Multiple-input Multiple-output Orthogonal Frequency Division Research Articles

Reliability Achievement of MIMO-OFDM Communication Systems

Wireless communication must have high spectral efficacy and the ability to resist fading channel in an environment of channel multipath, and it is difficult to achieve these requirements with traditional modulation techniques, but the Multiple system Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) It can fulfill these requirements. Research offers a way to improve the performance of OFDM- MIMO system by decreasing the Bit Error Rate (BER) using both (DWT) Discrete Wavelet Transform and Convolutional Coding (CC). The OFDM system is modeled with a single antenna at the transmitting end and a single antenna at the receiving end (DWT) . Multiple Input Single Output (MISO) By adding a second antenna at the transmitting end, and then improving it with Output Multiple Input Single technology SIMO (by adding a second antenna at the receiving end, and with MIMO technology by adding a second antenna at the receiving end The transmitter and a second antenna at the receiving end, thus optimizing the OFDM-MIMO system using convolution coding, Then using DWT instead of FFT (Fast Fourier Transform ). The research is based on the study of BER according to bit energy to noise energy density N0/Eb. The system was modeled and tested using the MATLAB program. The use of (DWT) and (CC) in OFDM-MIMO reduces N0/Eb )4.7( dB times in the item of the Rician fading channel and 3.2 dB times in the item of the Rayleigh fading channel when BER =10-3.

MmWave 6D Radio Localization With a Snapshot Observation From a Single BS

Accurate and ubiquitous localization is crucial for a variety of applications such as logistics, navigation, intelligent transport, monitoring, control, and also for the benefit of communications. Exploiting millimeter-wave (mmWave) signals in 5G and Beyond 5G systems can provide accurate localization with limited infrastructure. We consider the single base station (BS) localization problem and extend it to 3D position and 3D orientation estimation of an unsynchronized multi-antenna user equipment (UE), using downlink multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) signals. Through a Fisher information analysis, we show that the problem is often identifiable, provided that there is at least one multipath component in addition to the line-of-sight (LoS), even if the position of corresponding incidence point (IP) is a priori unknown. Subsequently, we pose a maximum likelihood (ML) estimation problem, to jointly estimate the 3D position and 3D orientation of the UE as well as several nuisance parameters (the UE clock offset and the positions of IPs corresponding to the multipath). The ML problem is a high-dimensional nonconvex optimization problem over a product of Euclidean and non-Euclidean manifolds. To avoid complex exhaustive search procedures, we propose a geometric initial estimate of all parameters, which reduces the problem to a 1-dimensional search over a finite interval. Numerical results show the efficiency of the proposed ad-hoc estimation, whose gap to the Cramér-Rao bound (CRB) is tightened using the ML estimation.

Hybrid Message Passing Channel Estimation Algorithm for Massive MIMO-OFDM Systems

In this letter, we propose the Bernoulli two-state Gaussian mixture (B-TSGM) probability model to characterize the angle-delay domain (ADD) channel of the massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system. Based on the hybrid message passing (HMP) rule, we design the HMP-B-TSGM channel estimation algorithm under the structured turbo-compressed sensing (STCS) framework. Simulation results show that the considered model better captures the channel characteristics, and the proposed algorithm outperforms the state-of-the-art methods under a wide range of simulation settings while having the same complexity.

Single-RF MIMO-OFDM Using ESPAR

A novel beamspace multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) transmission system using a single-radio-frequency (RF) electronically steerable parasitic array radiator (ESPAR) is described. It makes use of recent results for decomposing the patterns of any arbitrary ESPAR onto an orthogonal basis set using the Theory of Characteristic Modes (TCM). Leveraging this decomposition, OFDM symbols can be mapped into the beamspace domain so that the ESPAR can be used to transmit multiple streams of OFDM symbols using a single RF chain. In the proposed approach, we use the Linde-Buzo-Gray (LBG) algorithm to quantize the OFDM symbol vectors and form a codebook. A binary search tree is then utilized to efficiently find the reactive loads of ESPAR to excite the radiation patterns corresponding to the quantized symbols. It is shown by simulation that quantized codewords and the corresponding radiation patterns can be excited using ESPAR. In addition, the simulated bit error rate (BER) results of the proposed approach are shown to be close to that of conventional MIMO-OFDM systems, demonstrating the effectiveness of implementing a single-RF MIMO-OFDM transmitter.

Turbo-Coded MIMO-OFDM Channel Estimation Using the Chaotic Grey Wolf Optimizer and Genetic Algorithm

Turbo-Coded multiple-input multiple-output orthogonal frequency division multiplexing Channel Estimation, using Chaotic Grey Wolf Optimizer and the Genetic Algorithm (TC-MIMO-OFDM-Hybrid-CGWO-GA), is proposed in this manuscript. To enhance the performance of the bit error rate (BER), the proposed turbo code lessens the maximal correlation of the channel in the frequency domain. Initially, the channel is estimated using LS-MMSE individually using Chaotic Grey Wolf Optimizer (CGWO) and the LS-MMSE is combined with a genetic algorithm to scale the best channel for reducing the error. The encoding and decoding methods are done with the help of Turbo-codes for LS channel estimation. Then, the performance of the proposed method is analyzed with different performance metrics, such as the bit error rate, mean square error, channel estimation, computational cost, overhead, and symbol error rate. From the analysis, the proposed method attains lower computational costs of 99.67%, 98.38%, 92.34% and 97.45%, lower bit error rates of 98.33%, 89.34%, 83.12% and 88.96% and lower mean square errors of 93.15%, 91.25%, 79.90% and 92.88% analyzed to the existing methods, such as TSO-CE-TC-MIMO-OFDM, MFPA-CE-MIMO-OFDM, OSBS-CEA-MIMO-OFDM, and IAMO-CE-MIMO-OFDM.

Beam Domain MIMO-OFDM Optical Wireless Communications With PAPR Reduction

Multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM), confirmed as an efficient technique, has been applied in optical wireless communications (OWCs) to improve the transmission rate. However, the disadvantage of high peak-to-average power ratio (PAPR) also brings into the OWC systems, especially for multi-user communications, resulting in low power efficiency. This paper proposes beam domain MIMO-OFDM OWCs with PAPR reduction. We describe the MIMO-OFDM OWC system architecture and derive the achievable sum rate and PAPR for multi-user OFDM systems, which are dominated by the precoding vectors. To reduce the PAPR, we design the precoding vectors to maximize the sum rate under the PAPR constraint. We propose an iterative process to obtain a candidate optimal solution by the sequential parametric convex approximation (SPCA) method. Furthermore, we prove that the PAPR increases with the number of users and propose a low complexity beam scheduling and power allocation algorithm. Numerical results validate that the proposed schemes can efficiently improve the transmission rate and reduce the PAPR.

High-Performance Channel Estimation for mmWave Wideband Systems With Hybrid Structures

In this paper, a channel estimation problem for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with hybrid structures is studied. Firstly, a beamspace multiple signal classification (MUSIC) algorithm for mmWave wideband channels is proposed to simultaneously estimate the angles of arrival (AOAs), angles of departure (AODs) and transmission delays. Since the traditional spectral peak search method has high complexity, a multi-spectral peak search method is skillfully designed to search for multiple spectral peaks on the MUSIC spatial spectrum more quickly and accurately. Then, the proposed channel estimator is extended to more actual systems equipped with uniform planar arrays (UPAs). Finally, the Cramér–Rao bound (CRB) results of these channel parameters are derived for evaluating the performance of the proposed channel parameter estimator. Simulation results demonstrate that the proposed channel estimator has greatly high channel estimation accuracy.

Ergodic Achievable Rate Maximization of RIS-Assisted Millimeter-Wave MIMO-OFDM Communication Systems

Reconfigurable intelligent surface (RIS) has attracted extensive attention in recent years. However, most research focuses on the scenario of the narrowband and/or instantaneous channel state information (CSI), while wide bandwidth with the use of millimeter-wave (mmWave) (including sub-Terahertz) spectrum is a major trend in next-generation wireless communications, and statistical CSI is more practical to obtain in realistic systems. Thus, we {consider} the ergodic achievable rate of RIS-assisted mmWave multiple-input multiple-output orthogonal frequency division multiplexing communication systems. The widely used Saleh-Valenzuela channel model is adopted to characterize the mmWave channels and only the statistical CSI is available. We first derive the approximations of the ergodic achievable rate by means of the majorization theory and Jensen's inequality. Then, an alternating optimization based algorithm is proposed to maximize the ergodic achievable rate by jointly designing the transmit covariance matrix at the base station and the reflection coefficients at the RIS. Specifically, the design of the transmit covariance matrix is transformed into a power allocation problem and solved by spatial-frequency water-filling. The reflection coefficients are optimized by the Riemannian conjugate gradient algorithm. Simulation results corroborate the effectiveness of the proposed algorithms.

Deep Learning for Super-Resolution Channel Estimation in Reconfigurable Intelligent Surface Aided Systems

Reconfigurable intelligent surface (RIS) enables the configuration of the propagation environment. Channel estimation is an essential task in realizing the RIS-aided communication system. A RIS-aided multi-user multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) communication system involves cascaded channels with high dimensions and sophisticated statistics. Thus, implementing the optimal minimum mean square error (MMSE) with the integration computation is infeasible in practice. To accurately estimate channels with high accuracy in a RIS-aided multi-user MIMO-OFDM system, we model the channel state information (CSI) estimation as an image super-resolution (SR) problem to recover and denoise the channel matrix. Particularly, a convolutional neural network based on a super-resolution convolutional neural network (SRCNN) and denoising convolutional neural network (DnCNN), named SRDnNet, is then proposed. By taking estimated channels at pilot positions as a low-resolution image, the enhanced SRCNN can fully exploit the features of inputs to learn a suitable interpolation method and generate the coarse estimation of the channel matrix. The denoising model DnCNN with an element-wise subtraction structure can exploit features of the additive noise and recover channel coefficients from the coarse channel matrix. The simulation results demonstrate the effectiveness and excellent performance of the proposed SRDnNet.

Compressive Beam and Channel Tracking With Reconfigurable Hybrid Beamforming in mmWave MIMO OFDM Systems

An efficient beam and channel acquisition and tracking scheme is developed under a reconfigurable hybrid beamforming (R-HBF) architecture for wideband millimeter wave (mmWave) multiple input multiple output (MIMO) systems. Under such an R-HBF, a hierarchical multi-resolution beam scanning and space-frequency compressive sensing method is proposed to achieve fast beam and channel acquisitions in mmWave MIMO orthogonal frequency division multiplexing (OFDM) systems. With the initial frequency-domain beam space channel estimates, Kalman-based algorithms are further developed to track the transmit and receive beamforming directions and the channel coefficients of the sparse propagation paths in mmWave mobile channels. Simulations show that the proposed method can greatly reduce the beam training time, and that the channel capacity evaluated with the estimated channels is only 2 dB inferior to the one with ideal channels. Moreover, the beam selection error rates are less than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> in typical urban mmWave mobile channels and traveling speeds even if the transmit power is only 10 dBm.

Key Generation Technique Based on Channel Characteristics for MIMO-OFDM Wireless Communication Systems

Dynamic secret key generation from wireless channel characteristics is a promising technique for physical layer security. One of the important issues in this field is extending the secret key’s length while preserving its uniformity and randomness. This paper proposes a key generation method based on time-varying and the reciprocity of wireless channels for Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) wireless communication systems. In the proposed technique, the complex impulse response (CIR) of the estimated channel state information (CSI) is extracted, and a quantization algorithm is designed to convert the maximum peaks of the CIR into secret key bits. The effectiveness of the proposed key generation technique is assessed in terms of the randomness of the produced key bits with different key lengths by using a statistical test suite of the National Institute of Standards and Technology (NIST). The proposed technique is employed in the MIMO-OFDM systems with different modulation schemes through Additive White Gaussian Noise (AWGN) and Rayleigh channels. The simulation results show that the secret keys with various key lengths generated from the proposed technique for the MIMO-OFDM systems guarantee randomness. Moreover, the proposed CSI-based key generation technique provides better effectiveness in terms of security when compared to some existing techniques.

Efficient transmission of FECG signal using MIMO – OFDM

The extraction of Fetal Electrocardiogram (FECG) during labor or prenatal phases of pregnancy holds significant importance for early prediction of heart abnormalities. The accuracy of the extracted FECG is crucial for effective diagnosis, but the presence of external noises, particularly from the Maternal ECG (MECG), poses a major challenge in obtaining precise information. To address this issue in biomedical data processing, the study employs Finite Impulse Response (FIR) filters using an array multiplier. One notable challenge encountered in this process is the interference caused by external noises, leading to higher delay and power dissipation. In response, a modified High-Performance Multiplier (HPM) based modified booth multiplier is thoroughly reviewed and validated. This modification aims to enhance overall performance and enable high-speed operations in filtering the FECG signals. The effectiveness of these modified multipliers is assessed using ECG signal information collected from the MIT-BIH Arrhythmia Database, comprising 120 samples. In addition to noise filtering, the study explores the validation of Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) transceivers. These transceivers play a crucial role in ensuring the effective transmission of the extracted FECG signals. The research reveals a significant reduction, up to 80.4%, in both area and power dissipation during simulations conducted in Xilinx ISE 9.1 and Cadence Virtuoso. This achievement highlights the potential for improved efficiency and reliability in the processing and transmission of FECG signals, paving the way for advancements in early detection of fetal heart abnormalities.

An ESPRIT-Based Supervised Channel Estimation Method Using Tensor Train Decomposition for mmWave 3-D MIMO-OFDM Systems

This paper focuses on the downlink supervised channel estimation problem for the millimeter wave threedimensional multiple-input multiple-output orthogonal frequency division multiplexing (mmWave 3D MIMO-OFDM) systems, where both the transmitter and the receiver are equipped with uniform rectangular arrays (URAs). Based on the sparse scattering nature, the mmWave channel is modeled as a low-rank higher-order tensor. By formulating the channel and the received training signal as tensors, a fast ESPRIT-based Vandermondestructured tensor decomposition method is proposed to estimate the channel parameters involving angles of arrival and departure (AoAs/AoDs), delays and path gains. In specific, we first establish the relationship of the tensor ranks between the tensor train (TT) model and the higher-order tensor CANDECOMP/PARAFAC (CP) signal model. Then the TT decomposition is used to estimate the signal subspace and derive the shift invariance equation, which exploits the higher-order tensor low-rankness of the signal and the Vandermonde structure in the frequency domain. Based on the derived analytical estimation errors, theoretical justifications are provided to unveil the advantage of using TT decomposition. Moreover, we extend the proposed method in an iterative manner to pursue higher accuracy. According to our simulation results, compared with the state-of-the-art, the proposed method achieves higher estimation accuracy on both the channel parameters and the entire channel. In addition, up to 87% of computing time can be saved in comparison to the current best iterative algorithm in the experiments.

Sustainable and Robust Techniques of Wireless Communications for Industry 4.0: Towards Efficient PAPR Reduction Models

This work presents the concerns of reliable, comprehensive, and high-quality communication networks essential in wireless communications for Industry 4.0. These are considered critical requirements of wireless technology for Industry 4.0. For a reliable transmission of digital data over broadband widths and Giga Hertz channels, the corresponding Peak-to-Average-Power Ratio (PAPR) must be under control. Industry standards Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing With Offset Quadrature Amplitude Modulation (MIMO-OFDM/OQAM) system has been considered as the modulation technique with less Computational Complexity (CC). It usually produces phase sequence sets with different PAPR, a complex phenomenon to control. For robustness in PAPR control, the technique proposed has a receiver that initially restores the frequency domain rotation signal according to the sequence selection of the transmitter. Then it compares the distance between the reverse rotation sequence and the nearest constellation point to restore the original sequence. The proposed method simulation results can efficiently suppress the PAPR of MIMO-OFDM/OQAM signals. The proposed method is compared with the traditional Selective Mapping (SLM) algorithm. The proposed method reduces the CC, and can obtain the approximate Bit-Error Rate (BER) performance of the conventional SLM method when the sideband side information is known.

New Preamble Sequence for WiMAX System with Improved Synchronization Accuracy

Worldwide Interoperability for Microwave Access (WiMAX) trusts Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) combination for the deployment of physical layer functions and for connecting the medium access control to the wireless media. Even though Orthogonal Frequency Division Multiplexing (OFDM) facilitates reliable digital broadband transmission in the fading wireless channels, the presence of synchronization errors in the form of Carrier Frequency Offset (CFO) and Time Offset (TO) adversely affect the performance of OFDM based physical layers. The objective of this work is to improve the accuracy of the frequency and the time offset estimation in the WiMAX physical layer. A method to enhance the synchronization accuracy by fine-tuning the merit factor of the preamble sequence is suggested in this paper. Also, a new preamble with improved synchronization accuracy is proposed for the WiMAX system. The performance of the proposed preamble is evaluated in a Rayleigh fading channel and the results of simulations show that the Mean Square Error (MSE) in offset estimation is significantly reduced and it outperforms the standard WiMAX preamble.

Multiuser Massive MIMO-OFDM for Visible Light Communication Systems

Visible light communication (VLC) has emerged as a viable supplement to existing radio frequency (RF) communication systems. The limited modulation bandwidth of sources is one of the significant challenges in the multiuser (MU) VLC systems, which prohibits transmission at high data rates for each user. Since an array of LEDs is used to illuminate a room, a MU VLC system utilizing massive multiple-input multiple-output (mMIMO) orthogonal frequency-division multiplexing (OFDM) is proposed for achieving high data rates in this paper. Since the distances of the multiple transmitter-receiver links are different, their temporal delays are also different, resulting in complex channel gain and phase differences when transformed to the frequency domain in the OFDM technique. Complex channel vectors associated with different users are mutually spatially orthogonal as the number of transmitters increases. Therefore, inter-user interference (IUI) can be eliminated with simple linear signal processing, and more users can simultaneously communicate in the same time-frequency resource. In this paper, three linear precoding methods, including the maximum ratio transmission (MRT), the minimum mean square error (MMSE), and the zero-forcing (ZF), are investigated for VLC systems based on proposed MU-mMIMO-OFDM in intensity-modulation and direct-detection link. We evaluate and compare the performance of the aforementioned precoding methods in terms of the achievable spectral efficiency and total downlink optical power for different scenarios. Moreover, we derive a closed-form expression for the lower bound on the average achievable sum-rate, which we confirm the accuracy of the derived expression with the numerical simulation. The results demonstrate that the performance of the proposed method in this paper for all precoder techniques, i.e., ZF, MMSE, and MRT, is better than the previous works since the complex channel in the frequency domain is used in this paper. Furthermore, the ZF and MMSE methods are better than MRT when the ratio of the number of LEDs to the number of users is large. At the same time, when this ratio is relatively small, the MRT precoding technique outperforms the ZF and MMSE techniques.

Robust hybrid beamforming designs for full-duplex OFDM millimeter wave systems with imperfect channel estimates

In this paper, robust hybrid beamforming designs are developed for full-duplex millimeter wave multiple-input multiple-output orthogonal frequency division multiplexing (OFDM) systems. A zero-space projection based method is proposed to attenuate the self-interference. The imperfect channel estimations are considered to design the robust full digital beamformers. Then, the hybrid processors are solved via an alternating algorithm. The simulation results show that the proposed design prevails the existing designs.

Spatio-Temporal Neural Network for Channel Prediction in Massive MIMO-OFDM Systems

In massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, a challenging problem is how to predict channel state information (CSI) (i.e., channel prediction) accurately in mobility scenarios. However, a practical obstacle is caused by CSI non-stationary and nonlinear dynamics in temporal domain. In this paper, we propose a spatio-temporal neural network (STNN) to achieve better performance by carefully taking into account the spatio-temporal characteristics of CSI. Specifically, STNN uses its encoder and decoder modules to capture the spatial correlation and temporal dependence of CSI. Further, the differencing-attention module is designed to deal with the non-stationary and nonlinear temporal dynamics and realize adaptive feature refinement for more accurate multi-step prediction. Additionally, an advanced training scheme is adopted to reduce the discrepancy between STNN training and testing. Evaluated on a realistic channel model with enhanced mobility and spherical waves, experimental results show that STNN can effectively improve the accuracy of prediction and perform well with respect to different signal to noise ratios (SNRs). Visualization and testing for unit root illustrate STNN is able to learn CSI time-varying patterns by alleviating series non-stationarity.

Compressive sensing-based channel estimation for high mobile systems with delay Doppler effects

In this paper, channel overhead is reduced by exploiting channel sparsity for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system. Where, compressive sensing (CS) based dictionary design algorithms has been adopted as a channel estimation technique in high mobile systems with minimal number of pilots, such as high-speed train (HST) systems. A novel framework design of the dictionary-based CS was proposed considering both delay and Doppler effects in order to correctly recover the channel response. The channel under consideration is a 2 by 2 space-time block code (STBC) MIMO channel. Simulation tests according to the international telecommunication union (ITU) channel model demonstrated the suitability of the proposed dictionary for estimating the channel impulse response (CIR) of a liner time varying (LTV) channel with a mobility approaches 675 Km/h related to a Doppler frequency of 1500 Hz and 2.4 GHz carrier frequency. Two CS recovery algorithms were applied; orthogonal matching pursuit (OMP) and basis pursuit (BP), where by about 7 dB gain in signal to noise ratio (SNR) was achieved with mobility of 675 Km/h using OMP as compared to BP at a bit error rate (BER) of with 128 OFDM subcarriers.

Signal processing in passive radar with multi-user MIMO-OFDM signal

In this paper, we propose a signal processing method in passive radar using the multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) signal as the illuminator of opportunity. The MU-MIMO-OFDM modulation is adopted in the fifth-generation (5G) communication. We consider the scenario where multiple mobile users share the same time–frequency resources but separate spatially in different beams formed a prior by the MU-MIMO transmitter antenna array, which is a quite general case in realistic 5G communication. A beam-by-beam matched filter is proposed for weak target sensing. Interference suppression is also discussed. The interference in this scenario is more complicated than that in the traditional passive radar. In the MU-MIMO communication system, the base station (BS) transmits multiple signals to different beam regions simultaneously for the communication between the BS and mobile users. For a specific beam region where the radar receiver is located, the interference consists of not only the direct and multipath interference (DMI) of the signal transmitted by the BS to this beam region, but also the inter-user interference (IUI) from the other beam regions. The DMI and IUI degrade the weak target detection capability significantly. In this paper, we propose a suppression method for jointly eliminating the DMI and IUI. Simulations verify the effectiveness of these methods.