Massive Multiple-input Multiple-output Downlink Systems Research Articles

Serving Mobile Users in Intelligent Reflecting Surface Assisted Massive MIMO System

As the number of antennas increases, the massive multiple-input multiple-output (MIMO) system can precisely point to user equipments (UEs) with narrow beams. Accurate and timely channel state information (CSI) feedback is crucial to keep UEs in service. Mobile UEs, however, may suffer from the narrow beam nature of the massive MIMO system since UEs can move out of the beam coverage. When intelligent reflecting surface (IRS) is applied to the massive MIMO system, the adjustment of the IRS cannot be frequent as the IRS is controlled remotely by the base station (BS). Limiting the number of CSI feedback and the number of both BS and IRS adjustments significantly reduces the overhead of transmission and computation to the system. In this paper, we consider the UEs’ mobility adaptation problem in an IRS assisted multiuser massive MIMO downlink system with infrequent CSI feedback. We propose a beam control method that adapts to UEs’ mobility. The problem is constructed as a sum rate problem where both the BS and IRS are taken into account to jointly optimize the beamforming matrices. Simulation results show that our proposed algorithm can converge quickly and provide satisfactory performance for mobile UEs. At the same time, our proposed algorithm reduces the frequency of updating the beamforming matrices effectively both at the BS and at the IRS.

Energy Efficiency Optimization Based on Power Allocation in Massive MIMO Downlink Systems

To solve the energy efficiency (EE) optimization in a multi-cell (MU) massive multiple-input multiple-output (MIMO) downlink system, of which channels are of symmetry in the Time-Division Duplex (TDD) protocol, we utilize a spatially correlated channel model and adopt the minimum mean-squared error (MMSE) estimator to tailor linear precoding vectors. Then, we derive the expression of downlink spectral efficiency (SE), taking interference into account. Subsequently, we establish the EE optimization function, which is defined as the average capacity divided by power consumption. In an interference-limited scenario, the EE optimization is of high complexity to solve globally as it is not jointly concave. To this end, we propose the Dinkelbach-like power allocation algorithm to obtain a suboptimal solution. We transform the EE problem in a fractional form into a subtractive optimization form called an auxiliary subproblem. Then, we relax the sub-problem to a concave problem by initializing the interference and omitting the dynamic power term about throughput. Lastly, we solve iteratively the Karush–Kuhn–Tucker (KKT) conditions by bisection search. Consequently, we obtain a sub-solution with modest complexity. The simulation results justify the rationality of the Dinkelbach-like algorithm and demonstrate that the proposal outperforms the reference schemes and effectively improves the performance metrics EE and SE.

An Enhanced Precoder for Multi User Multiple-Input Multiple-Output Downlink Systems

Recently, as the demand for data rate of users has increased, wireless communication systems have aimed to offer high throughput. For this reason, various techniques which guarantee high performance have been invented, such as massive multiple-input multiple-output (MIMO). However, the implementation of huge base station (BS) antenna array and decrease of reliability as the number of users increases are chief obstacles. In order to mitigate these problems, this paper proposes an adaptive precoder which provides high throughput and bit error rate (BER) performances to achieve the desired data rate in multi user (MU) MIMO downlink systems which have a practical BS antenna array (up to 16). The proposed scheme is optimized with a modified minimum mean square error (MMSE) criterion in order to improve BER gain and reduce data streams in order to obtain diversity gain at low signal to noise ratio (SNR). It is shown that the BER and throughput performances of the proposed scheme are improved.

Nonlinear One-Bit Precoding for Massive MIMO Downlink Systems With l₀-Norm Constraint

Using one-bit digital-to-analog converters can significantly reduce the power consumption of base station in the massive multiple-input multiple-output downlink system. In this letter, we introduce $l_{0}$ -norm constraint on quantized transmit signals in the nonlinear one-bit precoding to further reduce the number of radio-frequency chains and thus the power consumption. This precoding is first formulated as a non-convex optimization problem, then the alternative direction method of multipliers (ADMM) is employed to solve this problem. Specifically, an efficient two-step implementation of the non-convex projection operator in ADMM is presented. Finally, the performance of proposed scheme and the effectiveness of proposed algorithm are validated by computer simulations.

Performance Analysis of OFDM-based Massive MIMO Downlink System

Massive MIMO systems are the key technology for evolution of 4G, 5G standards in telecommunication environment. One of the major limitations in OFDM based massive multiple-input multiple-output (MIMO) downlink system is peak to average power ratio (PAPR). Transmitting symbol vectors to different set of users, the main idea is estimate the low PAPR OFDM modulated signal with reduced multi user interference. Many techniques were used to mitigate the PAPR problem, but they consume more computational time, particularly in Massive MIMO systems. The proposed ETG (Expectation maximization Truncated Gaussian mixture Generalized approximate passing) employs truncated Gaussian mixture prior to get low PAPR signal. To understand the prior signal, expectation step helps in identifying hidden variables; maximization step helps in identifying deterministic parameters. Generalized approximate passing is applied to mitigate the computational complexity. Numerical simulated results in comparison with existing techniques suggests that desired level of PAPR is achieved with less computation time with minute degradation in symbol error rate(SER).By choosing proper normalization we can achieve same SER with reduced PAPR.

A low-complexity algorithm for the joint antenna selection and user scheduling in multi-cell multi-user downlink massive MIMO systems

The massive MIMO (multiple-input multiple-output) technology plays a key role in the next-generation (5G) wireless communication systems, which are equipped with a large number of antennas at the base station (BS) of a network to improve cell capacity for network communication systems. However, activating a large number of BS antennas needs a large number of radio-frequency (RF) chains that introduce the high cost of the hardware and high power consumption. Our objective is to achieve the optimal combination subset of BS antennas and users to approach the maximum cell capacity, simultaneously. However, the optimal solution to this problem can be achieved by using an exhaustive search (ES) algorithm by considering all possible combinations of BS antennas and users, which leads to the exponential growth of the combinatorial complexity with the increasing of the number of BS antennas and active users. Thus, the ES algorithm cannot be used in massive MIMO systems because of its high computational complexity. Hence, considering the trade-off between network performance and computational complexity, we proposed a low-complexity joint antenna selection and user scheduling (JASUS) method based on Adaptive Markov Chain Monte Carlo (AMCMC) algorithm for multi-cell multi-user massive MIMO downlink systems. AMCMC algorithm is helpful for selecting combination subset of antennas and users to approach the maximum cell capacity with consideration of the multi-cell interference. Performance analysis and simulation results show that AMCMC algorithm performs extremely closely to ES-based JASUS algorithm. Compared with other algorithms in our experiments, the higher cell capacity and near-optimal system performance can be obtained by using the AMCMC algorithm. At the same time, the computational complexity is reduced significantly by combining with AMCMC.

An Approach to Reduce the Overhead of Training Sequences in FDD Massive MIMO Downlink Systems

In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, a large number of antennas are equipped at the base station (BS). In order to accurately estimate the downlink channel state information (CSI), a significant portion of time slots are usually invoked to send training sequences. To reduce the training time slots, we propose a dimension reduction method, which uses a matrix to divide and combine the transmit antennas into several groups. Noticed that the matrix is only related to the parameters of system and independent of CSI. As a beneficial result, a new equivalent system is created, where the training time slots can be decreased dramatically. Simulation results show that the new equivalent system can provide higher throughput than the original system because of the dimension reduction.

A novel method for joint- PAPR mitigation in OFDM-based massive MIMO downlink systems

Massive MIMO has gained much attention with the increase in the high speed data communication. The problem of peak-to-average power ratio (PAPR) is considered, the detrimental aspects in OFDM based massive multiple-input multiple-output (MIMO) downlink systems. The previous works done in reduction of PAPR problem using convex optimization are computationally inefficient. We considered Bayesian approach to mitigate PAPR by utilizing the redundant degrees of freedom (DOF) of the transmit array, which effectively reduced the level of PAPR. The performance or numerical results indicate the applied algorithm achieved a good improvement over the existing techniques in terms of the PAPR reduction.

A ZF-Based Precoding Scheme With Phase Noise Suppression for Massive MIMO Downlink Systems

In wireless communications, massive multiple-input multiple-output (MIMO) systems in which the base station (BS) is equipped with a large number of antennas can provide significant spectral and energy efficiency by means of simple signal processing. Therefore, massive MIMO is an attractive technology for next-generation wireless communication systems and for green communications. However, phase noise (PN) introduced by the impairment of oscillators can cause a severe performance loss in wireless communication systems. Solving the PN problem for massive MIMO systems in both the downlink and uplink is challenging as it is a multivariate joint PN estimation and data detection problem. The optimal solution is difficult to derive and may lead to high complexity. In this paper, we propose a zero-forcing (ZF) based precoding scheme with PN suppression to solve the PN problem for massive MIMO downlink systems. By using the ideal PN free output signal-to-noise ratio (SNR) of the ZF precoding as a performance upper bound, analytical and numerical results show that when the required SNR is satisfied, the output SNR of the proposed scheme can approach the upper bound, except for very large PN variances. Moreover, the complexity of the proposed scheme is low on both the BS and the user sides, and the BS does not need any feedback information from the users.

Joint Power Allocation and User Association Optimization for Massive MIMO Systems

This paper investigates the joint power allocation and user association problem in multi-cell Massive MIMO (multiple-input multiple-output) downlink (DL) systems. The target is to minimize the total transmit power consumption when each user is served by an optimized subset of the base stations (BSs), using non-coherent joint transmission. We first derive a lower bound on the ergodic spectral efficiency (SE), which is applicable for any channel distribution and precoding scheme. Closed-form expressions are obtained for Rayleigh fading channels with either maximum ratio transmission (MRT) or zero forcing (ZF) precoding. From these bounds, we further formulate the DL power minimization problems with fixed SE constraints for the users. These problems are proved to be solvable as linear programs, giving the optimal power allocation and BS-user association with low complexity. Furthermore, we formulate a max-min fairness problem which maximizes the worst SE among the users, and we show that it can be solved as a quasi-linear program. Simulations manifest that the proposed methods provide good SE for the users using less transmit power than in small-scale systems and the optimal user association can effectively balance the load between BSs when needed. Even though our framework allows the joint transmission from multiple BSs, there is an overwhelming probability that only one BS is associated with each user at the optimal solution.

Joint Power Allocation and User Association Optimization for Massive MIMO Systems

This paper investigates the joint power allocation and user association problem in multi-cell Massive MIMO (multiple-input multiple-output) downlink (DL) systems. The target is to minimize the total transmit power consumption when each user is served by an optimized subset of the base stations (BSs), using non-coherent joint transmission. We first derive a lower bound on the ergodic spectral efficiency (SE), which is applicable for any channel distribution and precoding scheme. Closed-form expressions are obtained for Rayleigh fading channels with either maximum ratio transmission (MRT) or zero forcing (ZF) precoding. From these bounds, we further formulate the DL power minimization problems with fixed SE constraints for the users. These problems are proved to be solvable as linear programs, giving the optimal power allocation and BS-user association with low complexity. Furthermore, we formulate a max-min fairness problem that maximizes the worst SE among the users, and we show that it can be solved as a quasi-linear program. Simulations manifest that the proposed methods provide good SE for the users using less transmit power than in small-scale systems and the optimal user association can effectively balance the load between BSs when needed. Even though our framework allows the joint transmission from multiple BSs, there is an overwhelming probability that only one BS is associated with each user at the optimal solution.