Multiple-input Multiple-output Orthogonal Frequency Division Research Articles

Massive MIMO-OFDM Channel Estimation via Distributed Compressed Sensing

Massive multiple-input multiple-output orthogonal frequency division multiplexing (mMIMO-OFDM) channel estimation is considered recently utilizing compressed sensing (CS) based methods. Here, we proposed to use the joint sparsity of mMIMO-OFDM channels using stage-wise forward backward pursuit (StFBP) algorithm. In order to increase the speed of convergence and accuracy of estimation, we proposed to gather multiple good atoms in each step and to exploit common sparsity in the system model, respectively. Furthermore, the backward steps improve the accuracy by omitting bad previously gathered atoms. Simulation results represent the superiority of the proposed StFBP approach rather than the conventional CS-based and non-CS-based approaches.

Compensation of Phase Noise in Uplink Massive MIMO OFDM Systems

Due to the imperfection of practical oscillators, phase noise (PHN) inevitably occurs in the phase of desired signals, and may incur significant degradation in the performance of wireless communications. This paper addresses the PHN compensation in uplink massive multiple-input multiple-output orthogonal frequency division multiplexing systems, where multiple single-antenna users simultaneously communicate with a base station equipped with a large antenna array. The proposed compensation scheme considers a two-stage transmission protocol. In the first stage, training symbols are transmitted, and the channels of multiple users are estimated along with the PHN sequences involved. In the second stage, information-conveying data symbols are transmitted, and the channel estimates previously obtained are used to estimate the data symbols in the presence of unknown PHN. For both stages, the estimation methods are elaborately derived based on the variational expectation maximization methodology. Moreover, some simplifications are conducted to significantly reduce the computational complexity of the proposed scheme. The effectiveness of the proposed PHN compensation scheme is corroborated through extensive simulations.

Performance and ASIC Designs of the K-best LSD and LMMSE Detectors for LTE Downlink

We consider performance comparison and application specific integrated circuit (ASIC) designs of linear minimum mean-square error (LMMSE) and K-best list sphere detector (LSD) algorithms for 4 × 4 and 8 × 8 multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Requirements for higher data rate and lower power consumption set new challenges for implementation. In order to minimize the power consumption, an optimal detector would be able to switch the detection algorithm to suit the channel conditions. The detectors are designed for three different modulation schemes using 28 nm complementary metal oxide semiconductor (CMOS) technology. The communications performance is evaluated in the Third Generation Partnership Project (3GPP) Long-Term Evolution (LTE) system. The impact of transmit precoding is considered. The ASIC designs aim at providing the hardware design aspects to the comparison of detectors. The designs are synthesized and complexity and power consumption results are found. Based on the ASIC synthesis and communications performance results, we show the performance–energy efficiency and performance–complexity comparison. We also present the most suitable scenarios for a low-power detector and show how the transmit precoding impacts the detector selection.

Subspace Channel Identification for Multiuser MIMO STBC-OFDM Systems

This paper investigates the subspace channel identification for multiple-input multiple-output zero-padded orthogonal frequency-division multiplexing systems with space–time block code and virtual carriers (VCs). We first develop a new subspace channel identification model when the VCs exist. Then, two schemes, the forward–backward method and the repetition index scheme (RIS), are developed to generate many times equivalent symbols and then to enhance the performance of the subspace channel identification. With these methods, more accurate subspace channel identification can be obtained by using only a few received blocks. Further, the noise pre-whitening technique is investigated to decrease the nonwhite noise effect caused by the RIS scheme. We also provide complexity analyses of the proposed methods. With the acceptable computational cost, the proposed methods significantly improve the performances of the channel identification and the equalization. Simulations are carried out to demonstrate the effectiveness of the proposed methods.

Hybrid mmWave MIMO-OFDM Channel Estimation Based on the Multi-Band Sparse Structure of Channel

This paper presents a hybrid channel estimator for multiple-input multiple-output orthogonal frequency-division multiplexing mmWave systems based on the sparse nature of the angular channel and leveraging the compressed sensing (CS) tools. The angular support is recovered without any discretization by treating the angular channel in a continuous framework which resolves the limited angular resolution of the discrete sparse channel models used in the previous CS-based channel estimators. The power leakage problem is also addressed by modeling the continuous angular channel as a multi-band signal with the bandwidth of each sub-band being proportional to the amount of power leakage caused by the limited antenna array length. The RF combiner is implemented using a network of low-power switches for antenna subset selection based on a multi-coset sampling pattern. Simulation results validate the low reconstruction error and good performance of the proposed channel estimator.

An efficient scheduling scheme for MIMO-OFDM broadcast networks

A new limited feedback user/antenna scheduling algorithm is proposed for multiple-input multiple-output orthogonal frequency division multiplexing broadcast networks. Feedback of channel state information at users to base station (BS) is inevitable for efficient spectrum utilization by performing the best possible scheduling of users/antennas. The feedback overhead increases with number of users and subcarriers. This enormous feedback information consumes useful bandwidth in uplink channel. Hence, in literature scheduling schemes with clustering of adjacent subcarriers (per-cluster scheduling) are proposed to reduce feedback overhead by considering channel condition of only the center subcarrier of cluster. According to proposed algorithm, channel conditions of all subcarriers are taken into consideration in a novel manner for efficient scheduling. The benefit of this scheme is attaining higher system throughput than the per-cluster scheduling scheme without incurring any additional feedback overhead. Moreover, this scheme achieves higher system throughput for large cluster size and more number of channel taps as compared to per-cluster scheduling scheme. Further throughput is improved by assigning previously unscheduled users randomly to transmit antennas which were not sending data packets to any users previously.

Unequal error protection technique for video streaming over MIMO-OFDM systems.

In this paper, a novel unequal error protection (UEP) technique is proposed for video streaming over multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. Based on the concept of hierarchical quadrature amplitude modulation (HQAM) UEP and multi-antenna UEP, the proposed technique combines the relative protection levels (PLs) of constellation symbols and the differentiated PLs of the transmit antennas. In the proposed technique, standard square quadrature amplitude modulation (QAM) constellations are used instead of HQAM so that the QAM mapper at the transmitter side and the soft decision calculation at the receiver side remain unchanged, but the UEP benefit of HQAM is retained. The superior performance of the proposed technique is explained by the improved connections between data with various priorities and data paths with various PLs. The assumed video compression method is H.264/AVC, which is known to be commercially successful. The IEEE802.16m system is adopted as a data transmission system. With the aid of realistic simulations in strict accordance with the standards of IEEE802.16m systems and H.264/AVC video compression systems, the proposed technique HQAM-multi-antenna UEP is shown to improve the video quality significantly for a given average bit error rate when compared with previous techniques.

Robust Sub-Meter Level Indoor Localization With a Single WiFi Access Point—Regression Versus Classification

Precise indoor localization is an increasingly demanding requirement for various emerging applications, like Virtual/Augmented reality and personalized advertising. Current indoor environments are equipped with pluralities of WiFi access points (APs), whose deployment is expected to be massive in the future enabling highly precise localization approaches. Though the conventional model-based localization schemes have achieved sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is usually significant, especially in the current multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In order to address this issue, model-free localization techniques using deep learning frameworks have been lately proposed, where mainly classification methods were applied. In this paper, instead of classification based mechanism, we propose a logistic regression based scheme with the deep learning framework, combined with Cram\'er-Rao lower bound (CRLB) assisted robust training, which achieves more robust sub-meter level accuracy (0.97m median distance error) in the standard laboratory environment and maintains reasonable online prediction overhead under the single WiFi AP settings.

Channel Prediction for Millimeter Wave MIMO-OFDM Communications in Rapidly Time-Varying Frequency-Selective Fading Channels

The millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems communicate at the extremely high-frequency band. In the extremely high band, the channel state information (CSI) from channel estimation will be outdated quickly, and herein, seriously degrading the system performance. In this paper, we focus on the channel prediction to obtain prior CSI in mmWave MIMO-OFDM systems. First, the mmWave MIMO-OFDM channel is categorized and represented in four domains: the array-frequency, array-time, angle-frequency, as well as angle-time. Then, for the above four domains, we investigate the effects of the channel representations on channel prediction, and analyze the mean-squared error performance as well as the computational complexity of the investigated prediction methods. We derive that the angle-time-domain prediction method achieves higher accuracy than the other three prediction techniques. In addition, we propose an enhanced angle-time-domain channel predictor by exploiting the spatial-time sparsity of the MIMO-OFDM channel to further improve the prediction accuracy. Finally, the simulation results confirm the statistical analysis and verify the superiority of the proposed predictors.

Performance Analysis of Nonlinear SFBC OFDM Systems Over TWDP Fading Channel

In this paper, the practical performance of space-frequency block code (SFBC)-based multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system in the presence of nonlinear power amplifier with imperfect channel state information (CSI) over two-wave with diffuse power (TWDP) channel is analyzed. In the performance analysis, we first introduce the equivalent single-input single-output (SISO) scalar model with nonlinear power amplifier and then the SISO model is used to construct a model for the nonlinear MIMO-OFDM system. Considering the channel estimation error at the receiver, we study the effects of MMSE channel estimation error on the channel capacity. What is more, based on inter modulation product (IMP) analysis, we study the BER performance of nonlinear SFBC MIMO-OFDM systems, considering M-ary phase shift keying (MPSK) and M-ary quadrature amplitude modulation (MQAM). And the effects of channel estimation error on the BER performance are studied. Finally, this paper also derives the closed-form expressions of the system optimal operating point. The numerical results and comparisons are provided for several forms of SFBC MIMO-OFDM. From these analysis results, it is found that the analytical interpretation of the observed behavior in the simulation can be advanced, and the actual analysis of the performance optimization of the nonlinear MIMO-OFDM system is also provided.

Machine Learning Model for Adaptive Modulation of Multi-Stream in MIMO-OFDM System

In this paper, we propose the adaptive modulation (AM) model based on machine learning for a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system. Since 5G new radio (NR) system can be used in a large variety of Internet of Things fields than the conventional systems, the AM scheme that adjusts data rate and reliability according to channel condition can be effectively utilized. The conventional AM schemes are implemented by defining modulation schemes to be used according to each channel condition as table in advance. However, since the rule-based AM cannot analyze the communication performance according to the correlation of channels between antennas and the number of transmission modes is exponentially increased according to the number of available modulation schemes and antennas, it is not suitable for 5G NR system. The learning of the proposed AM model is based on the generated training signal by the extracted features from the received signal and assigned label through the performance analysis for signal detection. We focus on the application of deep neural network for AM and cover the precedence method of principal component analysis to improve the performance of the model. The simulations on the classification of optimal transmission mode for the MIMO-OFDM signal demonstrate that the proposed model supports the adaptability according to the condition of complex MIMO channel.

Sub-System SVD Hybrid Beamforming Design for Millimeter Wave Multi-Carrier Systems

In this paper, the hybrid beamforming design for multiple-input multiple-output orthogonal frequency-division multiplexing systems is studied over the indoor millimeter wave (mm-wave) channels. Under practical hybrid beamforming constraints for multi-carrier systems and the study of sub-systems, we propose the sub-system SVD (SS) hybrid beamforming design. To alleviate the complexity problem of channel estimation, reduction of the amount of the required channel state information (CSI) is also studied based on the statistical properties of indoor mm-wave channels. The SS hybrid beamforming algorithm is then extended to the limited feedback SS (LSS) scheme in which the transmit beamformers are designed based on limited information fed back from the receiver. A fast codeword selection algorithm is also developed to reduce the search complexity of the LSS algorithm. The simulation results show that our SS algorithm achieves the performance level of traditional full-digital beamforming with low complexity and the LSS algorithm has better performance and lower complexity than the previous algorithm, even if the amount of CSI is greatly reduced.

Joint channel estimation and turbo equalisation for MIMO-OFDM-IM systems

ABSTRACTMultiple-Input Multiple-Output (MIMO) communications are frequently employed to improve the transmitted data rate and the link quality. Index modulated orthogonal frequency division multiplexing (OFDM-IM) improves the error rate performance and the peak-to-average power ratio (PAPR) compared with those of the conventional OFDM system due to the activation of partial subcarriers. The MIMO OFDM-IM can transmit additional information bits via the indices of active subcarriers. Also, in order to reduce the transmission power of the OFDM system, the MIMO OFDM-IM scheme can be employed to approach the demanded data transmission rate and the error rate performance. Multiple-input multiple-output orthogonal frequency division multiplexing index modulation (MIMO-OFDM-IM) is an effective multicarrier transmission scheme and can be proposed as an alternative to conventional MIMO-OFDM system. In this scheme, OFDM-IM is combined with MIMO transmission to take the benefits of these two techniques. In this paper, we propose a joint channel estimation and turbo equalisation receiver for MIMO-OFDM-IM system. Some simulation examples are given to demonstrate the effectiveness of the proposed receiver.

Performance Evaluation of SFBC MIMO–OFDM Using FrFT Under TWDP Fading Channel

Multiple-input–multiple-output orthogonal frequency division multiplexing (MIMO–OFDM) is a promising 4G technology to increase data rate and capacity of a system. Further, the space–frequency-block-coding (SFBC) technique in MIMO–OFDM system has increased reliability of system in high mobility channels. Fourier transform in conventional OFDM system can obtain either in time or frequency domain, hence it can be acceptable to reduce inter-carrier-interference while it fails for doubly dispersive channels where both the domain are changing simultaneously. Another mathematical tool named as fractional Fourier transform (FrFT) cancels the effect of rapidly varying time–frequency-distortions by considering new rotating axis \(\left( {u,v} \right)\) at an angle \(\alpha\) from conventional time–frequency plane. In this paper, the performance of SFBC encoded MIMO OFDM system using FrFT in place of FFT has been analyzed under two-wave-diffused-power (TWDP) frequency selective fading channel. In this paper, the novel closed form expression for average bit-error-rate (BER) for coded and un-coded FrFT based MIMO–OFDM system under TWDP has also been presented in this paper. The results carried out using simulation shows that the proposed model achieves BER of 10−2 at 5 dB SNR whereas conventional achieves the same at 14 dB hence, concluded that the novel system performance improved by nearly 9 dB.

Efficient Joint Estimation of Carrier Frequency and Sampling Frequency Offsets for MIMO-OFDM ATSC Systems

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) is appealing for the provision of high spectral efficiency in digital terrestrial broadcast systems. To fully obtain its advantageous features, it is very important to remove the frequency mismatch between the transmitter and the receiver. In this paper, we present the performance analysis of joint estimation of carrier and sampling frequency offsets in the MIMO-OFDM-based advanced television systems committee (ATSC) 3.0 system. In the MIMO-OFDM ATSC system, the continual pilot (CP) is primarily utilized to perform frequency synchronization. To efficiently suppress an unwanted bias introduced by the presence of random-likely located CPs, an optimal pilot subset is selected to form the basis of least squares frequency-offset estimation. A closed-form mean squared error is derived in the context of MIMO-OFDM, considering the multipath fading channel. We show via computer simulations and numerical analysis that the proposed estimation method achieves higher estimation accuracy than the existing estimation method.

A Low Rank Channel Estimation Scheme in Massive Multiple-Input Multiple-Output

Aiming at the problem of computational complexity of channel estimation, this paper proposes a low-complexity block matching pursuit (BMP) algorithm based on antenna grouping and block sparsity for frequency division duplex (FDD) massive Multiple-input Multiple-output orthogonal frequency division multiplexing (OFDM) systems. The system coherence time may be exceeded as a result of time consumption when adopting an orthogonal pilot symbol in the time domain. To solve this problem, an antenna grouping transmission scheme is proposed to reduce the total channel estimation time by sacrificing the observed data length. The simulation results show that the proposed BMP algorithm has good anti-noise performance, and it can accurately determine the non-zero position of the sparse vector and adaptively determine the sparsity of the channel, which effectively translates to improved channel estimation performance and better overall system performance than the existing algorithms.

Analysis of nonlinear crosstalk impairment in MIMO-OFDM systems

An analytical modeling and performance analysis for a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) transceiver exhibiting crosstalk and nonlinear distortion in transmitting chain is presented. For this purpose, a two-dimensional memory polynomial is used to describe the dynamic nonlinear MIMO transmitter. It is shown that the impairment of nonlinear crosstalk can be characterized by two complex attenuations and a nonlinear additive noise. Moreover, an analytical formulation for the symbol error rate (SER) of MIMO-OFDM system considering a frequency selective channel is provided. Experimental results have been employed to extract a realistic model for the nonlinear transmitter in the presence of crosstalk. Also, a model of the linearized transmitter has been calculated after experimentally applying digital predistortion (DPD). The proposed model for the system is validated by the simulation results and a good agreement between the analytical and simulation results is observed. Finally, it is shown that nonlinear crosstalk degrades SER of the system, whereas, the DPD can effectively compensate for the nonlinearity and crosstalk infections, which, in turn, improves SER of the MIMO-OFDM system.

Pilot allocation approaches for channel estimation in MIMO relay networks

Distributed-compressed sensing (DCS)-based channel estimation of multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing for relay communication is considered in this study. Specifically, the pilot allocation is addressed to optimise the channel estimation performance. All the existing works on DCS-based channel estimation pilot placement discussed on the mean square error (MSE) of the estimation probabilistically based on the mutual coherence. On the contrary, the authors try to address the MSE of the estimation directly and optimise the MSE directly and design a pilot pattern to optimise the performance of the estimation. By taking into account the optimisation approach, two combinatorial stochastic algorithms have been presented. These two algorithms utilise cross entropy approach in sequential and parallel form. Simulation results represent that the DCS-based MIMO relay channel estimation using optimised pilot placements will increase the performance from 3 to 12 dB as compared with the conventional least squares (LS) method. Furthermore, parallelism has demonstrated the performance gain. Moreover, the DCS-based MIMO relay channel estimation shows 35 % improvement in spectrum efficiency under the same bit error rate performance over the traditional LS-based channel estimation approach, respectively.

Golay pair aided timing synchronization algorithm for distributed MIMO-OFDM system

In order to solve the problem of inaccurate synchronization for distributed multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system in both multipath and low signal to noise ratio (SNR) channels, a golay pair aided timing synchronization (GPATS) method is proposed in this paper. A new synchronous training sequence based on the golay pair with guard interval is designed in GPATS method. By utilizing the unique properties of the new training sequence, the different timing point and the inter-transmitter delays (ITD) are obtained at the receiver. Simulation results show that, compared with the traditional synchronization approaches, the proposed algorithm can provide high accuracy in detecting different time offsets caused by the distributed transmitters of the MIMO-OFDM system, especially over multipath and low SNR channels.

Channel Estimation Techniques over MIMO-OFDM System

The multiple input multiple output-orthogonal frequency division multiplexing or MIMO-OFDM frameworks are utilized to satisfy the necessities of high information transmission rates, as a result of their exceptional properties, for example, high otherworldly proficiency, high information rate and obstruction towards multipath proliferation. MIMO-OFDM frameworks are finding their applications in the advanced remote correspondence frameworks like IEEE 802.11n, 4G. In this paper, we consider the plan of LTE system and diagram the channel estimation. To achieve high data rate correspondence with flexibility, Long term evolution (LTE) has been displayed. LTE Downlink systems get Symmetrical Recurrence Division Different and MIMO to give upto 100 Mbps (expecting a 2x2 MIMO structure with 20MHz transmission limit). The execution get of MIMO overwhelmingly depends upon the correct estimation of Channel State information (CSI), which is crucial for every trade structure. In this paper, we show assorted channel estimation computations for LTE Downlink structures. This joins channel estimation using Pilot Images and Visually impaired Channel estimation counts. The estimation procedures incorporated the Slightest Square Mistake (LSE) and the Minimum mean square error (MMSE) is being inspected in conclusion broke down.