多小区Massive MIMO系统低复杂度ZF线性检测算法

针对多小区Massive MIMO上行链路系统中ZF线性检测涉及到大矩阵求逆而具有高复杂度的问题,提出了一种低复杂度的ZF检测算法。在考虑各小区信道状态信息已知条件下,利用SVD分解的方法求解干扰消除矩阵,从而将多小区信号接收模型等效转化为单小区模型,然后再利用ZF检测算法。但是,在ZF检测算法中涉及到大矩阵求逆运算的复杂度为O(K^3),其中K为本小区中的用户数。为了降低直接求逆的高复杂度,提出了将大矩阵分解为对角矩阵和空心矩阵之和,并采用诺依曼级数近似且通过优化展开项因子,使所提算法在性能损失很少的情况下复杂度降低了一个数量级为O(K^2)。此外,通过仿真实验验证了理论推导与分析的有效性。

多小区Massive MIMO系统中低复杂度MMSE线性检测算法研究

文章针对多小区Massive MIMO上行链路系统中MMSE线性检测中涉及大矩阵求逆具有高复杂度的问题,提出了一种低复杂度的MMSE检测算法。首先考虑已知目标小区的信道状态信息而其他小区信道状态信息未知条件下,通过求解干扰项与噪声之和的均值与方差,将多小区信道模型转化为单小区信道模型,再利用MMSE算法进行检测。为了降低求逆矩阵的复杂度,将大矩阵分解为对角矩阵和空心矩阵之和,再利用诺依洛曼级数近似将其展开,并优化展开项因子来增加算法收敛速度。仿真结果表明,所提出的改进算法在性能损失很少的情况下复杂度从O(K^3)降低到O(K^2),其中K为本小区中的用户数。

基于压缩感知的多小区MASSIVE MIMO信道估计

针对多小区多用户大规模多输入多输出(MASSIVE MIMO)系统信道估计在低信噪比情况下估计精度较差的问题,提出了一种基于群智能搜索的果蝇分段正交匹配追踪(FF-StOMP)压缩感知算法。该算法在分段正交匹配追踪(StOMP)求解不同阈值下的信道矩阵参数与归一化最小均方误差的基础上,采用果蝇优化算法动态搜索出最小归一化均方误差与其对应的阈值,达到自适应参数设定的目的。仿真结果表明,与StOMP算法相比,信噪比在0~10 dB情况下,所提出的FF-StOMP算法信道估计性能能够提升0.5~1 dB;信噪比在11~20 dB时,信道估计性能能够提升0.2~0.3 dB。当小区用户数发生变化时,所提出的算法能实现自适应信道估计,能够有效提升MASSIVE MIMO系统低信噪比情况下的信道估计精度。

多小区Massive MIMO系统的分布式导频优化分配

研究了多小区大规模多输入多输出系统用于缓解导频污染问题的导频资源优化分配。在假定导频序列个数与单个小区内用户数相同的前提下,仅利用小区基站与用户终端之间的大尺度衰落信息,以提升用户平均传输速率为目标,提出2种可逐个小区执行的分布式导频优化方案:交换搜索与干扰度量。交换搜索算法通过将任意小区内任意2个用户的导频序列相交换进而搜索得到最佳导频分配方案,而干扰度量方案基于对不同小区用户间干扰值的度量来进行导频优化。数值结果显示,新提出的2种方案与已有方案相比能以更低的实现复杂度获得更好的性能。尤其是,若将2种方案结合起来使用,其性能甚至接近于穷举搜索方式。

利用数据和导频的多小区Massive MIMO系统信道估计

本文提出一种通过同时利用上行数据和导频进行信道估计来对抗时分双工大规模多输入多输出(Massive multi-input multi-output,Massive MIMO)系统中导频污染的方法。考虑到Massive MIMO系统下空间相关信道在角度域具有近似稀疏性,期望用户信道和干扰信道的多径分量因而可分辨。同时上行数据子帧中的数据符号数远大于导频序列的长度。基于以上两点观察,首先利用上行导频对信道进行粗估计,然后将解码得到的上行数据符号视为导频序列,用于得到角度域信道估计中能量较大分量所处的位置。通过抽取信道粗估计中相应位置上的信道系数,可以得到信道的精估计。仿真表明,无论相邻小区采用同一组正交导频序列、还是不同组但具有相关性的导频序列,所提出的信道估计方法都能有效地消除导频污染。

基于Massive MIMO的5G高业务量场景组网方案

针对5G用户量大、流量密度大等高密重载特殊场景,引入分布式Massive MIMO技术,给出典型场景的速率和容量提升方案,并对方案进行了验证。最后总结了典型场景的组网应用建议,对提升5G用户感知,促进5G市场发展具有重要意义。

基于位置分配的去蜂窝massive MIMO系统导频分配功率控制算法

针对去蜂窝(cell free,CF)大规模多输入多输出(multiple-input multiple-output,MIMO)系统中存在严重的导频污染问题,提出了一种基于位置分配的贪婪导频分配功率控制算法(greedy pilot assignment based on location with pilot power control,GPABL with PPC).首先,遵循相邻用户不分配相同导频序列的原则进行贪婪导频分配(greedy pilot assignment,GPA);然后,在导频分配的基础上叠加了导频功率控制,选择合理的导频功率控制系数减小信道估计的均方误差.仿真结果表明,将两种方式结合起来进行导频优化,系统的吞吐能力有所提升.

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.

Extreme massive hydraulic fracturing in deep coalbed methane horizontal wells:A case study of the Linxing Block,eastern Ordos Basin,NW China

Deep coal seams show low permeability,low elastic modulus,high Poisson’s ratio,strong plasticity,high fracture initiation pressure,difficulty in fracture extension,and difficulty in proppants addition.We proposed the concept of large-scale stimulation by fracture network,balanced propagation and effective support of fracture network in fracturing design and developed the extreme massive hydraulic fracturing technique for deep coalbed methane(CBM)horizontal wells.This technique involves massive injection with high pumping rate+high-intensity proppant injection+perforation with equal apertures and limited flow+temporary plugging and diverting fractures+slick water with integrated variable viscosity+graded proppants with multiple sizes.The technique was applied in the pioneering test of a multi-stage fracturing horizontal well in deep CBM of Linxing Block,eastern margin of the Ordos Basin.The injection flow rate is 18 m^(3)/min,proppant intensity is 2.1 m^(3)/m,and fracturing fluid intensity is 16.5 m^(3)/m.After fracturing,a complex fracture network was formed,with an average fracture length of 205 m.The stimulated reservoir volume was 1987×10^(4)m^(3),and the peak gas production rate reached 6.0×10^(4)m^(3)/d,which achieved efficient development of deep CBM.

Optimization of resource allocation in FDD massive MIMO systems

The performance of massive MIMO systems relies heavily on the availability of Channel State Information at the Transmitter(CSIT).A large amount of work has been devoted to reducing the CSIT acquisition overhead at the pilot training and/or CsI feedback stage.In fact,the downlink communication generally includes three stages,i.e.,pilot training,CsI feedback,and data transmission.These three stages are mutually related and jointly determine the overall system performance.Unfortunately,there exist few studies on the reduction of csIT acquisition overhead from the global point of view.In this paper,we integrate the Minimum Mean Square Error(MMSE)channel estimation,Random Vector Quantization(RVQ)based limited feedback and Maximal Ratio Combining(MRC)precoding into a unified framework for investigating the resource allocation problem.In particular,we first approximate the covariance matrix of the quantization error with a simple expression and derive an analytical expression of the received Signal-to-Noise Ratio(SNR)based on the deterministic equivalence theory.Then the three performance metrics(the spectral efficiency,energy efficiency,and total energy consumption)oriented problems are formulated analytically.With practical system requirements,these three metrics can be collaboratively optimized.Finally,we propose an optimization solver to derive the optimal partition of channel coherence time.Experiment results verify the benefits of the proposed resource allocation schemes under three different scenarios and illustrate the tradeoff of resource allocation between three stages.

A Web-Based Approach for the Efficient Management of Massive Multi-source 3D Models

Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.

Network-Assisted Full-Duplex Cell-Free mmWave Massive MIMO Systems with DAC Quantization and Fronthaul Compression

In this paper,we investigate networkassisted full-duplex(NAFD)cell-free millimeter-wave(mmWave)massive multiple-input multiple-output(MIMO)systems with digital-to-analog converter(DAC)quantization and fronthaul compression.We propose to maximize the weighted uplink and downlink sum rate by jointly optimizing the power allocation of both the transmitting remote antenna units(T-RAUs)and uplink users and the variances of the downlink and uplink fronthaul compression noises.To deal with this challenging problem,we further apply a successive convex approximation(SCA)method to handle the non-convex bidirectional limited-capacity fronthaul constraints.The simulation results verify the convergence of the proposed SCA-based algorithm and analyze the impact of fronthaul capacity and DAC quantization on the spectral efficiency of the NAFD cell-free mmWave massive MIMO systems.Moreover,some insightful conclusions are obtained through the comparisons of spectral efficiency,which shows that NAFD achieves better performance gains than cotime co-frequency full-duplex cloud radio access network(CCFD C-RAN)in the cases of practical limited-resolution DACs.Specifically,their performance gaps with 8-bit DAC quantization are larger than that with1-bit DAC quantization,which attains a 5.5-fold improvement.

A Correlation-Based Stochastic Model for Massive MIMO Channel

In this paper,the channel impulse response matrix(CIRM)can be expressed as a sum of couplings between the steering vectors at the base station(BS)and the eigenbases at the mobile station(MS).Nakagami distribution was used to describe the fading of the coupling between the steering vectors and the eigenbases.Extensive measurements were carried out to evaluate the performance of this proposed model.Furthermore,the physical implications of this model were illustrated and the capacities are analyzed.In addition,the azimuthal power spectrum(APS)of several models was analyzed.Finally,the channel hardening effect was simulated and discussed.Results showed that the proposed model provides a better fit to the measured results than the other CBSM,i.e.,Weichselberger model.Moreover,the proposed model can provide better tradeoff between accuracy and complexity in channel synthesis.This CIRM model can be used for massive MIMO design in the future communication system design.

Deep Reinforcement Learning Based Joint Cooperation Clustering and Downlink Power Control for Cell-Free Massive MIMO

In recent times,various power control and clustering approaches have been proposed to enhance overall performance for cell-free massive multipleinput multiple-output(CF-mMIMO)networks.With the emergence of deep reinforcement learning(DRL),significant progress has been made in the field of network optimization as DRL holds great promise for improving network performance and efficiency.In this work,our focus delves into the intricate challenge of joint cooperation clustering and downlink power control within CF-mMIMO networks.Leveraging the potent deep deterministic policy gradient(DDPG)algorithm,our objective is to maximize the proportional fairness(PF)for user rates,thereby aiming to achieve optimal network performance and resource utilization.Moreover,we harness the concept of“divide and conquer”strategy,introducing two innovative methods termed alternating DDPG(A-DDPG)and hierarchical DDPG(H-DDPG).These approaches aim to decompose the intricate joint optimization problem into more manageable sub-problems,thereby facilitating a more efficient resolution process.Our findings unequivo-cally showcase the superior efficacy of our proposed DDPG approach over the baseline schemes in both clustering and downlink power control.Furthermore,the A-DDPG and H-DDPG obtain higher performance gain than DDPG with lower computational complexity.

Power Allocation for SE Maximization in Uplink Massive MIMO System Under Minimum Rate Constraint

In this paper,we optimize the spectrum efficiency(SE)of uplink massive multiple-input multiple-output(MIMO)system with imperfect channel state information(CSI)over Rayleigh fading channel.The SE optimization problem is formulated under the constraints of maximum power and minimum rate of each user.Then,we develop a near-optimal power allocation(PA)scheme by using the successive convex approximation(SCA)method,Lagrange multiplier method,and block coordinate descent(BCD)method,and it can obtain almost the same SE as the benchmark scheme with lower complexity.Since this scheme needs three-layer iteration,a suboptimal PA scheme is developed to further reduce the complexity,where the characteristic of massive MIMO(i.e.,numerous receive antennas)is utilized for convex reformulation,and the rate constraint is converted to linear constraints.This suboptimal scheme only needs single-layer iteration,thus has lower complexity than the near-optimal scheme.Finally,we joint design the pilot power and data power to further improve the performance,and propose an two-stage algorithm to obtain joint PA.Simulation results verify the effectiveness of the proposed schemes,and superior SE performance is achieved.

基于Massive MIMO的5G基站能效优化研究

5G网络已成熟商用,但是其能效问题一直影响着5G发展,MassiveMIMO技术是提升网络覆盖、用户体验和系统容量的核心技术,可作为提升5G网络能效的一种手段。开展了基于蚁群算法的迭代寻优算法的研究,解决海量MIMO参数组合最优解迭代问题。同时构建了基于数字孪生技术的功率优化模型,实现5G网络能效的优化。模型实验和现网验证结果显示:该算法能够达到预期效果,为后续基站的能效优化和MIMO智慧优化演进提供参考。

Optimal AP Deployment in Cell-Free Massive MIMO Systems with LoS/NLoS Transmissions:A Stochastic Geometry Approach

Cell-free massive multiple-input multipleoutput(MIMO)is a promising technology for future wireless communications,where a large number of distributed access points(APs)simultaneously serve all users over the same time-frequency resources.Since users and APs may locate close to each other,the line-of-sight(Lo S)transmission occurs more frequently in cell-free massive MIMO systems.Hence,in this paper,we investigate the cell-free massive MIMO system with Lo S and non-line-of-sight(NLo S)transmissions,where APs and users are both distributed according to Poisson point process.Using tools from stochastic geometry,we derive a tight lower bound for the user downlink achievable rate and we further obtain the energy efficiency(EE)by considering the power consumption on downlink payload transmissions and circuitry dissipation.Based on the analysis,the optimal AP density and AP antenna number that maximize the EE are obtained.It is found that compared with the previous work that only considers NLo S transmissions,the actual optimal AP density should be much smaller,and the maximized EE is actually much higher.

Massive Dirac particles based on gapped graphene with Rosen-Morse potential in a uniform magnetic field

We explore the gapped graphene structure in the two-dimensional plane in the presence of the Rosen-Morse potential and an external uniform magnetic field.In order to describe the corresponding structure,we consider the propagation of electrons in graphene as relativistic fermion quasi-particles,and analyze it by the wave functions of two-component spinors with pseudo-spin symmetry using the Dirac equation.Next,to solve and analyze the Dirac equation,we obtain the eigenvalues and eigenvectors using the Legendre differential equation.After that,we obtain the bounded states of energy depending on the coefficients of Rosen-Morse and magnetic potentials in terms of quantum numbers of principal n and spin-orbit k.Then,the values of the energy spectrum for the ground state and the first excited state are calculated,and the wave functions and the corresponding probabilities are plotted in terms of coordinates r.In what follows,we explore the band structure of gapped graphene by the modified dispersion relation and write it in terms of the two-dimensional wave vectors K_(x) and K_(y).Finally,the energy bands are plotted in terms of the wave vectors K_(x) and K_(y) with and without the magnetic term.

Progress and prospects of EOR technology in deep,massive sandstone reservoirs with a strong bottom-water drive

The Triassic massive sandstone reservoir in the Tahe oilfield has a strong bottom-water drive and is characterized by great burial depth,high temperature and salinity,a thin pay zone,and strong heterogeneity.At present,the water-cut is high in each block within the reservoir;some wells are at an ultrahigh water-cut stage.A lack of effective measures to control water-cut rise and stabilize oil production have necessitated the application of enhanced oil recovery(EOR)technology.This paper investigates the development and technological advances for oil reservoirs with strong edge/bottom-water drive globally,and compares their application to reservoirs with characteristics similar to the Tahe oilfield.Among the technological advances,gas injection from the top and along the direction of structural dip has been used to optimize the flow field in a typical bottom-water drive reservoir.Bottom-water coning is restrained by gas injection-assisted water control.In addition,increasing the lateral driving pressure differential improves the plane sweep efficiency which enhances oil recovery in turn.Gas injection technology in combination with technological measures like channeling prevention and blocking,and water plugging and profile control,can achieve better results in reservoir development.Gas flooding tests in the Tahe oilfield are of great significance to identifying which EOR technology is the most effective and has the potential of large-scale application for improving development of deep reservoirs with a strong bottomwater drive.

Off-Grid Compressed Channel Estimation with Parallel Interference Cancellation for Millimeter Wave Massive MIMO

Millimeter wave(mmWave)massive multiple-input multiple-output(MIMO)plays an important role in the fifth-generation(5G)mobile communications and beyond wireless communication systems owing to its potential of high capacity.However,channel estimation has become very challenging due to the use of massive MIMO antenna array.Fortunately,the mmWave channel has strong sparsity in the spatial angle domain,and the compressed sensing technology can be used to convert the original channel matrix into the sparse matrix of discrete angle grid.Thus the high-dimensional channel matrix estimation is transformed into a sparse recovery problem with greatly reduced computational complexity.However,the path angle in the actual scene appears randomly and is unlikely to be completely located on the quantization angle grid,thus leading to the problem of power leakage.Moreover,multiple paths with the random distribution of angles will bring about serious interpath interference and further deteriorate the performance of channel estimation.To address these off-grid issues,we propose a parallel interference cancellation assisted multi-grid matching pursuit(PIC-MGMP)algorithm in this paper.The proposed algorithm consists of three stages,including coarse estimation,refined estimation,and inter-path cyclic iterative inter-ference cancellation.More specifically,the angular resolution can be improved by locally refining the grid to reduce power leakage,while the inter-path interference is eliminated by parallel interference cancellation(PIC),and the two together improve the estimation accuracy.Simulation results show that compared with the traditional orthogonal matching pursuit(OMP)algorithm,the normalized mean square error(NMSE)of the proposed algorithm decreases by over 14dB in the case of 2 paths.