 50%energy efficiency improvement compared with other algorithms when the PS resolution is set as 3-bit.

Channel Estimation for Reconfigurable Intelligent Surface Aided Multiuser Millimeter-Wave/THz Systems

It is assumed that reconfigurable intelligent surface(RIS)is a key technology to enable the potential of mmWave communications.The passivity of the RIS makes channel estimation difficult because the channel can only be measured at the transceiver and not at the RIS.In this paper,we propose a novel separate channel estimator via exploiting the cascaded sparsity in the continuously valued angular domain of the cascaded channel for the RIS-enabled millimeter-wave/Tera-Hz systems,i.e.,the two-stage estimation method where the cascaded channel is separated into the base station(BS)-RIS and the RIS-user(UE)ones.Specifically,we first reveal the cascaded sparsity,i.e.,the sparsity exists in the hybrid angular domains of BS-RIS and the RIS-UEs separated channels,to construct the specific sparsity structure for RIS enabled multi-user systems.Then,we formulate the channel estimation problem using atomic norm minimization(ANM)to enhance the proposed sparsity structure in the continuous angular domains,where a low-complexity channel estimator via Alternating Direction Method of Multipliers(ADMM)is proposed.Simulation findings demonstrate that the proposed channel estimator outperforms the current state-of-the-arts in terms of performance.

Building mm Wave on the Evolving C-V2X:mm Wave NR-V2X

Since the evolving standardization of cellular V2X(C-V2X)technologies is continuously developed by 3GPP,research on new radio(NR-V2X)has been on schedule by academic and industries.Though millimeter wave(mm Wave)frequency band is supposed to provide large transmission bandwidth,yet the development of mm Wave band in NR-V2X is still in preliminary stage.In this article,a comprehensive discussion of mm Wave NR-V2X is given covering trends,standardization landscape,and enabling technologies,aiming at tackling the challenge of channel modeling,directional channel access,beamforming and blockage management.The vision of mm Wave NR-V2X is to fully support the development of automated driving,holographic control display and improved in-car infotainment for the future.

Hybrid millimeter wave heterogeneous networks with spatially correlated user equipment

In this paper,we analyze a hybrid Heterogeneous Cellular Network(HCNet)framework by deploying millimeter Wave(mmWave)small cells with coexisting traditional sub-6GHz macro cells to achieve improved coverage and high data rate.We consider randomly-deployed macro base stations throughout the network whereas mmWave Small Base Stations(SBSs)are deployed in the areas with high User Equipment(UE)density.Such user centric deployment of mmWave SBSs inevitably incurs correlation between UE and SBSs.For a realistic scenario where the UEs are distributed according to Poisson cluster process and directional beamforming with line-of-sight and non-line-of-sight transmissions is adopted for mmWave communication.By using tools from stochastic geometry,we develop an analytical framework to analyze various performance metrics in the downlink hybrid HCNets under biased received power association.For UE clustering we considered Thomas cluster process and derive expressions for the association probability,coverage probability,area spectral efficiency,and energy efficiency.We also provide Monte Carlo simulation results to validate the accuracy of the derived expressions.Furthermore,we analyze the impact of mmWave operating frequency,antenna gain,small cell biasing,and BSs density to get useful engineering insights into the performance of hybrid mmWave HCNets.Our results show that network performance is significantly improved by deploying millimeter wave SBS instead of microwave BS in hot spots.

基于改进DnCNN的RIS辅助毫米波系统信道估计

RIS是第六代移动通信系统中潜在的候选技术之一。然而,由于无源的RIS缺乏信号处理能力,这给RIS辅助毫米波大规模MIMO系统的信道估计带来了挑战。为了获得更精确的信道状态信息,将信道估计转化为图像去噪问题,提出改进的DnCNN来完成信道估计任务。具体地,采用LMMSE对信道进行粗估计。融合注意力机制网络和噪声水平估计子网络对DnCNN进行改进,以提高网络对噪声的提取性能和自适应性能,实现从信道的粗估计中得到高精度信道估计值。仿真实验表明,所提算法在低信噪比下具有较好的估计性能。

Trajectory Tracking for MmWave Communication Systems via Cooperative Passive Sensing

A cooperative passive sensing framework for millimeter wave(mmWave)communication systems is proposed and demonstrated in a scenario with one mobile signal blocker.Specifically,in the uplink communication with at least two transmitters,a cooperative detection method is proposed for the receiver to track the blocker’s trajectory,localize the transmitters and detect the potential link blockage jointly.To facilitate detection,the receiver collects the signal of each transmitter along a line-of-sight(LoS)path and a non-line-of-sight(NLoS)path separately via two narrow-beam phased arrays.The NLoS path involves scattering at the mobile blocker,allowing its identification through the Doppler frequency.By comparing the received signals of both paths,the Doppler frequency and angle-of-arrival(AoA)of the NLoS path can be estimated.To resolve the blocker’s trajectory and the transmitters’locations,the receiver should continuously track the mobile blocker to accumulate sufficient numbers of the Doppler frequency and AoA versus time observations.Finally,a gradient-descent-based algorithm is proposed for joint detection.With the reconstructed trajectory,the potential link blockage can be predicted.It is demonstrated that the system can achieve decimeterlevel localization and trajectory estimation,and predict the blockage time with an error of less than 0.1 s.

RIS-Assisted Cell-Free MIMO: A Survey

Cell-free(CF)multiple-input multiple-output(MIMO)is a promising technique to enable the vision of ubiquitous wireless connectivity for next-generation network communications.Compared to traditional co-located massive MIMO,CF MIMO allows geographically distributed access points(APs)to serve all users on the same time-frequency resource with spatial multiplexing techniques,resulting in better performance in terms of both spectral efficiency and coverage enhancement.However,the performance gain is achieved at the expense of deploying more APs with high cost and power consumption.To address this issue,the recently proposed reconfigurable intelligent surface(RIS)technique stands out with its unique advantages of low cost,low energy consumption and programmability.In this paper,we provide an overview of RIS-assisted CF MIMO and its interaction with advanced optimization designs and novel applications.Particularly,recent studies on typical performance metrics such as energy efficiency(EE)and spectral efficiency(SE)are surveyed.Besides,the application of RIS-assisted CF MIMO techniques in various future communication systems is also envisioned.Additionally,we briefly discuss the technical challenges and open problems for this area to inspire research direction and fully exploit its potential in meeting the demands of future wireless communication systems.

5G多天线系统中毫米波物理层安全设计

三维MIMO(3D Multiple Input Multiple Output,3D MIMO)技术是一种提高第5代(The Fifth Generation,5G)无线网络覆盖和频谱效率的潜在技术。三维信道空间模型(Three-Dimensional SCM,3D SCM)作为3D MIMO技术的一部分,它在2D SCM的基础上增加了垂直维自由度。在考虑毫米波段传输方案基础上,文中重点探讨了3D SCM对物理层安全设计的影响。首先简述了2D SCM和3D SCM模型,接着基于毫米波段路径损耗模型,主要研究3D SCM如何影响毫米波段的安全容量。文中采用1.9 GHz频段和28 GHz及73 GHz两个毫米波频段,引入相应的路径损耗模型,最后在不同载波频率以及基站下倾角下采用计算机仿真评估了毫米波通信的信道安全容量。结果表明,相对于传统2D SCM模型,3D SCM在28 GHz和73 GHz频段上的安全容量有一定程度的提升。与此同时,适当地调整基站天线阵列的下倾角也利于优化期望用户的安全容量。

基于迭代重加权的信道估计方案

针对传统的基于压缩感知的信道估计分辨率损失严重、精度不高问题,提出一种基于迭代重加权(iterative reweight,IR)的超分辨信道估计方案。通过部分自适应动量估计方法(partially adaptive momentum estimation method,Padam)迭代优化目标函数,使到达角/离开角(angle of arrivals/departures,AoAs/AoDs)趋向最优解,实现高分辨率信道估计。在优化过程中,使用加权参数来平衡稀疏性和数据拟合误差。仿真结果表明,与正交匹配追踪(orthogonal matching pursuit,OMP)信道估计方案、自适应码本信道估计方案相比,所提方案归一化均方误差和频谱效率等性能更优。

一种非正交多址接入迭代优化混合预编码算法

为了使大规模毫米波(millimeter wave,mmWave)多输入多输出(multiple-input multiple-output,MIMO)系统获得高频谱效率和高能量效率,采用非正交多址接入迭代优化算法和簇首选择算法,研究了无线携能通信场景中的混合预编码问题。仿真结果表明:与传统正交多址接入无线携能通信的混合预编码算法相比,所提算法能够获得更高的频谱效率和能量效率。

UAV Communications with Millimeter-Wave Beamforming:Potentials,Scenarios,and Challenges

Unmanned aerial vehicle(UAV)has been widely used in many fields and is arousing global attention.As the resolution of the equipped sensors in the UAV becomes higher and the tasks become more complicated,much higher data rate and longer communication range are required in the foreseeable future.As the millimeter-wave(mm Wave)band can provide more abundant frequency resources than the microwave band,much higher achievable rate can be guaranteed to support UAV services such as video surveillance,hotspot coverage,and emergency communications,etc.The flexible mm Wave beamforming can be used to overcome the high path loss caused by the long propagation distance.In this paper,we study three typical application scenarios for mm Wave-UAV communications,namely communication terminal,access point,and backbone link.We present several key enabling techniques for UAV communications,including beam tracking,multi-beam forming,joint Tx/Rx beam alignment,and full-duplex relay techniques.We show the coupling relation between mm Wave beamforming and UAV positioning for mm Wave-UAV communications.Lastly,we summarize the challenges and research directions of mm Wave-UAV communications in detail.

MmWave Beamforming for UAV Communications with Unstable Beam Pointing

Millimeter wave(mmWave) communications of unmanned aerial vehicles(UAVs) have drawn dramatic attentions for its flexibility on a variety of applications.Recently,channel tracking base on the spatial features has been proposed to solve the problem of beam misalignments due to the UAV navigation.However,unstable beam pointing caused by the non-ideal beam tracking environment may impact the performance of mmWave systems significantly.In this paper,an improved beamforming method is presented to overcome this shortcoming.Firstly,the effect of the beam deviation is analyzed through the establishment of the equivalent data rate.Then,combining the quantification of spatial angle and the improved orthogonal matching pursuit(OMP) algorithm,an optimized beam corresponding to the beam deviation is obtained.Simulation results show that the optimized beam of the proposed approach can effectively improve the spectral efficiency without improving the complexity when the beam pointing is unstable.

Hybrid Precoding for mm Wave Massive MIMO Systems with Different Antenna Arrays

In this paper, the performance of hybrid precoding is investigated for mmWave massive MIMO systems with different antenna arrays. The hybrid precoding with partially connected architecture (PCA) is adopted. The spectral efficiency (SE) and received energy efficiency (EE) are investigated by considering four types of antenna arrays, including uniform linear array (ULA), uniform rectangular planar array (URPA), uniform hexagonal planar array (UHPA), and uniform circular planar array (UCPA), respectively. We focus on analysis at the antenna response vector and utilize the idea of orthogonal matching pursuit algorithm to seek the optimal hybrid precoder. Furthermore, the trade-off of precoding architectures is studied between SE and received EE. Simulation results show that if the uniform planar array antenna is more concentrated, the SE and receive EE will be higher. Considering SE and received EE, the performance of planar arrays outperform linear array. There exist different optimal radio-frequency chain numbers to maximize the SE for planar array and linear array. In addition, the PCA can achieve relatively higher received EE while the SE is close to the fully connected architecture and the full digital architecture.

Radio Propagation and Wireless Coverage of LSAA-Based 5G Millimeter-Wave Mobile Communication Systems

Millimeter-wave(mm Wave) communications will be used in fifth-generation(5G) mobile communication systems, but they experience severe path loss and have high sensitivity to physical objects, leading to smaller cell radii and complicated network architectures. A coverage extension scheme using large-scale antenna arrays(LSAAs) has been suggested and theoretically proven to be cost-efficient in combination with ultradense small cell networks. To analyze and optimize the LSAA-based network deployments, a comprehensive survey of recent advances in statistical mmWave channel modeling is first presented in terms of channel parameter estimation, large-scale path loss models, and small-scale cluster models. Next, the measurement and modeling results at two 5G candidate mmWave bands(e.g., 28 GHz and 39 GHz) are reviewed and compared in several outdoor scenarios of interest, where the propagation characteristics make crucial contributions to wireless network designs. Finally, the coverage behaviors of systems employing a large number of antenna arrays are discussed, as well as some implications on future mmWave cellular network designs.

Machine Learning Empowered Beam Management for Intelligent Reflecting Surface Assisted MmWave Networks

Recently,intelligent reflecting surface(IRS)assisted mmWave networks are emerging,which bear the potential to address the blockage issue of the millimeter wave(mmWave)communication in a more cost-effective way.In particular,IRS is built by passive and programmable electromagnetic elements that can manipulate the mmWave propagation channel into a more favorable condition that is free of blockage via judicious joint base station(BS)-IRS transmission design.However,the coexistence of IRSs and mmWave BSs complicates the network architecture,and thus poses great challenges for efficient beam management(BM)that is one critical prerequisite for high performance mmWave networks.In this paper,we systematically evaluate the key issues and challenges of BM for IRS-assisted mmWave networks to bring insights into the future network design.Specifically,we carefully classify and discuss the extensibility and limitations of the existing BM of conventional mmWave towards the IRS-assisted new paradigm.Moreover,we propose a novel machine learning empowered BM framework for IRS-assisted networks with representative showcases,which processes environmental and mobility awareness to achieve highly efficient BM with significantly reduced system overhead.Finally,some interesting future directions are also suggested to inspire further researches.

Throughput Maximization for Multi-UAV Enabled Millimeter Wave WPCN: Joint Time and Power Allocation

In this paper,we investigate the effective deployment of millimeter wave(mmWave)in unmanned aerial vehicle(UAV)-enabled wireless powered communication network(WPCN).In particular,a novel framework for optimizing the performance of such UAV-enabled WPCN in terms of system throughput is proposed.In the considered model,multiple UAVs monitor in the air along the scheduled flight trajectory and transmit monitoring data to micro base stations(mBSs)with the harvested energy via mmWave.In this case,we propose an algorithm for jointly optimizing transmit power and energy transfer time.To solve the non-convex optimization problem with tightly coupled variables,we decouple the problem into more tractable subproblems.By leveraging successive convex approximation(SCA)and block coordinate descent techniques,the optimal solution is obtained by designing a two-stage joint iteration optimization algorithm.Simulation results show that the proposed algorithm with joint transmit power and energy transfer time optimization achieves significant performance gains over Q-learning method and other benchmark schemes.

Sparsity-Aware Channel Estimation for mmWave Massive MIMO: A Deep CNN-Based Approach

The deep convolutional neural network(CNN)is exploited in this work to conduct the challenging channel estimation for mmWave massive multiple input multiple output(MIMO)systems.The inherent sparse features of the mmWave massive MIMO channels can be extracted and the sparse channel supports can be learnt by the multi-layer CNN-based network through training.Then accurate channel inference can be efficiently implemented using the trained network.The estimation accuracy and spectrum efficiency can be further improved by fully utilizing the spatial correlation among the sparse channel supports of different antennas.It is verified by simulation results that the proposed deep CNN-based scheme significantly outperforms the state-of-the-art benchmarks in both accuracy and spectrum efficiency.

Vehicle-to-Infrastructure Channel Characterization in Urban Environment at 28 GHz

Both ultra-reliable low latency and high-data-rate communications are required by connective vehicles. Millimeter wave(mm Wave) with large bandwidth is a key technology to support high-data-rate communications. In this paper, the 28 GHz wideband vehicle-to-infrastructure channel is characterized for the urban environment in a major street in Manhattan. The deployment of the transmitter and the receiver, as well as the traffic models, are selected by considering the recommendation by 3GPP TR 37.885. Ray tracing simulator with calibrated electromagnetic parameters is employed in this work to practically conduct intensive simulations. The 3D environment model is reconstructed from OpenStreetMap. The power delay profile, path loss, root-meansquare delay spread, K-factor and so on, are extracted from the calibrated simulation results. The evolution of the parameters, as well as their statistical properties, are analyzed and modeled. The work of this paper helps the researchers understand the propagation channel for designing mmWave technologies and communication system in a similar scenario.

Clustering in the Wireless Channel with a Power Weighted Statistical Mixture Model in Indoor Scenario

Cluster-based channel model is the main stream of fifth generation mobile communications, thus the accuracy of clustering algorithm is important. Traditional Gaussian mixture model (GMM) does not consider the power information which is important for the channel multipath clustering. In this paper, a normalized power weighted GMM (PGMM) is introduced to model the channel multipath components (MPCs). With MPC power as a weighted factor, the PGMM can fit the MPCs in accordance with the cluster-based channel models. Firstly, expectation maximization (EM) algorithm is employed to optimize the PGMM parameters. Then, to further increase the searching ability of EM and choose the optimal number of components without resort to cross-validation, the variational Bayesian (VB) inference is employed. Finally, 28 GHz indoor channel measurement data is used to demonstrate the effectiveness of the PGMM clustering algorithm.