Multiple-input Multiple-output Orthogonal Frequency Division Research Articles

Deep neural network-based detection of index modulated MIMO-OFDM

Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) has been introduced as a new transmission method for 5G and beyond communications. On the other hand, deep neural networks (DNNs) have started to be effective in many fields, including wireless communication, due to their advantages such as low complexity, high performance, low processing times, etc. Since the optimum decoding for MIMO-OFDM-IM grows exponentially with higher modulation orders and the number of transmit and receive antennas, DNN-based decoding will be a potential choice for the next generation receiver architecture. In this work, a novel fully connected DNN based MIMO-OFDM-IM to jointly detect the transmitted symbols from each antenna is proposed and its performance is analyzed. As seen from the simulation results, the proposed DNN-based detector shows a close bit error rate performance to optimum detection with lower computational complexity.

Power allocation scheme in MIMO-OFDM UWOC system with varying receiver spacing channel gain analysis

Abstract For underwater blue–green light multiple-input multiple-output orthogonal frequency division multiplexing modulation system (MIMO-OFDM), the underwater MIMO channel gain matrix corresponding to the change of receiver spacing was analyzed and derived, and a proportional power allocation scheme combined with singular value decomposition (SVD) precoding was proposed, in order to eliminate inter-channel interferences of the MIMO system. The simulation results show that when the transmitter spacing is fixed, and the receivers adopt the same spacing as the transmitters, which is difficult to achieve in the complex underwater environment, the system bit error rate (BER) performance under the proposed proportional power allocation scheme is the same as that of the average power allocation. When the receiver spacing increases, the system BER under the proposed scheme can ensure better overall performance compared with that of the traditional average allocation scheme.

Analysis of the Impact of Detection Threshold Adjustments and Noise Uncertainty on Energy Detection Performance in MIMO-OFDM Cognitive Radio Systems.

Due to the capability of the effective usage of the radio frequency spectrum, a concept known as cognitive radio has undergone a broad exploitation in real implementations. Spectrum sensing as a core function of the cognitive radio enables secondary users to monitor the frequency band of primary users and its exploitation in periods of availability. In this work, the efficiency of spectrum sensing performed with the energy detection method realized through the square-law combining of the received signals at secondary users has been analyzed. Performance evaluation of the energy detection method was done for the wireless system in which signal transmission is based on Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing. Although such transmission brings different advantages to wireless communication systems, the impact of noise variations known as noise uncertainty and the inability of selecting an optimal signal level threshold for deciding upon the presence of the primary user signal can compromise the sensing precision of the energy detection method. Since the energy detection may be enhanced by dynamic detection threshold adjustments, this manuscript analyses the influence of detection threshold adjustments and noise uncertainty on the performance of the energy detection spectrum sensing method in single-cell cognitive radio systems. For the evaluation of an energy detection method based on the square-law combining technique, the mathematical expressions of the main performance parameters used for the assessment of spectrum sensing efficiency have been derived. The developed expressions were further assessed by executing the algorithm that enabled the simulation of the energy detection method based on the square-law combining technique in Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing cognitive radio systems. The obtained simulation results provide insights into how different levels of detection threshold adjustments and noise uncertainty affect the probability of detection of primary user signals. It is shown that higher signal-to-noise-ratios, the transmitting powers of primary user, the number of primary user transmitting and the secondary user receiving antennas, the number of sampling points and the false alarm probabilities improve detection probability. The presented analyses establish the basis for understanding the energy detection operation through the possibility of exploiting the different combinations of operating parameters which can contribute to the improvement of spectrum sensing efficiency of the energy detection method.

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).

Machine Learning-Based Fingerprint Positioning for Massive MIMO Systems

In this paper, we investigate a user terminal (UT) fingerprint positioning problem in massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing systems under non-line-of-sight scenario. Exploiting the advantages of a large-scale array and wide bandwidth, we introduce a spatial refined beam-based channel model and advocate a beam domain channel amplitude matrix as location-related fingerprint embedding abundant and stationary multi-path information, including amplitude, angle of arrival and delay of arrival, which are favorable to positioning. Taking the fingerprints as the input, we propose a novel machine learning enabled positioning method to localize the 2-dimension position of UTs. Specifically, we propose two fully-connected neural networks (NNs) to complete the task. The first NN is used to classify the position coordinates of the UTs, and the second NN functions as the regression module to estimate the UTs’ position coordinates. The simulation results present that the first NN can classify the UTs efficiently and the second NN can reliablely regress the UTs’ location. With the increasing of beams, the positioning performance of the proposed methods can achieve superior accuracy compared with conventional approaches.

Carrier frequency offset estimation for MIMO single-carrier FDMA system in wireless communication

Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency-Division Multiplexing (OFDM) systems and Single-Carrier Frequency-Division Multiple Access (SC-FDMA) are so susceptible to carrier frequency offset (CFO) between transceivers. In this paper, a novel sequence called CAZBAR is proposed. The proposed CAZBAR sequence is based on constant amplitude zero autocorrelation (CAZAC) sequence and barker code. CAZBAR sequence shows better autocorrelation with higher peak at zero shift without ripples, which facilitates estimating carrier frequency offset in communication systems compared to other compensation methods in terms of mean square error (MSE). By applying CAZBAR sequence in MIMO SC-FDMA system 2 × 2 and 4×4, the bit error rate (BER) performance has improved significantly. At a BER=10-2, a SNR reduction of 11 dB is achieved for 2 × 2 system and 7 dB reduction for 4 × 4 system compared to other sequences. Which improves the performance of the whole system MIMO SC-FDMA. The CAZBAR sequence can be applied in 5G techniques, MIMO-OFDM, and MIMO SC-FDMA systems.

Iterative Interference Cancellation for Multi-Carrier Modulation in MIMO-DWT Downlink Transmission

The Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) scheme represents the dominant radio interface for broadband multicarrier communication systems. However, with insufficient Cyclic Prefixes (CP), Inter-Symbol Interference (ISI) and Inter-Carrier Interference (ICI) occur due to the time-varying multipath channel. This means that the performance of the system will be degraded. In this paper, we investigate the interference problem for a MIMO Discrete Wavelet Transform (MIMO-DWT) system under the effect of the downlink LTE channel. A Low-Density Parity-Check (LDPC) decoder is used to estimate the decoded signal. The proposed iterative algorithm uses the estimated decoded signal to compute the components required for ICI/ISI interference reduction. In this paper, Iterative Interference Cancellation (IIC) is employed to mitigate the effects of interference that contaminates the received signal due to multiple antenna transmission and a multipath channel. An equalizer with minimum mean square error is considered. We compare the performance of our proposed algorithm with the traditional MIMO-OFDM scheme in terms of bit error probability under insufficient CP. Simulation results verify that significant improvements are achieved by using IIC and MIMO-IIC for both systems.

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.

Power Allocation and Parameter Estimation for Multipath-Based 5G Positioning

We consider a single-anchor multiple-input multiple-output orthogonal frequency-division multiplexing system with imperfectly synchronized transmitter (Tx) and receiver (Rx) clocks, where the Rx estimates its position based on the received reference signals. The Tx, having (imperfect) prior knowledge about the Rx location and the surrounding geometry, transmits reference signals based on a set of fixed beams. We develop strategies for the power allocation among the beams aiming to minimize the expected Cramér-Rao lower bound for Rx positioning. Additional constraints on the design are included to make the optimized power allocation robust to uncertainty on the line-of-sight (LOS) path direction. Furthermore, the effect of clock asynchronism on the proposed allocation strategies is studied. Our evaluation results show that, for non-negligible synchronization error, it is optimal to allocate a large fraction of the available power for the illumination of the non-LOS (NLOS) paths, which help resolve the clock offset. In addition, the complexity reduction achieved by our proposed suboptimal approach incurs only a small performance degradation. We also propose an off-grid compressed sensing-based position estimation algorithm, which exploits the information on the clock offset provided by NLOS paths, and show that it is asymptotically efficient.

Algorithm for Evaluating Energy Detection Spectrum Sensing Performance of Cognitive Radio MIMO-OFDM Systems.

Cognitive radio technology enables spectrum sensing (SS), which allows the secondary user (SU) to access vacant frequency bands in the periods when the primary user (PU) is not active. Due to its minute implementation complexity, the SS approach based on energy detection (ED) of the PU signal has been analyzed in this paper. Analyses were performed for detecting PU signals by the SU in communication systems exploiting multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) transmission technology. To perform the analyses, a new algorithm for simulating the ED process based on a square-law combining (SLC) technique was developed. The main contribution of the proposed algorithm is enabling comprehensive simulation analyses of ED performance based on the SLC method for versatile combinations of operating parameter characteristics for different working environments of MIMO-OFDM systems. The influence of a false alarm on the detection probability of PU signals impacted by operating parameters such as the signal-to-noise ratios, the number of samples, the PU transmit powers, the modulation types and the number of the PU transmit and SU receive branches of the MIMO-OFDM systems have been analyzed in the paper. Simulation analyses are performed by running the proposed algorithm, which enables precise selection of and variation in the operating parameters, the level of noise uncertainty and the detection threshold in different simulation scenarios. The presented analysis of the obtained simulation results indicates how the considered operating parameters impact the ED efficiency of symmetric and asymmetric MIMO-OFDM systems.

Space-Frequency Coded Quadrature Index Modulation With Linear Maximum Likelihood Detection

This paper focuses on the issue of achieving transmit diversity for the multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system working with quadrature index modulation (QIM). Inspired by the ideas of quadrature spatial modulation (QSM) and quadrature space-frequency index modulation (QSF-IM), parts of the space-frequency resource units in each resource block (RB) of the MIMO-OFDM system are activated twice independently to send the in-phase and quadrature components of a space-frequency code, respectively, to achieve the second order transmit diversity. Thus the proposed scheme is called space-frequency coded QIM (SFC-QIM). The SFC-QIM scheme allows for a linear-complexity maximum likelihood (ML) receiver, which benefits from the orthogonality between the real and imaginary parts of the symbols in the SFC codewords. Owing to obtaining higher diversity order, simulation results of bit error rate (BER) performance demonstrate that the new SFC-QIM scheme outperforms some existing IM schemes in both independent and identically distributed (i.i.d.) and correlated Rayleigh fading channels.

Millimeter Wave MIMO-OFDM With Index Modulation: A Pareto Paradigm on Spectral- Energy Efficiency Trade-Off

Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) has recently received increased attention, due to the potential advantage to balance the trade-off between spectral efficiency (SE) and energy efficiency (EE). In this paper, we investigate the application of MIMO-OFDM-IM to millimeter wave (mmWave) communication systems, where a hybrid analogy-digital (HAD) beamforming architecture is employed. Taking advantage of the Pareto-optimal beam design, we propose a feasible solution to approximately achieve a globally Pareto-optimal trade-off between SE and EE, and the collision constraints of the multi-objective optimization problem (MOP) can be solved efficiently. Correspondingly, the MOP of SE-EE trade-off can be converted into a feasible solution for energy-efficient resource usage, by finding the Pareto-optimal set (POS) towards the Pareto front. This combinatorial-oriented resource allocation approach on the SE-EE relation considers the optimal beam design and power control strategies for downlink multi-user mmWave transmission. To ease the system performance evaluation, we adopt the Poisson point process (PPP) to model the mobile data traffic, and the evolutionary algorithm is applied to speed up the search efficiency of the Pareto front. Compared with benchmarks, the experimental results collected from extensive simulations demonstrate that the proposed optimization approach is vastly superior to existing algorithms.

Wideband Precoding for MIMO-OFDM Systems With Per-Antenna Power Constraints

This letter describes a wideband precoding design for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems under per-antenna power constraints, wherein all subcarriers share one wideband precoder. With new insight into the objective function, we first reformulate the precoding designing as a stochastic optimization problem. Then, an algorithm based on the free probability theory is proposed to solve the stochastic optimization problem; consequently, a wideband precoder is obtained, with a computational complexity nearly equivalent to that required for a single narrowband precoder. The effectiveness of this wideband precoder is validated through simulations.

Robust Decentralized and Distributed Estimation of a Correlated Parameter Vector in MIMO-OFDM Wireless Sensor Networks

An optimal precoder design is conceived for the decentralized estimation of an unknown spatially as well as temporally correlated parameter vector in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) based wireless sensor network (WSN). Furthermore, exploiting the temporal correlation present in the parameter vector, a rate-distortion theory based framework is developed for the optimal quantization of the sensor observations so that the resultant distortion is minimized for a given bit-budget. Subsequently, optimal precoders are also developed that minimize the sum-MSE (SMSE) for the scenario of transmitting quantized observations. In order to reduce the computational complexity of the decentralized framework, distributed precoder design algorithms are also developed which design precoders using the consensus based alternating direction method of multipliers (ADMM), wherein each SN determines its precoders without any central coordination by the fusion center. Finally, new robust MIMO precoder designs are proposed for practical scenarios operating in the face of channel state information (CSI) uncertainty. Our simulation results demonstrate the improved performance of the proposed schemes and corroborate our analytical formulations.

Multilevel Redundant Discrete Wavelet Transform (ML-RDWT) and Optimal Red Deer Algorithm (ORDA) Centred Approach to Mitigate the Effect of ICI, BER and CIR in a MIMO-OFDM System

Nowadays, there is a great demand for ultra-high data rate (UHDR) transmission on most 5th generation wireless networks. In this concern, the multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) scheme is used on a large scale to achieve UHDR transmission with reduced inter-symbol interference (ISI) and inter-carrier interference (ICI). Discrete wavelet transform-based OFDM (DWT-OFDM) provides better orthogonality due to presence of orthogonal wavelets, which mitigates the effects caused by ISI and ICI. Also, it has extended bandwidth than the traditional OFDM systems. But a major drawback in this system is that it suffers from down sampling. The down-sampling effect reduces the actual size of the input bit streams. As a result, the system performance is degraded. For solving this problem, a multilevel redundant discrete wavelet transform (ML-RDWT) is used instead of DWT to achieve improved spectral performance. Here, complex down-sampling operation is eliminated. From the simulation outcomes, it is clearly viewed that effects caused by ICI, ISI and BER are mitigated by improving the performance of CIR. The proposed method employs optimal red deer algorithm (ORDA) to locate the optimized weights for the ICI cancellation system. This algorithm enhances the spectral efficiency by achieving high CIR with reduced BER, ISI and ICI. The BER in the proposed MIMO-ML-RDWT-OFDM-ORDA method is 68%, 76%, 38% and 75%, which is very low when compared to the BER in the existing techniques like MIMO-DWT-OFDM-RDA, MIMO-RNS-OFDM-PNMA, MIMO-OFDM-BMA and MIMO-OFDM-ICIMA. The ISI in the proposed method is 94%, 91%, 95% low when compared to the ISI in the existing techniques. The ICI in the proposed work is 71%, 57%, 73% and 86% low when compared to the ICI in the existing techniques. Therefore, the general performance of the proposed MIMO-ML-RDWT-OFDM-ORDA method is improved in an efficient way with less complexity, error rate and processing delay.

Variational-autoencoder signal detection for MIMO-OFDM-IM

This paper proposes a deep learning (DL) based method for signal detection in multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) systems using variational autoencoder (VAE). A framework of variational optimization is constructed and the learning deep neural network structure with fully connected layers is also designed. The network is trained offline by exploiting the dataset generated by simulations, and then the well-trained model is applied for signal detection in MIMO-OFDM-IM system. The derivation on the loss function for network training is presented from the perspective of variational inference. Also, a regularization parameter is introduced in order to achieve better performance. Numerical results are provided and the performance demonstrates the merits of the proposed method by comparison with the traditional detection algorithms. Finally, complexity measured by the runtime of the program utilizing existing detectors and the proposed method are also presented. The bit error rate (BER) performance which is close to the existing hand-crafted detectors can be obtained with lower runtime using the proposed DL network.

Generalized Quadrature Space-Frequency Index Modulation for MIMO-OFDM Systems

In this paper, motivated by the framework of diversity-achieving quadrature spatial modulation (DA-QSM), we propose a generalized quadrature space-frequency index modulation (GQSF-IM) transmission scheme for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. The dispersion matrices potentially transmit some combination of each symbol from each antenna at every subcarrier, and their structures lead to desirable coding properties. By setting two well-designed dispersion-matrix sets for both the in-phase (I-) and quadrature (Q-) dimensions, each resource block (RB) of MIMO-OFDM independently selects part of dispersion matrices from each set to modulate the real and imaginary parts of complex symbols, respectively. Thus a simple and systematic method which easily generates dispersion-matrix set with large size is presented. It can be verified that the proposed GQSF-IM scheme subsumes the existing QSF-IM scheme as its special case. Moreover, it can be proved that the GQSF-IM scheme automatically achieves the second order transmit diversity without requiring any parameter or matrix optimization. Owing to obtaining higher transmit diversity order, simulation results of bit error rate (BER) performance show that GQSF-IM outperforms QSF-IM in different channels such as independent and identically distributed (i.i.d.), correlated Rayleigh fading, and Rician channels. Furthermore, GQSF-IM can achieve obvious better BER performance when it combines with interleaving for more diversity gains.

Sparse, Group-Sparse, and Online Bayesian Learning Aided Channel Estimation for Doubly-Selective mmWave Hybrid MIMO OFDM Systems

Sparse, group-sparse and online channel estimation is conceived for millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We exploit the angular sparsity of the mmWave channel impulse response (CIR) to achieve improved estimation performance. First a sparse Bayesian learning (SBL)-based technique is developed for the estimation of each individual subcarrier's quasi-static channel, which leads to an improved performance versus complexity trade-off in comparison to conventional channel estimation. Then a novel group-sparse Bayesian learning (G-SBL) scheme is conceived for reducing the channel estimation mean square error (MSE). The salient aspect of our G-SBL technique is that it exploits the frequency-domain (FD) correlation of the channel's frequency response (CFR), while transmitting pilots on only a few subcarriers, thus it has a reduced pilot overhead. A low complexity (LC) version of G-SBL, termed LCG-SBL, is also developed that reduces the computational cost of the G-SBL significantly. Subsequently, an online G-SBL (O-SBL) variant is designed for the estimation of doubly-selective mmWave MIMO OFDM channels, which has low processing delay and exploits temporal correlation as well. This is followed by the design of a hybrid transmit precoder and receive combiner, which can operate directly on the estimated beamspace domain CFRs, together with a limited channel state information (CSI) feedback. Our simulation results confirms the accuracy of the analysis.

Magnitude and Wrap-Phase OFDM for MIMO Visible Light Communication Systems

Generalized light emitting diode (LED) index modulation (GLIM) has been proposed as an effective multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) scheme for visible light communications (VLC) systems. In this letter, we propose a magnitude and wrap-phase OFDM (MW-OFDM) scheme for MIMO VLC systems. The proposed MW-OFDM system relies on the conversion of complex signals into polar form with the magnitudes and wrap-phases to decrease the restriction on the number of LEDs compared to the conventional GLIM-OFDM. Furthermore, maximum likelihood and best linear unbiased estimators of the proposed scheme are derived. The proposed MW-OFDM scheme improves the average bit error rate and provides a significant reduction in the decoding complexity, compared to the GLIM-OFDM. Moreover, a half number of LEDs are required for the proposed scheme to deliver the same spectral efficiency in a comparison with the GLIM-OFDM. Numerical results are presented over MIMO VLC channels to elaborate the superiority of our proposed scheme.

Learning to Localize: A 3D CNN Approach to User Positioning in Massive MIMO-OFDM Systems

In this paper, we investigate user positioning in massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems where the base station (BS) is equipped with a uniform planar array (UPA). Taking advantage of the UPA geometry and wide bandwidth, we advocate the use of the angle-delay channel power matrix (ADCPM) as a new type of fingerprint to replace the traditional ones. The ADCPM embeds the stable and stationary multipath characteristics, e.g., delay, power, and angles in the vertical and horizontal directions, which are beneficial to positioning. We further exploit the sparsity of the ADCPM to reduce the noise contamination in the ADCPM. Taking ADCPM fingerprints as the inputs, we propose a novel three-dimensional (3D) convolution neural network (CNN) enabled learning method to localize the 3D positions of the mobile terminals (MTs). In particular, such a 3D CNN model consists of a convolution refinement module to refine the elementary feature maps from the ADCPM fingerprints, three extended Inception modules to extract the advanced feature maps, and a regression module to estimate the 3D positions. By intensive simulations, the proposed 3D CNN-enabled positioning method is demonstrated to achieve higher positioning accuracy than the traditional searching-based ones, with reduced computational complexity and storage overhead, and robust to noise contamination.