Large MIMO Systems Research Articles

Reduction of Large Scale Linear Dynamic MIMO Systems Using ACO-PID Controller

The MIMO technique is an essential element in the standards of communication systems (IEEE 802.11n, IEEE 802.11ac, WiMAX, and LTE) because it helps to increase their capacity. This paper employs a model order reduction (MOR) technique with a PID controller, an ACO algorithm, and the ITAE fitness function to reduce the large-scale linearity of the MIMO technique. The numerator and denominator parameters are set by minimizing the ITAE fitness function between the transient responses of the original and the reduced model. The objectives are achieved with the PID controller and the ACO algorithm for the unit step input. The simulation results show a good system performance. The controller performance is presented with regard to the dynamic response in terms of rising time, settling time, and overshoot/undershoot. Moreover, the results of the proposed method are compared with four literature reports for validation purposes. Evaluating the parameters within the time frame and the error values with and without the PID controller and ACO algorithm allowed validating the functioning of the proposed method. Furthermore, the simulation results revealed that the proposed scheme exhibited sufficient robustness and demonstrated a reduction in the time-domain response and error values.

Channel Estimation in Radiative Near Field Region of 6G Extremely Large MIMO System

Channel Estimation in Radiative Near Field Region of 6G Extremely Large MIMO System

Performance analysis of alternating minimization based low complexity detection for MIMO communication system

Several antennas are used for sending and receiving in large MIMO (Multiple-Input-Multiple-Output) devices and assist in enhanced performances of wireless communication systems. One important component of Large MIMO systems is that MIMO detectors are placed at receiver ends, whose functions are to regain symbols broadcasts from multiple antennas. In this paper, novelAMLCD (Alternating Minimizationbased Low Complexity Detections) method is proposed in which AMs (Alternating Minimizations) are applied in initial stages to detect signals. Soft value generation is used for the second stage to estimate the signals. Finally, the more optimal estimated signal value will be chosen by applying the MPSOs (Modified Particle Swarm Optimizations). The system's functions are evaluated using CPMs (Continuous Phase Modulations) and channel’s AWGNs (Additive White Gaussian Noises). According to the results obtained, the suggested AMLCD method with modulations of CPMs outperform known methods using QAMs (Quadrature Amplitude Modulations) under multiple antennas in terms of BERs (Bit Error Rates). The AMLCD method also reduces the time complexity and computational complexity compared to the existing methods.

A Novel Detector based on Compressive Sensing for Uplink Massive MIMO Systems

Massive multiple-input multiple-output is a promising technology in future communication networks where a large number of antennas are used. It provides huge advantages to the future communication systems in data rate, the quality of services, energy efficiency, and spectral efficiency. Linear detection algorithms can achieve a near-optimal performance in large-scale MIMO systems, due to the asymptotic orthogonal channel property. But, the performance of linear MIMO detectors degrades when the number of transmit antennas is close to the number of receive antennas (loaded scenario). Therefore, this paper proposes a series of detectors for large MIMO systems, which is capable of achieving promising performance in loaded scenarios. The main idea is to improve the performance of the detector by finding the hidden sparsity in the residual error of the received signal. At the first step, the conventional MIMO model is converted into the sparse model via the symbol error vector obtained from a linear detector. With the aid of the compressive sensing methods, the incorrectly detected symbols are recovered and performance improvement in the detector output is obtained. Different sparse recovery algorithms have been considered to reconstruct the sparse error signal. This study reveals that error recovery by imposing sparse constraint would decrease the bit error rate of the MIMO detector. Simulation results show that the iteratively reweighted least squares method achieves the best performance among other sparse recovery methods.

Joint Activity and Channel Estimation for Extra-Large MIMO Systems

Extra large MIMO (XL-MIMO) systems are subject to spatial non-stationarity forming visibility regions (VRs), which leads to a sub-array-wise sparse structure of the channel matrix. When XL-MIMO systems operate in grant-free access mode, in which only a fraction of the potential users are active during a given time slot, it follows that the channel matrix possesses a doubly-sparse and user-specific structure such that the activity of each user and each sub-array can be jointly modeled by a nested Bernoulli-Gaussian distribution. This article considers the joint activity and channel estimation (JACE) problem in XL-MIMO systems subject to this so-defined spatial non-stationarity, tackling this challenging inference problem. Our main contributions are 1) to introduce the novel Bernoulli-Gaussian model to simultaneously capture the aforementioned two distinct structured sparsities, and 2) a new bilinear Bayesian inference algorithm capable of jointly estimating the associated channel coefficients, user activity patterns, sub-array activity patterns ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$a.k.a$ </tex-math></inline-formula> . spatial non-stationarity), boosted by expectation maximization (EM)-based auto-parameterization. In addition, to shed light on a realistic modeling of VRs, we also introduce a Matérn-cluster point process (MCPP)-based approach to imitate the clustered activity pattern due to spatial non-stationarity. The efficacy of the proposed bilinear JACE algorithm is confirmed by numerical simulations, which show that the proposed method not only significantly outperforms the state-of-the-art (SotA) but also can reach the performance of a genie-aided scheme over wide signal-to-noise-ratio (SNR) ranges, in both uniformly-random and MCPP-based sub-array activity scenarios.

Two-stage deep learning-based hybrid precoder design for very large scale massive MIMO systems

Wireless networking is approaching a new era, which necessitates new frequency ranges and novel strategies. With recent circuit growth, communications over the Terahertz (THz) band is proving to be a viable option because of the tremendous bandwidth and low cost. On the other hand, THz signals suffer from significant direction loss, necessitating the use of precoding. In this paper, Deep Learning (DL) based precoding techniques for upcoming 6G networks were examined, along with their complexities. Based on the signal-to-noise ratio (SNR) and spectral efficiency (SE), the proposed DL-based precoding scheme is compared to traditional model-based precoding schemes. The proposed DL-based precoding technique is ideal for 6G networks, according to simulation results. Furthermore, the proposed DL-based precoding technique has lower computational complexity, making it suitable for parallel processing and high-speed data transmission.

Estimation of Channel for Millimeter-Wave Hybrid Massive MIMO Systems using Orthogonal Matching Pursuit (OMP)

In a millimeter-wave (mm-Wave) large MIMO system, hybrid precoding is a critical component for lowering radio-frequency hardware costs as compared to the traditional full-digital precoding strategy. Knowledge of channels is required for hybrid precoding. Though, estimation of the channel is problematic for the mm-Wave system because of the usage of a massive array of antenna and hybrid architecture with analog precoding. Due to the extremely directed nature of wireless propagation, wireless channels have spatial sparsity. In this paper, we exploit this spatial sparsity nature of wireless channels to develop orthogonal matching pursuit technique-based estimation of channels for hybrid millimeter-wave (mm-Wave) wireless systems. Genie (Ideal) channel estimation is also performed in which we presume that actual angle of departures (AoD) and angle of arrivals (AoA) are known to us. Finally, the simulation results reveal that suggested OMP algorithm-based channel estimation has a significant advantage over conventional approaches like least-squares channel estimation.

Localization and Channel Reconstruction for Extra Large RIS-Assisted Massive MIMO Systems

Reconfigurable intelligent surface (RIS) is a promising material that can passively manipulate electromagnetic waves and improve the quality of mobile communication services at a low cost. It can be made large to extend the service region and acquire the ability for localization enhancement. However, the lack of a signal processing module at the RIS makes channel estimation a difficult problem. When employing an extra large RIS, users are in the near field of the RIS and the channel shows spatial nonstationarity, making the problem more complicated. In this paper, these challenges are addressed by a low-overhead joint localization and channel reconstruction scheme proposed for extra large RIS-assisted massive multi-input multi-output systems. This scheme can accurately identify the visibility region (VR) of each user, find the user positions by exploiting the near-field characteristics, and reconstruct the channels by jointly utilizing the pilots of multiple users. Numerical results demonstrate that the identified VR covers more than 97% of the real VR, and the user localization accuracy reaches centimeter level at the millimeter-wave frequency band. A more accurate channel reconstruction result than that of existing works can also be obtained. This work verifies the great potential of RIS and is a solid step toward the integration of communication and sensing.

Joint Transmit and Receive Antenna Selection in Mimo Systems Based on Swarm Intelligence Algorithm

Multiple-input-multiple-output (MIMO) can provide superior performances such as system capacity, linkage, etc. But also it will bring high RF costs and system complexity, especially in large scale MIMO systems. Antenna selection (AS) is proved to be a trade-off between good performances and complexity. Specifically, from the perspective of both transmit and receive antennas, the joint transmit and receive antenna selection (JTRAS) is employed in MIMO systems. Up to now, some algorithms of JTRAS have been studied in MIMO systems. However, most of them are mainly focused on just one aspect about accuracy or complexity. Especially, compared to numerical analysis, the implementation of swarm intelligence algorithm in JTRAS needs to be studied extensively. In the paper, three intelligent algorithms, i.e. genetic algorithm, cat swarm algorithm and particle swarm algorithm are studied and compared in terms of accuracy, cost, and complexity. In addition, fractional coding is proposed in the algorithms instead of binary integer coding. The simulation results demonstrate that all three algorithms can efficiently accomplish the antenna selection. PSO has the best accuracy and stability, but the complexity of PSO is also highest. If we take overall performances in consideration, CSO is the best choice especially in practical implementation. Moreover, fractional coding will provide better performance than binary integer coding.

Dynamic branch pruning aided low switching fixed complexity sphere decoding for small scale and massive MIMO detection

AbstractIn this article, we have proposed a dynamic branch pruning aided low switching fixed complexity sphere decoding (LSFSD) algorithm that can reduce the number of node visits in conventional fixed‐complexity sphere decoder (FSD) or imbalanced fixed‐complexity sphere decoder (IFSD) detector dynamically based on the channel condition. Further, we propose a low complexity QR‐based FSD ordering scheme in the real domain that requires half of the number of real arithmetic operations in the conventional case. As a case study, we have simulated the proposed algorithm for and MIMO system with 64‐QAM modulation. The simulation results show that the proposed algorithm can reduce approximately 50% of node visits to achieve a BER of . Additionally, a low complexity solution of the soft bit information generation method is also proposed. The concept is further extended to massive MIMO applications where the number of receiving antenna to transmit antenna ratio is close to one. A smaller node extension parameter is defined for the proposed detector to reduce the complexity in massive MIMO detection. Simulation results demonstrate that the proposed detection algorithm has significantly lower computational complexity than other recent state‐of‐the‐art detection algorithms for massive MIMO and large MIMO systems.

Optimization of Channel Estimation Using ELMx-based in Massive MIMO

In communication channel estimation, the Least Square (LS) technique has long been a widely accepted and commonly used principle. This is because the simple calculation method is compared with other channel estimation methods. The Minimum Mean Squares Error (MMSE), which is developed later, is devised as the next step because the goal is to reduce the error rate in the communication system from the conventional LS technique which still has a higher error rate. These channel estimations are very important to modern communication systems, especially massive MIMO. Evaluating the massive MIMO channel is one of the most researched and debated topics today. This is essential in technology to overcome traditional performance barriers. The better the channel estimation, the more accurate it is. This paper investigated machine learning (ML) for channel estimation. ML channel estimations based on the Extreme Learning Machine (ELMx) group are also implemented. These estimations, known as the ELMx group, include Regularized Extreme Learning Machine (RELM) and Outlier Robust Extreme Learning Machine (ORELM). Then, it was compared with LS and MMSE. The simulation results reveal that the ELMx group outperforms LS and MMSE in channel capacity and bit error rate. Additionally, this paper has proven complexity for verified computational times. The RELM method is less time consuming and has low complexity which is suitable for future use in large MIMO systems.

Spatial channel correlation for local scattering with linear MMSE‐based estimator and detector in multi‐cell large scale MU‐MIMO networks

AbstractScalable Multi‐antenna systems are designed to gain improvement in energy and spectral efficiency under different propagation conditions by equipping the BSs with hundreds or even more antennas. A better understanding of such systems leads to overcome the essential challenges, which is important for the beneficial deployment in future networks. In wireless communication systems, channel models describe the main characteristics of the propagation environment and are essential for systems performance evaluation. This article considers a multi‐cell large scale multiuser MIMO (LS‐MU‐MIMO) system with a physical correlated channel model and linear‐based MMSE estimator and detector. A physical local scattering geometric‐based stochastic channel model for dense urban and suburban scenarios is presented in this article to illustrate the impact of propagation environment on signal transmission in a general framework of antenna spatial correlation. Also, a pilot signaling‐based channel estimation in the uplink scenario, the effect of pilot contamination (PC), and spatial correlation on the channel estimation are studied. The effect of PC and channel estimation on the performance of LS‐MU‐MIMO system is investigated by applying MMSE‐based channel estimator and detector over uniform linear arrays (ULA) of BS antennas in different scattering environments. Moreover, different pilot reuse patterns are considered, and their impact on the average sum SE has been investigated. The simulation results show that LS‐MU‐MIMO system can provide higher performance for dense urban scenarios where the user terminals (UTs) have limited mobility and higher coherence time than suburban scenarios. Furthermore, while higher spatial correlation will result in improvement in the channel estimation quality, it contributes imperfectly to the channel hardening effect.

Stochastic channel models for massive and extreme large multiple‐input multiple‐output systems

AbstractIn this article, stochastic channel models for massive multiple‐input multiple‐output (M‐MIMO) and extreme large MIMO (XL‐MIMO) system applications are described, evaluated, and systematically compared. This work aims to cover new aspects of M‐MIMO stochastic channel models in a comprehensive and systematic way. For that, we compare different models, presenting graphically and intuitively the behavior of each model. Each M‐MIMO channel model emulates the environment using different methodologies and properties. Using metrics such as capacity, SINR, singular values decomposition, and condition number, one can understand the influence of each characteristic on the modeling and how it differentiates from other models. Moreover, in new XL‐MIMO scenarios, where the near‐field and visibility region effects arise, our finding demonstrate that for the two assumed schemes of clusters distribution, the clusters location influences the performance of the conjugate beamforming and zero‐forcing precoding due to the correlation effect, which have been analyzed from the geometric M‐MIMO channel models.

Spectral Efficient Precoding Design for Multi-cell Large MU-MIMO System

Large scale multiuser multi-input multi-output (MU-MIMO) system is an emerging wireless technology, which can impart huge spectral efficiency, enhanced link reliability, and serves several users simultaneously in the same frequency–time resource. Pilot contamination (PC) is the major bottleneck in achieving the optimum performance of a multi-cell large MU-MIMO system. We propose a new multi-cell block diagonalization-QR-minimum mean square error (M-BD-QR-MMSE) precoding scheme to diminish the effects of pilot contamination in a multi-cell MIMO system. The reduced effects of Pilot contamination result in the enhancement of spectral efficiency for the multi-cell MU-MIMO system. The design of the proposed scheme utilizes block diagonalization (BD) in combination with MMSE precoding that obtains large null space using QR decomposition operation to reduce intercell and intracell interference much efficiently than conventional precoding schemes. The gain in spectral efficiency with the proposed scheme is enhanced through non-universal pilot reuse in contrast to single-cell schemes. Furthermore, the downlink spectral efficiency expression has been derived for a large scale MIMO system, and the same is computed using Monte Carlo simulations to analyze the results. The numerical results depict that the proposed M-BD-QR-MMSE provides a significant improvement in the spectral efficiency in comparison with traditional single-cell MMSE (S-MMSE), regularized zero-forcing (R-ZF), ZF, Maximum ratio transmission (MRT) and multi-cell MMSE (M-MMSE) schemes. The proposed precoding design provides the spectral enhancements of 32.19 and 50.13 bit/s/Hz/cell in comparison to conventional multi-cell MMSE and single-cell MMSE precoding schemes respectively.

Large MIMO Systems and Hybrid Beamforming in 5G or Future Communication

Large MIMO Systems and Hybrid Beamforming in 5G or Future Communication

Maximum likelihood low-complexity GSM detection for large MIMO systems

Hard-Output Maximum Likelihood (ML) detection for Generalized Spatial Modulation (GSM) systems involves obtaining the ML solution of a number of different MIMO subproblems, with as many possible antenna configurations as subproblems. Obtaining the ML solution of all of the subproblems has a large computational complexity, especially for large GSM MIMO systems. In this paper, we present two techniques for reducing the computational complexity of GSM ML detection.The first technique is based on computing a box optimization bound for each subproblem. This, together with sequential processing of the subproblems, allows fast discarding of many of these subproblems. The second technique is to use a Sphere Detector that is based on box optimization for the solution of the subproblems. This Sphere Detector reduces the number of partial solutions explored in each subproblem. The experiments show that these techniques are very effective in reducing the computational complexity in large MIMO setups.

MCMC based SOR Detector for Large Scale MIMO Systems

MCMC based SOR Detector for Large Scale MIMO Systems

Learning-Aided Deep Path Prediction for Sphere Decoding in Large MIMO Systems

In this paper, we propose a novel learning-aided sphere decoding (SD) scheme for large multiple-input-multiple-output systems, namely, deep path prediction-based sphere decoding (DPP-SD). In this scheme, we employ a neural network (NN) to predict the minimum metrics of the “deep” paths in sub-trees before commencing the tree search in SD. To reduce the complexity of the NN, we employ the input vector with a reduced dimension rather than using the original received signals and full channel matrix. The outputs of the NN, i.e., the predicted minimum path metrics, are exploited to determine the search order between the sub-trees, as well as to optimize the initial search radius, which may reduce the computational complexity of SD. For further complexity reduction, an early termination scheme based on the predicted minimum path metrics is also proposed. Our simulation results show that the proposed DPP-SD scheme provides a significant reduction in computational complexity compared with the conventional SD algorithm, despite achieving near-optimal performance.

ISSOR Signal Detection for Energy Efficient and Low Complexity Large Scale MIMO System

Scalable version of multiuser MIMO called Large-scale MIMO is a one of the powerful technology in future wireless communication systems in which huge amount of BS (base station) antennas utilized to process multiple user equipment. Energy consumed is high with more antennas and also it leads to increase the signal detection complexity and overall circuit power consumption. Designing energy efficient and low complexity MIMO system is considered as a challenging issue. This paper presents the ISSOR signal detection for energy efficient and low complexity large scale MIMO system. VA-GSM (Variable Antenna Generalized spatial modulation) is used in which the number of active antenna transmissions are varied for every transmission in the large scale MIMO. In transmitter side, Eigen value based approach is used for antenna selection. Then, improved symmetric successive over relaxation (ISSOR) approach is proposed for low complexity signal detection in receiver side. The number of user equipment, transmit power, as well as the amount of antennas at the base station, are considered as the optimal system parameters which are chosen for enhancing the efficiency of utilized energy in the system. The proposed scheme implemented in MATLAB software. The proposed scheme attained the high energy efficiency compared to other approaches. Moreover, the BER is utilized to estimate the performance of an offered algorithm and also compared to the previously determined algorithm of existing literatures.

ISSOR Signal Detection for Energy Efficient and Low Complexity Large Scale MIMO System

Scalable version of multiuser MIMO called Large-scale MIMO is a one of the powerful technology in future wireless communication systems in which huge amount of BS (base station) antennas utilized to process multiple user equipment. Energy consumed is high with more antennas and also it leads to increase the signal detection complexity and overall circuit power consumption. Designing energy efficient and low complexity MIMO system is considered as a challenging issue. This paper presents the ISSOR signal detection for energy efficient and low complexity large scale MIMO system. VA-GSM (Variable Antenna Generalized spatial modulation) is used in which the number of active antenna transmissions are varied for every transmission in the large scale MIMO. In transmitter side, Eigen value based approach is used for antenna selection. Then, improved symmetric successive over relaxation (ISSOR) approach is proposed for low complexity signal detection in receiver side. The number of user equipment, transmit power, as well as the amount of antennas at the base station, are considered as the optimal system parameters which are chosen for enhancing the efficiency of utilized energy in the system. The proposed scheme implemented in MATLAB software. The proposed scheme attained the high energy efficiency compared to other approaches. Moreover, the BER is utilized to estimate the performance of an offered algorithm and also compared to the previously determined algorithm of existing literatures.