Multi-output Orthogonal Frequency Division Multiplexing Research Articles

MIMO Signal Detection Based on IM-LSTMNet Model

Signal detection is crucial in multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, yet classical detection methods often struggle with nonlinear issues in wireless channels. To handle this challenge, we propose a novel signal detection method for MIMO-OFDM system based on the fractional Fourier transform (FrFT), leveraging the robust time series processing capabilities of long short-term memory (LSTM) networks. Our innovative approach, termed IM-LSTMNet, integrates LSTM with convolutional neural networks (CNNs) and incorporates a Squeeze and Excitation Network to emphasize critical information, enhancing neural network performance. The proposed IM-LSTMNet is applied to the FrFT-based MIMO-OFDM system to improve signal detection performance. We compare the detection results of IM-LSTMNet with zero forcing (ZF), minimum mean square error (MMSE), simple LSTM neural network, and CNN–LSTM network by evaluating the bit error rate. Experimental results demonstrate that IM-LSTMNet outperforms ZF, MMSE, LSTM, and other methods, significantly enhancing system signal detection performance. This work offers a promising advancement in MIMO-OFDM signal detection, presenting a deep learning-based solution that effectively improves the system signal detection performance.

RIS-Aided Multiuser MIMO-OFDM With Linear Precoding and Iterative Detection: Analysis and Optimization

In this paper, we consider a reconfigurable intelligent surface (RIS) aided uplink multiuser multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, where the receiver is assumed to conduct low-complexity iterative detection. We aim to minimize the total transmit power by jointly designing the precoder of the transmitter and the passive beamforming of the RIS. This problem can be tackled from the perspective of information theory. But this information-theoretic approach may involve prohibitively high complexity since the number of rate constraints that specify the capacity region of the uplink multiuser channel is exponential in the number of users. To avoid this difficulty, we formulate the design problem of the iterative receiver under the constraints of a maximal iteration number and target bit error rates of users. To tackle this challenging problem, we propose a groupwise successive interference cancellation (SIC) optimization approach, where the signals of users are decoded and canceled in a group-by-group manner. We present a heuristic user grouping strategy, and resort to the alternating optimization technique to iteratively solve the precoding and passive beamforming sub-problems. Specifically, for the precoding sub-problem, we employ fractional programming to convert it to a convex problem; for the passive beamforming sub-problem, we adopt successive convex approximation to deal with the unit-modulus constraints of the RIS. We show that the proposed groupwise SIC approach has significant advantages in both performance and computational complexity, as compared with the counterpart approaches.

Structured Hybrid Message Passing Based Channel Estimation for Massive MIMO-OFDM Systems

This paper investigates uplink channel estimation for massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with uniform planar array (UPA) antennas at the base station (BS). We first establish a triple beam-based channel model using sampled steering vectors. Based on the presented channel model, we further develop a three-dimensional (3D) Markov random field (MRF) probability model to capture the structured channel sparsity. Then constrained Bethe free energy (BFE) minimization is introduced to provide a systematic theoretical framework for message passing. Under this framework, we derive a structured hybrid message passing (SHMP) algorithm to address the channel estimation problem. The proposed algorithm can significantly improve the estimation accuracy by exploiting the clustered sparse structure of channels with low complexity. Moreover, aiming at the fine factors of the triple beam-based channel model and the coupling parameter of the 3D-MRF sparsity model, we analyze the effect of their different settings in the numerical simulation. Finally, extensive simulation results verify the superiority of the proposed SHMP algorithm.

Highly Accurate Technique for CO-OFDM Channel Estimation Technique Using Extreme Learning Machine (ELM)

In wireless systems, channel estimation is considered a problematic technology, due to the fact of the difference in time between wireless channels and the noise effect. Orthogonal frequency-division multiplexing (OFDM) is a promising candidate for future optical communications and has received wide concern. The article proposed a Coherent Optical (CO) orthogonal frequency division multiplexing (OFDM) scheme, which gives a scalable and flexible solution for increasing the transmission rate, being extremely robust to chromatic dispersion as well as polarization mode dispersion. Nevertheless, both coherent detection and OFDM are prone to phase noise due to the phase mismatch between the laser oscillators at the transmitter and receiver sides and the relatively long OFDM symbol duration compared to that of single carrier communications. An Extreme Learning Machine (ELM) with Pilot Assisted Equalization (PEM) is proposed for compensation of impairments caused by fibre nonlinearity in coherent optical communication systems. Channel estimation using ELM and the value of distortion is sent to the OSTBC receiving end based on the distortion information the data is decoded and pilot data is removed. FFT is applied to the data and QPSK demodulation is done in the data to get its original form. In addition, the article utilized a free-space optical communication system of multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) with a modified receiver structure. Simulation reveals that the proposed model exhibits significant BER (0.0112) performance and provides better spectral efficiency as compared with conventional systems and less computational complexity. This suggested that the proposed method shows better performance by using the CO-OFDM-FSO-MIMO-ELM-based channel estimation technique for high-speed data communication networks in real-time scenarios respectively.

Performance Evaluation of MIMO-OFDM-FSO with Modified Receiver

The performance of free space optical communication system using multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) with a modified receiver structure is evaluated in this paper referred to as MIMO-OFDM-FSO with the modified receiver (MIMO-OFDM-FSO-MR). Utilizing equal gain combining the analysis is done by taking closed-form expression of average bit error rate and throughput. The simulation is performed to estimate various performance parameters such as throughput and bit error rate (BER) for the proposed MIMO-OFDM-FSO-MR model under different turbulence and weather conditions. Simulation reveals that the proposed model exhibits significant BER performance and provides better throughput and spectral efficiency as compared with conventional FSO systems, OFDM-FSO systems, MIMO-FSO systems and Low-density Parity Check (LDPC) coded MIMO-OFDM-FSO systems.

Hierarchical BEM Based Channel Estimation With Very Low Pilot Overhead for High Mobility MIMO-OFDM Systems

Channel estimation for multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is a challenging issue in high mobility scenarios due to prohibitive pilot overhead and complexity. In this paper, a hierarchical basis expansion model (BEM) based channel estimation scheme is proposed for doubly selective channels, which requires a low pilot overhead and computational complexity. A mixed-BEM is proposed for time-varying channels alongside a complex exponential-BEM (CE-BEM), resulting in very few unknown parameters required for channel estimation. Based on the hierarchical BEM structure, an analytical channel estimation model is derived. By employing the block-structured sparsity and a small number of mixed-BEM coefficients in the antenna-time-BEM domain, a low-complexity estimator is proposed to recover the mixed-BEM coefficients accurately. The lower bound on the mean square error (MSE) of channel estimation is derived to verify the effectiveness of the proposed scheme. Simulation results show that the proposed channel estimation scheme significantly outperforms the existing methods in terms of MSE of channel estimation and bit error rate, with much lower pilot overhead and computational complexity.

Mine Intelligent Receiver: MIMO-OFDM Intelligent Receiver for Mine Information Recovery

With the advancement of an intellectual and numerical society, the coal mining industry has also begun to change to intelligence. As an important aspect of intelligent coal mine construction, coal mine communication has put forward more stringent standards for communication quality. For the complex communication environment in mines, the transmission of communication signals is always damaged by various noises and interferences, resulting in serious distortion of the communication signals received at the receiving end. Therefore, the use of traditional receivers for information recovery has the problem of high bit error rate (BER), which cannot meet the standard of intelligent coal mine construction. Based on this, the aim of this research is to combine convolutional neural networks (CNN) and multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) communication systems to design an intelligent receiver model for complex mine communication systems. At the receiver side, CNNs are used to take the place of all the information processing processes. First, features are extracted from the received IQ signal by the convolutional neural network, and then the original information bit is recovered using a multi-label classifier to finally realize end-to-end information restoration. The experimental results show that the intelligent receiver model designed in this research has more accurate information recovery capability in the complex mine channel environment compared with the traditional receiver. In addition, they also verify that the intelligent receiver can still recover information effectively when the traditional receiver cannot recover information properly in the case of partial loss of received data.

Energy Efficiency of User-Centric, Cell-Free Massive MIMO-OFDM with Instantaneous CSI.

In the user-centric, cell-free, massive multi-input, multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, a large number of deployed access points (APs) serve user equipment (UEs) simultaneously, using the same time–frequency resources, and the system is able to ensure fairness between each user; moreover, it is robust against fading caused by multi-path propagation. Existing studies assume that cell-free, massive MIMO is channel-hardened, the same as centralized massive MIMO, and these studies address power allocation and energy efficiency optimization based on the statistics information of each channel. In cell-free, massive MIMO systems, especially APs with only one antenna, the channel statistics information is not a complete substitute for the instantaneous channel state information (CSI) obtained via channel estimation. In this paper, we propose that energy efficiency is optimized by power allocation with instantaneous CSI in the user-centric, cell-free, massive MIMO-OFDM system, and we consider the effect of CSI exchanging between APs and the central processing unit. In addition, we design different resource block allocation schemes, so that user-centric, cell-free, massive MIMO-OFDM can support enhanced mobile broadband (eMBB) for high-speed communication and massive machine communication (mMTC) for massive device communication. The numerical results verify that the proposed energy efficiency optimization scheme, based on instantaneous CSI, outperforms the one with statistical information in both scenarios.

Channel Estimation for Massive MIMO: An Information Geometry Approach

In this paper, we investigate the channel estimation for massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Using the sampled steering vectors in the space and frequency domain, we first establish a space-frequency (SF) beam based statistical channel model. The accuracy of the channel model can be guaranteed with sufficient sampling steering vectors. With the channel model, the channel estimation is formulated as obtaining the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$a \ posteriori$</tex-math></inline-formula> information of the beam domain channel. We solve this problem by calculating an approximation of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$a\ posteriori$</tex-math></inline-formula> distribution's marginals within the information geometry framework. Specifically, by viewing the set of Gaussian distributions and the set of the marginals as a manifold and its <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$e$</tex-math></inline-formula> -flat submanifold, we turn the calculation of the marginals into an iterative projection process between submanifolds with different constraints. We derive the information geometry approach (IGA) for channel estimation by calculating the solutions of projections. We prove that the mean of the approximate marginals at the equilibrium of IGA is equal to that of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$a \ posteriori$</tex-math></inline-formula> distribution. Simulations demonstrate that the proposed IGA can accurately estimate the beam domain channel within limited iterations.

Simultaneous distribution of wired and two 2 × 2 MIMO wireless OFDM signals over an integrated RoF-PON system

Abstract In this paper, the integration of the radio over fiber technology into a passive optical network (RoF-PON) is proposed to simultaneously distribute wired and two wireless standards each one transports 2 × 2 multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) signals. This simultaneous transmission is operated based on a single centralized optical wavelength and enabled by a polarization division multiplexing technique. The proposed RoF-PON system offers high spectral efficiency and is designed for unlicensed industrial, scientific, and medical bands at 2.4 and 5.8 GHz. The system performance is assessed by measuring the bit error rates (BERs) according to different access distances up to 30 km. After determining the optimum extinction ratio (ER) to modulate the baseband wired signal that allows jointly optical modulation of wireless signals using the same optical wavelength; all the 2 × 2 quadrature amplitude modulation (QAM) MIMO-OFDM wireless signals with a wired signal are successfully delivered without using forward error correction limit. Additionally, the system reliability has been confirmed by adopting other modulations schemes such as 8 and 16 phase-shift keying (PSK) that maintained good constellation diagrams performances. Whereas, the wired signal is analyzed according to its BER and quality Q-factor including high-quality eye diagram patterns. Also, the results revealed that the MIMO-OFDM signals mapped by 16 QAM scheme are given a better performance than PSK schemes when they are accompanied with high data rate baseband wired signal via a PON distance of 30 km.

Site-specific millimeter-wave compressive channel estimation algorithms with hybrid MIMO architectures

In this paper, we present and compare three novel model-cum-data-driven channel estimation procedures in a millimeter-wave Multi-Input Multi-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) wireless communication system. The transceivers employ a hybrid analog-digital architecture. We adapt techniques from a wide range of signal processing methods, such as detection and estimation theories, compressed sensing, and Bayesian inference, to learn the unknown virtual beamspace domain dictionary, as well as the delay-and-beamspace sparse channel. We train the model-based algorithms with a site-specific training dataset generated using a realistic ray tracing-based wireless channel simulation tool. We assess the performance of the proposed channel estimation algorithms with the same site's test data. We benchmark the performance of our novel procedures in terms of normalized mean squared error against an existing fast greedy method and empirically show that model-based approaches combined with data-driven customization unanimously outperform the state-of-the-art techniques by a large margin. The proposed algorithms were selected as the top three solutions in the "ML5G-PHY Channel Estimation Global Challenge 2020" organized by the International Telecommunication Union.

Phase Rotations of SVD-Based Precoders in MIMO-OFDM for Improved Channel Estimation

In a multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, the conventional singular value decomposition (SVD) based precoders may destroy the smoothness of the effective channel, - i.e., the concatenation of the precoders and the channel - in the frequency domain, causing difficulties to the channel estimation. This letter proposes a phase-rotated SVD (PR-SVD) via exploiting the phase non-uniqueness of SVD to make the effective channel as smooth as possible in the frequency domain to facilitate the channel estimation. The simulation results verify that the proposed PR-SVD based precoding method can improve the channel estimation and consequently can significantly outperform the state-of-the-art method in bit error rate (BER).

Channel Estimation and Robust Detection for IQ Imbalanced Uplink Massive MIMO-OFDM With Adjustable Phase Shift Pilots

In this paper, we investigate channel estimation and robust detection for uplink massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with in-phase and quadrature-phase imbalances (IQI). We represent the received signal with a real-valued model, where the combination of the IQI and the wireless channel is treated as the effective channel. Based on the real-valued model, we then perform the minimum mean square error (MMSE) criterion based estimation for the effective channel. We use adjustable phase shift pilots (APSPs) to reduce the pilot overhead and verify that the MSE of the effective channel estimation can be minimized by scheduling the pilot phase shifts properly. We propose a pilot phase shift scheduling algorithm based on the covariance matrices of the effective channels. Since the channel estimation performance might be degraded due to using APSPs, we develop an MMSE criterion based data detection scheme which is robust to the channel estimation error. Using operator-valued free probability theory, we derive an asymptotic deterministic equivalent for the ergodic achievable sum rate of the proposed detection scheme. The performance of the channel estimation with APSPs and the robust data detection is demonstrated via extensive numerical results.

HYBRIDIZATION OF SUBCARRIER INDEX MODULATION AND OFDM WITH MIMO SYSTEM APPLIED TO FSO

Presently free space optical communication (FSO) is a promising technique on which various researches are carried on. The integration of orthogonal frequency division multiplexing (OFDM) and Subcarrier index modulation, conventional OFDM, Multi input multi output OFDM (MIMO-OFDM) concepts are analyzed in this paper. In free space optical communication these techniques are utilized as well as the performance analysis is simulated using MATLAB. This work investigates the significant of SIM modulated MIMO-OFDM in FSO. Also it analyses the superiority of SIM modulated MIMO-OFDM than SIM modulated classical OFDM and simple Subcarrier index modulation over free space optical communication. Here different turbulence conditions are taken to analyze the concept of SIM along with MIMO-OFDM, classical OFDM in free space. Ultimately SIM modulated MIMO-OFDM exhibits better BER performance in the order of 10-6 in comparison to simple SIM and SIM associated OFDM in free space.

Extended Spatial-Index LED Modulation for Optical MIMO-OFDM Wireless Communication

An efficient optical modulation technique for multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) visible light communication system is proposed in this paper. The proposed modulation technique is termed as extended spatial-index light-emitting diode (LED) modulation. In the proposed technique, the indices (the spatial domain) of the LEDs are exploited in a dynamic style to not only get rid of the optical OFDM time-domain ( OFDM t d ) shaping problem but also to expand the LED indices spatial modulation domain. The indices of the active LEDs in the proposed technique are changed from the two LEDs active situation to the situation where all or several LEDs are active. Moreover, within the selected active LED indices, the power weight distribution and the positions of the OFDM components are varied to expand the resultant spatial domain. Therefore, the proposed technique offers a considerable spectral efficiency improvement over the up-to-date LED index OFDM modulation schemes even with a lower number of LEDs. The key idea of the proposed technique is to maximize the LEDs’ indices spatial position (spatial domain) utilization, where both the power weight allocation and the positions of the complex OFDM time domain components are varying several times over the same active LED indices combination, which improve the optical system spectral efficiency. The simulation results asserted the superiority of the proposed technique, as it improves both the average bit error rate (ABER) and the achievable data rate (R) compared with existing up-to-date OFDM-LED index modulations with even lower computational complexity.

Weighted TSVR Based Nonlinear Channel Frequency Response Estimation for MIMO-OFDM System

A channel frequency response estimation method based on weighted twin support vector regression (TSVR) for pilot-aided multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system is proposed in this work. Nonlinearity of channel in wireless communication system is considered. The channel is fading in time domain produced by Doppler effect and in frequency domain by propagation multipath. An improved TSVR-weighted TSVR is adopted to estimate channel parameters in MIMO-OFDM system. The weights obtained by wavelet transform method are used to improve the regression performance of TSVR. The characteristic of the proposed algorithm is that different training samples are given weights calculated according to the distance from the samples to the mean values filtered by wavelet transform method. Due to the regression characteristics of TSVR, the channel frequency response estimation algorithm proposed in this work has good estimation performance and anti noise ability. Experimental results show that compared with the classical pilot aided channel estimation method, the proposed algorithm has better performance in estimating mean square error.

Enhanced Peak Cancellation With Simplified In-Band Distortion Compensation for Massive MIMO-OFDM

This paper presents an enhanced peak cancellation method with simplified in-band distortion compensation for massive multi-input multi-output (mMIMO) orthogonal frequency division multiplexing (OFDM). The method compensates an in-band distortion due to peak cancellation by utilizing extra transmit antennas, where a compensation signal is designed and transmitted using extra antenna elements so that in-band distortion is canceled at the receiver end. Consequently, deep peak cancellation is possible without degrading bit error rate performance. The proposed method is further extended to non-linear precoded mMIMO-OFDM systems, where the perturbation vector cancellation signal is superimposed on the compensation signal so that the received signal is demodulated without non-linear processing to remove the perturbation vector. Thus, the proposed method does not require the iterative calculation to compensate for an in-band distortion. Our results show the effectiveness of the proposed method in terms of peak-to-average power ratio (PAPR) characteristics, signal to noise and distortion power ratio (SDNR), bit error rate (BER), and throughput in comparison with the state-of-the-art.

Posture-Specific Breathing Detection.

Human respiratory activity parameters are important indicators of vital signs. Most respiratory activity detection methods are naïve abd simple and use invasive detection technology. Non-invasive breathing detection methods are the solution to these limitations. In this research, we propose a non-invasive breathing activity detection method based on C-band sensing. Traditional non-invasive detection methods require special hardware facilities that cannot be used in ordinary environments. Based on this, a multi-input, multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system based on 802.11n protocol is proposed in this paper. Our system improves the traditional data processing method and has stronger robustness and lower bit relative error. The system detects the respiratory activity of different body postures, captures and analyses the information, and determines the influence of different body postures on human respiratory activity.

Performance of Pilot-Aided 3D- OFDM Channel Estimation using Different Antenna Configurations

&lt;p&gt;This paper aims, a 3D-Pilot Aided Multi-Input Multi-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) Channel Estimation (CE) for Digital Video Broadcasting -T2 (DVB-T2)for the 5 different proposed block and comb pilot patterns model and performed on different antenna configuration. The effects of multi-transceiver antenna on channel estimation are addressed with different pilot position in frequency, time and the vertical direction of spatial domain framing. This paper first focus on designing of 5- different proposed spatial correlated pilot pattern model with optimization of pilot overhead. Then it demonstrates the performance comparison of Least Square (LS) &amp;amp;Linear Minimum Mean Square Error (LMMSE), two linear channel estimators for 3D-Pilot Aided patterns on different antenna configurations in terms of Bit Error Rate. The simulation results are shown for Rayleigh fading noise channel environments. Also, 3x4 MIMO configuration is recommended as the most suitable configuration in this noise channel environments.&lt;/p&gt;

CFO robust blind receivers for MIMO-OFDM systems based on PARALIND factorizations

Carrier frequency offsets (CFO) is a well known impairment that destroys the orthogonality amongst subcarriers in multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, and the resulting inter-carrier interference significantly degrades the performance of such systems. This paper proposes the CFO robust blind receivers for MIMO-OFDM systems based on parallel profiles with linear dependencies (PARALIND) factorizations. First, we model MIMO-OFDM signal in the presence of CFO to satisfy the PARALIND model, where the symbols, the CFO, and the MIMO channel matrices can be viewed as three independent factor matrices. Such a hybrid tensorial modeling enables a blind and joint estimation of symbols and CFO. Then, in order to improve the fitting precision of the PARALIND model, we introduce two algorithms for fitting the PARALIND model. The first one, called delta alternating least squares (DALS) algorithm, where we predict coarsely by exploiting the increment values between two iterations of the factor matrices, refine these predictions by using the enhanced line search and use these refined values to initialize factor matrices, this procedure helps us avoid the random initialization found in the traditional ALS algorithm. The second one, called orthogonal DALS (ODALS) algorithm that uses the solution of orthogonal constraint for the DALS algorithm to provide the initialization for one more factor matrix. The performance of proposed receivers is illustrated by means of simulation results, and a comparison is made with other approaches that allow to estimate symbols and CFO in two separated steps. Beside a robust to CFO characteristics, our approach gives a better BER performance than other compared blind approaches.