Beam Tracking Particle Filter for Hybrid Beamforming and Precoding Systems

Because millimeter wave (mmWave) systems can span notably wide spectral bands, mmWave systems are expected to dominate fifth-generation (5G) communication systems. Due to the short wave-length of mmWave radiation, multiple-input multiple-output (MIMO) systems can use massive antennas and precoding technology to overcome signal attenuation in mmWave channels. However, the cost and power consumption of radio frequency (RF) chains would increase substantially with the number of antennas. Hence, hybrid beamforming was proposed to reduce the number of RF chains in massive MIMO systems. Hybrid beamforming involves RF beamforming matrix construction and baseband precoding matrix derivation. This study focused on the design and implementation of an algorithm for the RF beamforming matrix construction for mobile environments. Accordingly, this study presents a mixture particle filter that exploits the temporal continuity of beam clusters in a mobile mmWave channel to reduce the computational complexity of RF beamforming matrix construction. Moreover, this beam-tracking particle filter is based on parallel processing architecture to support the tracking of multiple beam clusters in the mmWave channel. Finally, the beam-tracking particle filter was implemented on a field-programmable gate array platform and was verified in a hybrid beamforming system for mmWave MIMO systems. The particle filter processor achieved a maximal throughput of 9.198k matrices/s with a clock rate of 192 MHz, which could support a speed of up to 88.5 km/h for mobile users.

     

Hybrid Beamforming for Massive MIMO Using Rectangular Antenna Array Model in 5G Wireless Networks

Fifth and future generation (5G and B5G) wireless networks aim to serve users with higher data rates and lower latency. Data traffic due to the rapid growth in communication has motivated the study of Multiple Input Multiple Output (MIMO) systems. They utilize multiple antennas in both transmitter and receiver sides. It is necessary to improve the existing technology to achieve fast and reliable communication. In this research work, a rectangular array antenna based hybrid beamforming in a massive MIMO model has been proposed to improve the spectral efficiency of the system. Thus channel capacity with small RF chains is used. To achieve the high signal strength in the main lobe, Chebyshev tapering has been used to suppress the side lobes signals. In this manner, the proposed Hybrid Beamforming for Massive Output MIMO has been realized with a small complexity and higher spectral efficiency. In this research work, the spectral efficiency of both proposed Hybrid and fully-digital beamforming with a different number of RF chains for a various number of antennas at the transmitter, the receiver side has been analyzed. From the simulation results, it has been observed that the proposed rectangular array antenna based Hybrid beamforming in a massive MIMO system reduces the computational complexity up to 99% as compared with conventional fully digital beamforming to achieve the same spectral efficiencies, which is a productive model for 5G wireless networks.

     

Energy efficiency of massive MIMO wireless communication systems with antenna selection

Massive multiple-input multiple-output (MIMO) requires a large number (tens or hundreds) of base station antennas serving for much smaller number of terminals, with large gains in energy efficiency and spectral efficiency compared with traditional MIMO technology. Large scale antennas mean large scale radio frequency (RF) chains. Considering the plenty of power consumption and high cost of RF chains, antenna selection is necessary for Massive MIMO wireless communication systems in both transmitting end and receiving end. An energy efficient antenna selection algorithm based on convex optimization was proposed for Massive MIMO wireless communication systems. On the condition that the channel capacity of the cell is larger than a certain threshold, the number of transmit antenna, the subset of transmit antenna and servable mobile terminals (MTs) were jointly optimized to maximize energy efficiency. The joint optimization problem was proved in detail. The proposed algorithm is verified by analysis and numerical simulations. Good performance gain of energy efficiency is obtained comparing with no antenna selection.     

Robust hybrid beamforming design for millimeter-wave massive MIMO systems

Hybrid analog-digital beamforming is recognized as a promising solution for a practical implementation of massive multiple-input multiple-output(MIMO) systems based on millimeter-wave(mmWave) technology. In view of the overwhelming hardware cost and excessive power consumption and the imperfection of the channel state information(CSI), a robust hybrid beamforming design is proposed for the mmWave massive MIMO systems, where the robustness is defined with respect to imperfect knowledge or error of the CSI at the transmitter due to limited feedback and/or imperfect channel estimation. Assuming the errors of the CSI are bounded, the optimal hybrid beamforming design with robustness is formulated to a mean squared error(MSE) minimization problem. An iterative semidefinite programming(SDP) based algorithm is proposed to obtain the beamforming matrices. Simulation results show that the proposed robust design can provide more than 4 dB performance gain compared to that of non-robust design.     

Extended Length and Error-Correcting-Capacity of First-Order Reed–Muller Code in Extended Length Range

The massive multiple-input multiple-output (massive MIMO) system is the major section of the fifth generation (5G) future wireless cellular systems. It consists of hundreds of antennas in the base station that serves more number of users, concurrently. Thus, this system will get optimized energy usage, high data rate, and more precision because of their larger degrees of freedom. The computation power to the total power consumption ratio is considered for rapid increment owing to the more data traffic at the baseband unit that seeks more attention in the exploitation of massive MIMO systems for 5G wireless systems. The main intent of this paper is to develop the multi-user massive MIMO systems by deriving the joint optimization problem of computation and communication power. In the existing energy efficiency analysis, there is a negative effect on energy efficiency when increasing the count of RF chains and antennas by considering only computation power or communication power in massive MIMO. In order to overwhelm this problem, this paper focuses on two optimization problems. The first problem is focusing on the improvement of upper bound on energy efficiency with the optimal baseband and RF precoding matrices based on a new hybrid meta-heuristic algorithm. The combination of two well-performing meta-heuristic algorithms like electric fish optimization and dragonfly algorithm is used as the new algorithm, which is named as hybrid dragonfly with electric fish optimization (HD-EFO) for enhancing the efficiency of massive MIMO system. In the second phase, the joint optimization of both computation and communication power is performed by the same HD-EFO for developing the optimized hybrid precoding matrix. The extensive results have shown that the implemented multi-user massive MIMO systems with partially-connected structures using HD-EFO increase the cost and energy efficiencies, and save the maximum power.

     

基于GSIC的上行链路毫米波大规模MIMO-NOMA系统低功耗传输方法

非正交多址接入（non-orthogonal multiple access,NOMA）和毫米波大规模多输入多输出（multiple-input multiple-output,MIMO）的结合能够支持未来无线通信网络的巨流量大连接需求。研究了上行链路毫米波大规模MIMO-NOMA系统中的功率最小化问题，提出了基于群体串行干扰消除（group-levelsuccessiveinterference cancellation,GSIC）的混合波束成形毫米波MIMO-NOMA上行传输系统新架构。具体来说，根据信道增益对用户进行群体划分，不同群体用户由NOMA服务，群体内用户采用空分多址区分。通过给不同群体设计模拟波束成形矩阵，对数字波束成形和功率控制进行联合优化，提出了一种并行迭代算法来解决优化问题。仿真结果表明，所提出的新架构在总功率方面优于传统的基于分簇和用户级串行干扰消除的毫米波大规模MIMO-NOMA。     

Energy and spectral efficiency improvement using improved shark smell-coyote optimization for massive MIMO system

Massive multiple-input multiple-output (MIMO) can considerably enhance the “spectral efficiency and energy efficiency” since it is a major technique for future wireless networks. Thus, the performance needs a huge count of base station antennas to serve a smaller number of terminals in conventional MIMO methodology. Large-scale radio frequency (RF) chains represent the large-scale antennas. There is a need of implementing an effective massive MIMO system for maximizing the efficient performance of the system with high “spectral efficiency and energy efficiency” owing to the high cost of RF chains, and the higher power consumption. In this paper, a massive MIMO communication system is implemented to satisfy the requirements regarding “energy efficiency and spectral efficiency.” Here, the number of base station antennas, the transmit power, and beam forming vectors are optimized to maximize “energy efficiency and spectral efficiency” when the channel capacity is known to be higher than some threshold values. The novelty of this work is a new hybrid optimization adaptive shark smell-coyote optimization (ASS-CO) algorithm is developed for improving energy efficiency. The optimization is done with the help of the hybrid optimization ASS-CO Algorithm. The proposed ASS-CO algorithm-based massive MIMO communication system is evaluated by experimental analysis. From the result analysis, the maximum resource efficiency is observed by SS-WOA, which is 6.6%, 50%, 6.6%, 6.6%, and 6.6% maximized than rider optimization algorithm (ROA), spotted hyena optimization (SHO), lion algorithm (LA), Shark Smell Optimization (SSO), and Coyote Optimization Algorithm (COA) by taking the count of base stations as 4. The superior performance enhancement regarding “spectral efficiency and energy efficiency” is accomplished over the traditional systems.     

Space‐time block coding in millimeter wave large‐scale MIMO‐NOMA transmission scheme

In this paper, we introduce a new wireless system architecture using space‐time block coding schemes (STBC) and non‐orthogonal multiple access (NOMA) in millimeter wave (mmWave) large‐scale MIMO systems. The proposed STBC mmWave large‐scale MIMO‐NOMA system utilizes two MIMO subarrays, transmitting data over two channel vectors to mobile users. To reduce the communication overhead and latency in the system, we utilize random beamforming with optimal coefficients at the base station and random‐near random‐far user pairing in implementing the NOMA scheme. Our results show that the proposed STBC mmWave large‐scale MIMO‐NOMA technique significantly outperforms the previous counterparts.     

Hybrid beamforming with discrete phase shifters for millimeter wave backhaul networks

Hybrid beamforming (HBF) technology becomes one of the key technologies in the millimeter wave (mmWave) mobile backhaul systems, for its lower complexity and low power consumption compared to full digital beamforming (DBF). Two structures of HBF exist in the mmWave mobile backhaul system, namely, the fully connected structures (FCS) and partially connected structures (PCS). However, the existing methods cannot be applied to both structures. Moreover, the ideal phase shifter is considered in some current HBF methods, which is not realistic. In this paper, a HBF algorithm for both structures based on the discrete phase shifters is proposed in the mmWave mobile backhaul systems. By using the principle of alternating minimization, the optimization problem of HBF is decomposed into a DBF optimization problem and an analog beamforming (ABF) optimization problem. Then the least square (LS) method is enabled to solve the optimization model of DBF. In addition, the achievable data rate for both structures with closed-form expression which can be used to convert the optimization model into a single-stream beamforming optimization model with per antenna power constraint is derived. Therefore, the ABF is easily solved. Simulation results show that the performance of the proposed HBF method can approach the full DBF by using a lower resolution phase shifter.     

携能大规模MIMO资源分配算法研究综述

将大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)技术与无线能量传输(Wireless Power Transfer,WPT)技术相结合,能够帮助实现节能降耗,契合国内外绿色通信发展浪潮。针对WPT技术在大规模MIMO研究领域的应用问题,总结了当前携能大规模MIMO技术的研究现状及发展趋势,从频效、能效、安全性等多个方面对携能大规模MIMO资源分配算法进行综述,探讨了学术界在携能大规模MIMO资源分配算法上的重要研究成果。在现有算法研究进展分析的基础上,对当前研究中携能大规模MIMO资源分配算法研究情况存在的问题进行分析,并对未来的发展方向进行了展望。     

基于MIMO系统的天线选择

多天线MIMO（Multiple Input Multiple Output）系统利用多个收、发天线有效地改善无线通信系统性能，提高系统容量，增强系统可靠性。然而，由于使用多天线同时收发，这要求发射机和接收机使用与天线一样多的射频链路，增加了系统成本和复杂度。使用天线选择技术可以降低系统成本和复杂度，同时保留MIMO系统的优越性能。文中首先介绍了MIMO系统的实现方式，然后讨论天线选择的方法及性能，最后提出天线选择技术还存在的问题，并得出相关的结论。     

Asymptotic RZF cooperative beamforming algorithm based on energy efficiency

A low complexity asymptotic regularized zero forcing cooperative beamforming algorithm based on energy efficiency in heterogeneous massive MIMO system was proposed,aiming at the problem that the current multi-flow regularization zero forcing beamforming algorithm sets the power constraint of each antenna in the regularization term as a fixed value and ignores the influences of factors such as the number of antennas,the number of users and QoS.The algorithm selects the optimal antenna power constraint set through the optimization method,and the optimal beamforming was asymptotically ob-tained to balance the interference among users to achieve the optimal energy efficiency,considering the impact of the number of antennas and users with the constraints of the antenna power and QoS.In view of the importance of backhaul in massive MIMO system,a backhaul power consumption model and the impact of backhaul power consumption on system performance was analyzed.Analysis and simulation results show that the proposed algorithm has great improvement of the performance,especially when the number of antennas is large.The algorithm is close to optimal performance,especially suitable for massive MIMO system of next generation communication.     

基于联合用户分组及天线选择的大规模MIMO波束成形

大规模MIMO系统中,当小区用户数与基站天线数较大时,各用户的信道条件不尽相同,提出一种适用于大规模MIMO下行链路的基于联合用户分组及天线选择的迫零波束成形算法。将用户分成两组,选择信道条件较优的一组用户来接收信号,并为每一个发送数据流选择最优的基站天线组合进行通信,以较小的性能损失,换取大规模MIMO 射频电路的成本与功耗的大幅度降低。仿真结果证明,该算法能够较好地实现系统性能与硬件复杂度的折中。     

Joint estimation of the channel and I/Q imbalance for two-way relay networks

In modern day communication systems, the massive MIMO architecture plays a pivotal role in enhancing the spatial multiplexing gain, but vice versa the system energy efficiency is compromised. Consequently, resource allocation in-terms of antenna selection becomes inevitable to increase energy efficiency without having any obvious effect or compromising the system spectral efficiency. Optimal antenna selection can be performed using exhaustive search. However, for a massive MIMO architecture, exhaustive search is not a feasible option due to the exponential growth in computational complexity with an increase in the number of antennas. We have proposed a computationally efficient and optimum algorithm based on the probability distribution learning for transmit antenna selection. An estimation of the distribution algorithm is a learning algorithm which learns from the probability distribution of best possible solutions. The proposed solution is computationally efficient and can obtain an optimum solution for the real time antenna selection problem. Since precoding and beamforming are also considered essential techniques to combat path loss incurred due to high frequency communications, so after antenna selection, successive interference cancellation algorithm is adopted for precoding with selected antennas. Simulation results verify that the proposed joint antenna selection and precoding solution is computationally efficient and near optimal in terms of spectral efficiency with respect to exhaustive search scheme. Furthermore, the energy efficiency of the system is also optimized by the proposed algorithm, resulting in performance enhancement of massive MIMO systems.

     

Joint hybrid beamforming and power splitting design in millimeter-wave simultaneous wireless information and power transfer system

A joint hybrid beamforming and power splitting(JHBPS) design problem for simultaneous wireless information and power transfer(SWIPT) in millimeter-wave(mmWave) system is studied.The considered scenario is a multi-antenna base station(BS) transfers information and energy simultaneously to multiple single-antenna receivers.BS adopts hybrid digital and analog beamforming architecture to reduce hardware costs.Receivers separate acquired signals with power splitters either for information decoding(ID) or energy harvesting(EH).The aim is minimizing total transmission power by joint design of hybrid beamforming and PS under ID and EH requirements.It is difficult to obtain the optimal hybrid beamformer directly since the analog beamformer and digital beamformer are multiplied.Therefore,a two-stage algorithm is proposed to solve the problem.In the first stage,the optimal beamformer and PS ratios are obtained by solving the joint transmission beamforming and PS design problem.In the second stage,the optimal beamformer is approximated with the product of analog beamformer and digital beamformer.The superiority of proposed algorithm over the existing algorithms is demonstrated through simulations.Moreover,the effectiveness of approximation algorithm is testified.     

基于低分辨率模数转换的全双工大规模MIMO中继系统频谱效率分析

为了降低由于大规模基站天线阵列模数转换(analog-to-digital converters, ADCs)所造成的巨大硬件损耗,同时有效地提高系统的能量和频谱效率,基于迫零传输/迫零接收(zero-forcing transmitting/ zero-forcing receiving, ZFT/ZFR)预处理方案,文章提出了低分辨率模数转换的多用户全双工大规模多入多出(massive multiple-input multiple-output, massive MIMO)中继系统,基站采用放大转发(amplify-and-forward, AF)协议,并对系统频谱效率进行了分析。文章首先获得了任一用户对频谱效率的闭式表达式,然后分别对三种不同功率缩放方案下系统的频谱效率进行了渐近分析。研究结果表明,当基站天线数量足够大时,三种不同的功率缩放方案对系统的环路干扰和量化误差有不同的影响,且当信源功率固定、基站的传输功率与发送天线数量成反比时,系统能够有效地抑制系统的环路干扰和量化误差,这对低分辨率全双工massive MIMO 中继系统的部署具有一定的指导意义。      

毫米波大规模MIMO系统混合波束成形技术综述

毫米波通信和大规模多输入多输出（Multiple-Input Multiple-Output,MIMO）技术是5G的关键候选技术,在提高5G系统各项性能指标上潜力巨大。混合波束成形作为毫米波大规模MIMO系统中的关键点,能在系统性能和实现复杂度上取得较好平衡,受到业界和学术界广泛关注。首先给出了混合波束成形经典系统模型和常用信道模型,根据信道状态信息获取方式的不同,从基于理想信道条件和基于波束配对两个方面分析和归纳了现有的混合波束成形方案,最后指出了混合波束成形未来发展趋势以及尚未解决的难点。     

毫米波视距MIMO安全通信技术研究

移动通信和信息社会的高速发展对宽带高速数据传输提出了较高的要求,而毫米波多输入多输出（multiple-input multiple-output,MIMO）技术成为实现高速安全数据传输的重要技术途径.考虑到一些特殊的需求和应用场景,比如对偏远地区的覆盖,构建应急通信系统,特别是军事宽带战术互联网,基于空中移动平台的毫米波MIMO技术成为当前研究的一个热点.本文充分调研国内外相关文献资料,阐述了毫米波视距（line-of-sight,LoS）MIMO信道建模、天线阵列优化、混合波束成形设计以及物理层安全等相关技术的当前进展、存在的挑战,并指出未来的研究方向,推动基于空中平台的毫米波MIMO系统的工程化应用.     

MIMO系统中天线选择的关键技术及其应用

多天线MIMO系统利用多个收、发天线有效地改善无线通信系统性能,提高系统容量,增强系统可靠性.然而使用多个射频的MIMO系统增加了天线的体积、功率和硬件,从而增加了成本.天线选择是一种低成本、低复杂度的有效方法,可以利用多天线系统的多数优点.使用天线选择技术可以降低系统成本和复杂度,同时保留MIMO系统的优越性能.文中考虑了天线选择系统的一些实际应用问题,RF预处理,训练序列等的实际应用.最后,给出了一些天线选择的结论.     

混合动态连接结构的多用户多流混合预编码

混合预编码对于提高多用户毫米波大规模多输入多输出(MIMO)系统的性能至关重要,但目前基于全连接结构与子连接结构的混合预编码分别存在高能耗与性能损失严重的问题。该文综合考虑系统的频谱效率与能量效率,提出混合动态连接结构,并设计该结构下的混合预编码算法。该算法通过最大化信干噪比(SINR)的增量来设计混合动态连接结构的模拟域预编码,然后基于等效信道运用块对角化(BD)设计数字域预编码抑制多用户多流干扰。仿真实验表明,该文所提出的混合动态连接结构的频谱效率介于全连接结构与混合固定连接结构之间且获得的能量效率最高。