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

Implementing intelligent‐based sparse channel estimation in Multi User‐Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing system with hybridized optimization algorithm

SummaryThe channel plays a significant role as the multipath in Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) systems. The precoding‐based channel estimation algorithms estimate the channel capacity at the receiver node and they provide efficient channel estimates under worst case channel scenarios. To estimate the channel capacity in the mobile node, semi‐blind scenario is a crucial task in Multi User (MU) MIMO systems. Therefore, a semi blind sparse channel estimation algorithm is demonstrated in the MU‐MIMO system to provide better performance. In the transmitter side, the signal modulation is performed with the help of Quadrature Phase Shift Keying (QPSK), and the modulated signal is given to Pulse Shaping Algorithm (PSA) to reduce the inter‐carrier interference as well as inter symbol interference. At each transmitter, the Inverse Fast Fourier Transforms (IFFT) technique is used for mapping the symbols and then the mapped signal is send to the receiver. In the receiver side, the FFT, pulse reshaping, and QPSK demodulation blocks are serially connected and perform inverse operations of the transmitter in the receiver. Then, the sparse channel is estimated at the receiver by using the semi blind sparse algorithm, where the parameters are optimized by using the Hybridized Drosophila with Virus Colony Search Optimization (HD‐VCSO). The objective function of the developed sparse channel estimation is the reduction of Minimum Mean Square Error (MMSE) in the channel. The performance of the developed sparse channel estimation algorithm is analyzed through different conventional algorithms to validate the effectiveness of the proposed algorithm.

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.

Modeling an Enhanced Modulation Classification Approach using Arithmetic Optimization with Deep Learning for MIMO-OFDM Systems

Abstract In a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) method, multiple antennas can be used on either the transmitter or receiver end to improve the system capacity, data throughput, and robustness. OFDM has been used as the modulation system that divides the data stream into multiple parallel low-rate subcarriers. MIMO enhances the system by utilizing spatial diversity and multiplexing abilities. Modulation classification in the MIMO-OFDM systems describes the process of recognizing the modulation scheme used by the communicated signals in a MIMO-OFDM communication system. This is a vital step in receiver design as it enables proper demodulation of the received signals. In this paper, an Enhanced Modulation Classification Approach using an Arithmetic Optimization Algorithm with Deep Learning (EMCA-AOADL) is developed for MIMO-OFDM systems. The goal of the presented EMCAAOADL technique is to detect and classify different types of modulation signals that exist in MIMO-OFDM systems. To accomplish this, the EMCA-AOADL technique performs a feature extraction process based on the Sevcik Fractal Dimension (SFD). For modulation classification, the EMCA-AOADL technique uses a Convolution Neural Network with Long Short-Term Memory (CNN-LSTM) approach. Finally, the hyperparameter values of the CNN-LSTM algorithm can be chosen by using AOA. To highlight the better recognition result of the EMCA-AOADL approach, a comprehensive range of simulations was performed. The simulation values illustrate the better results of the EMCA-AOADL algorithm.

Non-orthogonal pilot pattern design and sparse channel estimation for underwater acoustic MIMO-OFDM systems

This paper proposes a novel non-orthogonal pilot pattern design method for underwater acoustic (UWA) multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, leveraging compressive sensing (CS) channel estimation. This approach adopts the mutual incoherence property (MIP) and enhances pilot density without increasing pilot overhead. We conducted rigorous mathematical derivation to generate the explicit formula for the non-orthogonal pilot pattern with the mutual coherence of zero. In addition, we estimate multipath delay and amplitude by employing parallel orthogonal matching pursuit (OMP) channel estimation. The co-channel interference (CCI) is eliminated from the systems with the proposed non-orthogonal pilot pattern to estimate the channel coefficients from two transmitters without interference in a single iteration. Simulations and lake trial results demonstrate the superior performance of the proposed non-orthogonal pilot pattern over the orthogonal and existing non-orthogonal pilot patterns in terms of normalized mean square error (NMSE) of channel estimation and bit error rate (BER). The 2×8 UWA MIMO-OFDM system achieves a data rate of 78.11 kbps with a BER of 1.42×10−4 in the lake trial over the communication distance of 773 m.

CSI-based WIFI Device Identification Solution

In recent years, wireless devices such as WIFI routers have become increasingly common in various fields. Rogue access point (RAP) is one of the threats that has persisted in wireless LAN (WLAN) for many years and can cause varying degrees of property damage and privacy leaks. In response to these threats, we propose a new security mechanism, called Rdi, which uses environment-independent features extracted from channel state information (CSI) as fingerprints for device identification. We found that the phase errors between multiple antennas on a single MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) transmitter are not the same. This phase offset is due to the I/Q imbalance and imperfect oscillator of each WiFi network card, and it will not change with factors such as environment and time. Therefore, we inferred and verified that there must be some relationship between the phase errors of multiple groups of antennas, that is, the relative phase error (RPE). In addition, RPE will also vary with different WiFi devices. Compared with some similar fingerprint detection methods in the past, the use of specific connections between group phase errors between antennas can better reveal the different attributes between devices, thereby enhancing the uniqueness of features. Therefore, we believe that RPE can be used as an effective fingerprint to detect RAP attacks. We conduct a large number of experimental demonstrations on this, and innovatively built a multi-modal convolutional neural network (CNN) model to perform efficient classification work for our solution. Experiments on 22 WiFi devices and various scenarios show that the detection rate of Rdi can reach more than 98% in both dynamic and static device states.

The Use of Deep Learning Techniques in OFDM Receivers for 5G NR: A Survey

The Fifth Generation (5G) New Radio (NR) wireless system is the most promising next-generation solution to meet the needs of the increasing demands of mobile market. Orthogonal Frequency Division Multiplexing (OFDM) is the fundamental transmission technique due to the great improvement in spectral efficiency and the flexibility in fast varying environments. The use of multiple-input-multiple-output (MIMO) OFDM and Massive MIMO (Ma-MIMO) OFDM promises the increase in quality, throughput, and capacity of the communication system. The use of Deep Learning techniques in wireless communication networks has shown significant achievements to overcome the rising challenges as compared to the conventional solutions. In this paper, first we give a brief background on OFDM, MIMO-OFDM and Ma-MIMO. Second, we provide a survey on the use of deep learning techniques in OFDM receivers. We categorize the state-of-the-art into Block based and End-to-End deep learning models. Third, we provide a survey on the use of deep learning techniques in MIMO-OFDM and Ma-MIMO-OFDM. We categorize the state-of-the-art as model driven and data driven deep learning models. Finally, we present future research directions.

Performance Analysis of SNR in MIMO OFDM Downlink System

The goal of future wireless communications systems is to provide a wide variety of high quality high-rate services with minimum requirements on spectrum, power consumption and hardware complexity. Toward this end, proper system structures as well as robust system designs are required to meet the challenges in wireless transmissions, such as multipath fading, limited spectrum resource, and interference. Recent research results have unveiled the multiple-input multiple-output (MIMO) system as a potential candidate to play a key role in future wireless. A MIMO wireless system is commonly deployed by using multiple transmit and receive antennas. Early work on multi-antenna systems involves the use of antenna arrays at the receiver to provide spatial diversity against the random destructive effect of fading. There is a recent rich literature on employing multiple antennas at the transmitter and achieving diversity through space-time coding when there is no channel state information at the transmitter (CSIT), or through transmit beam forming when there is perfect CSIT.

Optimized deep learning based hypernet convolution neural network and long short term memory for joint pilot design and channel estimation in MIMO‐OFDM model

AbstractIn multiple input multiple output‐orthogonal frequency division multiplexing (MIMO‐OFDM) systems, efficient pilot design (PD) and channel estimation (CE) greatly influences the reliability and robustness of pilot‐based CE methods. But, accurate estimationof the channel remains a challenge in a high‐mobility environment with non‐linear channel characteristics. Many techniques have been introduced to overcome the pilot contamination and CE problems in the MIMO‐OFDM system to overcome this issue. However, these techniques take multiple paths at the receiver, resulting in delay spread and interference in the communication. Hence, this study presents a novel deep learning (DL) based technique for channel estimation based on channel state information (CSI). A DL technique based on a hyper convolutional neural network (Hyper‐CNN) is introduced for the optimal pilot design. The selection of the pilot position can be made using the tunicate swarm optimization (TSO) approach. Finally, a DL‐based long short‐term memory (LSTM) model is proposed for the CE in the MIMO‐OFDM system. The proposed method is implemented in the MATLAB platform, and the outcome is compared under different metrics like mean square error (MSE) and bit error rate (BER). In the experimental scenario, the proposed method attains the BER of 0.20 and 0.03 for CP (cyclic prefix) and without CP, respectively. In addition, the proposed method attains the MSE of 1.14, 0.99, 1.08 and 0.97 for 8, 16, 48 and 64 pilots, respectively. The performance of a proposed method is compared with the existing method and proves the efficacy of the proposed method.

Efficient and Low-Complex Signal Detection with Iterative Feedback in Wireless MIMO-OFDM Systems.

To solve error propagation and exorbitant computational complexity of signal detection in wireless multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, a low-complex and efficient signal detection with iterative feedback is proposed via a constellation point feedback optimization of minimum mean square error-ordered successive interference cancellation (MMSE-OSIC) to approach the optimal detection. The candidate vectors are formed by selecting the candidate constellation points. Additionally, the vector most approaching received signals is chosen by the maximum likelihood (ML) criterion in formed candidate vectors to reduce the error propagation by previous erroneous decision, thus improving the detection performance. Under a large number of matrix inversion operations in the above iterative MMSE process, effective and fast signal detection is hard to be achieved. Then, a symmetric successive relaxation iterative algorithm is proposed to avoid the complex matrix inversion calculation process. The relaxation factor and initial iteration value are reasonably configured with low computational complexity to achieve good detection close to that of the MMSE with fewer iterations. Simultaneously, the error diffusion and complexity accumulation caused by the successive detection of the subsequent OSIC scheme are also improved. In addition, a method via a parallel coarse and fine detection deals with several layers to both reduce iterations and improve performance. Therefore, the proposed scheme significantly promotes the MIMO-OFDM performance and thus plays an irreplaceable role in the future sixth generation (6G) mobile communications and wireless sensor networks, and so on.

TDLoc: Passive Localization for MIMO-OFDM System via Tensor Decomposition

Passive localization is an important aspect of integrated sensing and communication (ISAC). However, it is challenging to estimate the target position and velocity accurately from the receiving signals due to complex multipath propagation. This paper presents TDLoc, a multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) based passive localization and tracking system, using channel state information (CSI). We first proposed a fourth-order tensor model that contains angle-of-departure (AoD), angle-of-arrival (AoA), time-of-flight (ToF), and Doppler frequency shifts (DFS) information, followed by developing an efficient joint estimation algorithm. We also show that with more than one pair of transceivers, our method can obtain the target velocity from the relativistic Doppler effects, leading to additional DFS-based trajectory information. Moreover, the Cramér-Rao lower bound (CRLB) for multipath parameter estimation and positioning is derived for performance evaluation. Numerical results show that TDLoc outperforms state-of-the-art methods in terms of localization accuracy.

FPGA based modified multi-repeat distribution matcher for probabilistic amplitude shaping

The technique of probabilistic amplitude modulation, based on distribution matching, has garnered considerable attention in recent years as a means to enhance spectral efficiency and diminish the constellation energy of coded modulation. This paper introduces the implementation of Probabilistic Amplitude Modulation (PAS) using a Modified Multi-Repeat Distribution Matcher (MMRDM) on a Field Programmable Gate Array (FPGA). The Modified Multiple Repetition Distribution Matcher (MMRDM) is integrated into a 2×2 Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system, realized through the Xilinx System Generator (XSG). Simple Zero-Forced and Minimum Mean Squared Error (MMSE) equalizers are applied to the receiver for signal detection across the MIMO channel. The system incorporates enhanced security through chaos-based scrambling with 16 and 64 Quadrature Amplitude Modulation (QAM). VHDL code files for this system are generated for the Xilinx Kintex-7 (xc7k325t-3fbg676) for hardware implementation. Performance evaluation includes an assessment of required storage capacity, complexity, and bit error rate (BER). Using Vivado 2017.4, the system is successfully routed with resource utilization, for example, 0.67% Block RAM (BRAM), 68.6% Look-Up Tables (LUT), 83% DSP 48s, and 1.5% registers for 64-QAM uniform modulation. Similarly, for 64-QAM 10 level (shaper output 60 bit) shaped modulation, the resource utilization is 0.67% BRAM, 68.8% LUT, 83% DSP 48s, and 1.6% registers on the specified device. Simulation results demonstrate an improvement in the net shaping gain of approximately (2-4 dB) at 1×10^(-4) for different equalizer cases compared to uniform QAM, along with a notable reduction in required storage capacity and computational complexity.

OFDM-MIMO radar assisted dual-function radar communication system using index modulation

Dual-function radar communication (DFRC) system exploits a hardware platform to achieve both radar and communication functions. Compared to utilizing separate radar and communication systems, DFRC has better performance in spectrum utilization. In this paper, assisted by orthogonal frequency division multiplexing-multiple input multiple output (OFDM-MIMO) radar, we propose a DFRC system architecture combining generalized space shift keying (GSSK) modulation and subcarrier index modulation (SIM) techniques. In this scheme, linear frequency modulation (LFM) signal is explored as the transmission waveform of OFDM-MIMO radar to complete the radar function. Furthermore, the proposed scheme combines GSSK and SIM to carry the communication information to the antenna index and subcarrier index to complete the communication function. Based on this architecture, a novel low complexity frequency detection communication demodulation algorithm is proposed, and the achievable rate communication performance of the system is analyzed. Numerical and simulation results verify the feasibility and effectiveness of the proposed system.

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.

Superposed three-dimensional 64QAM constellation design for MIMO-OFDM visible light communication systems.

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) has been widely used to improve data rate in visible-light communication (VLC) systems. However, the high correlation of channels restricts the application of MIMO. A superposed constellation, combined with MIMO, can achieve considerable diversity gain even in highly correlated MIMO channels. In this study, what we believe to be novel superposed three-dimensional 64-quadrature amplitude modulation (3D-64QAM) constellation schemes are proposed for MIMO-OFDM VLC systems. First, a superposed 3D-64QAM constellation scheme using two transmitted light emitting diodes (LEDs) is proposed, where two independent signals with 3D-4QAM and 3D-16QAM modulation formats are superposed to form a 3D-64QAM signal at the receiver. Then, for what we believe is the first time, we expand the superposed constellation solution to three-LED application scenarios, wherein the 3D-64QAM constellation is superposed by three different 3D-4QAM constellations. Both schemes benefit from a higher minimum Euclidean distance of 3D-64QAM constellation, 1.67 times that of traditional two-dimensional (2D) 64QAM constellation. Meanwhile, the equal-power superposition design of transmitted signals reduces the nonlinearity of LEDs and power competition of photodiodes. Moreover, the three-LED scheme further improves the transmitted power without increasing the risk of nonlinear distortion. To improve spectral efficiency and reduce complexity, we also propose a 3D OFDM modulation scheme. The superposed 3D-64QAM schemes are first studied through theoretical analysis and computer simulation. Then, an experimental demonstration is established to investigate the system performance comprehensively. Experimental results prove that the superposed 3D-64QAM constellation schemes achieve a superior bit error rate (BER) performance than the traditional superposed 2D-64QAM constellation scheme. Compared with two-LED scheme, the three-LED scheme not only obtains a lower BER, but also improves the dynamic range of driving peak-to-peak voltage significantly.

PER Prediction in MIMO-OFDM Systems With Approximate Demodulation: A GMI-Based Method

In multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, the symbols in a packet may undergo different signal-to-interference-plus-noise ratio (SINR) levels, making packet error rate (PER) prediction a challenging task. Effective SINR mapping (ESM) simplifies this problem by mapping the SINRs on different symbols to a single packet-level effective SINR. A novel ESM method, referred to as mean generalized mutual information mapping (MGMIM), is put forward in this paper. The proposed method transforms the SINR of each symbol to the generalized mutual information (GMI) and then derives an effective SINR from the mean GMI measure over different symbols. The derived effective SINR summarizes the effect of all the SINRs on the packet, which would produce the same PER performance under an additive white Gaussian noise (AWGN) channel. Compared to existing techniques, MGMIM does not require MCS-dependent tuning parameters but delivers competitive or even better PER prediction accuracy. More importantly, MGMIM allows reliable PER prediction when the receiver employs approximate demodulation, which boosts its applicability in practice. These features are demonstrated by comparative numerical simulations.

Theoretical Foundation and Design Guideline for Reservoir Computing-Based MIMO-OFDM Symbol Detection

In this paper, we derive a theoretical upper bound on the generalization error of reservoir computing (RC), a special category of recurrent neural networks (RNNs). The specific RC implementation considered in this paper is the echo state network (ESN), and an upper bound on its generalization error is derived via the empirical Rademacher complexity (ERC) approach. While recent work in deriving risk bounds for RC frameworks makes use of a non-standard ERC measure and a direct application of its definition, our work uses the standard ERC measure and tools allowing fair comparison with conventional RNNs. The derived result shows that the generalization error bound obtained for ESNs is tighter than the existing bound for vanilla RNNs, suggesting easier generalization for ESNs. With the ESN applied to symbol detection in MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) systems, we show how the derived generalization error bound can guide underlying system design. Specifically, the derived bound together with the empirically characterized training loss is utilized to identify the optimum reservoir size in neurons for the ESN-based symbol detector. Finally, we corroborate our theoretical findings with results from simulations that employ 3GPP standards-compliant wireless channels, signifying the practical relevance of our work.

The Joint Channel Coding and Pre-Distortion Technique on the USRP-Based MIMO-OFDM System

Modern wireless communication systems use orthogonal frequency division multiplexing (OFDM), a multi-carrier modulation method that resists multipath channels and provides bandwidth efficiency. OFDM is generally used with a Multiple-Input Multiple-Output (MIMO) system to boost diversity gain and channel capacity. MIMO-OFDM has several advantages, but its high PAPR value is a drawback. A non-linear high-power amplifier (HPA) can distort signals with high PAPR values. This issue can be resolved by employing predistortion, which compensates for nonlinear HPA. In addition to PD, channel coding can be used to improve the quality of systems with high PAPR values by adding redundant bits to the bits to be sent. In this paper, we report the experimental evaluations of the joint channel coding and pre-distortion (PD) technique on a 2x2 MIMO OFDM system using USRP hardware. The experiments are conducted in two scenarios: line-of-sight (LOS) and nonline-of-sight (NLOS) scenarios. The channel coding used in this scenario is convolutional code with code rates of 1/2, 2/3, and 3/4. From the results of the experiment, it can be seen that the system that uses PD combined with the convolution code produces better performance in the LOS and NLOS scenarios compared to the system without PD. In the LOS scenario, the use of PD can improve the SNR value of code rates 1/2, 2/3, and 3/4 by approximately 58.74%, 75.97%, and 96.20%. In the NLOS scenario, the use of PD can improve the SNR value of code rates 1/2, 2/3, and 3/4 by about 60.71%, 73.59%, and 71.84%. The measurement of the LOS scenario gives a better SNR value than the NLOS scenario, with a maximum SNR value of 30.86 dB, while the maximum SNR value of the NLOS scenario is 30.23 dB. This happened because the LOS scenario suffered minimal multipath fading compared to the NLOS scenario

Bi‐directional LSTM based channel estimation in 5G massive MIMO OFDM systems over TDL‐C model with Rayleigh fading distribution

SummaryIn this work, a deep learning (DL)‐based massive multiple‐input multiple‐output (mMIMO) orthogonal frequency division multiplexing (OFDM) system is investigated over the tapped delay line type C (TDL‐C) model with a Rayleigh fading distribution at frequencies ranging from 0.5 to 100 GHz. The proposed bi‐directional long short‐term memory (Bi‐LSTM) channel state information (CSI) estimator uses online learning during training and offline learning during the practical implementation phase. The design of the estimator takes into account situations in which prior knowledge of channel statistics is limited and targets excellent performance, even with limited pilot symbols (PS). Three separate loss functions (mean square logarithmic error [MSLE], Huber, and Kullback–Leibler Distance [KLD]) are assessed in three classification layers. The symbol error rate (SER) and outage probability performance of the proposed estimator are evaluated using a number of optimization techniques, such as stochastic gradient descent (SGD), momentum, and the adaptive gradient (AdaGrad) algorithm. The Bi‐LSTM‐based CSI estimator is trained considering a specific number of PS. It can be readily seen that by incorporating a cyclic prefix (CP), the system becomes more resilient to channel impairments, resulting in a lower SER. Simulations show that the SGD optimization approach and Huber loss function‐trained Bi‐LSTM‐based CSI estimator have the lowest SER and very high estimation accuracy. By using deep neural networks (DNNs), the Bi‐LSTM method for CSI estimation achieves a superior channel capacity (in bps/Hz) at 10 dB than long short‐term memory (LSTM) and other conventional CSI estimators, such as minimum mean square error (MMSE) and least squares (LS). The simulation results validate the analytical results in the study.

MmWave 6D Radio Localization With a Snapshot Observation From a Single BS

Accurate and ubiquitous localization is crucial for a variety of applications such as logistics, navigation, intelligent transport, monitoring, control, and also for the benefit of communications. Exploiting millimeter-wave (mmWave) signals in 5G and Beyond 5G systems can provide accurate localization with limited infrastructure. We consider the single base station (BS) localization problem and extend it to 3D position and 3D orientation estimation of an unsynchronized multi-antenna user equipment (UE), using downlink multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) signals. Through a Fisher information analysis, we show that the problem is often identifiable, provided that there is at least one multipath component in addition to the line-of-sight (LoS), even if the position of corresponding incidence point (IP) is a priori unknown. Subsequently, we pose a maximum likelihood (ML) estimation problem, to jointly estimate the 3D position and 3D orientation of the UE as well as several nuisance parameters (the UE clock offset and the positions of IPs corresponding to the multipath). The ML problem is a high-dimensional nonconvex optimization problem over a product of Euclidean and non-Euclidean manifolds. To avoid complex exhaustive search procedures, we propose a geometric initial estimate of all parameters, which reduces the problem to a 1-dimensional search over a finite interval. Numerical results show the efficiency of the proposed ad-hoc estimation, whose gap to the Cramér-Rao bound (CRB) is tightened using the ML estimation.

Efficient Index Modulation-Based MIMO OFDM Data Transmission and Detection for V2V Highly Dispersive Channels

Vehicle-to-vehicle (V2V) communication networks are based on vehicles that wirelessly exchange data, traffic congestion, and safety warnings between them. The design of new V2V systems requires increasingly energetically and spectrally efficient systems. Conventional multiple-input–multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems have been used successfully for the last decade. However, MIMO-OFDM systems need to be improved to face future communication networks in high-mobility environments. This article proposes an efficient index modulation (IM)-based MIMO-OFDM system for V2V channels. The proposed transmission system is evaluated in high Doppler-spread channels. The results demonstrate that the proposed scheme reduces the required computational complexity in data detection and exhibits gains of up to 3 dB in bit error rate (BER) performance when compared to the conventional MIMO-OFDM system under the same conditions and parameters, along with achieving superior spectral efficiency. The results show the viability of implementing the proposed system in practical applications for high-transmission-rate V2V channels.