Multiple-output Orthogonal Frequency-division Multiplexing Systems Research Articles

Manifold Optimization-Based Data Detection Algorithm for Multiple-Input–Multiple-Output Orthogonal Frequency-Division Multiplexing Systems under Time-Varying Channels

Recently, multiple-input–multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems have gained significant attention in the field of wireless communications. The utilization of the Riemannian manifold has become prevalent in MIMO-OFDM systems. However, the existing data detection algorithms for MIMO-OFDM systems are mostly designed for block fading channels. Additionally, these algorithms often suffer from high computational complexity. In this paper, we propose a data detection algorithm on the basis of Riemannian manifold optimization for MIMO-OFDM systems under time-varying channels. The core concept of this algorithm is to optimize the transmitted signals by solving the manifold optimization problem in the case of time-varying channels. In order to reduce the computational complexity of the algorithm, we improve the proposed algorithm by dividing the transmitted signals into multiple subframes for solving the optimization problem separately and using the pilots to maintain the performance of the algorithm. In the simulation, the performance of multiple proposed algorithms and the forced-zero detection algorithm under different parameter settings are compared. The simulation results show that the proposed algorithm demonstrates good bit error rate and computational complexity performances.

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

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

Enhancing channel estimation accuracy in polar-coded MIMO–OFDM systems via CNN with 5G channel models

In this paper, a novel channel estimation technique for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO–OFDM) systems in fifth generation (5G) networks is proposed, which leverages the power of convolutional neural networks (CNNs) along with the application of polar coding. By employing polar encoding at the transmitter and polar decoding at the receiver, the proposed system enhances error correction performance, reliability, and data transmission rate in 5G networks. A novel optimized CNN-based channel estimation technique, named CNN-CENet, has been integrated into the polar-coded MIMO–OFDM receiver. This integration aims to address the interference and noise challenges in 5G systems. The proposed channel estimation method utilizes multiple layers of CNN to accurately characterize the properties of the real channel by incorporating least squares (LS) estimations. Hence, the proposed CNN-CENet provides an accurate estimation of the channel coefficients, enabling efficient signal detection at the receiver. The MATLAB simulation has been conducted using deep learning and 5G toolboxes to obtain results for various channel parameters. These results are then compared with the LS and minimum mean square error (MMSE) methods for performance evaluation. The proposed model surpasses the performance of the LS and MMSE methods, achieving a significant 95.6% reduction in mean square error (MSE) compared to LS and a substantial 59.7% reduction compared to MMSE. Additionally, it delivers notable bit error rate (BER) reductions of 59.8% and 53.2% for LS and MMSE, respectively, in low mobility scenarios, and 57.4% and 48.5% reductions in high mobility scenarios. This improvement leads to enhanced reliability and overall system efficiency.

Hybrid Message Passing Channel Estimation Algorithm for Massive MIMO-OFDM Systems

In this letter, we propose the Bernoulli two-state Gaussian mixture (B-TSGM) probability model to characterize the angle-delay domain (ADD) channel of the massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system. Based on the hybrid message passing (HMP) rule, we design the HMP-B-TSGM channel estimation algorithm under the structured turbo-compressed sensing (STCS) framework. Simulation results show that the considered model better captures the channel characteristics, and the proposed algorithm outperforms the state-of-the-art methods under a wide range of simulation settings while having the same complexity.

Estimation of bit error rate in 2×2 and 4×4 multi-input multi-output-orthogonal frequency division multiplexing systems

<span lang="EN-US">Multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with multiple input antennas and multiple output antennas in dynamic environments face the challenge of channel estimation. To overcome this challenge and to improve the performance and signal-to-noise ratio, in this paper we used the Kalman filter for the correct estimation of the signal in dynamic environments. To obtain the original signal at the receiver end bit error rate factor plays a major role. If the signal to noise ratio is high and the bit error rate is low then signal strength is high, the signal received at the receiver end is almost similar to the </span><em><span lang="EN-US">i<sup>th</sup></span></em><span lang="EN-US"> transmitted signal. The dynamic tracking characteristic of Kalman filter is used to establish a dynamic space-time codeword and a collection of orthogonal pilot sequences to prevent interference among transmissions in this paper. Using the simulation, the Kalman filter method can be compared to the other channel estimation method presented in this paper that can track time-varying channels rapidly.</span>

Implementation of Omar Pigeon Space-Time (OPST) Algorithm to Mitigate the Interference and Peak-to-Average Power Ratio (PAPR) Using RPR Mobile and HST-HM in the 5G

Nowadays, the 5G parameters play an eminent role in the massive Multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system for enriching high signal to noise ratio (SNR). 5G application has emerged in the role of artificial intelligence for involving the reduction of Peak to Average Power Ratio (PAPR) and Bit Error Rate (BER). In MIMO – OFDM system, the high PAPR is a tremendous drawback during the transmission of bit symbol with the number of sub-carriers in the signal. To avoid Intercarrier Interference (ICI) during transmission of the number of sub-carriers, the Omar Pigeon Space-Time (OPST) algorithm is implemented. Then, to overcome high PAPR in the uplink, the Hybrid Space-Time - Hadamard matrix (HST-HM) techniques are proposed and the Bit Error Rate (BER) is decreased abruptly. 5G parameters and specifications are incorporated in this OPST algorithm for avoiding interference during the data bit transmission in the MIMO – OFDM system. Realtors Property Resource (RPR) mobile app is developed for an experimental display of the information that occurs in the real-time uplink MIMO – OFDM system. Thus, the descriptive analysis and simulated results of PAPR, SNR, and BER are executed using the proposed system of HST with HM in the 5G Communication. The RPR mobile executes the outcomes through the OPST algorithm with a better system performance of the MIMO-OFDM system based on the 5G.

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.

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.

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.

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.

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.

Quantized Polar Transmitters for Power Efficient Massive MIMO Systems

This letter investigates the feasibility of the quantized digital polar transmitter architecture in a downlink multi-user massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system, where a low amplitude resolution and a moderate phase resolution are utilized. We derive a lower bound on the average sum-rate achievable with Gaussian signaling inputs and linear precoding by a simple diagonal approximation for the quantization distortion. The analysis and simulations demonstrate that the quantized polar transmitter can enable excellent performance in terms of average sum-rate throughput, symbol error rate (SER), and out-of-band (OOB) emission level, thus providing an attractive option for the traditional Cartesian architecture.

Moving Toward Intelligence: Detecting Symbols on 5G Systems Through Deep Echo State Network

Due to the nonlinear distortion caused by radio-frequency (RF) components in the transceiver, detecting transmitted symbols for multiple-input and multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems can be challenging and resource consuming. In this work, we introduce a Deep Echo State Network (DESN) to serve as the symbol detector for 5G communication networks. Our DESN employs memristive synapses as the dynamic reservoir layer to accelerate the learning algorithm and computation. By cascading multiple dynamic reservoir layers in a hierarchical processing structure, our DESN processes received signal in both spatial and temporal domains. The resulting hybrid memristor-CMOS co-design provides the nonlinear computation required by the reservoir layer while significantly reduces the power consumption. From the benchmark on nonlinear system prediction, our DESN exhibits 10.31 X reduction on the prediction error compared to state-of-the-art neural network designs. Moreover, our DESN records a bit error rate (BER) of $5.76 \times 10^{-2}$ on the high-speed transmitted symbol detection task for MIMO-OFDM systems, yielding 47.73% more precise than state-of-the-art techniques in the literate for 5G communication networks.

Hybrid Turbo Coding PTS with Enhanced Switching Algorithm Employing DE to Carry Out Reduction in PAPR in AUL-Based MIMO-OFDM

High peak-to-average power ratio (PAPR) is considered as a critical issue in modern multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. This underlying drawback can be overcome by employing partial transmit sequence (PTS) method. The complexity of computation by this method is quite high since phase factors are searched comprehensively to optimally reduce PAPR. Enhanced switching differential (ESD) algorithm is incorporated into PTS scheme to solve the problem of high search complexity. In this work, adaptive update lifting (AUL)-based wavelet transform method is proposed that enhanced the system performance by decreasing high side lobes, while enhancing implementation flexibility and sensitivity to time and frequency synchronization. Lastly, a combination of turbo coding and ESD-based PTS scheme is presented for improvement in the reduction in PAPR, bit error rate (BER) and computational complexity in AUL-based MIMO-OFDM system. Turbo decoder allows error correction and phase factor searching function in an efficient way. Analysis of the theoretical performance of the suggested scheme is performed with relevant formulas, and comparative studies with the existing techniques are also presented here. MATLAB simulation outcomes confirm that the suggested hybrid technique is able to reduce PAPR and improve BER to a significant extent with low search computational complexity compared to others.

Semi‐blind MIMO‐OFDM channel estimation using expectation maximisation like techniques

This study deals with semi-blind (SB) channel estimation of multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system using maximum likelihood (ML) technique. For the ML cost optimisation function, new expectation maximisation (EM) algorithms for the channel taps estimation are introduced. Different approximation/simplification approaches are proposed for the algorithm's computational cost reduction. The first approach consists of decomposing the MIMO-OFDM system into parallel multiple-input single-output OFDM systems. The EM algorithm is then applied to estimate the MIMO channel in a parallel way. The second approach takes advantage of the SB context to reduce the EM cost from exponential to linear complexity by reducing the size of the search space. Finally, the last proposed approach uses a parallel interference cancellation technique to decompose the MIMO-OFDM system into several single-input multiple-output OFDM systems. The latter are identified in a parallel scheme and with a reduced complexity. The performance of the proposed approaches are discussed, assessed through numerical experiments and compared with respect to the Cramer Rao Bound and to other EM-based solutions reported in the literature.

Fingerprint-Based Localization for Massive MIMO-OFDM System With Deep Convolutional Neural Networks

Fingerprint technique is a promising enabler for mobile terminals (MTs) localization in rich scattering environments, such as urban areas and indoor corridors. In this paper, we investigate fingerprint based location for massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems with deep convolutional neural networks(DCNNs). By taking full advantage of the high resolution in the angle domain and the delay domain in massive MIMO-OFDM systems, we first propose an efficient angle-delay channel amplitude matrix (ADCAM) fingerprint extraction method. Then a DCNN enabled localization method is proposed, in which the modeling error for fingerprint similarity calculation can be overcome. Both DCNN classification and DCNN regression are considered. For practical implementation, a hierarchical DCNN architecture is proposed. The performance of the proposed DCNN localization method is evaluated via simulation performed in a geometry-based ray tracing signal propagation scene. Numerical results demonstrate that DCNN performs well in achieving high localization accuracy as well as reducing storage overhead and computational complexity.

A Scaled Cyclic Delay Diversity Based Precoding for Coded MIMO-OFDM System

In this paper, we propose a scaled cyclic delay diversity (SCDD) based precoding for coded multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. In order to improve the bit error rate (BER) performance, the proposed precoding reduces the maximum correlation of the precoded channel in frequency domain. In addition, a decision criterion for the scaling factor is suggested by analyzing the bit error probability of repetition codes, convolutional codes, and turbo codes. We present the simulation results for the BER performance to verify the effect of proposed SCDD-based precoding.

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.

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.