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

Range-Division Multiplexing for MIMO OFDM Joint Radar and Communications

An orthogonal frequency-division multiplexing (OFDM) waveform enables simultaneous radar sensing and communications. In the multiple-input multiple-output (MIMO) case with several transmit (Tx) antennas, multiplexing the transmit signals is usually implemented using equidistant subcarrier interleaving (ESI), thus allowing the separation of the transmit signals in frequency domain. In this work, a multiplexing technique denoted as range-division multiplexing (RDMult) is analyzed, which allows separating signals radiated by different Tx antennas along the range axis of a range-Doppler map. This work also investigates the effects of RDMult on the communication task. It turns out that RDMult heavily distorts the channel statistics by introducing a repetitive pattern of deep fading holes, which severely distort the transmission of data. To combat this issue, this work proposes a communication system especially designed for RDMult, including methods for data estimation, channel estimation and synchronization. Simulations confirm the effectiveness of these concepts in terms of bit error ratio (BER) performance.

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.

A Deep Learning-Based Framework for Low Complexity Multiuser MIMO Precoding Design

Using precoding to suppress multi-user interference is a well-known technique to improve spectra efficiency in multiuser multiple-input multiple-output (MU-MIMO) systems, and the pursuit of high performance and low complexity precoding method has been the focus in the last decade. The traditional algorithms including the zero-forcing (ZF) algorithm and the weighted minimum mean square error (WMMSE) algorithm failed to achieve a satisfactory trade-off between complexity and performance. In this paper, leveraging on the power of deep learning, we propose a low-complexity precoding design framework for MU-MIMO systems. The key idea is to transform the MIMO precoding problem into the multiple-input single-output precoding problem, where the optimal precoding structure can be obtained in closed-form. A customized deep neural network is designed to fit the mapping from the channels to the precoding matrix. In addition, the technique of input dimensionality reduction, network pruning, and recovery module compression are used to further improve the computational efficiency. Furthermore, the extension to the practical MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) system is studied. Simulation results show that the proposed low-complexity precoding scheme achieves similar performance as the WMMSE algorithm with very low computational complexity.

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.

Using tensor contractions to derive the structure of slice-wise multiplications of tensors with applications to space–time Khatri–Rao coding for MIMO-OFDM systems

The slice-wise multiplication of two tensors is required in a variety of tensor decompositions (including PARAFAC2 and PARATUCK2) and is encountered in many applications, including the analysis of multidimensional biomedical data (EEG, MEG, etc.) or multi-carrier multiple-input multiple-output (MIMO) systems. In this paper, we propose a new tensor representation that is not based on a slice-wise (matrix) description, but can be represented by a double contraction of two tensors. Such a double contraction of two tensors can be efficiently calculated via generalized unfoldings. It leads to new tensor models of the investigated system that do not depend on the chosen unfolding (in contrast to matrix models) and reveal the tensor structure of the data model, such that all possible unfoldings can be seen at the same time. As an example, we apply this new concept to the design of new receivers for multi-carrier MIMO systems in wireless communications. In particular, we consider MIMO-orthogonal frequency division multiplexing (OFDM) systems with and without Khatri–Rao coding. The proposed receivers exploit the channel correlation between adjacent subcarriers, require the same amount of training symbols as traditional OFDM techniques, but have an improved performance in terms of the symbol error rate. Furthermore, we show that the spectral efficiency of the Khatri–Rao-coded MIMO-OFDM can be increased by introducing cross-coding such that the “coding matrix” also contains useful information symbols. Considering this transmission technique, we derive a tensor model and two types of receivers for cross-coded MIMO-OFDM systems using the double contraction of two tensors.

Performance Analysis of ML-based Low Complex CFO Estimation for MIMO-OFDMA Uplink Systems

Carrier frequency offset (CFO) estimation in multiuser multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system is investigated in this study. MIMO-OFDM is very sensitive to CFOs due to oscillator frequency mismatch and/or Doppler shift. Inaccurate CFO estimation results in intercarrier interference (ICI) through the loss of orthogonality among subcarriers. In this paper, the performance of the ML and APFE algorithm is analyzed for CFO estimation. ML becomes extremely complex due to the multidimensional exhaustive search issue, which is the basic concern in ML estimation. However, making use of the iterative low complex APFE method, here this multidimensional search is replaced with a sequence of mono-dimensional searches. This results in an estimation algorithm of reasonable complexity which is suitable for practical applications. In addition, ML accuracy is compared with the Cramer-Rao bound (CRB).

Generalized superimposed training for RIS-aided cell-free massive MIMO-OFDM networks

In this paper, a generalized superimposed training (GST) is proposed for an uplink cell-free multiple-input multiple-output orthogonal frequency-division multiplexing (mMIMO-OFDM) system, which is aided by reconfigurable intelligent surfaces (RISs) to enhance the spectral efficiency in the system. For the GST scheme, the pilots and data symbols are transmitted simultaneously in the coherence time. This scheme is different from traditional separate transmission methods, such as regular pilots (RP) transmission. In the OFDM multi-carrier case, a part of the subcarriers is based on the GST, whereas the other part of subcarriers is used for data transmission only. The channel and data estimations are carried out and the normalized mean-squared error (NMSE), bit error rate (BER), and sum-rate in different schemes are compared. Different receiver cooperation levels are analyzed in this case, including fully centralized processing and local processing. The distributed time processing and iterative process are also used to improve the performance of the data estimation in this system.

Performance enhancing of MIMO‐OFDM system utilizing different interleaving techniques with rate‐less fountain raptor code

Due to the importance of both rate-less digital fountain codes and Multi-Input Multi-Output (MIMO) schemes in Fifth and Sixth Generation (5G/6G) wireless networks, this paper investigates enhancing the performance of MIMO-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) based system by employing the rate-less fountain codes. The rate-less codes, such as the Tornado codes and Raptor codes, have a flexible code rate that differs from the classical channel coding schemes. This paper uses Raptor code to study the MIMO system's performance with the rate-less codes over different wireless communications channels. The proposed MIMO-fountain code-based system has been tested over the various wireless communication channel conditions concerning the varied Frequency Doppler (FD). In the proposed rate-less wireless communication system, multiple interleaving techniques and equalizers are used to improve its performance. Computer-based simulation experiments are assigned to evaluate the MIMO-fountain code system using the different data transmission scenarios. Simulation results confirm the superiority of the presented wireless rate-less system compared to fixed-rate wireless systems in terms of Bit Error Rate (BER) and throughput.

Massive Connectivity Over MIMO-OFDM: Joint Activity Detection and Channel Estimation With Frequency Selectivity Compensation

In this paper, we study how to efficiently and reliably detect active devices and estimate their channels in a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) based grant-free non-orthogonal multiple access (NOMA) system to enable massive machine-type communications (mMTC). First, by exploiting the correlation of the channel frequency responses in narrow-band mMTC, we propose a block-wise linear channel model. Specifically, the continuous OFDM subcarriers in the narrow-band are divided into several sub-blocks and a linear function with only two variables (mean and slope) is used to approximate the frequency-selective channel in each sub-block. This significantly reduces the number of variables to be determined in channel estimation and the sub-block number can be adjusted to reliably compensate the channel frequency-selectivity. Second, we formulate the joint active device detection and channel estimation in the block-wise linear system as a Bayesian inference problem. By exploiting the block-sparsity of the channel matrix, we develop an efficient turbo message passing (Turbo-MP) algorithm to resolve the Bayesian inference problem with near-linear complexity. We further incorporate machine learning approaches into Turbo-MP to learn unknown prior parameters. Numerical results demonstrate the superior performance of the proposed algorithm over state-of-the-art algorithms.

Deep Neural Network Based Channel Estimation for Massive MIMO-OFDM Systems With Imperfect Channel State Information

Massive multi-input multi-output (MIMO) has aroused extensive interest in communication industry, as it can effectively increase communication system capacity and reduce transmit power. For massive MIMO-orthogonal frequency division multiplexing (OFDM) systems, traditional pilot assisted channel estimation methods need to obtain the channel response of data subcarriers by interpolation. However, when the channel response of continuous data subcarriers changes dramatically, the accuracy of channel estimation will be decreased. In this article, therefore, a novel channel prediction framework that integrates the imperfect channel estimation of the massive MIMO-OFDM into the deep neural network scheme is proposed, thus replacing the interpolation method and achieving higher accuracy of channel estimation. From algorithm analysis and simulation results, the performance of proposed deep neural network channel prediction method based on the imperfect channel estimation outperforms the conventional least square method based on interpolation. Furthermore, compared with minimum mean square error, there are no need for channel autocorrelation matrix, prior statistics of noise variance, and the complex matrix inversion operations. In summary, deep neural network is an efficient method for the imperfect channel estimation in massive MIMO-OFDM systems.

DATA TRANSMISSION IN MIMO-OFDM TECHNOLOGIES FOR CURRENT AND FUTURE WIRELESS COMMUNICATION SYSTEMS: FROM THEORY TO PRACTICE

This paper presents a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) communication system with space-time and space-Frequency block coded (STBC/SFBC) of present generation downlink channel. Space-time block coding (STBC) and space-frequency block coding are transmit diversity schemes used in a wireless communication system to mitigate the effect of channel fading. The system is aimed to reduce the error rate (BER) by mitigating the effect of multipath fading which increase the quality of the system. In the proposed system, 2 transmit and 2 receive antennas are used, to transmit modulated symbols of block N across an OFDM channel, using SFBC Alamouti’s based encoding algorithm (SFBC-OFDM) derives from the basic criteria for space-time block codes (STBC). The signals from the first and second antennas are encoded and transmitted simultaneously in space and frequency over frequency-selective Rayleigh fading channel using two adjacent carriers. The work examines the SFBC and spatial diversity benefits realized based on the BER performance. The result is simulated effectively using MATLAB application software and the BER performance is compared with the conventional STBC-OFDM. It is shown that both STBC and SFBC techniques achieve similar performance with a diversity gain of four (4) over a slow fading channel with 2 antennas at both ends of the link under simple processing across the transmit and receive antennas.

Impact of Subcarrier Allocation and User Mobility on the Uplink Performance of Multiuser Massive MIMO-OFDM Systems

This paper considers the uplink performance of a multi-user massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system with mobile users. Mobility brings two major problems to a MIMO-OFDM system: inter carrier interference (ICI) and channel aging. In practice, it is common to allot multiple contiguous subcarriers to a user as well as schedule multiple users on each subcarrier. Motivated by this, we consider a general subcarrier allocation scheme and derive expressions for the ICI power, uplink signal to interference plus noise ratio and the achievable uplink sum-rate, taking into account the ICI and the multi-user interference due to channel aging. We show that the system incurs a near-constant ICI power that depends linearly on the ratio of the number of users per subcarrier to the number of subcarriers per user, nearly independently of how the UEs distribute their power across the subcarriers. Further, we exploit the coherence bandwidth of the channel to reduce the length of the pilot sequences required for uplink channel estimation. We consider both zero-forcing and maximal-ratio combining at the receiver and compare the respective sum-rate performances. In either case, the proposed subcarrier allocation scheme leads to significantly higher sum-rates compared to previous work, owing to the near-constant ICI property as well as the reduced pilot overhead.

Model-Driven Deep Learning-Based MIMO-OFDM Detector: Design, Simulation, and Experimental Results

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM), a fundamental transmission scheme, promises high throughput and robustness against multipath fading. However, these benefits rely on the efficient detection strategy at the receiver and come at the expense of the extra bandwidth consumed by the cyclic prefix (CP). We use the iterative orthogonal approximate message passing (OAMP) algorithm in this paper as the prototype of the detector because of its remarkable potential for interference suppression. However, OAMP is computationally expensive for the matrix inversion per iteration. We replace the matrix inversion with the conjugate gradient (CG) method to reduce the complexity of OAMP. We further unfold the CG-based OAMP algorithm into a network and tune the critical parameters through deep learning (DL) to enhance detection performance. Simulation results and complexity analysis show that the proposed scheme has significant gain over other iterative detection methods and exhibits comparable performance to the state-of-the-art DL-based detector at a reduced computational cost. Furthermore, we design a highly efficient CP-free MIMO-OFDM receiver architecture to remove the CP overhead. This architecture first eliminates the intersymbol interference by buffering the previously recovered data and then detects the signal using the proposed detector. Numerical experiments demonstrate that the designed receiver offers a higher spectral efficiency than traditional receivers. Finally, over-the-air tests verify the effectiveness and robustness of the proposed scheme in realistic environments.

PAPR Reduction and Bit Error Rate Evaluation in OFDM System using Hybrid Techniques

Orthogonal frequency division multiplexing (OFDM) has become an important component of waveform generation in wideband transmission. it's a superior technology for the high-speed rate of wired and wireless communication systems. Currently, multiple-input multiple-output orthogonal frequency division multiplexing (MIMO OFDM) systems are crucial wireless communication systems like 4G and 5G networks & tactical communication. The OFDM has many advantages over other techniques like its high capacity and immunity against multipath fading channels. However, one amongst the foremost drawbacks of the OFDM system is that the high-peak-to-average power ratio (PAPR) that leads the system to provide in-band distortion and out-of-band radiation and reduces its efficiency. This problem increases with a rise within the number of transmit antennas. Therefore, it's highly desirable to cut back the PAPR of an OFDM signal. For this, numerous techniques are proposed to beat the PAPR problem like i) Selective mapping (SLM) ii) Partial transmit sequence (PTS), iii) Clipping, iv) Clipping and filtering. All of those are reduced the PAPR by generating alternative subcarrier vector that are statistically independent OFDM symbols for a given data frame and transmitting the symbol with rock bottom peak power. During this paper we also proposed, some hybrid techniques. The hybrid techniques are the technique of clipping is employed in conjunction with SLM and PTS to cut back computational complexity. And also the combination of SLMPTS to scale back PAPR. Simulations are acquainted with analyze the efficiency of the techniques used which signifies OFDM to be providing much better PAPR reduction and a way better Bit Error Rate (BER). it's shown in simulation results that the proposed scheme performs well reducing PAPR. But the proposed scheme is more complex than the prevailing techniques.

Independent vector analysis based blind interference reduction and signal recovery for MIMO IoT green communications

In application to time convolutive mixing model or frequency domain blind separation model for wireless receiving applications, frequency domain independent component analysis (FDICA) has been a very popular method but with adverse random permutation ambiguity influence. In order to solve this inherent problem in FDICA assisted multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) based the Internet of Thing (IoT) systems, this paper proposes an new detection mechanism, named independent vector analysis (IVA), for realizing blind adaptive signal recovery in MIMO IoT green communication to reduce inter-carrier interference (ICI) and multiple access interference (MAI). IVA jointly implements separation work for different frequency bin data while the FDICA deals with it separately. In IVA, the dependencies of frequency bins can be exploited in mitigating the random permutation problem. In addition, multivariate prior distributions are employed to preserve the inter-frequency dependencies for individual sources, which can result in separation performance enhancement. Simulation results and analysis corroborate the effectiveness of the proposed method.

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.

MIMO-OFDM Modulation Classification Using Three-Dimensional Convolutional Network

Automatic modulation classification (AMC) plays a vital role in cognitive radio to improve spectrum utilization efficiency, however, most of the existing works have focused on single-carrier communications in single-input single-output systems. In this paper, we propose an efficient AMC method for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) communication systems with the assumption of unknown frequency-selective fading channels and signal-to-noise ratio. At the receiver, the complex envelope samples of a burst signal acquired by multiple antennas are decomposed into in-phase and quadrature samples, which are then structured into a high-dimensional data array. To learn the modulation patterns from received signals, we develop a deep network, namely three-dimensional MIMO-OFDM convolutional neural network (MONet). With cuboidal convolution filters, the proposed MONet allows the network to capture underlying features as intra- and inter-antenna correlations at multi-scale signal representations. Relying on simulations, MONet achieves the classification accuracy of over 95% at 0 dB SNR under various channel impairments and shows the robustness with different MIMO antenna configurations.

A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO

Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels wherein optimization-based or greedy algorithms were employed to derive hybrid beamformers. In this paper, we introduce a deep learning (DL) approach for channel estimation and hybrid beamforming for frequency-selective, wideband mm-Wave systems. In particular, we consider a massive MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and propose three different DL frameworks comprising convolutional neural networks (CNNs), which accept the raw data of received signal as input and yield channel estimates and the hybrid beamformers at the output. We also introduce both offline and online prediction schemes. Numerical experiments demonstrate that, compared to the current state-of-the-art optimization and DL methods, our approach provides higher spectral efficiency, lesser computational cost and fewer number of pilot signals, and higher tolerance against the deviations in the received pilot data, corrupted channel matrix, and propagation environment.

Variable pilot assisted channel estimation in MIMO-OFDM system with STBC and different modulation modes

High speed and low latency have become the development trend of mobile communication network. Orthogonal frequency division multiplexing (OFDM) technology has become the key technology of mobile communication network due to its efficient transmission method. Space time block coding (STBC) introduces time and space correlation in the signals sent by different antennas, so that diversity gains and coding gains are obtained for the multiple input multiple output-OFDM (MIMO-OFDM) receiver without sacrificing bandwidth. STBC is a signal coding technology that combines higher transmission rates and it is a combination of space and time transmission signals. STBC uses the spatial diversity of multiple transmitters and multiple receivers in space to improve the capacity and information rate of the OFDM system. It proceeds parallel multiplex transmission in the transmission channel, which effectively improves spectrum utilization. For obtaining diversity gains and spectrum efficiency, this paper introduces a variable pilot assisted channel estimation method with different modulation modes. In this paper, the new type pilot sequences with different modulation orders are proposed for improving spectrum efficiency. The proposed variable pilot assisted channel estimation method in MIMO-OFDM systems is adopted with different low and high order modulations. Simulation results show that the proposed variable pilot assisted channel estimation method has 1–2 dB signal-to-noise ratio (SNR) gains for bit error rate (BER) performance of MIMO-OFDM systems.