Uplink Massive MIMO Systems Research Articles

Power allocation for SE maximization in uplink massive MIMO system under minimum rate constraint

Power allocation for SE maximization in uplink massive MIMO system under minimum rate constraint

An effective algorithm in uplink massive MIMO systems for pilot decontamination

The mobile Internet business is rapidly developing, leading to a demand for higher data service rates. To address this, massive Multiple-Input Multiple-Output (MIMO) technology has gained widespread attention due to its ability to fully exploit spatial degree of freedom and improve spectral efficiency while maintaining high energy efficiency. However, there are challenges with channel estimation in multi-user massive MIMO systems, such as pilot overhead, accuracy, and complexity. To tackle these challenges, this paper proposes a highly demanding beam Domain Decomposition (DD) and Singular Value Decomposition (SVD) method-based channel estimation methodology. By dividing into various single-user MIMO systems, the multi-user massive MIMO system, the proposed approach models the beam domain channel. The channel autocorrelation matrix is optimized using the SVD method, which lowers pilot overhead and estimates error while increasing computing efficiency. The suggested approach also provides closed-form analytical formulations for the calculated error covariance matrix. The numerical outcomes show that the suggested algorithm outperforms conventional schemes in performance. Therefore, the proposed algorithm could provide an effective solution for channel estimation in multi-user massive MIMO systems, which is increasingly critical as the demand for high data rates in the mobile Internet business continues to grow.© 2017 Elsevier Inc. All rights reserved.

Efficient detectors for uplink massive MIMO systems

Efficient detectors for uplink massive MIMO systems

An Ordered QR Decomposition based Signal Detection Technique for Uplink Massive MIMO System

Signal detection turns out to be a critical challenge in massive MIMO (m-MIMO) system due to the deployment of large number of antennas at the base station. Although, the mini mum mean square error (MMSE) is one of the popular signal detection method, but, it requires matrix inversion with cubic complexity. In order to reduce computational complexity, sev eral suboptimal signal detection methods were proposed such as Gauss-Seidel, successive over relaxation, Jacobi, Richardson methods. Although, these methods provide low complexity but their performance are limited to MMSE method. In this paper, we have proposed two signal detection techniques namely QR decompositions (QRD) and ordered QRD (OQRD). Finally, the performances of proposed signal detection methods are com pared with various conventional methods in terms of symbol er ror rate (SER) and computational complexity. The simulation re sults validate that the proposed methods outperform the MMSE method with substantially lower computational complexity.

Low complexity detection algorithms based on ADMIN for massive MIMO

This paper proposes the alternating direction method of multipliers-based infinity-norm (ADMIN) with threshold (ADMIN-T) and with percentage (ADMIN-P) detection algorithms, which make full use of the distribution of the signal to interference plus noise ratio (SINR) for an uplink massive MIMO system. The ADMIN-T and ADMIN-P detection algorithms are improved visions of the ADMIN detection algorithm, in which an appropriate SINR threshold in the ADMIN-T detection algorithm and a certain percentage in the ADMIN-P detection algorithm are designed to reduce the overall computational complexity. The detected symbols are divided into two parts by the SINR threshold which is based on the cumulative probability density function (CDF) of SINR and a percentage, respectively. The symbols in higher SINR part are detected by MMSE. The interference of these symbols is then cancelled by successive interference cancellation (SIC). Afterwards the remaining symbols with low SINR are iteratively detected by ADMIN. The simulation results show that the ADMIIN-T and the ADMIN-P detection algorithms provide a significant performance gain compared with some recently proposed detection algorithms. In addition, the computational complexity of ADMIN-T and ADMIN-P are significantly reduced. Furthermore, in the case of same number of transceiver antennas, the proposed algorithms have a higher performance compared with the case of asymmetric transceiver antennas.

Efficient Iterative Detection Based on Conjugate Gradient and Successive Over-Relaxation Methods for Uplink Massive MIMO Systems

Being a crucial aspect of fifth-generation (5G) mobile communications systems, massively multiple-input multiple-output (mMIMO) architectures are expected to help achieve the highest key performance indicators. However, the huge numbers of antennas used in such systems make it difficult to determine the inversion of the signal channel matrix relied upon by several detection methods, hence posing a problem with accurate estimation of the symbols sent. In this paper, conjugate gradient (CG) and successive over-relaxation (SOR) methods are selected to construct a new iterative approach that avoids the matrix inversion computation issue. This suggested approach for uplink mMIMO detection is based on a joint cascade structure of both iterative methods. The CG method is first applied and adjusted for the initial solution, followed by the SOR method in the final iterations for terminal computations, resulting in an algorithm with robust performance and low computational complexity. Furthermore, the new hybrid scheme operates based on the relaxation parameter, whose value has a great impact on error performance and, whose optimal determination is necessary. Numerical simulations reveal that the proposed scheme is capable of significantly improving signal detection accuracy with minimum complexity. The simulation results indicated that the proposed detector outperforms CG and SOR detectors, achieves close to optimal performance, requires fewer iterations, and reduces complexity.

Sub-6-GHz Uplink Massive MIMO System Using Holographic Beamforming Metasurfaces: A Conceptual Development

We propose an uplink massive MIMO system using an array of holographic metasurfaces as a sector antenna operating at 3.5 GHz. The antenna consists of a set of rectangular waveguide-fed metasurfaces combined along the elevation direction into a planar aperture, each with subwavelength-sized metamaterial elements as radiators. The metamaterial radiators are designed such that the waveguide-fed metasurface implements a holographic solution for the guided (or reference) mode, generating a directional beam towards a prescribed direction, thereby forming a multibeam antenna system. We demonstrate that a narrowband uplink massive MIMO system using the metasurfaces can achieve the sum capacity close to that offered by the Rayleigh channel. We show that metasurfaces supporting multiple beams can achieve high spatial resolution in the azimuth directions in sub-6 GHz channels, and thereby form uncorrelated MIMO channels between the base station and users. Also, the proposed metasurface antenna is structurally simple, low-cost, and efficient, and thus is suitable to alleviate RF hardware issues common to massive MIMO systems equipped with a large antenna system.

Optimal Probabilistic Repetition for Massive MIMO-Aided Grant-Free Short-Packet Transmissions

In this letter, we propose a novel probabilistic repetition scheme for the uplink massive MIMO system with grant-free access. Compared with canonical K-repetition, the proposed scheme is more flexible and covers the K-repetition as a special case. With the consideration of multiple practical factors, including the sporadic arrivals, the short-packet transmissions, probabilistic repetition, and channel estimation error, the reliability and throughput of the proposed probabilistic repetition assisted grant-free access scheme are theoretically analyzed. It is also proved that the optimal transmission probability for reliability maximization exists and can be found through gradient descent method. Simulation results validate the accuracy of our theoretical analysis, and also show that the proposed Optimal Probabilistic Repetition assisted grant-free Access scheme (OPRA) is superior to the popular modern grant-free random access with repetition transmission, that is, irregular repetition slotted ALOHA (IRSA).

Learning Linear MMSE Precoder for Uplink Massive MIMO Systems With One-Bit ADCs

In this letter, we present a deep learning based linear precoder design to improve the performance for uplink multiuser multiple-input multiple-output (MU-MIMO) systems with one-bit analog-to-digital converters (ADCs). Specifically, by utilizing the Bussgang decomposition based linear minimum mean squared error (MMSE) receiver at the base station (BS), we aim to further minimize the system MSE by optimizing the precoder at each user equipment (UE). To address this problem efficiently, we propose a deep neural network based approach by unfolding the developed projected gradient descent (PGD) algorithm and treating the step size in each PGD iteration as trainable network parameters. Simulation results show that the proposed network design outperforms the conventional PGD algorithm under both perfect and estimated channel state information (CSI) and the improvement is achieved with reduced online complexity.

Data detection method for uplink massive MIMO systems based on the long recurrence enlarged conjugate gradient

<p>Although the mean square error (MMSE) approach is recognized to be near optimal for uplinking large-scale multiple-input-multiple-output (MIMO) systems, there are certain difficulties in the procedure related to matrix inversion. The long recurrence enlarged conjugate gradient (LRE-CG) approach is proposed in this study as a way to iteratively realize the MMMS algorithm while avoiding the complications of matrix inversion. In addition, a diagonal-approximate starting solution to the LRE-CG approach was used to speed up the conversion rate and reduce the complications required. It has been discovered that the LRE-CG-based approach has the ability to significantly reduce computational complexity. By comparing simulation results, it is clear that this new methodology surpasses well-established wayslike the Neumann series approximation-based method and the Gauss-Siedel iterative method. With a small number of iterations, the suggested approach achieves near-optimal performance of a standard MMSE algorithm.</p>

Variational Bayes’ Joint Channel Estimation and Soft Symbol Decoding for Uplink Massive MIMO Systems With Low Resolution ADCs

We consider the problem of joint channel estimation and data decoding in uplink massive multiple input multiple output systems with low resolution analog-to-digital converters (ADCs) at the base station. The nonlinearities introduced by the ADCs make the problem challenging: in particular, the existing linear detectors perform poorly. Also, the channel coding used in commercial wireless systems necessitates soft symbol detection to obtain satisfactory performance. In this paper, we present a low-complexity variational Bayesian (VB) inference procedure to jointly solve the (possibly correlated) channel estimation and soft symbol decoding problem. We present the approach in progressively more complex scenarios, including the case where even the channel statistics are not available at the receiver. Finally, we combine our proposed VB procedure with a belief propagation (BP) based channel decoder, which further enhances the performance without any additional complexity. We numerically evaluate the bit error rate (BER) and the normalized mean squared error (NMSE) in the channel estimates obtained by our algorithm as a function of various system parameters, and benchmark the performance against genie-aided and state-of-the-art receivers. The results show that VB procedure is a promising technique for the design of low-complexity advanced receivers in low resolution ADC based systems.

Spectral Efficiency Augmentation in Uplink Massive MIMO Systems by Increasing Transmit Power and Uniform Linear Array Gain.

Improved Spectral Efficiency (SE) is a prominent feature of Massive Multiple-Input and Multiple-Output systems. These systems are prepared with antenna clusters at receiver and transmitter . In this paper, we examined a massive MIMO system to increase SE in each cell that ultimately improves the area throughput of the system. We are aiming to find appropriate values of average cell-density (D), available bandwidth (B), and SE to maximize area throughput because it is the function of these parameters. Likewise, a SE augmentation model was developed to attain an increased transmit power and antenna array gain. The proposed model also considers the inter-user interference from neighboring cells along with incident angles of desired and interfering users. Moreover, simulation results validate the proposed model that is implementable in real-time scenarios by realizing maximum SE of 12.79 bits/s/Hz in Line of Sight (LoS) and 12.69 bits/s/Hz in Non-Line of Sight (NLoS) scenarios, respectively. The proposed results also substantiate the SE augmentation because it is a linear function of transmit power and array gain while using the Uniform Linear Array (ULA) configuration. The findings of this work ensure the efficient transmission of information in future networks.

Reconstruction of Clipped Signals in Quantized Uplink Massive MIMO Systems

This paper considers the uplink of a single-cell multiuser massive multiple-input multiple-output system. Each receiver antenna of the base station (BS) is assumed to be equipped with a pair of analog-to-digital converters to quantize the real and imaginary part of the received signal. We propose a novel clipping-aware receiver (CA-MMSE), which performs minimum mean square error (MMSE) reconstruction only on the clipped received samples, while the granular samples are left unchanged after the quantization. On this basis, we present an iterative algorithm to implement the CA-MMSE receiver and derive a sufficient condition for its geometrical convergence to a fixed point. We show that as long as the number of BS antennas or the quantization resolution is sufficiently high, then, the performance of the CA-MMSE is as good as the optimal MMSE receiver which reconstructs all quantized received symbols. Additionally, we propose a novel Bussgang-based weighted zero-forcing (B-WZF) receiver which distinguishes the clipping and granular distortion and it is shown that as long as the received training symbols per antenna are correlated, the CA-MMSE brings significant improvements compared to conventional receivers in the literature while for users that do not experience deep large-scale fading the simpler B-WZF is near to the CA-MMSE for sufficiently high signal-to-noise ratio and quantization resolution.

Deep Learning for Massive MIMO With 1-Bit ADCs: When More Antennas Need Fewer Pilots

This letter considers uplink massive MIMO systems with 1-bit analog-to-digital converters (ADCs) and develops a deep-learning based channel estimation framework. In this framework, the prior channel estimation observations and deep neural networks are leveraged to learn the non-trivial mapping from quantized received measurements to channels. For that, we derive the sufficient length and structure of the pilot sequence to guarantee the existence of this mapping function. This leads to the interesting, and counter-intuitive, observation that when more base-station antennas are employed, our proposed deep learning approach achieves better channel estimation performance, for the same pilot sequence length. Equivalently, for the same channel estimation performance, this means that when more antennas are employed, fewer pilots are required. This observation is also analytically proved for some special channel models. Simulation results confirm our observations and show that more antennas lead to better channel estimation in terms of the normalized mean squared error and the receive signal-to-noise ratio per antenna.

A Novel Iterative Discrete Estimation Algorithm for Low-Complexity Signal Detection in Uplink Massive MIMO Systems

In this paper, a novel iterative discrete estimation (IDE) algorithm, which is called the modified IDE (MIDE), is proposed to reduce the computational complexity in MIMO detection in uplink massive MIMO systems. MIDE is a revision of the alternating direction method of multipliers (ADMM)-based algorithm, in which a self-updating method is designed with the damping factor estimated and updated at each iteration based on the Euclidean distance between the iterative solutions of the IDE-based algorithm in order to accelerate the algorithm’s convergence. Compared to the existing ADMM-based detection algorithm, the overall computational complexity of the proposed MIDE algorithm is reduced from O N t 3 + O N r N t 2 to O N t 2 + O N r N t in terms of the number of complex-valued multiplications, where Ntand Nr are the number of users and the number of receiving antennas at the base station (BS), respectively. Simulation results show that the proposed MIDE algorithm performs better in terms of the bit error rate (BER) than some recently-proposed approximation algorithms in MIMO detection of uplink massive MIMO systems.

Energy Efficiency of Massive MIMO Systems With Low-Resolution ADCs and Successive Interference Cancellation

This paper studies the influence of signal detection schemes on the energy efficiency (EE) of uplink multiple-input-multiple-output (MIMO) systems with low-resolution analog-to-digital converters (ADCs). Assuming equal transmission rates for all users, we derive the optimal power allocation and their analytical approximations for zero-forcing (ZF) and ZF successive interference cancellation (ZF-SIC) receivers. Both the cases with perfect channel state information (CSI) and with imperfect CSI are considered. The EE with different receivers is compared. The results indicate that for uplink massive MIMO systems with low-resolution ADCs, the radio-frequency circuit power consumption can be significant because a large number of antennas are required to compensate for the loss due to quantization errors while the number of base station antennas needed with the ZF-SIC receiver is significantly smaller than that with the ZF receiver. Meanwhile, the increase of power consumption of signal processing with ZF-SIC can be moderate, due to the fact that the receiver weights are reused in a coherent block and the dimensionality is reduced. Consequently, the ZF-SIC receiver is able to improve the overall EE for massive MIMO systems with practical ADCs. We also conduct an approximation analysis for a multi-cell scenario with the pilot contamination and inter-cell-interference considered.

LMMSE Receivers in Uplink Massive MIMO Systems With Correlated Rician Fading

We carry out a theoretical analysis of the uplink (UL) of a massive MIMO system with per-user channel correlation and Rician fading, using two processing approaches. First, we examine the linear-minimum-mean-square-error receiver under training-based imperfect channel estimates. Second, we propose a statistical combining technique that is more suitable in environments with strong line-of-sight (LoS) components. We derive closed-form asymptotic approximations of the UL spectral efficiency (SE) attained by each combining scheme in single and multi-cell settings, as a function of the system parameters. These expressions are insightful in how different factors such as LoS propagation conditions and pilot contamination impact the overall system performance. Furthermore, they are exploited to determine the optimal number of training symbols, which is shown to be of significant interest at low Rician factors. The study and numerical results substantiate that stronger LoS signals lead to better performances, and under such conditions, the statistical combining entails higher SE gains than the conventional receiver.

An LMMSE-Based Receiver for Uplink Massive MIMO Systems With Randomized IQ Imbalance

In-phase/quadrature-phase imbalance (IQI) is one of the hardware impairments in uplink massive multiple-input multiple-output systems. In this context, by modeling the IQI parameters as random variables, this letter first analyzes the statistic property of the effective channel in which both the IQI parameters and channel state information are included. By doing so, an LMMSE-based receiver is then investigated for channel estimation and data detection. Furthermore, we provide tightness analysis on asymptotic achievable sum rate for the proposed scheme using results from random matrix theory. Finally, simulation results show that our proposed scheme has small performance loss but with the advantage that estimating IQI parameters can be avoided.

A Low-Complexity Signal Detection Approach in Uplink Massive MIMO Systems

A Low-Complexity Signal Detection Approach in Uplink Massive MIMO Systems

Differential 16-QAM and 16-APSK for Uplink Massive MIMO Systems

A differential quadrature amplitude modulation (QAM) scheme for uplink massive multiple-input-multiple-output systems was recently proposed; this scheme outperformed the conventional differential amplitude-phase shift keying (APSK) scheme for 1000 receiver antennas. However, the detectors of these schemes are different, and the QAM detector suffers from error floors if the number of antennas is not sufficiently large. In this letter, we generalize the QAM detector and propose a new detector that presents better error performance at low bit error rates when the number of available antennas is not extremely large. We find that, using the same detector, 16-APSK outperforms 16-QAM for any number of antennas.