Output Orthogonal Frequency Division Multiplexing Research Articles

Ultra-reliable intelligent link scheduling based on DRL for manned/unmanned aerial vehicle cooperative scenarios

When manned/unmanned aerial vehicle (MAV–UAV) perform cooperative missions in complex and variable situations, the communication link between manned aerial vehicle (MAV) and unmanned aerial vehicle (UAV) must be extremely dependable to guarantee the reliable transfer of command and control information. To address the above issues, this study provides an intelligent link scheduling algorithm based on MAVlink protocol that leverages deep reinforcement learning (DRL) for highly reliable communication in MAV–UAV multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) communication systems. This algorithm combines a deep neural network (DNN) with a Q-learning (QL) algorithm to get a deep Q-network (DQN). The downlink communication link between MAV and UAV uses it to intelligently schedule the modulation and coding scheme (MCS) and the number of spatial multiplexing layers. Results from system simulations indicate that the proposed algorithm outperforms other representative scheduling algorithms in the communication between MAV and UAV in complex scenarios with high-speed movement and large noise interference, improving the reliability of communication in MAV–UAV cooperative missions.

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.

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.

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.

Reliability Achievement of MIMO-OFDM Communication Systems

Wireless communication must have high spectral efficacy and the ability to resist fading channel in an environment of channel multipath, and it is difficult to achieve these requirements with traditional modulation techniques, but the Multiple system Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) It can fulfill these requirements. Research offers a way to improve the performance of OFDM- MIMO system by decreasing the Bit Error Rate (BER) using both (DWT) Discrete Wavelet Transform and Convolutional Coding (CC). The OFDM system is modeled with a single antenna at the transmitting end and a single antenna at the receiving end (DWT) . Multiple Input Single Output (MISO) By adding a second antenna at the transmitting end, and then improving it with Output Multiple Input Single technology SIMO (by adding a second antenna at the receiving end, and with MIMO technology by adding a second antenna at the receiving end The transmitter and a second antenna at the receiving end, thus optimizing the OFDM-MIMO system using convolution coding, Then using DWT instead of FFT (Fast Fourier Transform ). The research is based on the study of BER according to bit energy to noise energy density N0/Eb. The system was modeled and tested using the MATLAB program. The use of (DWT) and (CC) in OFDM-MIMO reduces N0/Eb )4.7( dB times in the item of the Rician fading channel and 3.2 dB times in the item of the Rayleigh fading channel when BER =10-3.

A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems

In the past decades, researchers/scientists have paid attention to the physical layer of underwater communications (UWCs) due to a variety of scientific, military, and civil tasks completed beneath water. This includes numerous activities critical for communication, such as survey and monitoring of oceans, rescue, and response to disasters under the sea. Till the end of the last decade, many review articles addressing the history and survey of UWC have been published which were mostly focused on underwater sensor networks (UWSN), routing protocols, and underwater optical communication (UWOC). This paper provides an overview of underwater acoustic (UWA) physical layer techniques including cyclic prefix orthogonal frequency division multiplexing (CP-OFDM), zero padding orthogonal frequency division multiplexing (ZP-OFDM), time-domain synchronization orthogonal frequency division multiplexing (TDS-OFDM), multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM), generalized frequency division multiplexing (GFDM), unfiltered orthogonal frequency division multiplexing (UF-OFDM), continuous phase modulation orthogonal frequency division multiplexing (CPM-OFDM), filter bank multicarrier (FBMC) modulation, MIMO, spatial modulation technologies (SMTs), and orthogonal frequency division multiplexing index modulation (OFDM-IM). Additionally, this paper provides a comprehensive review of UWA channel modeling problems and challenges, such as transmission loss, propagation delay, signal-to-noise ratio (SNR) and distance, multipath effect, ambient noise effect, delay spread, Doppler effect modeling, Doppler shift estimation. Further, modern technologies of the physical layer of UWC have been discussed. This study also discusses the different modulation technology in terms of spectral efficiency, computational complexity, date rate, bit error rate (BER), and energy efficiency along with their merits and demerits.

Power Optimization of UOWC-MIMO-OFDM System Based on PSO-WF Algorithm

Under the constraint that the total transmission power is constant, when multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) technology is applied to the signal transmission design in the underwater optical wireless communication system (UOWC), the traditional water-filling algorithm (WF) uses mathematical derivation to solve the optimization problem, which requires multiple Lagrangian optimization solutions. In order to reduce the computational complexity and accelerate the convergence speed, a particle swarm optimization (PSO-WF) algorithm for water injection is proposed. It randomly selects particles in the target area, calculates the fitness, and gradually approximates the optimal solution to avoid negative power distribution. The simulation results show that, compared with the average power allocation scheme, the algorithm improves the system capacity and operation efficiency by about 80%; Compared with WF algorithm, it greatly reduces the complexity.

Target location and velocity estimation with the multistatic MU‐MIMO‐OFDM modulation signal

AbstractIn passive radar and joint communication and radar sensing (JCRS), target sensing with the multiuser multiple input multiple output orthogonal frequency division multiplexing (MU‐MIMO‐OFDM) modulation signal is gaining increasing interest. Multiple transmit nodes emitting the MU‐MIMO‐OFDM modulation signals at the same carrier frequency and one receiver collecting the target‐reflected signals for target location and velocity estimation are considered. This is a typical scenario when using the fifth‐generation (5G) communication network signal for target sensing. In this scenario, the echo signals corresponding to different transmit nodes are not resolved in the receiver, and the modulated data symbols cannot be removed from the received signals. Most traditional parameter estimation methods in passive radar and JCRS may not be suitable here. A location and velocity estimation method with the received echo signals is proposed. Specifically, the location parameters are extracted directly from the received echo signals. The location estimation is cast into a block sparse vector reconstruction problem. The variational Bayesian sparsity learning (VBSL) method is exploited for the reconstruction of the block sparse vector. Accelerated VBSL methods are developed for improving the computational efficiency. Simulations verify the effectiveness of the proposed methods.

Hardware-Based Architecture for DNN Wireless Communication Models.

Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO OFDM) is a key technology for wireless communication systems. However, because of the problem of a high peak-to-average power ratio (PAPR), OFDM symbols can be distorted at the MIMO OFDM transmitter. It degrades the signal detection and channel estimation performance at the MIMO OFDM receiver. In this paper, three deep neural network (DNN) models are proposed to solve the problem of non-linear distortions introduced by the power amplifier (PA) of the transmitters and replace the conventional digital signal processing (DSP) modules at the receivers in 2 × 2 MIMO OFDM and 4 × 4 MIMO OFDM systems. Proposed model type I uses the DNN model to de-map the signals at the receiver. Proposed model type II uses the DNN model to learn and filter out the channel noises at the receiver. Proposed model type III uses the DNN model to de-map and detect the signals at the receiver. All three model types attempt to solve the non-linear problem. The robust bit error rate (BER) performances of the proposed receivers are achieved through the software and hardware implementation results. In addition, we have also implemented appropriate hardware architectures for the proposed DNN models using special techniques, such as quantization and pipeline to check the feasibility in practice, which recent studies have not done. Our hardware architectures are successfully designed and implemented on the Virtex 7 vc709 FPGA board.

Data Aided Channel Estimation for MIMO-OFDM Wireless Systems Using Reliable Carriers

Wireless channel estimation is one of the challenging problems in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) wireless systems. The MIMO-OFDM exploits the spatial resources and increases the reliability and capacity of wireless systems. However, the performance of these systems depends on accurate channel estimation since the receivers require perfect Channel State Information (CSI) for coherent signal detection. Thus, wireless channel estimation is a necessary component of OFDM systems. In this article, we have proposed an algorithm for MIMO-OFDM systems that combines pilot symbols with reliable data symbols for channel estimation. The reliable data symbols serve as virtual pilots and enhance the spectral efficiency. The proposed Data Aided Channel Estimation (DACE) algorithm eliminates the requirement of any additional resources such as excessive number of training symbols to attain the desired performance. Also, it outperforms the traditional Least Square (LS) and Linear Minimum Mean Square Error (LMMSE) methods for channel estimation in terms of Mean Square Error (MSE) and Bit Error Rate (BER) performance of the system.

Performance Assessment of a RIS-Empowered Post-5G/6G Network Operating at the mmWave/THz Bands

Reconfigurable Intelligent Surfaces (RISs) are considered to be a key enabling technology for 6G as they can potentially provide a boost in performance with a high energy efficiency. RISs rely on the use of arrays with a large number of low-cost quasi-passive reflecting elements which can be individually tuned in order to shape the radio wave propagation. This can effectively enable the implementation of smart radio environments, increasing the capacity and improving the coverage of the system. Since most RISs related studies focus on evaluating the gains of RIS based solutions in simplified communication scenarios, in this paper we investigate the potential benefits of RISs when integrated into future wireless networks within the context of post-5G/ 6G systems. With this aim, we present an iterative algorithm for accomplishing the joint design of the access point precoder and phase-shifts of the RIS elements considering a multi-stream multiple-input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) link. Based on this approach, we then present the system-level evaluation of a RIS-aided post-5G/6G network deployment operating in two different bands, mmWave and sub-THz, and which considers both near-field and far-field propagation models. The results obtained in two different environments namely, Indoor Open Office (IOO) and Urban Micro Truncated (UMT), show that the adoption of the proposed RIS-based approach can effectively improve the throughput and coverage area.

New Preamble Sequence for WiMAX System with Improved Synchronization Accuracy

Worldwide Interoperability for Microwave Access (WiMAX) trusts Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) combination for the deployment of physical layer functions and for connecting the medium access control to the wireless media. Even though Orthogonal Frequency Division Multiplexing (OFDM) facilitates reliable digital broadband transmission in the fading wireless channels, the presence of synchronization errors in the form of Carrier Frequency Offset (CFO) and Time Offset (TO) adversely affect the performance of OFDM based physical layers. The objective of this work is to improve the accuracy of the frequency and the time offset estimation in the WiMAX physical layer. A method to enhance the synchronization accuracy by fine-tuning the merit factor of the preamble sequence is suggested in this paper. Also, a new preamble with improved synchronization accuracy is proposed for the WiMAX system. The performance of the proposed preamble is evaluated in a Rayleigh fading channel and the results of simulations show that the Mean Square Error (MSE) in offset estimation is significantly reduced and it outperforms the standard WiMAX preamble.

Compressive sensing-based channel estimation for high mobile systems with delay Doppler effects

In this paper, channel overhead is reduced by exploiting channel sparsity for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system. Where, compressive sensing (CS) based dictionary design algorithms has been adopted as a channel estimation technique in high mobile systems with minimal number of pilots, such as high-speed train (HST) systems. A novel framework design of the dictionary-based CS was proposed considering both delay and Doppler effects in order to correctly recover the channel response. The channel under consideration is a 2 by 2 space-time block code (STBC) MIMO channel. Simulation tests according to the international telecommunication union (ITU) channel model demonstrated the suitability of the proposed dictionary for estimating the channel impulse response (CIR) of a liner time varying (LTV) channel with a mobility approaches 675 Km/h related to a Doppler frequency of 1500 Hz and 2.4 GHz carrier frequency. Two CS recovery algorithms were applied; orthogonal matching pursuit (OMP) and basis pursuit (BP), where by about 7 dB gain in signal to noise ratio (SNR) was achieved with mobility of 675 Km/h using OMP as compared to BP at a bit error rate (BER) of with 128 OFDM subcarriers.

Comparison of PAPR Reduction Using ABC-FF Algorithm and OGWO in MIMO-OFDM

In the transmitted signal, the high peak-to-average power ratio (PAPR) is the real disadvantage of multiple input, multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Among different PAPR reduction techniques, selected mapping (SLM) is a famous strategy that accomplishes good PAPR reduction performance without signal distortion. Likewise, Partial transmit sequence (PTS) is additionally solitary of the successful techniques to decrease the PAPR in OFDM. Though, result an optimal segment issue in PTS system is measured to exist a critical concern. To progress the existing PAPR reduction techniques, we have incorporated ideal SLM and PTS based PAPR reduction strategy in parallel. By utilizing, the OGWO algorithm; the transmit succession was chosen with least PAPR above all communication antennas. The proposed PAPR reduction approach is applied independently on each transmitted antenna, and so the PAPR can be extremely reduced. Moreover, the OGWO optimization based PAPR reduction technique will provide better performance and it was been promoted as an uncomplicated way for PAPR reduction. The proposed approach will be analyzed with ABC-FF Algorithm PAPR reduction scheme to show the effectiveness

Proactive Eavesdropping in Massive MIMO-OFDM Systems via Deep Reinforcement Learning

It is challenging to monitor suspicious links in massive multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems with directional beamforming. When the monitor and suspicious user are not in the coverage of the same beam, to implement legitimate monitoring, we propose the proactive eavesdropping scheme in which the monitor acts as a spoofing relay to realize beam misleading and data eavesdropping. In the phase of beam sweep, the beam misleading algorithm induces the transmitter to choose the beam which is beneficial to the monitor by optimizing the relay precoding matrices. In the phase of data transmission, the data eavesdropping algorithm elevates the monitoring rate by optimizing the relay power split factors and power gain factors of all subcarriers. Due to the unknown channel state information between suspicious pairs, we propose to find the optimal precoders and power split factors by the multi-agent deep deterministic policy gradient (MADDPG) algorithm. The simulation results verify the effectiveness and superiority of the proposed eavesdropping scheme.

A systematic literature review on channel estimation in MIMO-OFDM system: performance analysis and future direction

Abstract Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is a familiar modern wireless broadband technology due to its resistance against multipath fading, high data transmission rate, and spectral efficiency. This technology delivers dependable communication as well as a large range of coverage. The precise recovery of Channel State Information (CSI) and synchronization among the receiver and transmitter are two major challenges for MIMO-OFDM systems. Several estimate procedures, like blind, pilot-aided, and semi-blind channel estimating, are used to recover channel state information. Yet, those systems have several flaws that cause them to perform poorly. Hence, this paper describes the basic introduction of the Channel Estimation (CE) process in the MIMO-OFDM system. The main goal of this survey is to study analyzing and categorizing the channel estimation algorithms, and simulation tools in different contributions. Further, the performance analysis with different performance metrics from diverse contributions is pointed out. Thus, this review article presents a detailed overview of the various channel estimation schemes that have been exploited in the OFDM channel to enhance the estimation of the CSI in the MIMO-OFDM systems. This work presents and discusses the relevant comparative results and computational complexity for all of these CE systems. Furthermore, there is a list of open study directions for further exploration.

Comparison on BER Performance Using Linear Filtering Least Square Method and Least Square Method for MIMO-OFDM Based Communication Systems

In Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, the Bit Error Rate is poor by using Linear Filtering Least Square method (LFLS) and Least Square (LS) method for massive MIMO-OFDM systems to extend the strength of the signal and to scale back the noise. Therefore the objective of the project is to maintain better Bit Error Rate performance. Materials and methods: Using Linear Filtering Least Square method, different values are used to enhance the Bit Error Rate (BER). The proposed method is compared with the least square method, the results are obtained by using Matlab simulation software. Results: The performance of Bit Error Rate is calculated by using the Linear Filtering Least Square method and enhanced Least Square method parameters. At 20 dB SNR, the linear filtering least-square provides the BER value is 10-3, whereas the Novel enhanced least-square scheme provides the BER value around 10-5. Conclusion: Based on experimental results and statistical analysis using the independent sample T-test, the Least Square method significantly performs better than the Linear Filtering Least Square method.

A better signal recovery method for an urban environment

Communication systems have had such a profound impact on every aspect of humanity. Wireless broadband infrastructures with high throughput and improved spectral efficiency should ensure greater networking and services throughout all areas. Multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) has excessive bandwidth capacity compared with other techniques; also it can be used in high-frequency applications and fast wireless environments. However, accurate channel estimation is a major problem. Compressive sensing is one of the latest techniques that is paying an overwhelming response to wireless communication networks. This can overcome the channel estimation drawbacks of MIMO-OFDM. Compressed sensing's extraordinary qualities have prompted scholars to use it in a variety of certain other domains, and its innumerable applications have a key role in wireless systems. In Compressive sensing, a priori aided compressive sampling matching pursuit (PA-CoSaMP) algorithm is used here to ensure good performance and to optimize the exact mean squared error rate information. This paper gives an overview of MIMO-OFDM, compressive sensing, and the performance evaluation of the proposed algorithm with OFDM. Simulation results gives the recovered signal details and comparison of error rate performance of PA-CoSaMP and conventional OFDM techniques.

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).