Multiple-input Multiple-output Orthogonal Frequency Division Multiplexing Systems Research Articles

Peak-to-average power ratio minimization and complexity reduction in MIMO-OFDM systems using spatial circular shifting and temporal interleaving method

Multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) technological support for the simultaneous and frequent access by a large number of users to radio resources. For 5G cellular systems, this exhaust is not enough to provide physical layer services. An appropriate Peak-to-average power ratio (PAPR) minimization principle, which maximizes data capacity and channel utility, has been used to address this issue. In this paper, mainly focus on minimize the high PAPR of the candidate sequence of the OFDM sub-block using modified enhancement asymmetric arithmetic coding scheme (M-EAAC). According to this, circular shifting of the candidate sequence is established in the spatial circular shifting and temporal interleaving (SCS-TI) form to generated different set of conjugated phases which is multiplied with candidate sequence. Then, the transmitting antenna is identified the best lowest PAPR of the candidate sequence is chosen for entire OFDM data transmission. The simulation results conveys that the proposed SCS-TI method provide acceptable improvement in the PAPR reduction as compared with conventional selective mapping(SLM)and pseudo-random SLM(PR-SLM). Moreover, the complexity evaluation which ensure the proposed method provides better improvement at three important stages includes inverse fast Fourier transform (IFFT) operation, optimization process, and PAPR calculation at each candidate sequence.

Sparse Reconstruction-Based Joint Signal Processing for MIMO-OFDM-IM Integrated Radar and Communication Systems

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is widely used in integrated radar and communication systems (IRCSs). Moreover, index modulation (IM) is a reliable OFDM transmission scheme in the field of communication, which transmits information by arranging several distinguishable constellations. In this paper, we propose a sparse reconstruction-based joint signal processing scheme for integrated MIMO-OFDM-IM systems. Combining the advantages of MIMO and OFDM-IM technologies, the integrated MIMO-OFDM-IM signal design is realized through the reasonable allocation of bits and subcarriers, resulting in better intercarrier interference (ICI) resistance and a higher transmission efficiency. Taking advantage of the sparseness of OFDM-IM, an improved target parameter estimation method based on sparse signal reconstruction is explored to eliminate the influence of empty subcarriers on the matched filtering at the receiver side. In addition, an improved sequential Monte Carlo signal detection method is introduced to realize the efficient detection of communication signals. The simulation results show that the proposed integrated system is 5 dB lower in the peak sidelobe ratio (PSLR) and 1.5 ×105 lower in the number of complex multiplications than the latest MIMO-OFDM system and can achieve almost the same parameter estimation performance. With the same spectral efficiency, it has a lower bit error rate (BER) than existing methods.

Dynamic thresholding logarithmic companding for PAPR reduction in MIMO-OFDM systems for 5G wireless communication

In order to advance 5G wireless communication technology, this paper presents a novel method for lowering the Peak-to-Average Power Ratio (PAPR) in Multiple-Input Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) systems. It is called the Dynamic Thresholding-based μ-law (DT-μ-law) logarithmic companding technique, and it greatly improves the performance of the system by effectively broadcasting data streams on a single frequency across multiple channels. This technique's ability lies in compressing the amplitude of the transmitting signal beyond a specific threshold value before the parallel-to-serial conversion block in the MIMO-OFDM system. It employs the direct insertion of an offset value into the guard bits, rather than using a step function for the amplitude reduction process, which can reduce additional system complexity and data loss during signal expansion at the receiver end. This technique is more adequately suited for dynamic data rate shifting and interference within a limited threshold value. The dynamic thresholding value (α) is determined by calculating the median and standard deviation of the μ-law companding. This ensures the effective compression of higher amplitude signals at the transmitter, using μ-law log companding. To streamline the decompanding process at the receiver, an offset is introduced, facilitating a smoother transition and reducing complexity. The results demonstrate that the proposed DT- μ-law companding technique significantly improves PAPR reduction while maintaining low Bit Error Rates (BER) and high average spectral power efficiency. This efficiency is optimal when the μ value is maintained within the range of 4–4.5. Furthermore, it was observed that an increase in the μ value corresponds to a decrease in both PAPR value and BER. Specifically, at a signal-to-noise ratio of 15.2 dB, the BER stabilizes at 10−1. Compared to existing methods like the Max-Min Decomp-SLM approach, this technique offers lower Signal-to-Noise Ratio (SNR) values, thereby enhancing spectral efficiency and maximizing data capacity.

Modeling an Enhanced Modulation Classification Approach using Arithmetic Optimization with Deep Learning for MIMO-OFDM Systems

Abstract In a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) method, multiple antennas can be used on either the transmitter or receiver end to improve the system capacity, data throughput, and robustness. OFDM has been used as the modulation system that divides the data stream into multiple parallel low-rate subcarriers. MIMO enhances the system by utilizing spatial diversity and multiplexing abilities. Modulation classification in the MIMO-OFDM systems describes the process of recognizing the modulation scheme used by the communicated signals in a MIMO-OFDM communication system. This is a vital step in receiver design as it enables proper demodulation of the received signals. In this paper, an Enhanced Modulation Classification Approach using an Arithmetic Optimization Algorithm with Deep Learning (EMCA-AOADL) is developed for MIMO-OFDM systems. The goal of the presented EMCAAOADL technique is to detect and classify different types of modulation signals that exist in MIMO-OFDM systems. To accomplish this, the EMCA-AOADL technique performs a feature extraction process based on the Sevcik Fractal Dimension (SFD). For modulation classification, the EMCA-AOADL technique uses a Convolution Neural Network with Long Short-Term Memory (CNN-LSTM) approach. Finally, the hyperparameter values of the CNN-LSTM algorithm can be chosen by using AOA. To highlight the better recognition result of the EMCA-AOADL approach, a comprehensive range of simulations was performed. The simulation values illustrate the better results of the EMCA-AOADL algorithm.

Non-orthogonal pilot pattern design and sparse channel estimation for underwater acoustic MIMO-OFDM systems

This paper proposes a novel non-orthogonal pilot pattern design method for underwater acoustic (UWA) multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, leveraging compressive sensing (CS) channel estimation. This approach adopts the mutual incoherence property (MIP) and enhances pilot density without increasing pilot overhead. We conducted rigorous mathematical derivation to generate the explicit formula for the non-orthogonal pilot pattern with the mutual coherence of zero. In addition, we estimate multipath delay and amplitude by employing parallel orthogonal matching pursuit (OMP) channel estimation. The co-channel interference (CCI) is eliminated from the systems with the proposed non-orthogonal pilot pattern to estimate the channel coefficients from two transmitters without interference in a single iteration. Simulations and lake trial results demonstrate the superior performance of the proposed non-orthogonal pilot pattern over the orthogonal and existing non-orthogonal pilot patterns in terms of normalized mean square error (NMSE) of channel estimation and bit error rate (BER). The 2×8 UWA MIMO-OFDM system achieves a data rate of 78.11 kbps with a BER of 1.42×10−4 in the lake trial over the communication distance of 773 m.

Efficient and Low-Complex Signal Detection with Iterative Feedback in Wireless MIMO-OFDM Systems.

To solve error propagation and exorbitant computational complexity of signal detection in wireless multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, a low-complex and efficient signal detection with iterative feedback is proposed via a constellation point feedback optimization of minimum mean square error-ordered successive interference cancellation (MMSE-OSIC) to approach the optimal detection. The candidate vectors are formed by selecting the candidate constellation points. Additionally, the vector most approaching received signals is chosen by the maximum likelihood (ML) criterion in formed candidate vectors to reduce the error propagation by previous erroneous decision, thus improving the detection performance. Under a large number of matrix inversion operations in the above iterative MMSE process, effective and fast signal detection is hard to be achieved. Then, a symmetric successive relaxation iterative algorithm is proposed to avoid the complex matrix inversion calculation process. The relaxation factor and initial iteration value are reasonably configured with low computational complexity to achieve good detection close to that of the MMSE with fewer iterations. Simultaneously, the error diffusion and complexity accumulation caused by the successive detection of the subsequent OSIC scheme are also improved. In addition, a method via a parallel coarse and fine detection deals with several layers to both reduce iterations and improve performance. Therefore, the proposed scheme significantly promotes the MIMO-OFDM performance and thus plays an irreplaceable role in the future sixth generation (6G) mobile communications and wireless sensor networks, and so on.

PER Prediction in MIMO-OFDM Systems With Approximate Demodulation: A GMI-Based Method

In multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, the symbols in a packet may undergo different signal-to-interference-plus-noise ratio (SINR) levels, making packet error rate (PER) prediction a challenging task. Effective SINR mapping (ESM) simplifies this problem by mapping the SINRs on different symbols to a single packet-level effective SINR. A novel ESM method, referred to as mean generalized mutual information mapping (MGMIM), is put forward in this paper. The proposed method transforms the SINR of each symbol to the generalized mutual information (GMI) and then derives an effective SINR from the mean GMI measure over different symbols. The derived effective SINR summarizes the effect of all the SINRs on the packet, which would produce the same PER performance under an additive white Gaussian noise (AWGN) channel. Compared to existing techniques, MGMIM does not require MCS-dependent tuning parameters but delivers competitive or even better PER prediction accuracy. More importantly, MGMIM allows reliable PER prediction when the receiver employs approximate demodulation, which boosts its applicability in practice. These features are demonstrated by comparative numerical simulations.

Theoretical Foundation and Design Guideline for Reservoir Computing-Based MIMO-OFDM Symbol Detection

In this paper, we derive a theoretical upper bound on the generalization error of reservoir computing (RC), a special category of recurrent neural networks (RNNs). The specific RC implementation considered in this paper is the echo state network (ESN), and an upper bound on its generalization error is derived via the empirical Rademacher complexity (ERC) approach. While recent work in deriving risk bounds for RC frameworks makes use of a non-standard ERC measure and a direct application of its definition, our work uses the standard ERC measure and tools allowing fair comparison with conventional RNNs. The derived result shows that the generalization error bound obtained for ESNs is tighter than the existing bound for vanilla RNNs, suggesting easier generalization for ESNs. With the ESN applied to symbol detection in MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) systems, we show how the derived generalization error bound can guide underlying system design. Specifically, the derived bound together with the empirically characterized training loss is utilized to identify the optimum reservoir size in neurons for the ESN-based symbol detector. Finally, we corroborate our theoretical findings with results from simulations that employ 3GPP standards-compliant wireless channels, signifying the practical relevance of our work.

The Joint Channel Coding and Pre-Distortion Technique on the USRP-Based MIMO-OFDM System

Modern wireless communication systems use orthogonal frequency division multiplexing (OFDM), a multi-carrier modulation method that resists multipath channels and provides bandwidth efficiency. OFDM is generally used with a Multiple-Input Multiple-Output (MIMO) system to boost diversity gain and channel capacity. MIMO-OFDM has several advantages, but its high PAPR value is a drawback. A non-linear high-power amplifier (HPA) can distort signals with high PAPR values. This issue can be resolved by employing predistortion, which compensates for nonlinear HPA. In addition to PD, channel coding can be used to improve the quality of systems with high PAPR values by adding redundant bits to the bits to be sent. In this paper, we report the experimental evaluations of the joint channel coding and pre-distortion (PD) technique on a 2x2 MIMO OFDM system using USRP hardware. The experiments are conducted in two scenarios: line-of-sight (LOS) and nonline-of-sight (NLOS) scenarios. The channel coding used in this scenario is convolutional code with code rates of 1/2, 2/3, and 3/4. From the results of the experiment, it can be seen that the system that uses PD combined with the convolution code produces better performance in the LOS and NLOS scenarios compared to the system without PD. In the LOS scenario, the use of PD can improve the SNR value of code rates 1/2, 2/3, and 3/4 by approximately 58.74%, 75.97%, and 96.20%. In the NLOS scenario, the use of PD can improve the SNR value of code rates 1/2, 2/3, and 3/4 by about 60.71%, 73.59%, and 71.84%. The measurement of the LOS scenario gives a better SNR value than the NLOS scenario, with a maximum SNR value of 30.86 dB, while the maximum SNR value of the NLOS scenario is 30.23 dB. This happened because the LOS scenario suffered minimal multipath fading compared to the NLOS scenario

Bi‐directional LSTM based channel estimation in 5G massive MIMO OFDM systems over TDL‐C model with Rayleigh fading distribution

SummaryIn this work, a deep learning (DL)‐based massive multiple‐input multiple‐output (mMIMO) orthogonal frequency division multiplexing (OFDM) system is investigated over the tapped delay line type C (TDL‐C) model with a Rayleigh fading distribution at frequencies ranging from 0.5 to 100 GHz. The proposed bi‐directional long short‐term memory (Bi‐LSTM) channel state information (CSI) estimator uses online learning during training and offline learning during the practical implementation phase. The design of the estimator takes into account situations in which prior knowledge of channel statistics is limited and targets excellent performance, even with limited pilot symbols (PS). Three separate loss functions (mean square logarithmic error [MSLE], Huber, and Kullback–Leibler Distance [KLD]) are assessed in three classification layers. The symbol error rate (SER) and outage probability performance of the proposed estimator are evaluated using a number of optimization techniques, such as stochastic gradient descent (SGD), momentum, and the adaptive gradient (AdaGrad) algorithm. The Bi‐LSTM‐based CSI estimator is trained considering a specific number of PS. It can be readily seen that by incorporating a cyclic prefix (CP), the system becomes more resilient to channel impairments, resulting in a lower SER. Simulations show that the SGD optimization approach and Huber loss function‐trained Bi‐LSTM‐based CSI estimator have the lowest SER and very high estimation accuracy. By using deep neural networks (DNNs), the Bi‐LSTM method for CSI estimation achieves a superior channel capacity (in bps/Hz) at 10 dB than long short‐term memory (LSTM) and other conventional CSI estimators, such as minimum mean square error (MMSE) and least squares (LS). The simulation results validate the analytical results in the study.

MaReSPS for energy efficient spectral precoding technique in large scale MIMO-OFDM

Spectral pre-coding is a capable method to restrain Out-Of-Band Emission (OOBE) and act in accordance with leaking parameters over neighboring frequency channels while masking unnecessary emissions. Nevertheless, spectral pre-coding might deform the real data vector that is articulated as the Error Vector Magnitude (EVM), which shows a harmful effect on the performance of Multiple-Input Multiple-Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM)-oriented schemes. In this research, a new Mapper Reducer for spectral pre-coded signal (MaReSPS) for energy-constrained signal receiver is proposed for energy efficient spectral precoding in the MIMO-OFDM system. This model involves Mapper Reducer (MR) framework for detecting the received signal, which renders an error rate, and graceful degradation is observed in the throughput under channel uncertainty. The proposed scheme alleviates the resultant Transmit EVM (TxEVM) observed at the receiver by capitalizing on the massive MIMO system, and as a result the throughput is improved. The comparison is done with respect to Block Error Rate (BLER), throughput, and Power Spectral Density (PSD) for proving the betterment of the proposed precoding model for MIMO-OFDM. In particular, the normalized throughput for conventional No OOBE Reduction (OOBER), Mask Compliant Spectral Pre-coder (MSP), Notching Spectral Pre-coder + Zero Forcing (NSP + ZF), P1/P2: CVX and P1/P2: Top- Alternating Direction Method of Multipliers (ADMM) models, as well as proposed MaReSPS model, is lower at a Signal to Noise Ratio (SNR) from 0 dB to 15 dB. With an increase in SNR, the normalized throughput increases and when SNR =40 dB, the normalized throughput values reach their peak values. However, compared to existing models, the proposed MaReSPS model showed high normalized throughput.

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.

Data Aided Channel Estimation for MIMO-OFDM Wireless Systems Using Reliable Carriers

Wireless channel estimation is one of the challenging problems in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) wireless systems. The MIMO-OFDM exploits the spatial resources and increases the reliability and capacity of wireless systems. However, the performance of these systems depends on accurate channel estimation since the receivers require perfect Channel State Information (CSI) for coherent signal detection. Thus, wireless channel estimation is a necessary component of OFDM systems. In this article, we have proposed an algorithm for MIMO-OFDM systems that combines pilot symbols with reliable data symbols for channel estimation. The reliable data symbols serve as virtual pilots and enhance the spectral efficiency. The proposed Data Aided Channel Estimation (DACE) algorithm eliminates the requirement of any additional resources such as excessive number of training symbols to attain the desired performance. Also, it outperforms the traditional Least Square (LS) and Linear Minimum Mean Square Error (LMMSE) methods for channel estimation in terms of Mean Square Error (MSE) and Bit Error Rate (BER) performance of the system.

VHDL implementation of circular shifting‐partial transmit sequence in MIMO OFDM systems

SummaryOne of the basic issues in orthogonal frequency‐division multiplexing (OFDM) frameworks is the peak‐to‐average power ratio (PAPR). There are several PAPR reduction methods available. The partial transmit sequence (PTS) technique is considered one of the superior techniques for overcoming this issue. In this study, an efficient VHSIC hardware description language VHDL implementation of the PTS design utilising roundabout circular shifting CS was planned. The proposed structured method was designed as a real‐time prototype circuit over a field programmable gate array (FPGA) utilising the VHDL language. The implementation results of the prototype circuit in terms of the resources used in the FPGA using the Xilinx synthesis tool on the Virtex‐5 board for SISO, 2 × 2 MIMO and 4 × 4 MIMO models for N‐IFFT 64 and 256 points are presented. The structured framework effectively combined to be a compact framework with low intricacy and high speed. Testing of the implemented circuit verified its functionality. A comparison of our proposed CS‐PTS scheme with previously used PTS schemes, such as the subblock scheme, showed that our proposed method is superior in terms of hardware complexity reduction.

Multiuser neural network detectors for large scale MIMO OFDM over Rayleigh Winner‐II channel

Large scale (LS) MIMO orthogonal frequency division multiplexing (OFDM) technique satisfies the demands on performance and the service quality preferred in wireless communication system. Since numerous antenna terminals have been incorporated in the base station, multiuser detection is crucial for retrieving the data appropriately. Thus, the complexities of the detectors increase rapidly in large scale MIMO OFDM schemes. A unique neural network detection approach for multiuser detection in LS MIMO OFDM model is presented. The performance of multilayer perceptron (MLPNN) and radial basis (RBNN) detectors with minimum mean square (MMSE) MUDs are analyzed. The neural network multiuser detecting methodologies are also analyzed by the loss function in LS MIMO OFDM systems. The performance analyses of the detectors are also done by analyzing the radiation patterns of the antennas which is equipped at the both ends of the uplink channels. Radial basis MUDs have 23 dB bit error rate which gains over linear MMSE detector and of 14 dB than multilayer perceptron detectors specifically at 10(−5) bit error rate. According to the bit error rate analysis of estimations at the output of the multiuser detector, the suggested neural network models are more effective in reducing multiuser interference in MU LS MIMO OFDM. Analysis also verifies, radial basis function (RBF) multiuser detector are exhibiting an optimal BER performance than multilayer perceptron detector with sufficient number of hidden neurons. This study thus emphasis a RBF MUD scheme for detecting multiuser signals in LS MIMO OFDM system due to its dynamic performance in low probability of bit error.

Fine Timing and Frequency Synchronization for MIMO-OFDM: An Extreme Learning Approach

Multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) is a key technology component in the evolution towards cognitive radio (CR) in next-generation communication in which the accuracy of timing and frequency synchronization significantly impacts the overall system performance. In this paper, we propose a novel scheme leveraging extreme learning machine (ELM) to achieve high-precision synchronization. Specifically, exploiting the preamble signals with synchronization offsets, two ELMs are incorporated into a traditional MIMO-OFDM system to estimate both the residual symbol timing offset (RSTO) and the residual carrier frequency offset (RCFO). The simulation results show that the performance of the proposed ELM-based synchronization scheme is superior to the traditional method under both additive white Gaussian noise (AWGN) and frequency selective fading channels. Furthermore, comparing with the existing machine learning based techniques, the proposed method shows outstanding performance without the requirement of perfect channel state information (CSI) and prohibitive computational complexity. Finally, the proposed method is robust in terms of the choice of channel parameters (e.g., number of paths) and also in terms of “generalization ability” from a machine learning standpoint.

The Effectiveness of LDPC Decoding Algorithms in 5G Channel Modelling of MIMO-OFDM System under the Influence of Spatial Correlation

This paper investigates the spatial cross-correlation properties of 5G channel modeling in non-line of sight (NLOS) of Urban Micro Cell (UMi), Rural Macro Cell (RMa) and Indoor Cell (InH) at 6 GHz frequency band as Third Generation Partnership Project (3GPP) specification. In our spatial correlation Multiple-input multiple-output (MIMO) channel, when increasing the distances of BS antenna elements, the correlation characteristics of the channel dramatically vary, which leads to altering the observed system behavior. By analyzing the 5G LDPC code and using different decoding algorithms and code rates, we evaluate the system’s performance by Bit Error Rate (BER) in a wideband correlated Multiple-input multiple-output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system. In our spatial correlation, the more rising of the distances of antenna elements on the BS side, the better the effectiveness of the LDPC decoding algorithms achieved. Of the Belief Propagation based (BP-based) Algorithm, Offset Minimum-Sum (OMS) Algorithm and Linear Approximation Minimum-Sum (LAMS) Algorithm, we propose to use the LAMS decoding algorithm with a high code rate of 5/6 in our spatial correlated wideband channel because of lower complexity and more efficiency in term of the system’s performance.

Comparison on BER Performance Using Linear Filtering Least Square Method and Least Square Method for MIMO-OFDM Based Communication Systems

In Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, the Bit Error Rate is poor by using Linear Filtering Least Square method (LFLS) and Least Square (LS) method for massive MIMO-OFDM systems to extend the strength of the signal and to scale back the noise. Therefore the objective of the project is to maintain better Bit Error Rate performance. Materials and methods: Using Linear Filtering Least Square method, different values are used to enhance the Bit Error Rate (BER). The proposed method is compared with the least square method, the results are obtained by using Matlab simulation software. Results: The performance of Bit Error Rate is calculated by using the Linear Filtering Least Square method and enhanced Least Square method parameters. At 20 dB SNR, the linear filtering least-square provides the BER value is 10-3, whereas the Novel enhanced least-square scheme provides the BER value around 10-5. Conclusion: Based on experimental results and statistical analysis using the independent sample T-test, the Least Square method significantly performs better than the Linear Filtering Least Square method.

A Novel Non-Hermitian Symmetry Orthogonal Frequency Division Multiplexing System for Visible Light Communications

This paper proposes a novel non-Hermitian symmetry ${2 \times 2}$ MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) system based on fast Hartley transform (FHT) with unipolar encoding (referred to as HU-OFDM) for visible light communications. The new scheme uses FHT instead of FFT (fast Fourier transform) to process the complex-valued signals reducing the computational complexity and hardware cost and employs two LEDs (light-emitting diodes) to transmit the real and imaginary parts of a complex-valued OFDM signal respectively. Unipolar encoding is applied to the transmitted signal from each LED to ensure non-negative. The restrictions of Hermitian symmetry in traditional optical OFDM systems and real constellation mapping in FHT based optical OFDM systems can be removed simultaneously in the proposed system. Compared with MIMO-ACO/DCO-OFDM system, HU-OFDM has significant performance improvement along with a tremendous decrease in hardware cost and computational complexity. Compared with other non-Hermitian symmetry MIMO-OFDM systems, HU-OFDM has significant advantages in terms of power efficiency, system design flexibility, computational complexity, or hardware cost without losing reliability.