Pilot Contamination Reduction by Shifted Frame Structure in Massive MIMO TDD Wireless System

Pilot contamination limits the performance of massive multiple-input multiple-output（MIMO） system severely due to the degraded channel estimation. An efficient way to solve this problem in time division duplex（TDD） wireless system is shifting the location of pilots in time frames used in neighboring cells. However, the shifted frame structure has only been studied in MIMO system with the ideal independent and identically distributed（i.i.d.） channel coefficients. In this paper, the shifted frame structure is studied in a measured channel with a large number of antennas for a certain class of channel fading statistics. To deal with the high inter-cell interference caused by the shifted frame structure in such a measured channel condition, we propose a scenario with a covariance-aided channel estimator. This scenario shows that pilot contamination can be solved and the high inter-cell interference can be made to vanish asymptotically with the number of antennas. The key of the interference rejection is obtaining the precise condition on the second order statistics for the desired and interference signals. The most significant information of the second order statistics comes from two parts, one is the channel state information（CSI） among the base stations（BSs）, the other comes from the estimated information exchanged by the BSs, which depicts the channel between the BSs and the users. The simulations give powerful results of the interference rejection and the achievable rate promotion.

MUSIC-Based Pilot Decontamination and Channel Estimation in Multiuser Massive MIMO System

This paper addresses the problem of channel estimation in a multiuser multi-cell wireless communications system in which the base station(BS)is equipped with a very large number of antennas(also referred to as"massive multiple-input multiple-output(MIMO)").We consider a time-division duplexing(TDD)scheme,in which reciprocity between the uplink and downlink channels can be assumed.Channel estimation is essential for downlink beamforming in massive MIMO,nevertheless,the pilot contamination effect hinders accurate channel estimation,which leads to overall performance degradation.Benefitted from the asymptotic orthogonality between signal and interference subspaces for non-overlapping angle-of arrivals(AOAs)in the large-scale antenna system,we propose a multiple signals classification(MUSIC)based channel estimation algorithm during the uplink transmission.Analytical and numerical results verify complete pilot decontamination and the effectiveness of the proposed channel estimation algorithm in the multiuser multi-cell massive MIMO system.

iPAS:A deep Monte Carlo Tree Search-based intelligent pilot-power allocation scheme for massive MIMO system

Massive Multiple-Input-Multiple-Output(MIMO)is a promising technology to meet the demand for the connection of massive devices and high data capacity for mobile networks in the next generation communication system.However,due to the massive connectivity of mobile devices,the pilot contamination problem will severely degrade the communication quality and spectrum efficiency of the massive MIMO system.We propose a deep Monte Carlo Tree Search(MCTS)-based intelligent Pilot-power Allocation Scheme(iPAS)to address this issue.The core of iPAS is a multi-task deep reinforcement learning algorithm that can automatically learn the radio environment and make decisions on the pilot sequence and power allocation to maximize the spectrum efficiency with self-play training.To accelerate the searching convergence,we introduce a Deep Neural Network(DNN)to predict the pilot sequence and power allocation actions.The DNN is trained in a self-supervised learning manner,where the training data is generated from the searching process of the MCTS algorithm.Numerical results show that our proposed iPAS achieves a better Cumulative Distribution Function(CDF)of the ergodic spectral efficiency compared with the previous suboptimal algorithms.

Joint Pilot Design and Beamforming Optimization in Massive MIMO Surveillance Systems

This paper proposes a novel joint channel estimation and beamforming scheme for the massive multiple-input-multiple-output(MIMO)frequency-division duplexing(FDD) wireless legitimate surveillance system. With the proposed scheme,the monitor with the full duplex capability realizes the proactive eavesdropping of the suspicious link by leveraging the pilot attack approach. Specifically, exploiting the effective eavesdropping rate and the mean square error as performance metrics and setting a total power budget at the training and transmission phases,while guaranteeing the information from suspicious source can be successfully decode, joint pilot design,power allocation and beamforming strategy are formulated as optimization problems for the two objective functions: MSE and effective eavesdropping rate. A closed-form expression of the optimal pilot with the limited length can be obtained via the channel correlation. The optimal power problem at the training phase can be solved by a simple bisection method. Then,based on the obtained imperfect estimated channel,the jamming beamforming at monitor optimization algorithm is proposed by utilizing the convex Semidefinite Programming approach to maximize the effective eavesdropping rate. Numerical results show that the proposed joint pilot design, power allocation and beamforming optimization scheme can improve the surveillance performance of the legitimate monitor as compared to the existing passive eavesdropping and jamming-assisted eavesdropping.

Enhanced EVD based channel estimation and pilot decontamination for Massive MIMO networks

The enhanced eigenvalue decomposition （EEVD） based channel estimation algorithm, which could solve the pilot contamination problem in massive multiple-input and multiple-output （Massive MIMO） channel estimation when the number of antennas at base stations （NABT） tends to infinity, is proposed in this paper. The algorithm is based on the close relationship between covariance matrix of received pilot signal and the channel fast fading coefficient matrix, i.e. the latter is the eigenvector matrix of the former when NABT tends to infinity. Therefore, we can get a set of normalized base vectors from the eigenvalue decomposition （EVD） of sample covariance matrix in practical Massive MIMO networks. By multiplying the received pilot signal with conjugate transpose of normalized base vector matrix, the channel matrix is projected to a lower dimensional matrix, and the intra-cell and inter-cell interference can be eliminated completely when NABT tends to infinity. Thus, we only need to estimate the lower dimensional projected matrix during the channel estimation. Simulation results show that the mean square error （MSE） performance of channel estimation is improved with approximately two orders of magnitude when the signal-to-noise ratio （SNR） is 40 dB, compared with EVD based channel estimation algorithm. And the signal-to-interference ratio （SIR） is improved greatly as well. The increment of SIR becomes larger and larger as SNR increasing.

Pseudo-Channel Matrix Truncation Based Spatial Correlation Mitigation in Massive MIMO

Massive multiple-input multiple-output(MIMO),a technique that can greatly increase spectral efficiency(SE)of cellular networks,has attracted significant interests in recent years.One of the major limitations of massive MIMO systems is pilot contamination,which will deteriorate the SE.The superimposed pilot-based scheme has been proved to be a viable method for pilot contamination reduction.However,it cannot break through another limitation of massive MIMO,i.e.,spatial correlation.In addition,it will also lead to interference between the pilot and user data since they are imposed together.In this paper,we try to tackle these two issues,which will be described as follows.Firstly,a column-wise asymptotically orthogonal matrix,named as pseudo-channel matrix,is developed by orthogonalization of received signal.To recover the information about the large-scale fading(LSF)coefficients,the pseudo-channel matrix is truncated according to the cardinality of adjacent users set(CAUS).By this means,spatial correlation can be mitigated effectively.Secondly,robust independent component analysis(RobustICA)is used to reduce the interference caused by user data,and as a result the system performance can be further improved.Numerical simulation results demonstrate the effectiveness of the proposed method.

Energy-Efficient Low-Complexity Algorithm in 5G Massive MIMO Systems

Energy efficiency(EE)is a critical design when taking into account circuit power consumption(CPC)in fifth-generation cellular networks.These problems arise because of the increasing number of antennas in massive multiple-input multiple-output(MIMO)systems,attributable to inter-cell interference for channel state information.Apart from that,a higher number of radio frequency(RF)chains at the base station and active users consume more power due to the processing activities in digital-to-analogue converters and power amplifiers.Therefore,antenna selection,user selection,optimal transmission power,and pilot reuse power are important aspects in improving energy efficiency in massive MIMO systems.This work aims to investigate joint antenna selection,optimal transmit power and joint user selection based on deriving the closed-form of the maximal EE,with complete knowledge of large-scale fading with maximum ratio transmission.It also accounts for channel estimation and eliminating pilot contamination as antennas M→∞.This formulates the optimization problem of joint optimal antenna selection,transmits power allocation and joint user selection to mitigate inter-cellinterference in downlink multi-cell massive MIMO systems under minimized reuse of pilot sequences based on a novel iterative low-complexity algorithm(LCA)for Newton’s methods and Lagrange multipliers.To analyze the precise power consumption,a novel power consumption scheme is proposed for each individual antenna,based on the transmit power amplifier and CPC.Simulation results demonstrate that the maximal EE was achieved using the iterative LCA based on reasonable maximum transmit power,in the case the noise power is less than the received power pilot.The maximum EE was achieved with the desired maximum transmit power threshold by minimizing pilot reuse,in the case the transmit power allocationρd=40 dBm,and the optimal EE=71.232 Mb/j.

Optimal Multipoint-to-Multipoint Transceiver Design with Massive MIMO Relay Station in OFDM Systems

Based on massive MIMO(multiple-input multiple-output)multipoint-to-multipoint(M2M) systems,in order to avoid pilot contamination and improve the performance of capacity,a pilot training transmission scheme was designed for pilot decontamination by utilizing orthogonal subcarriers of OFDM(orthogonal frequency division multiplexing) during pilot transmission phase and a joint optimized transceiver design for multi-antenna user pairs was proposed during the data transmission phase.The massive M2 M system included a single relay station,multiple paired source nodes and destination nodes.Source nodes precoding matrices and relay station precoding matrix were jointly optimized by maximizing the weighted sum-rate in OFDM systems.After some mathematical manipulation to sum-rate,the cost function of sum-rate was expressed as quadratic optimizing expressions which could be solved by regular convex optimization softwares.Different from existing algorithms,the proposed precoding design was based on massive MIMO OFDM systems with multi-antenna users pairs together pilot decontamination transmission arrangement.Simulations indicate the effectiveness of the proposed optimal precoding system.The proposed scheme not only can reduce pilot contamination,but also can improve performance of bit-error-rate(BER) as well as sumrate contrast to existing algorithms.In addition,it shows that the proposed M2 M massive MIMO system works steadily when the number of users increases in large scale.

Interference alignment scheme for massive MIMO system

The performance of massive multiple-input multiple-output （MIMO） system is limited by pilot contamination. To reduce the pilot contamination, uplink and downlink precoding algorithms are put forward based on interference alignment criterion. In the uplink receiving processing, the target function aligns the pilot contamination and the interference signals to the same null space and acquires the maximal space degree of the desired signals. The uplink receiving precoding matrix is solved on maximal signal to interference plus noise ratio （SINR） criterion considering the impact of the pilot contamination on channel estimations. The uplink receiving precoding matrix is used as the downlink transmitting precoding matrix. Exploiting the channel reciprocity, it is proved that, if the uplink receiving precoding matrix achieves maximal S1NR, the identical precoding matrix can be used in the downlink transmission and acquires maximal signal to leakage plus noise ratio （SLNR）. Simulations show that the spectrum efficiency of the proposed algorithm can reach about 1.5 times higher than that of popular matched filtering （MF） precoding algorithm, and about 1.1 times higher than multi-cell minimum mean square error （MMSE） precoding algorithm. The performance of the proposed algorithm can be improved approximately linearly with the increasing of the number of antennas.