Novel scheduling strategy for downlink multiuser MIMO system: Particle swarm optimization

In this paper the scheduling problem in downlink multiuser MIMO system is described as an optimization problem and particle swarm optimization （PSO） algorithm is introduced to address such problem. Two PSO scheduling methods with different objective functions applicable to different requirements on capacity and complexity are investigated. One is the capacity based PSO（C-PSO） scheduling method aiming at achieving the near optimal capacity; and the other is the lower bound of eigenvalue based PSO （LBE-PSO） scheduling method with the purpose of reducing computational complexity and at the same time achieving as large as possible capacity gain. Furthermore, convergence analysis of PSO from both the particle and the velocity aspects is also presented to derive the convergent condition, which is validated by several examples of different parameter values. Simulation results reveal that the C-PSO can obtain nearly the same capacity as the exhaustive search method with lower complexity, while the LBE-PSO provides a viable approach by striking a better tradeoff between capacity and computational complexity.

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.

Low-complexity soft-output signal detector based on adaptive pre-conditioned gradient descent method for uplink multiuser massive MIMO systems

In multiuser massive Multiple Input Multiple Output(MIMO)systems,a large amount of antennas are deployed at the Base Station(BS).In this case,the Minimum Mean Square Error(MMSE)detector with soft-output can achieve the near-optimal performance at the cost of a large-scale matrix inversion operation.The optimization algorithms such as Gradient Descent(GD)method have received a lot of attention to realize the MMSE detection efficiently without a large scale matrix inversion operation.However,they converge slowly when the condition number of the MMSE filtering matrix(the coefficient matrix)increases,which can compromise the efficiency of their implementation.Moreover,their soft information computation also involves a large-scale matrix-matrix multiplication operation.In this paper,a low-complexity soft-output signal detector based on Adaptive Pre-conditioned Gradient Descent(APGD-SOD)method is proposed to realize the MMSE detection with soft-output for uplink multiuser massive MIMO systems.In the proposed detector,an Adaptive Pre-conditioner(AP)matrix obtained through the Quasi-Newton Symmetric Rank One(QN-SR1)update in each iteration is used to accelerate the convergence of the GD method.The QN-SR1 update supports the intuitive notion that for the quadractic problem one should strive to make the pre-conditioner matrix close to the inverse of the coefficient matrix,since then the condition number would be close to unity and the convergence would be rapid.By expanding the signal model of the massive MIMO system and exploiting the channel hardening property of massive MIMO systems,the computational complexity of the soft information is simplified.The proposed AP matrix is applied to the GD method as a showcase.However,it also can be used by Conjugate Gradient(CG)method due to its generality.It is demonstrated that the proposed detector is robust and its convergence rate is superlinear.Simulation results show that the proposed detector converges at most four iterations.Simulation results also show that the proposed approach achieves a better trade-off between the complexity and the performance than several existing detectors and achieves a near-optimal performance of the MMSE detector with soft-output at four iterations without a complicated large scale matrix inversion operation,which entails a big challenge for the efficient implementation.

Spectral and Energy Efficiency of Downlink MU-MIMO Systems with MRT

This paper studies the achievable spectral efficiency(SE)of downlink multiuser multiple-input-multiple-output(MIMO)system,where the base station(BS)is deployed an arbitrary finite antenna number and communicates simultaneously with many users. We assume that the BS has accurate channel state information(CSI)and adopt maximum ratio transmission(MRT)precoding. An accurate analytical result for the achievable SE is obtained. Based on the analytical result on the achievable SE,we further study the achievable energy efficiency(EE)of multiuser MIMO system by considering an energy consumption model. Results indicate that the increasing number of BS antennas can boost the achievable SE of system,whilst the achievable SE tends to a saturated rate in the high signal-tonoise ratios(SNR)regime. Furthermore,an important conclusion is that the increasing number of users is beneficial for the achievable EE and there is an optimal antenna number to maximize the EE of system.

SINR-BASED ANTENNA COMBINING FOR MULTIUSER MIMO-OFDM WITH LIMITED FEEDBACK

A multiuser Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) broadcast channel is considered where both transmitter and receivers are equipped with multiple antennas. The Channel State Information (CSI) is quantized and provided through limited feedback links. In the first part of this work, the Maximum Expected SINR Combining (MESC) strategy for feedback decrease is investigated. Then a combination of MESC with subcarrier assigning is presented to further reduce the feedback load. The basic idea is the subcarriers are partitioned into resource blocks. For each block, only one quantized channel vector is feedback. The simulation results show MESC can obtain the performance gain over the relevant combiner strategies in MIMO-OFDM system. Moreover the effectiveness on the reduction of feedback overhead by the use of partitioning blocks in the studied system is evaluated.

Lattice-Basis Reduction Precoding Based on Successive Interference Cancellation Design for Multiuser MIMO Downlink System

Two kinds of lattice-basis reduction precoding schemes based on successive interference cancellation are proposed.The successive interference cancellation(SIC) structure can be obtained by either orthogonal and a right triangular matrix(QR) decomposition,or the Vertical Bell Labs Layered Space Time(VBLAST) algorithm which provides optimal user ordering.Moreover,the extended channel approach is applied to the proposed SIC-based schemes.Simulation results show that the proposed schemes can achieve comparable BER performance to vector precoding(VP).

Multiuser MIMO Downlink Transmission Schemes over Time-Varying Channels

The performance of multiuser multiple-input-multiple-output （MIMO） downlink systems with block diagonalization （BD） depends on the accuracy of the channel state information （CSI） available at the trans- mitter and the receiver. In time-varying channels, the CSI available at the transmitter （CSIT） is always out-dated due to an inherent time delay between the uplink channel estimation and the downlink data transmission in time division duplexing （TDD） systems. This leads to a drastic degradation of system capacity. This paper first analyzes the effect of the outdated CSIT on multiuser MIMO downlink systems using the BD method and then proposes two linear processing methods, BD precoding with user selection and scheduling at the transmitter and total minimum mean squared error （MMSE） decoding at the receiver （TBDUSS-RTMMSE） and BD preceding at the transmitter with partial MMSE decoding at the receiver （TBD-RPMMSE）, to mitigate the interference among data streams and users. Analysis and simulation results show that these methods can effectively reduce the impairment of the outdated CSIT to increase the system sum capacity in a suitable time delay region of the CSIT.

Grassmannian precoding MU-MIMO scheme

In this article, a Grassmannian precoding multiuser multiple-input multiple-output （MU-MIMO） scheme for downlink transmission is proposed. The proposed MU-MIMO scheme will perform scheduling and precoding simultaneously at the base station, to obtain both the multiuser diversity gain and the precoding gain, to maximize the system capacity. The precoding method is related to Grassmannian precoding, which extends the point-to-point single-user Grassmannian precoding to point-to-multipoint multiuser Grassmannian precoding. It provides further significant system capacity enhancement than the single user MIMO （SU-MIMO） system and also outperforms the block dia^onalization （BD） algorithm under the same simulation environment.