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.

Absorption-Dominant mmWave EMI Shielding Films with Ultralow Reflection using Ferromagnetic Resonance Frequency Tunable M-Type Ferrites

Although there is a high demand for absorption-dominant electromagnetic interference(EMI) shielding materials for 5G millimeter-wave(mmWave) frequencies, most current shielding materials are based on reflection-dominant conductive materials. While there are few absorption-dominant shielding materials proposed with magnetic materials, their working frequencies are usually limited to under 30 GHz. In this study, a novel multi-band absorption-dominant EMI shielding film with M-type strontium ferrites and a conductive grid is proposed. This film shows ultralow EMI reflection of less than 5% in multiple mmWave frequency bands with sub-millimeter thicknesses, while shielding more than 99.9% of EMI. The ultralow reflection frequency bands are controllable by tuning the ferromagnetic resonance frequency of M-type strontium ferrites and composite layer geometries. Two examples of shielding films with ultralow reflection frequencies, one for 39 and 52 GHz 5G telecommunication bands and the other for 60 and 77 GHz autonomous radar bands, are presented. The remarkably low reflectance and thinness of the proposed films provide an important advancement toward the commercialization of EMI shielding materials for 5G mmWave applications.

Multi-User MmWave Beam Tracking via Multi-Agent Deep Q-Learning

Beamforming is significant for millimeter wave multi-user massive multi-input multi-output systems.In the meanwhile,the overhead cost of channel state information and beam training is considerable,especially in dynamic environments.To reduce the overhead cost,we propose a multi-user beam tracking algorithm using a distributed deep Q-learning method.With online learning of users’moving trajectories,the proposed algorithm learns to scan a beam subspace to maximize the average effective sum rate.Considering practical implementation,we model the continuous beam tracking problem as a non-Markov decision process and thus develop a simplified training scheme of deep Q-learning to reduce the training complexity.Furthermore,we propose a scalable state-action-reward design for scenarios with different users and antenna numbers.Simulation results verify the effectiveness of the designed method.

An Optimized Approach for Spectrum Utilization in mmWave Massive MIMO 5G Wireless Networks

Massive multiple-input multiple-output(MIMO)systems that use the millimeter-wave(mm-wave)band have a higher frequency and more antennas,which leads to significant path loss,high power consumption,and server interference.Due to these issues,the spectrum efficiency is significantly reduced,making spectral efficiency improvement an important research topic for 5G communication.Together with communication in the terahertz(THz)bands,mmWave communication is currently a component of the 5G standards and is seen as a solution to the commercial bandwidth shortage.The quantity of continuous,mostly untapped bandwidth in the 30–300 GHz band has presented a rare opportunity to boost the capacity of wireless networks.The wireless communications and consumer electronics industries have recently paid a lot of attention to wireless data transfer and media streaming in the mmWave frequency range.Simple massive MIMO beamforming technology cannot successfully prevent interference between multiple networks in the current spectrum-sharing schemes,particularly the complex interference dispersed in indoor communication systems such as homes,workplaces,and stadiums.To effectively improve spectrum utilization and reduce co-channel interference,this paper proposes a novel algorithm.The main idea is to utilize the spectrum in software-defined mmWave massive MIMO networks through coordinated and unified management.Then,the optimal interference threshold is determined through the beam alignment method.Finally,a greedy optimization algorithm is used to allocate optimal spectral resources to the users.Simulation results show that the proposed algorithm improved spectral efficiency and reduced interference.

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.

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.

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.

Hybrid and Full-Digital Beamforming in mmWave Massive MIMO Systems: A Comparison Considering Low-Resolution ADCs

In a millimeter-wave(mmWave)Massive multiple-input multiple-output(MIMO)systems,full-digital beamforming(i.e.,connecting each antenna with a specific radio-frequency(RF)chain)becomes inefficient due to the hardware cost and power consumption.Therefore,hybrid analog and digital transceiver where the number of RF chains are much smaller than that of the antennas has drawn great research interest.In this work,we investigate the use of low-resolution analog-to-digital converters(ADCs)in the uplink of multi-user hybrid and full-digital mmWave Massive MIMO systems.To be specific,we compare the performance of full-digital minimum mean square error(MMSE)and hybrid MMSE beamforming in both sum rates and energy efficiency.Accurate approximations of sum rates and energy efficiency are provided for both schemes,which captures the dominant factors.The analytical results show that full-digital beamforming outperforms hybrid beamforming in terms of sum rates and requires only a small portion(γ)of antennas used by hybrid beamforming to achieve the same sum rates.We given sufficient condition for full-digital beamforming to outperform hybrid beamforming in terms of energy efficiency.Moreover,an algorithm is proposed to search for the optimal ADC resolution bits.Numerical results demonstrate the correctness of the analysis.

Multipath multihop mmWave backhaul in ultra-dense small-cell network

Ultra-Dense Network （UDN） is considered to be the key enabler for realizing capacity goals set by 5G. The major concern in UDN deployment is the backhaul network, which should be scalable, cost-effective, and have sufficient capacity to support massive small cell traffic. Otherwise, the backhaul can become the bottleneck of the network. In this paper, we propose a wireless backhaul solution for UDN deployment by considering MultiPath-MultiHop （MPMH） backhaul architecture in mmWave frequency band. In addition, we propose a distributed routing scheme to forward the backhaul traffic over the multihop network. Backhaul capacity and line-of-sight probability of the proposed backhaul architecture for various picocell densities were compared with direct, multiple-association, and multihop backhaul schemes under interference limited scenarios in outdoor and indoor small cell deployments. The simulation results indicate that the MPMH mmWave backhaul is the most cost-effective and scalable solution for UDN deployment.

Directional neighbor discovery in mmWave wireless networks

The directional neighbor discovery problem,i.e,spatial rendezvous,is a fundamental problem in millimeter wave(mmWave)wireless networks,where directional transmissions are used to overcome the high attenuation.The challenge is how to let the transmitter and the receiver beams meet in space under deafness caused by directional transmission and reception,where no control channel,prior information,and coordination are available.In this paper,we present a Hunting based Directional Neighbor Discovery(HDND)scheme for ad hoc mmWave networks,where a node follows a unique sequence to determine its transmission or reception mode,and continuously r0-tates its directional beam to scan the neighborhood for other mmWave nodes.Through a rigorous analysis,we derive the conditions for ensured neighbor discovery,as well as a bound for the worst-case discovery time and the impact of sidelobes.We validate the analysis with extensive simulations and demonstrate the superior perfor-mance of the proposed scheme over several baseline schemes.

A MmWave Communication Testbed Based on IEEE 802.11ad with Scalable PtMP Configuration

The sub-6 G band is too crowded to accommodate higher data rate, while the millimeter wave(mmWave) bands have abundant spectrum resources and massive MIMO can provide high spectral and energy efficiency. Therefore, the combination of the two,namely mmWave-MIMO system, has attracted intensive research interests. In this paper, we develop a high-speed mmWave-MIMO communication system and conduct exhaustive field tests. The detail of the system design is provided and the key modules of the testbed are analyzed. The testbed exploits high gain of mmWave RF and flexible configuration of embedded system. The validation and field tests show that the developed testbed can provide up to 2.3 Gbps network layer data rate in single channel with low latency and support point-to-multi-point(PtMP) transmission aided by relay. The testbed can be used in future B5 G and 6 G systems to provide high reliability and low latency wireless coverage.

A Resource Allocation Scheme with Delay Optimization Considering mmWave Wireless Networks

This paper presents a resource allocation scheme for wireless networks, aiming at optimizing the users’ data delay. It is proposed to provide optimal delay al-location by solving an optimization problem using idle state prediction and considering 5G characteristics such as mmWave propagation. The perfor-mance of the resource allocation algorithm is verified and compared with oth-ers from the literature using computational simulations in terms of Quality of Service (QoS) parameters such as throughput, delay, fairness index, loss rate and computational complexity. In these simulations, it is also considered the mmWave propagation and carrier aggregation technology for wireless next generation systems, in order to verify the system performance in a high data rate scenario.

5G网络中的mmWave和Massive MIMO

5G作为第五代移动通信技术,对其的研发工作是全球,特别是各移动通信领先国家、大型移动通信公司争相抢占的先机。我国也积极致力于5G技术的研究工作。本文主要介绍5G网络中mmWave技术以及在毫米波波段上使用的大规模MIMO技术。

Long-Range VNA-Based Channel Sounder: Design and Measurement Validation at MmWave and Sub-THz Frequency Bands

With the increasing demand for high bandwidth wireless communication systems,and with a congested spectrum in the sub-6 GHz frequency bands,researchers have been looking into exploration of millimeter wave(mmWave)and sub-terahertz(subTHz)frequency bands.Channel modeling is essential for system design and performance evaluation of new wireless communication systems.Accurate channel modeling relies on reliable measured channel data,which is collected by high-fidelity channel sounders.Furthermore,it is of importance to understand to which extent channel parameters are frequency dependent in typical deployment scenario(including both indoor short-range and outdoor long-range scenarios).To achieve this purpose,this paper presents a stateof-art long-range 28 GHz and 300 GHz VNA-based channel sounder using optical cable solutions,which can support a measurement range up to 300 m and 600 m in principle,respectively.The design,development and validation of the long-range channel sounders at mmWave and sub-THz bands are reported,with a focus on their system principle,link budget,and backto-back measurements.Furthermore,a measurement campaign in an indoor corridor is performed using the developed 300 GHz system and 28 GHz channel sounding systems.Both measured channels at the 28 GHz and 300 GHz channels are shown to be highly sparse and specular.A higher number of Multi Path Components(MPC)are observed for the 28 GHz system,while the same main MPC are observed for both systems.

A Sparse Optimization Approach for Beyond 5G mmWave Massive MIMO Networks

Millimeter-Wave(mmWave)Massive MIMO is one of the most effective technology for the fifth-generation(5G)wireless networks.It improves both the spectral and energy efficiency by utilizing the 30–300 GHz millimeter-wave bandwidth and a large number of antennas at the base station.However,increasing the number of antennas requires a large number of radio frequency(RF)chains which results in high power consumption.In order to reduce the RF chain’s energy,cost and provide desirable quality-ofservice(QoS)to the subscribers,this paper proposes an energy-efficient hybrid precoding algorithm formm Wave massive MIMO networks based on the idea of RF chains selection.The sparse digital precoding problem is generated by utilizing the analog precoding codebook.Then,it is jointly solved through iterative fractional programming and successive convex optimization(SCA)techniques.Simulation results show that the proposed scheme outperforms the existing schemes and effectively improves the system performance under different operating conditions.

A Highly Efficient Algorithm for Phased-Array mmWave Massive MIMO Beamforming

With the rapid development of the mobile internet and the internet of things(IoT),the fifth generation(5G)mobile communication system is seeing explosive growth in data traffic.In addition,low-frequency spectrum resources are becoming increasingly scarce and there is now an urgent need to switch to higher frequency bands.Millimeter wave(mmWave)technology has several outstanding features—it is one of the most well-known 5G technologies and has the capacity to fulfil many of the requirements of future wireless networks.Importantly,it has an abundant resource spectrum,which can significantly increase the communication rate of a mobile communication system.As such,it is now considered a key technology for future mobile communications.MmWave communication technology also has a more open network architecture;it can deliver varied services and be applied in many scenarios.By contrast,traditional,all-digital precoding systems have the drawbacks of high computational complexity and higher power consumption.This paper examines the implementation of a new hybrid precoding system that significantly reduces both calculational complexity and energy consumption.The primary idea is to generate several sub-channels with equal gain by dividing the channel by the geometric mean decomposition(GMD).In this process,the objective function of the spectral efficiency is derived,then the basic tracking principle and least square(LS)techniques are deployed to design the proposed hybrid precoding.Simulation results show that the proposed algorithm significantly improves system performance and reduces computational complexity by more than 45%compared to traditional algorithms.

Improved Hybrid Beamforming for mmWave Multi-User Massive MIMO

Massive multiple input multiple output(MIMO)has become essential for the increase of capacity as the millimeter-wave(mmWave)communication is considered.Also,hybrid beamforming systems have been studied since full-digital beamforming is impractical due to high cost and power consumption of the radio frequency(RF)chains.This paper proposes a hybrid beamforming scheme to improve the spectral efciency for multi-user MIMO(MU-MIMO)systems.In a frequency selective fading environment,hybrid beamforming schemes suffer from performance degradation since the analog precoder performs the same precoding for all subcarriers.To mitigate performance degradation,this paper uses the average channel covariance matrix for all subcarriers and considers an iterative algorithm to design analog precoder using approximation techniques.The analog precoder is iteratively updated for each column until it converges.The proposed scheme can reduce errors in the approximating process of the overall spectral efciency.This scheme can be applied to fully-connected and partially-connected structures.The simulation results show that spectral efciency performance for the proposed scheme is better than the conventional schemes.The proposed scheme can achieve similar performance with full-digital beamforming by using a sufciently large number of RF chains.Also,this paper shows that the proposed scheme outperforms other schemes in the frequency selective fading environment.This performance improvement can be achieved in both structures.

Propagation Characterization and Analysis for 5G mmWave Through Field Experiments

The 5G network has been intensively investigated to realize the ongoing early deployment stage as an effort to match the exponential growth of the number of connected users and their increasing demands for high throughput,bandwidth with Quality of Service(QoS),and low latency.Given that most of the spectrums below 6 GHz are nearly used up,it is not feasible to employ the traditional spectrum,which is currently in use.Therefore,a promising and highly feasible effort to satisfy this insufficient frequency spectrum is to acquire new frequency bands for next-generation mobile communications.Toward this end,the primary effort has been focused on utilizing the millimeter-wave(mmWave)as the most promising candidate for the frequency spectrum.However,though the mmWave frequency band can fulfill the desired bandwidth requirements,it has been demonstrated to endure several issues like scattering,atmospheric absorption,fading,and especially penetration losses compared to the existing sub-6 GHz frequency band.Then,it is fundamental to optimize the mmWave band propagation channel to facilitate the practical 5G implementation for the network operators.Therefore,this study intends to investigate the outdoor channel characteristics of 26,28,36,and 38 GHz frequency bands for the communication infrastructure at the building to the ground floor in both Line of Sight(LOS)and Non-Line of Sight(NLOS)environments.The experimental campaign has studied the propagation path loss models such as Floating-Intercept(FI)and Close-In(CI)for the building to ground floor environment in LOS and NLOS scenarios.The findings obtained from the field experiments clearly show that the CI propagation model delivers much better performance in comparison with the FI model,thanks to its simple setup,accuracy,and precise function.

Sparse array of sub-surface aided blockage-free multi-user mmWave communication systems

Recently,Reconfigurable Intelligent Surfaces(RISs)have drawn intensive attention in the realization of the smart radio environment.However,existing works mainly consider the RIS as a whole uniform plane,which may be unrealistic to be installed on the facade of buildings when the RIS is extremely large.In contrast,this paper investigates a practical Sparse Array of Sub-surface(SAoS)deployment of the RIS for uplink multi-user millimeter Wave(mmWave)communication systems,in which the Mobile Stations(MSs)are distributed in the blind coverage area due to the blockage.In order to exploit the benefits of the sparse deployment,the correlation of the effective channel is firstly investigated.Then the approximation and lower bounds of the ergodic spectral efficiency are derived under frequency and spatial multiplexing scenarios,respectively.Based on the autocorrelation of the effective channel,we obtain an optimal reflect coefficient design as well as the deployment guidelines of RIS tiles.Moreover,the RIS tile scheduling algorithms are also proposed.Numerical results show that the ergodic spectral efficiency approximation matches well with the Monte Carlo result under frequency multiplexing scenarios,and the lower bound is tight under spatial multiplexing scenarios only when the effective channel is strongly correlated.On the basis of the RIS tile scheduling algorithm and the reflect coefficient design,the system performance can be significantly improved under frequency multiplexing scenarios.On the other hand,by deploying more sparse RIS tiles,we can increase the multiplexing gain under spatial multiplexing scenarios.

Efficient Interference Mitigation in mmWave Backhaul Network for High Data Rate 5G Wireless Communications

This paper investigates the performance of the W band millimeter wave (mmWave) backhaul network proposed by our EU TWEETHER project. We focus on the downlink transmission of the mmWave backhaul network, in which each of the hubs serves a cluster of base stations (BSs). In the considered backhaul network, available frequency resources are first allocated to the downlink links with the consideration of fairness issue. In order to mitigate interference in the mmWave backhaul network, each hub operates the proposed algorithm, namely cooperation and power adaptation (CPA). Our simulation results show that, the backhaul network with mmWave capabilities can achieve a significant better throughput performance than the sub-6 GHz ultra high frequency (UHF) backhaul network. Furthermore, our simulations also reveal that the proposed CPA algorithm can efficiently combat interference in the backhaul network.