大规模MIMO系统中基于能效最大化的资源联合优化算法

针对信道条件未知的多小区大规模多输入多输出（MIMO）系统,提出一种对导频序列长度、导频符号功率以及数据符号功率进行联合优化的资源分配算法。采用最大比合并（MRC）接收,考虑电功率和导频污染的影响,并对最大传输功率进行约束从而建立起以能效（EE）最大化为目标的非凸函数模型。根据分数规划的性质,首先将分数形式转化成减式形式,进而分解成一系列凸函数之差（DC）的问题,最后采用交替优化算法联合调整 3 个变量从而达到能效最大化的目标。仿真结果表明,随着最大符号传输功率的增加,所提方案仍然能保持良好系统能效性能。     

大规模MIMO系统中动态迁移导频分配

大规模多输入多输出(MIMO)技术是5G的关键技术之一,由导频资源有限引起的导频污染是制约其发展的主要瓶颈之一,为此必须对宝贵的导频资源进行合理地分配以减小导频污染。已有的导频分配方案大多考虑静态导频分配,无法很好地适应用户位置不断变化的移动通信环境。文章针对大规模MIMO系统中用户移动时的导频分配问题,提出了一种动态迁移导频分配方法。该方法在两个相邻时隙间仅针对移动幅度较大的用户进行额外的导频优化,根据分配方案选择一个用户与之进行导频交换,对于其他的用户不改变原分配导频。复杂度分析及仿真结果表明,该算法相较于传统的导频分配方法可以显著地降低计算复杂度,并且与传统的导频分配方法具有相近的频谱效率,具有较高的实用价值。     

Deep and robust resource allocation for random access network based with imperfect CSI

A deep and robust resource allocation framework was proposed for the random access based wireless networks,where both the communication channel state information (C-CSI) and the interference channel state information (I-CSI) were uncertain.The proposed resource allocation framework considered the optimization objective of wireless networks as a learning problem and employs deep neural network (DNN) to approximate optimal resource allocation policy through unsupervised manner.By modeling the uncertainties of CSI as ellipsoid sets,two concatenated DNN units were proposed,where the first was uncertain CSI processing unit and the second was the power control unit.Then,an alternating iterative training algorithm was developed to jointly train the two concatenated DNN units.Finally,the simulations verify the effectiveness of the proposed robust leaning approach over the nonrobust one.     

基于人工智能的大规模MIMO信道状态信息反馈综述

大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)技术凭借其高能量效率和高频谱效率的优势成为下一代移动通信的核心技术之一,其系统增益的基础在于基站能够精确获知信道状态信息(Channel State Information,CSI).由于大规模MIMO系统中基站天线数量巨大,基站获取下行信道状态信息将造成巨大的系统开销,传统基于码本或矢量量化的反馈方法受到挑战,频分双工(Frequency Division Duplex,FDD)模式下5G通信的实际应用也受到制约,而人工智能技术尤其是深度学习(Deep Learning,DL)为解决大规模MIMO系统中的CSI反馈问题提供了新的思路.围绕大规模MIMO系统CSI反馈存在的问题,阐述了CSI反馈的背景,构建了FDD大规模MIMO系统模型,详细描述了代表性的国内外基于DL的CSI反馈方案,最后对基于人工智能的大规模MIMO信道状态信息反馈进行了展望和总结.     

无小区大规模MIMO系统中基于叠加导频的上行频谱效率理论上限分析

无小区大规模多输入多输出 (Cell-Free Massive MIMO)突破传统蜂窝小区的概念,被认为是未来移动通信中十分富有潜力的技术。该技术通过部署大量的分布式接入点(Access Point, AP)以获得充分的宏分集增益,以提高用户服务质量。受信道相干时间的限制,系统中可供用户分配的正交导频资源非常有限,部分用户将不得不复用相同的导频,从而常规的复用导频(Multiplexed Pilot, MP)配置模式将引起严重的导频污染问题。因此,本文利用叠加导频(Superimposed Pilot, SP)配置模式,基于线性最小均方误差(Linear Minimum Mean Square Error, LMMSE)准则和最大比合并(Maximum Ratio Combining, MRC)技术,研究了无小区大规模MIMO系统的信道估计,并从理论上推导了系统上行频谱效率(Spectrum Efficiency, SE)的上限闭合表达式。仿真结果表明,采用SP配置模式可有效减轻导频污染问题带来的影响,从而明显地提升系统的SE。      

Performance optimization of cell-free massive MIMO system with power control approach

The cell-free massive MIMO (multiple-input multiple-output) system involves a large number of access points serving a smaller number of mobile users (MUs) over identical time/ frequency resource. By providing large number of service antennas closer to the MUs, the cell-free massive MIMO can offer great spectral efficiency, better macro-diversity and minimal path loss. Despite several advantages, the cell-free massive MIMO suffers from energy overloading caused by uncontrolled backhaul power consumption for large number of distributed access points (APs) and pilot contamination during channel estimation. In this paper, we have taken into consideration a cell-free massive MIMO system with APs equipped with multiple antennas performing time-division-duplex (TDD) operation. Here, all the APs coordinate through a constrained backhaul network for joint transmission of signals to all the users simultaneously by multiplying the received signal with the normalized conjugate of the estimated channel state information (CSI) and send back a rounded off version of the weighted pattern to the central processing unit (CPU). Finally, an effective user defined algorithm is presented involving selection and grouping of various APs based on their individual contributions for a particular MU to improve the overall performance of the system.     

基于干扰消除辅助稀疏连接神经网络的大规模MIMO信号检测

近年来,深度学习成为无线通信领域的关键技术之一。在基于深度学习的一系列MIMO信号检测算法中,大多未充分考虑相邻天线之间的干扰消除问题,无法彻底消除多用户干扰对误码率性能的影响。为此,该文提出一种将深度学习与串行干扰消除(SIC)算法进行结合的方法用于大规模MIMO系统上行链路信号检测。首先,通过优化传统的检测网络(DetNet)及改进ScNet检测算法,该文提出一种基于深度神经网络(DNN)的检测算法,称为ImpScNet。在此基础上,进一步将SIC思想应用到深度学习框架结构设计中,提出一种基于深度学习的大规模MIMO多用户SIC检测算法,称为ImpScNet-SIC。此算法在每个检测层上分为两级,其中,第1级由该文提出的ImpScNet算法提供初始解,再将初始解解调至相应的星座点上作为SIC的输入,由此构成该算法的第2级。此外,在SIC中也使用了ImpScNet算法估计传输符号,以便获得最优性能。仿真结果表明,与已有的各种典型代表算法相比,该文所提ImpScNet-SIC检测算法特别适合大规模MIMO信号检测,具有收敛速度快、收敛稳定及复杂度相对较低的优势,并且在10–3误码率上有至少0.5 dB以上的增益。     

基于瞬时相位差和深度学习的丢包隐藏方法

实时IP 语音通信在数据包会丢失的情况下,语音质量会受到严重影响。为了恢复传输过程中丢失的语音信息,本文提出了一种基于瞬时相位差(Instantaneous Phase Deviation, IPD)和深度神经网络(Deep Neural Network, DNN)的丢包隐藏 (Packet Loss Concealment, PLC)方法。在训练阶段,将语音的对数功率谱(Log Power Spectrum, LPS)和IPD作为训练DNN的输入特征,以学习从接收包到丢失包的映射关系;在重构阶段,将丢包前接收到的语音包送入训练好的DNN中,恢复出丢失包的语音。实验结果表明,在不同丢包率下,所提方法的性能优于传统的基于LPS和DNN的PLC方法。      

Deep reinforcement learning based resource allocation algorithm in cellular networks

In order to solve multi-objective optimization problem,a resource allocation algorithm based on deep reinforcement learning in cellular networks was proposed.Firstly,deep neural network (DNN) was built to optimize the transmission rate of cellular system and to complete the forward transmission process of the algorithm.Then,the Q-learning mechanism was utilized to construct the error function,which used energy efficiency as the rewards.The gradient descent method was used to train the weights of DNN,and the reverse training process of the algorithm was completed.The simulation results show that the proposed algorithm can determine optimization extent of optimal resource allocation scheme with rapid convergence ability,it is obviously superior to the other algorithms in terms of transmission rate and system energy consumption optimization.     

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。     

无蜂窝大规模MIMO中基于深度强化学习的无人机辅助通信与资源调度

无蜂窝大规模多入多出(MIMO)网络中分布式接入点(AP)同时服务多个用户,可以实现较大区域内虚拟MIMO的大容量传输;而无人机辅助通信能够为该目标区域热点或边缘用户提供覆盖增强.为了降低反馈链路负载,并有效提升无人机辅助通信的频谱利用率,该文研究了基于AP功率分配、无人机服务区选择和接入用户选择的联合调度;首先将AP...     

大规模MIMO OFDMA下行系统能效资源分配算法

针对大规模多输入多输出（MIMO）正交频分多址(OFDMA)下行移动通信系统,提出了一种基于能效最优的资源分配算法。所提算法在采用迫零(ZF)预编码的情况下,以最大化系统能效的下界为准则,同时考虑每个用户的最低速率要求,通过调整带宽分配、功率分配和基站天线数分配来优化能效函数。首先根据优化条件提出了一种迭代算法确定每个用户的带宽分配,然后利用分数规划的性质并采用凸优化方法,通过联合调整基站端的发射天线数和用户的发射功率来优化能效函数。仿真结果表明,所提算法在较少迭代次数的同时能够取得较好的系统能效性能和吞吐量性能。     

Multiuser MIMO OFDM Based TDD/TDMA for Next Generation Wireless Communication Systems

Multiple-input multiple-output (MIMO) wireless technology in combination with orthogonal frequency division multiplexing (MIMO OFDM) is an attractive air-interface solution for next-generation wireless local area networks (WLANs), wireless metropolitan area networks (WMANs), and fourth-generation mobile cellular wireless systems. In this paper, one multiuser MIMO OFDM systems with TDD/TDMA was proposed for next-generation wireless mobile communications, i.e., TDD/TDMA 4G, which can avoid or alleviate the specific limitations of existing techniques designed for multiuser MIMO OFDM systems in broadband wireless mobile channel scenarios, i.e., bad performance and extreme complexity of multiuser detectors for rank-deficient multiuser MIMO OFDM systems with CDMA as access modes, extreme challenges of spatial MIMO channel estimators in rank-deficient MIMO OFDM systems, and exponential growth complexity of optimal sub-carrier allocations for OFDMA-based MIMO OFDM systems. Furthermore, inspired from the Steiner channel estimation method in multi-user CDMA uplink wireless channels, we proposed a new design scheme of training sequence in time domain to conduct channel estimation. Training sequences of different transmit antennas can be simply obtained by truncating the circular extension of one basic training sequence, and the pilot matrix assembled by these training sequences is one circular matrix with good reversibility. A novel eigenmode transmission was also given in this paper, and data symbols encoded by space–time codes can be steered to these eigenmodes similar to MIMO wireless communication systems with single-carrier transmission. At the same time,, an improved water-filling scheme was also described for determining the optimal transmit powers for orthogonal eigenmodes. The classical water-filling strategy is firstly adopted to determine the optimal power allocation and correspondent bit numbers for every eigenmode, followed by a residual power reallocation to further determine the additional bit numbers carried by every eigenmode. Compared with classical water-filling schemes, it can also obtain larger throughputs via residual power allocation. At last, three typical implementation schemes of multiuser MIMO OFDM with TDMA, CDMA and OFDMA, i.e., TDD/TDMA 4G, VSF-OFCDM and FuTURE B3G TDD, were tested by numerical simulations. Results indicated that the proposed multiuser MIMO OFDM system schemes with TDD/TDMA, i.e., TDD/TDMA 4G, can achieve comparable system performance and throughputs with low complexity and radio resource overhead to that of DoCoMo MIMO VSF-OFCDM and FuTURE B3G TDD.     

基于近似信息传递算法的大规模MIMO信号检测

大规模多输入多输出(MIMO)技术通过增加天线的数目可以有效降低发送功率,提高能量效率,被认为是5G移动通信的一项关键技术。随着天线数目的大幅增加,信号检测的复杂度随之增加。分析了大规模MIMO 信号检测的研究现状,提出了近似信息传递(AMP)算法,并比较了 AMP 算法、Richarson 算法以及Neumann级数迭代近似算法的复杂度。仿真结果表明,该算法使用较少的迭代次数即可达到和MMSE近似的系统差错性能。     

毫米波MIMO天线电磁辐射上限分析研究

第五代移动通信技术采用毫米波多输入多输出天线为高质量无线通信提供技术支撑,对传统的辐射安全性评估带来新的挑战。提出了采用无监督深度学习方法,分析毫米波MIMO设备电磁辐射上限,进而实现对MIMO设备辐射安全性的评估。首先从理论上分析了利用无监督学习方法分析MIMO设备电磁辐射上限的可行性,并以工作频率28 GHz的不同天线阵列为例,验证所提方法的可行性。此外,利用蒙特卡洛法作为参照对比验证了所提算法的可靠性。结果表明,所提方法与蒙特卡洛法的功率密度分布最大值和均值的误差均可控制在0.07%以下,可以准确高效地分析毫米波MIMO设备辐射上限,为快速评估毫米波MIMO设备的辐射安全性提供了新的方案。     

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.

     

无小区大规模MIMO系统中结合贪婪导频分配的导频功率控制的算法

无小区大规模多输入多输出(multiple-input multiple-output,MIMO)系统具有广阔的频谱,但是导频训练阶段的导频污染严重影响了系统的性能,因此减少导频污染成为必要.为了减少导频污染,文中提出了一种结合贪婪导频分配的导频功率控制的算法.首先进行贪婪导频分配,此阶段提高了较低性能用户的有限性能....     

Low power ring oscillator for IoT applications

In order to scale with the demand of higher data rates and improved spectral efficiency in next generation wireless communication systems, a large-scale multiple-input and multiple-output (MIMO) technology called massive MIMO has been proposed. In massive MIMO, appropriate signal-to-noise ratio (SNR) values can be achieved by the addition of base station (BS) antennas in place of increasing transmit power. Pilot-based channel estimation is widely used in conventional MIMO systems, where pilot signal sequences are sent from the user terminals (UTs) to the BS to estimate the channel. In massive MIMO-based cellular networks, channel estimation in a given cell will be impaired by the pilot signal sequences transmitted by users in other cells—rendering the addition of antennas or transmit power ineffective. This effect is called pilot contamination. Therefore, pilot-based channel estimation limits the performance of massive MIMO. Semi-blind and blind methods are alternatives to pilot-based channel estimation that perform channel estimation with short pilot signal sequences and without pilot signal sequences, respectively. Blind channel estimation is one of the promising solutions to the pilot contamination problem in massive MIMO. This paper compares, using MATLAB simulations of a cluster-based COST 2100 channel model, the performance of pilot-based, semi-blind, blind, and adaptive-blind channel estimation methods. The pilot contamination effect on different channel estimation methods and how channel estimation methods can be used to overcome pilot contamination are shown. Finally, an adaptive independent component analysis (ICA)-based channel estimation method, which outperforms conventional ICA in terms of computational complexity, is proposed.     

基于K-臂赌博机的多无人机空地网络动态资源分配方法

针对配置大规模MIMO的多无人机空地网络中的动态资源分配问题,从最大化系统吞吐量的角度出发,该文提出一种基于K-臂赌博机的强化学习算法联合优化多个无人机的用户选择与功率分配策略。首先根据地理位置对用户进行分簇,利用簇中心节点规划无人机飞行路径;其次在不考虑无人机之间端到端通信的情况下,将多无人机资源分配问题转化为相互独立的多个智能体强化学习问题;最后提出分幕式多智能体多状态K-臂赌博机算法来实现用户选择与功率分配的联合优化。通过将无人机每个时刻的位置索引定义为状态空间,从而使得无人机可动态适配自身位置及信道的动态变化。仿真结果表明,所提方案可根据环境状态变化自主智能调整资源分配策略,相比于已有方案能有效提升系统总吞吐量。     

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.