Performance Of MIMO Systems Research Articles

On the Performance of RIS-Aided Cell-Free Massive MIMO Systems Under Channel Aging

On the Performance of RIS-Aided Cell-Free Massive MIMO Systems Under Channel Aging

Design and performance analysis of L‐slotted MIMO antenna with improved isolation using defected ground structure for S‐band satellite application

SummaryThe increasing prevalence of MIMO technology in wireless communication networks is attributed to its ability to augment system capacity and dependability. Consequently, there is a notable enthusiasm for the advancement and implementation of S‐band applications. Nevertheless, the mutual coupling between numerous antennas presents a crucial obstacle to MIMO system performance. This paper presents a novel technique that utilizes a defective ground structure with parasitic components to increase antenna isolation and gain. Additionally, an L‐shaped slot is incorporated into the patch element to enhance the antennas impedance bandwidth. Antennas are intentionally placed in a perpendicular arrangement to minimize the impact of the coupling effects. The antenna exhibits an impedance bandwidth in the frequency range of 2.62–2.79 GHz. Furthermore, the utilization of a four‐element MIMO antenna setup with a defective ground structure yields outstanding isolation properties, with values notably lower than −25 dB. To achieve an optimal MIMO performance, we assessed various diversity parameters, including diversity gain, channel capacity loss, total active reflection coefficient, and envelope correlation coefficient. The parameter values were within acceptable limits (ECC < 0.04, DG = 10 dB, TARC < 0 dB, and CCL < 0.1 bits/s/Hz). This research paper therefore offers essential insights into the design and optimization of MIMO antennas specifically for 2.7 GHz applications.

Simulation of Massive MIMO Antennas for Improved Link Throughput

Massive MIMO technology involves deploying multiple antennas on both ends of a communication link, which boosts spatial diversity and signal-to-noise ratio. By using multi-user MIMO transmission and techniques like spatial multiplexing and beamforming, it increases capacity, spectral efficiency, and data rates. Simulations conducted with tools like Octave software help engineers to optimize designs, predict system behavior, and assess the impact of antenna quantity on downlink throughput. This study addresses the increasing demand for high downlink data rates within modern communication networks. It explores the performance of Massive MIMO systems through simulations using Octave software, focusing on the quantity of antenna elements (M), and considering both spectral efficiency (SE) and data throughput. Results show higher M enhances both spectral efficiency and data rates. Efficiency dips at M=10, surges at M=40, and slowly, but steadily grows from M=50 to 100. Similar trends in data rates indicate M's impact on transmission performance. SE values range from 5.8442 bps/Hz (M = 10) to 24.1608 bps/Hz (M = 100), while data rates range from 116.8834 Mbps (M = 10) to 483.2158 Mbps (M = 100). These insights offer practical guidance for engineers and could facilitate the optimization of wireless communication setups and addressing the imperative for improved data rates. The study advances the understanding of Massive MIMO's potential, providing a roadmap for effectively leveraging its capabilities across diverse communication network scenarios.

Enhanced resource allocation strategies to improve the spectral efficiency in massive MIMO systems

The accuracy of the channel state information is important for correct channel estimation. However, when conducting channel estimation, more resources are allocated to pilots for estimation compared to data transmission. Furthermore, when the number of users increases, the number of pilots for estimation increases. Subsequently, there is an increase in the transmission overhead and hence reduces the spectral efficiency. Therefore, the advantage of obtaining channel state information is significantly reduced. To improve the performance of massive MIMO systems, the study analyses the tradeoff between the number of resources required to correctly estimate the channel using pilots to avoid interference while maintaining optimum spectral efficiency in massive MIMO antennas. Therefore, this study proposes an algorithm to address the challenge of optimum resource allocation in a massive MIMO. Pilot Frequency reuse, max–min fairness algorithm, and Zadoff–Chu sequences were adopted to achieve optimal allocation of resources and reduce interference for users in different cells using the same frequencies. The results reveal improved performance in terms of spectral efficiency with the adoption of the resource optimization approach. The study contributes to the performance improvement of massive MIMO antennas for 5 G communications.

Numerical Evaluation of the MU-MIMO Beamforming Performance with Channel Estimation

This paper presents the numerical evaluation of the ZF beamforming algorithm and DFT precoding using the LS channel estimation in the multiuser multiantenna (MU-MIMO) downlink system. The sum rate performance depending on a number of users are presented. The arising inter user correlation degrades the sum rate (spectral efficiency) performance of multiuser MIMO system especially in scenarios where the number of users is larger than the number of antennas at the BS. For MIMO channel simulation the QuaDRiGa channel model reflecting the real propagation conditions is used. The obtained performance of MU-MIMO ZF precoding in spatially correlated channel are compared with DFT precoding based on the empirical cumulative density function of the sum rate of multiple users. Numerical results show that the ZF precoder outperforms the DFT precoder in channel with less spatial correlation. The DFT precoder in spatially more correlated channel has advantage over ZF precoder.

A reconfigurable wideband MIMO antenna combines RF energy harvesting

SummaryThis article introduces a novel and groundbreaking approach combining multiple‐input‐multiple‐output (MIMO) technology with radio frequency (RF) energy harvesting. The proposed antenna consists of two semi‐circular monopole antenna components, optimized with dimensions of 89 × 51.02 × 1.6 mm3, that share a common ground plane to achieve MIMO characteristics. A series of split‐ring resonators on the ground plane significantly enhances the isolation between the two radiating components. Band‐notched features are performed in the 3.5 GHz WiMAX and 5.5 GHz WLAN bands through modified C‐shaped slots in the radiating patch and two rectangular split‐ring resonators serving as parasitic devices near the feed line. The reconfiguration of band‐notching is made possible by controlling the modes of the embedded PIN diodes. The two antenna elements maintain mutual coupling below −18 dB from 1.5–13 GHz, achieving an impressive 158.62% impedance bandwidth. The antenna's efficiency and gain experience significant drop, indicating effective interference suppression at the center frequencies of the notch bands, and its performance in MIMO systems is assessed through parameters including envelope correlation coefficient, port isolation, radiation patterns, efficiency, gain, and diversity gain. The simulated properties of the designed antenna closely align with the measured outcomes, demonstrating its reliability and consistency. Moreover, the article evaluates the antenna's potential for RF energy harvesting, achieving a maximum harvested energy of 4.88 V. This proposed antenna can be used in multiple applications, like wideband, band‐notching MIMO, and RF energy harvesting. This proposed antenna is an efficient, reconfigurable wideband MIMO antenna with novel RF energy harvesting capability.

Channel Estimation in MIMO OFDM Systems with Tapped Delay Line Model

The continuous increase in the user demands fornew-generation communication systems, is making the wireless channel more complex and challenging for estimation, developing a simulation model for the channel,and evaluating the performance of different MIMO systems. In this work, a simulation model for multipath fading channels in wireless communication is performed. The model includes a selection of typical Tapped-Delay-Line channel models that can be implemented to reproduce the effects of representative channel distortion and interference. Based on the simulation results, the proposed method exhibits accurate channel estimation performance for frequency-selective fading channels. The proposed work employed LS, MMSE, and ML methods for channel estimation, using 16 and 32 pilots and fixed pilot locations in each frame. Results are obtained for 4x4, 8x8, 16x16, 16x8, and 16x4 MIMO systems and tapped delay line systems.

Corrections to “On the Achievable Performance of Nonlinear MIMO Systems”

Corrections to “On the Achievable Performance of Nonlinear MIMO Systems”

Impact of mobility on the downlink performance of cell-free massive MIMO systems

In this paper, we analyze the achievable downlink spectral efficiency of cell-free massive multiple input multiple output (CF-mMIMO) systems, accounting for the effects of channel aging (caused by user mobility) and pilot contamination. We consider two cases, one where user equipments (UEs) rely on downlink pilots beamformed by the access points (APs) to estimate downlink channel, and another where UEs utilize statistical channel state information (CSI) for data decoding. For comparison, we also consider cellular mMIMO and derive its achievable spectral efficiency with channel aging and pilot contamination in the above two cases. Our results show that, in CF-mMIMO, downlink training is preferable over statistical CSI when the length of the data sequence is chosen optimally to maximize the spectral efficiency. In cellular mMIMO, however, either one of the two schemes may be better depending on whether user fairness or sum spectral efficiency is prioritized. Furthermore, the CF-mMIMO system generally outperforms cellular mMIMO even after accounting for the effects of channel aging and pilot contamination. Through numerical results, we illustrate the effect of various system parameters such as the maximum user velocity, uplink/downlink pilot lengths, data duration, network densification, and provide interesting insights into the key differences between cell-free and cellular mMIMO systems.

Towards an optical MIMO system performance improvement using MGDM multiplexing and EDFA optical amplification

This paper proposes a creative (3 × 3) MIMO transmission system employing multimode fibers MMF and MGDM spatial multiplexing technology. Based on spatial injection and spatial detection of light beams, the MGDM approach allows the MMF fiber to benefit from its entire bandwidth and increase its transmission capacity. To increase the quality and transmission capacity, the injection and detection parameters have been optimized. The transmission and reception conditions on the optical fiber are adjusted to ensure the transfer of high-speed services. Thus, ideal injection and detection conditions increase the quality of signals received on an MMF fiber. The proposed transmission chain decreases the modal and chromatic dispersions during transmission, for this reason, three subsystems interconnected by spatial connectors are created. To decrease interference between the modes of the same groups and to correct for attenuation of the signal on a 1200 m cable, two EDFA optical amplifiers were integrated. The simulation of the proposed chain (3 × 3) MIMO of distance 1.2 Km is realized to transmit data with a total rate of about 4.5 Gb/s (1.5 Gb/s for each user). Then, the comparison between the BER and quality factor Q performance was done for a system with and without optical amplifier.

Performance and Capacity Optimization for High Speed Railway Communications Using UAV-IRS Assisted Massive MIMO System

In this paper, we study the communication performance of applying unmanned aerial vehicles (UAVs) combined with intelligent reflective surfaces (IRS) in a high speed railway (HSR) scenario. This study investigates the design and performance of (multiple-input-multiple-output) MIMO systems with UAV and IRS assistance technology in high-mobility scenarios. Direct links between base stations (BS) and trains are often obstructed in suburban environments, especially in mountainous areas. We mount the IRS on the UAVs so that it can assist in the communication between the trains and the BS. With the help of the UAV-IRS, straight-line links can be established effectively, which greatly improves communication for train passengers. This paper considers the employment of large-scale antenna arrays at both the BS and train ends. Train passengers communicate with UAVs via antennas assembled on the roof of the train as gateways, which in turn communicate with the BS. We consider two types of antenna layouts on the train: all antennas are located in the center of the train named Co-located antennas (CA) layout and uniformly distributed along the train called distributed antennas (DA) layout. We can obtain the analytical up-link capacity by averaging over all locations in a cell for the above two layouts by considering the radio frequency consumption. Overall, the CA layout is found to be a better option for trains when attempting to maximize cell mean value of capacity, and DA layout achieves a more uniformly distribution of capacity over the entire cell. Ultimately, the best solution will depend on the specific requirements and constraints of the selected deployment scenario.

Numerical Evaluation of the MU-MIMO Beamforming Performance with User Selection Algorithm

This paper presents the numerical evaluation of the ZF beamforming algorithm using the user selection in the multiuser multiantenna (MU-MIMO) downlink system. Two user selection algorithm – semiorthogonal user selection and greedy user selection are numerically evaluated based on the open source MIMO channel model. The sum rate performance depending on number of users are presented. The arising inter user correlation degrades the sum rate (spectral efficiency) performance of multiuser MIMO system especially in scenarios where the number of users is larger than the number of antennas at the BS. The selection of users is based on the orthogonality of the channels among selected users. For MIMO channel simulation the QUADRIGA channel model reflecting the real propagation conditions is used. The obtained performance of MU-MIMO ZF precoding in spatially correlated channel are compared based on the empirical cumulative density function of the sum rate of multiple users. Numerical results show that the ZF precoder using user selection (G ZF) outperforms the ZF precoder with random user selection in spectral efficiency. The greedy user selection in spatially correlated channel has advantage to semi-orthogonal user selection. It isobserved that as the increasing the number of served users used for selection the greedy user selection gives better performance than semi-orthogonal algorithm.

Deep learning based MIMO systems using open-loop autoencoder

This article introduces two novel multiple input multiple output spatial division multiplexing (MIMO-SDM) systems based on deep learning techniques using bit-wise (BW) and symbol-wise (SW) open-loop autoencoders (AE), abbreviated as SWAE-MIMO and BWAE-MIMO. Based on the detection methods, we introduce three detectors at the receiver: Radio Transformation Network (RTN), Minimum Mean Square Error (MMSE) technique, and the combination of MMSE technique and neural network (MMSEnet), which can suppress co-channel interference (CCI) among received signal streams resulting in low bit error rates (BER). Furthermore, the considered systems are trained in a single phase to optimally synchronize the transmitter’s and receiver’s learning parameters, thus successfully exploiting spatial diversities to improve the error performance of conventional MIMO systems using the MMSE detector with different numbers of transmit-receive antennas. In a specific case, the BWAE-MIMO system using the RTN detector (BWAE-MIMO-RTN) achieves a BER comparable to that of the conventional MIMO system with a maximum likelihood detector (MIMO-ML) when both systems are equipped with two transmitting and receiving antennas.

Design of Hybrid Beamforming System Based on Practical Circuit Parameter of 6-Bit Millimeter-Wave Phase Shifters

This paper addresses the design of a hybrid beamforming system considering the circuit parameter of six-bit millimeter-wave phase shifters based on the process design kit. The phase shifter design adopts 45 nm CMOS silicon on insulator (SOI) technology at 28-GHz. Various circuit topologies are utilized, and in particular, a design is presented based on switched LC components, connected in a cascode manner. The phase shifter configuration is connected in a cascading manner to get the 6-bit phase controls. Six different phase shifters are obtained, which are 180°, 90°, 45°, 22.5°, 11.25°, and 5.6°, with a minimum number of LC components. The circuit parameters of the designed phase shifters are then incorporated in a simulation model of hybrid beamforming for a multiuser MIMO system. The number of OFDM data symbols used in the simulation is ten for eight users, 16 QAM modulation schemes, −25 dB SNR, 120 simulation runs, and around 170 h runtime. Simulation results are obtained considering four and eight users, assuming accurate technology-based models of RFIC components of the phase shifter as well as ideal phase shifter parameters. The results indicate that the performance of the multiuser MIMO system is affected by the accuracy level of the phase shifter RF component models. The outcomes also reveal the performance tradeoff based on user data streams and the number of BS antennas. By optimizing the amount of parallel data streams per user, higher data transmission rates are achieved, while maintaining acceptable error vector magnitude (EVM) values. In addition, stochastic analysis is conducted to investigate the distribution of the RMS EVM. The outcomes show that the best fitting of RMS EVM distribution of the actual and ideal phase shifters agreed with the log-logistic and logistic distributions, respectively. The obtained (mean, variance) values of the actual phase shifters based on accurate library models are (46.997, 481.36), and for ideal components the values are (36.47, 10.44).

NEW QUASI-OPTIMAL ALGORITHMS OF ANTENNA SELECTION WITH LOW COMPLEXITY

Classical MIMO system model without antenna selection and MIMO system model with antenna selection are presented. New quasi-optimal low complexity algorithms of antenna selection are proposed for MIMO communication systems. These algorithms allow to achieve high spectral efficiency without significant increase of complexity. The statistical modeling method is used to evaluate the efficiency of the proposed algorithms and to compare their characteristics with the characteristics of the known algorithms of full and partial combined selection. The computational complexity of the proposed algorithms is evaluated and been compared with the computational complexity of the well-known algorithms of full and partial combined selection in low-order and high-order MIMO system configurations. The possibility of practical application of new quasi-optimal algorithms in real communication systems is clearly demonstrated by comparative analysis of error-rate performance in conjunction with an assessment of computational complexity. These applications provide significantly improving of the energy performance of MIMO communication systems and it allows to improve quality characteristics of communications for subscribers or could be exchanged to the lower price of construction and maintenance of real communication systems.

Optimization of resource allocation in FDD massive MIMO systems

The performance of massive MIMO systems relies heavily on the availability of Channel State Information at the Transmitter (CSIT). A large amount of work has been devoted to reducing the CSIT acquisition overhead at the pilot training and/or CSI feedback stage. In fact, the downlink communication generally includes three stages, i.e., pilot training, CSI feedback, and data transmission. These three stages are mutually related and jointly determine the overall system performance. Unfortunately, there exist few studies on the reduction of CSIT acquisition overhead from the global point of view. In this paper, we integrate the Minimum Mean Square Error (MMSE) channel estimation, Random Vector Quantization (RVQ) based limited feedback and Maximal Ratio Combining (MRC) precoding into a unified framework for investigating the resource allocation problem. In particular, we first approximate the covariance matrix of the quantization error with a simple expression and derive an analytical expression of the received Signal-to-Noise Ratio (SNR) based on the deterministic equivalence theory. Then the three performance metrics (the spectral efficiency, energy efficiency, and total energy consumption) oriented problems are formulated analytically. With practical system requirements, these three metrics can be collaboratively optimized. Finally, we propose an optimization solver to derive the optimal partition of channel coherence time. Experiment results verify the benefits of the proposed resource allocation schemes under three different scenarios and illustrate the tradeoff of resource allocation between three stages.

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.

Deep-Learning-Based Adaptive Symbol Decision for Visual MIMO System with Variable Channel Modeling.

A channel modeling method and deep-learning-based symbol decision method are proposed to improve the performance of a visual MIMO system for communication between a variable-color LED array and camera. Although image processing algorithms using color clustering are available to correct distorted color information in a channel, color-similarity-based approaches are limited by real-world distortions; to overcome such limitations, symbol decision is defined as a multiclass classification problem. Further, to learn a robust classifier against channel distortion, a deep neural network learning technique is applied to adaptively determine symbols from channel distortion. The network designed herein comprises the channel identification and symbol decision modules; the channel identification module extracts a channel identification vector for symbol determination from an input image using a two-dimensional deep convolutional neural network (CNN); the symbol decision module then generates a feature map by combining the channel identification vector and information on adjacent symbols to determine the symbol via learning correlations between adjacent symbols using a one-dimensional CNN. The two modules are connected together and learned simultaneously in an end-to-end manner. We also propose a new channel modeling method that intuitively reflects real-world distortion factors rather than the conventional additive white Gaussian noise channel to efficiently train deep-learning networks. Lastly, in the proposed channel distortion environment, the proposed method shows performance improvement by an average of about 41.8% (up to about 54.8%) compared to the existing Euclidean distance method, and about 6.3% (up to about 9.2%) on average compared to the SVM method.

A Novel Design of Hybrid Precoder in Multicell, Multiuser Heterogeneous Network

In this paper, the precoders capable of maximizing the weighted sum rate (WSR) for multicell scenarios under power budget constraint conditions per cell were considered based on the MIMO system performance. This rate is strongly affected by intracellular interference, so we recommend using coordinated beam forming (CBF). If the number of user equipment provided per cell is small enough to the number of transmit antennas, simple linear beamformers, such as matched filter (MF), achieve higher performance rates. In general cases, two algorithms meet the best performance in terms of the total achievable rate; these are KG (Kim Giannakis) and WSMSE (Weighted Sum Minimum Squared Error) algorithms. In the KG method, the objective function of the sum rate is suggested to be divided into two functions, one function for the desired user rate and the other for the sum rate of the remaining users. In the WSMSE algorithm, maximizing the sum rate is solved by redefining it as the problem of minimizing the MSE (Mean Square Error) function. These functions are convex and nonconvex functions, respectively. In the proposed method, the WSMSEs were modified to reduce the complexity and calculations to provide optimal performance in beamforming.

Two-level-enforced security with hybrid precoding for multicast massive MIMO wiretap systems

Multicast hybrid precoding reaches a compromise among hardware complexity, transmission performance and wireless resource efficiency in massive MIMO systems. However, system security is extremely challenging with the appearance of eavesdroppers. Physical layer security (PLS) is a relatively effective approach to improve transmission and security performance for multicast massive MIMO wiretap systems. In this paper, we consider a transmitter with massive antennas transmits the secret signal to many legitimate users with multiple-antenna, while eavesdroppers attempt to eavesdrop the information. A fractional problem aims at maximizing sum secrecy rate is proposed to optimize secure hybrid precoding in multicast massive MIMO wiretap system. Because the proposed optimized model is an intractable non-convex problem, we equivalently transform the original problem into two suboptimal problems to separately optimize the secure analog precoding and secure digital precoding. Secure analog precoding is achieved by applying singular value decomposition (SVD) of secure channel. Then, employing semidefinite program (SDP), secure digital precoding with fixed secure analog precoding is obtained to ensure quality of service (QoS) of legitimate users and limit QoS of eavesdroppers. Complexity of the proposed SVD-SDP algorithm related to the number of transmitting antennas squared is lower compared with that of constant modulus precoding algorithm (CMPA) which is in connection with that number cubed. Simulation results illustrate that SVD-SDP algorithm brings higher sum secrecy rate than those of CMPA and SVD-SVD algorithm.