Frequency Division Multiplexing Communication Systems 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.

Joint Sub-Band and Transmission Rate Selection for Anti-Jamming Non-Contiguous Orthogonal Frequency Division Multiplexing System: An Upper Confidence Bound Based Reinforcement Learning Approach

Reinforcement Learning (RL) has been employed to assign transmission parameters to all sub-carriers in a set frequency band for anti-jamming Orthogonal Frequency Division Multiplexing (OFDM) systems. However, prior works often overlooked the influence of wireless environment fading and convergence issues stemming from overly large parameter sets. To address these challenges, an anti-jamming scheme was proposed based on the Non-Contiguous Orthogonal Frequency Division Multiplexing (NC-OFDM) communication system integrated with reinforcement learning. First, all sub-carriers were divided into sub-bands, and a Finite State Markov Sub-bands (FSMS) model was established to describe the time-varying fading characteristics of each sub-band by combining Adaptive Modulation and Coding (AMC) technology. To mitigate instability due to the fading channel, a joint sub-band and modulation anti-jamming decision scheme was adopted, enabling the transmitter to select the optimal sub-band and transmission rate. Ultimately, this decision-making process was modeled as a Markov Decision Process (MDP), and an Upper Confidence Bound based Q-learning (UCB-Q) anti-jamming algorithm was proposed for obtaining the joint sub-band and transmission rate selection strategies. Simulation results indicate that the proposed algorithm demonstrates enhanced speed and superior average throughput. Additionally, the algorithm showcases the same commendable anti-jamming performance in scenarios with time-varying dynamic jamming.

An Optimized Algorithm for Peak to Average Power Ratio Reduction in Orthogonal Frequency Division Multiplexing Communication Systems: An Integrated Approach

An Optimized Algorithm for Peak to Average Power Ratio Reduction in Orthogonal Frequency Division Multiplexing Communication Systems: An Integrated Approach

Resource allocation optimization in multiuser OFDM relay-assisted underwater acoustic sensor networks

Resource allocation optimization (RAO) is a crucial design issue for providing green communications in the sixth generation (6G) of underwater acoustic (UWA) sensor networks (SNs). To have a suitable convergence speed in the machine learning (ML)-based algorithms used for finding the solution of the online RAO problems, the optimal or suboptimal solution of the offline form of the online problem should be utilized as the initial setting. Moreover, knowing the closed-form of the best initial setting in terms of the channel parameters leads to more efficiency in the ML-based algorithms from the robustness standpoint. In this paper, we formulate and solve two new offline RAO problems for joint relay selection and power allocation in the orthogonal frequency division multiplexing UWA cooperative communication systems. We first obtain a new formula for the signal to noise ratio (SNR) per-subcarrier in the cooperative UWA communication system with amplify and forward relaying including multiple users and multiple relays. In our analyses, unlike terrestrial channel and by considering the physical facts seen in the practical UWA channel, we assume non-white Gaussian noise along with the frequency-dependent pathloss for obtaining the SNR per-subcarrier function. Then, we use it in the definition of our RAO problems. Our proposed problems are non-convex and we present a promising method for converting them to the convex problems. In our problems, the objective function is the total power transmitted over the network. In addition, the sum-rate and probability of error are constrained to control the quality of service. Also, we derive some new closed-form formulas for reliable cooperation, relay selection, and power loading. Extensive simulation studies are carried out to assess the convergence, effectiveness, and robustness of our proposed algorithms to the channel impairments for different conditions.

Ergodic Achievable Rate Maximization of RIS-Assisted Millimeter-Wave MIMO-OFDM Communication Systems

Reconfigurable intelligent surface (RIS) has attracted extensive attention in recent years. However, most research focuses on the scenario of the narrowband and/or instantaneous channel state information (CSI), while wide bandwidth with the use of millimeter-wave (mmWave) (including sub-Terahertz) spectrum is a major trend in next-generation wireless communications, and statistical CSI is more practical to obtain in realistic systems. Thus, we {consider} the ergodic achievable rate of RIS-assisted mmWave multiple-input multiple-output orthogonal frequency division multiplexing communication systems. The widely used Saleh-Valenzuela channel model is adopted to characterize the mmWave channels and only the statistical CSI is available. We first derive the approximations of the ergodic achievable rate by means of the majorization theory and Jensen's inequality. Then, an alternating optimization based algorithm is proposed to maximize the ergodic achievable rate by jointly designing the transmit covariance matrix at the base station and the reflection coefficients at the RIS. Specifically, the design of the transmit covariance matrix is transformed into a power allocation problem and solved by spatial-frequency water-filling. The reflection coefficients are optimized by the Riemannian conjugate gradient algorithm. Simulation results corroborate the effectiveness of the proposed algorithms.

Deep Learning for Super-Resolution Channel Estimation in Reconfigurable Intelligent Surface Aided Systems

Reconfigurable intelligent surface (RIS) enables the configuration of the propagation environment. Channel estimation is an essential task in realizing the RIS-aided communication system. A RIS-aided multi-user multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) communication system involves cascaded channels with high dimensions and sophisticated statistics. Thus, implementing the optimal minimum mean square error (MMSE) with the integration computation is infeasible in practice. To accurately estimate channels with high accuracy in a RIS-aided multi-user MIMO-OFDM system, we model the channel state information (CSI) estimation as an image super-resolution (SR) problem to recover and denoise the channel matrix. Particularly, a convolutional neural network based on a super-resolution convolutional neural network (SRCNN) and denoising convolutional neural network (DnCNN), named SRDnNet, is then proposed. By taking estimated channels at pilot positions as a low-resolution image, the enhanced SRCNN can fully exploit the features of inputs to learn a suitable interpolation method and generate the coarse estimation of the channel matrix. The denoising model DnCNN with an element-wise subtraction structure can exploit features of the additive noise and recover channel coefficients from the coarse channel matrix. The simulation results demonstrate the effectiveness and excellent performance of the proposed SRDnNet.

A turbo‐based encryption and coding scheme for multiple‐input multiple‐output orthogonal frequency division multiplexing wireless communication systems affected by Doppler frequency offset

AbstractMultiple‐Input Multiple‐Output Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) is an essential technology in the wireless communication system. However, its performance is significantly affected by Carrier Frequency Offset (CFO), which leads to inter‐carrier interference (ICI). The CFO estimation and compensation techniques are used to mitigate the Doppler effect. In addition, the confidentiality of data is a key factor of the system. This paper proposes a Turbo‐based encryption and coding scheme to improve reliability and security for the MIMO‐OFDM wireless communication systems affected by the Doppler frequency offset. In our proposed scheme, the secret key is produced from channel parameters between legitimate users and it is used as a seed for generating a pseudo‐random bit sequence using an Advanced Encryption Standard (AES) generator with variable key lengths. The Turbo code's puncturing mechanism is controlled by this pseudo‐random bit sequence. The proposed method is applied to the system with different modulation schemes through the Rayleigh channel. The simulation results show that the system affected by the Doppler effect would obtain the best performance when using the highest key length of the AES generator, the BPSK, and a great number of receive antennas. Furthermore, the proposed solution outperforms several previous approaches for the MIMO‐OFDM systems.

Deep reinforcement learning-based adaptive modulation for OFDM underwater acoustic communication system

Due to the time-varying and space-varying characteristics of the underwater acoustic channel, the communication process may be seriously disturbed. Thus, the underwater acoustic communication system is facing the challenges of alleviating interference and improving communication quality and communication efficiency through adaptive modulation. In order to select the optimal modulation mode adaptively and maximize the system throughput ensuring that the bit error rate (BER) meets the transmission requirements, this paper introduces deep reinforcement learning (DRL) into orthogonal frequency division multiplexing acoustic communication system. The adaptive modulation is mapped into a Markov decision process with unknown state transition probability. Thereby, the underwater communication channel environment is regarded as the state of DRL, and the modulation mode is regarded as action. The system returns channel state information (CSI) and signal–noise ratio in every time slot through the feedback link. Because the Deep Q-Network optimizes in the changing state space of each time slot, it is suitable for a variety of different CSI. Finally, simulations in different underwater environments (SWellEx-96) show that the proposed adaptive modulation scheme can obtain lower BER and improve the system throughput effectively.

Deep neural network-based intercarrier interference detection for optical spectral efficient frequency division multiplexing system

In the past few decades, many efforts have been made to solve the intercarrier interference (ICI) because of the loss of the orthogonality for the spectral efficient frequency division multiplexing (SEFDM) optical communication systems. In this paper, a deep neural network (DNN) is introduced to deal with the ICI. The intrinsic relationship between the mechanism of ICI damage and the DNN is studied and analyzed. Based on this analysis, the performance of DNN-ICI decoder compared with the conventional algorithms is demonstrated by simulation for optical SEFDM intensity modulation/direct detection (IM/DD) communication systems. The results show that the DNN-ICI decoder is greatly superior to other schemes in terms of bit error rate (BER) with a simple designed DNN under different bandwidth compression factors. Besides, the proposed methods are also robust to the fiber lengths. All the results indicate that the proposed DNN-ICI decoder has great potential to be used in SEFDM IM/DD optical systems.

An efficient reconfigurable peak cancellation model for peak to average power ratio reduction in orthogonal frequency division multiplexing communication system

<span lang="EN-US">The peak to average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) communication system will be reduced using reconfigurable peak cancellation (RPC). RPC will also aid in improves the error vector magnitude (EVM) and reduces adjacent channel leakage ratio (ACLR) in OFDM communication system. The proposed RPC design methodology and practical implementation using field programmable gate array (FPGA) are discussed. The proposed RPC has been demonstrated using VIRTEX-7 XC7Z100 dual-core FPGA device with less hardware difficulty and minimum utilization of FPGA resources. The proposed RPC improves the efficiency of OFDM communication process by reducing complementary cumulative distribution function (CCDF) with respect to instantaneous power in dB. A comparison analysis was done between the existing selective mapping (SLM) method with proposed RPS method with respect FPGA resource utilization. The proposed RPC is implemented using VIRTEX-7 XC7Z100 dual-core FPGA device. Its effectively utilizing sub-carriers, fast Fourier transform (FFT) filter, bandwidth, and sampling frequency. Due to parallel switching operation, it reduces the PAPR, ACLR and improves EVM in OFDM signal with less hardware complexity.</span>

Design and performance evaluation of a novel ranging signal based on an LEO satellite communication constellation

ABSTRACT Driven by improvements in satellite internet and Low Earth Orbit (LEO) navigation augmentation, the integration of communication and navigation has become increasingly common, and further improving navigation capabilities based on communication constellations has become a significant challenge. In the context of the existing Orthogonal Frequency Division Multiplexing (OFDM) communication systems, this paper proposes a new ranging signal design method based on an LEO satellite communication constellation. The LEO Satellite Communication Constellation Block-type Pilot (LSCC-BPR) signal is superimposed on the communication signal in a block-type form and occupies some of the subcarriers of the OFDM signal for transmission, thus ensuring the continuity of the ranging pilot signal in the time and frequency domains. Joint estimation in the time and frequency domains is performed to obtain the relevant distance value, and the ranging accuracy and communication resource utilization rate are determined. To characterize the ranging performance, the Root Mean Square Error (RMSE) is selected as an evaluation criterion. Simulations show that when the number of pilots is 2048 and the Signal-to-Noise Ratio (SNR) is 0 dB, the ranging accuracy can reach 0.8 m, and the pilot occupies only 50% of the communication subcarriers, thus improving the utilization of communication resources and meeting the public demand for communication and location services.

Non-coherent modulation with random phase configurations in RIS-empowered cellular MIMO systems

The Reconfigurable Intelligent Surface (RIS) constitutes one of the prominent technologies for the 6th Generation (6G) of wireless communications. It is envisioned to enhance signal coverage in cases where obstacles block direct communication from Base Stations (BSs), and when high carrier frequencies are used that are sensitive to attenuation losses. In the literature, the exploitation of RISs based on traditional coherent demodulation, relies on the availability of accurate Channel State Information (CSI). Given that CSI estimation, a multi-antenna BS or a dedicated orchestration controller jointly computes the pre-coder/combiner and the RIS configuration. The latter tasks require a significant amount of time and resources, which may not be affordable when the channel is time-varying or the CSI is not accurate enough. In this paper, we consider the uplink between a single-antenna user and a multi-antenna BS, and present a novel RIS-empowered Orthogonal Frequency Division Multiplexing (OFDM) communication system, which is based on differential phase shift keying combined with random phase configurations at the RIS, thus, avoiding the channel estimation and any complex optimization processes. This feature renders our RIS-enabled system operation proposal suitable for high noise and/or mobility scenarios. Considering both an idealistic and a realistic channel model, analytical expressions for the Signal-to-Interference and Noise Ratio (SINR) and the Symbol Error Probability (SEP) of the proposed non-coherent RIS-empowered communication system are presented. Our extensive computer simulation results verify the accuracy of the presented analysis and showcase the proposed system's performance superiority over coherent demodulation in different mobility and spatial correlation scenarios.

Mine Intelligent Receiver: MIMO-OFDM Intelligent Receiver for Mine Information Recovery

With the advancement of an intellectual and numerical society, the coal mining industry has also begun to change to intelligence. As an important aspect of intelligent coal mine construction, coal mine communication has put forward more stringent standards for communication quality. For the complex communication environment in mines, the transmission of communication signals is always damaged by various noises and interferences, resulting in serious distortion of the communication signals received at the receiving end. Therefore, the use of traditional receivers for information recovery has the problem of high bit error rate (BER), which cannot meet the standard of intelligent coal mine construction. Based on this, the aim of this research is to combine convolutional neural networks (CNN) and multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) communication systems to design an intelligent receiver model for complex mine communication systems. At the receiver side, CNNs are used to take the place of all the information processing processes. First, features are extracted from the received IQ signal by the convolutional neural network, and then the original information bit is recovered using a multi-label classifier to finally realize end-to-end information restoration. The experimental results show that the intelligent receiver model designed in this research has more accurate information recovery capability in the complex mine channel environment compared with the traditional receiver. In addition, they also verify that the intelligent receiver can still recover information effectively when the traditional receiver cannot recover information properly in the case of partial loss of received data.

DATA TRANSMISSION IN MIMO-OFDM TECHNOLOGIES FOR CURRENT AND FUTURE WIRELESS COMMUNICATION SYSTEMS: FROM THEORY TO PRACTICE

This paper presents a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) communication system with space-time and space-Frequency block coded (STBC/SFBC) of present generation downlink channel. Space-time block coding (STBC) and space-frequency block coding are transmit diversity schemes used in a wireless communication system to mitigate the effect of channel fading. The system is aimed to reduce the error rate (BER) by mitigating the effect of multipath fading which increase the quality of the system. In the proposed system, 2 transmit and 2 receive antennas are used, to transmit modulated symbols of block N across an OFDM channel, using SFBC Alamouti’s based encoding algorithm (SFBC-OFDM) derives from the basic criteria for space-time block codes (STBC). The signals from the first and second antennas are encoded and transmitted simultaneously in space and frequency over frequency-selective Rayleigh fading channel using two adjacent carriers. The work examines the SFBC and spatial diversity benefits realized based on the BER performance. The result is simulated effectively using MATLAB application software and the BER performance is compared with the conventional STBC-OFDM. It is shown that both STBC and SFBC techniques achieve similar performance with a diversity gain of four (4) over a slow fading channel with 2 antennas at both ends of the link under simple processing across the transmit and receive antennas.

An identification of the tolerable time-interleaved analog-to-digital converter timing mismatch level in high-speed orthogonal frequency division multiplexing systems

<span>High-speed Terahertz communication systems has recently employed orthogonal frequency division multiplexing approach as it provides high spectral efficiency and avoids inter-symbol interference caused by dispersive channels. Such high-speed systems require extremely high-sampling <br /> time-interleaved analog-to-digital converters at the receiver. However, timing mismatch of time-interleaved analog-to-digital converters significantly causes system performance degradation. In this paper, to avoid such performance degradation induced by timing mismatch, we theoretically determine maximum tolerable mismatch levels for orthogonal frequency division multiplexing communication systems. To obtain these levels, we first propose an analytical method to derive the bit error rate formula for quadrature and pulse amplitude modulations in Rayleigh fading channels, assuming binary reflected gray code (BRGC) mapping. Further, from the derived bit error rate (BER) expressions, we reveal a threshold of timing mismatch level for which error floors produced by the mismatch will be smaller than a given BER. Simulation results demonstrate that if we preserve mismatch level smaller than 25% of this obtained threshold, the BER performance degradation is smaller than 0.5 dB as compared to the case without timing mismatch.</span>

PAPR reduction of OFDM systems using H‐SLM method with a multiplierless IFFT/FFT technique

This study proposes a novel low-complexity algorithm for computing inverse fast Fourier transform (IFFT)/fast Fourier transform (FFT) operations in binary phase shift keying-modulated orthogonal frequency division multiplexing (OFDM) communication systems without requiring any twiddle factor multiplications. The peak-to-average power ratio (PAPR) reduction capacity of an efficient PAPR reduction technique, that is, H-SLM method, is evaluated using the proposed IFFT algorithm without any complex multiplications, and the impact of oversampling factor for the accurate calculation of PAPR is analyzed. The power spectral density of an OFDM signal generated using the proposed multiplierless IFFT algorithm is also examined. Moreover, the bit-error-rate performance of the H-SLM technique with the proposed IFFT/FFT algorithm is compared with the classical methods. Simulation results show that the proposed IFFT/FFT algorithm used in the H-SLM method requires no complex multiplications, thereby minimizing power consumption as well as the area of IFFT/FFT processors used in OFDM communication systems.

Adaptive PTS scheme based on fuzzy neural network for PAPR reduction in OFDM system

High peak-to-average power ratio (PAPR) is one of the major drawbacks of orthogonal frequency division multiplexing (OFDM) communication system, which causes signal distortion and affects communication performance. Partial transmission sequence (PTS) is an effective method to reduce the PAPR, but it cannot adaptively select processing parameters for the signal and has high computational complexity. Aiming at the defects of PTS method, this paper propose an adaptive PTS method based on fuzzy neural network (FNN-APTS). The method considers the signal and system characteristics, combining the learning ability of the neural network with the reasoning ability of the fuzzy control method, to select the processing parameters for the signal adaptively. The simulation results show that the proposed FNN-APTS method can adapt to different OFDM models, reducing the computational complexity and improving the system performance while ensuring the reasonable PAPR.

Implementation of COFDM Reed Solomon using TMS320C6713

This sheet research presents the implementation of Coded Orthogonal Frequency Division Multiplexing (COFDM) communication system encoded using Reed Solomon (RS) Coding implemented on TMS320C6713 platform. The collaboration of MATLAB integrated development environment (IDE) and Code Composer Studio (CCS) are adopted. The real time implementation is one of the most difficult issues of OFDM implementation. The problem of real time implementation is the memory consumption and the processing time. The other problem is Synchronization of OFDM receiver. Time and frequency synchronization to distinguish the begin of the OFDM symbol. The capability of RS for encoding the error correction ability makes it the most used error correction codes algorithm, so we use it with OFDM systems to combining them make the system has excellent performance potentiality in multipath and fading channels. The bit error rate and the number of error bits are calculated during each test on the system.

Provable Chaotically Authenticated Encrypted Biomedical Image Using OFDM Transmission

In this research, a unique multiband random chaotic key generator based provable authenticated encrypted technique for biomedical picture for the healthcare biomedical system, which can be used in 5G communication system is presented. In addition, the encryption method employed in this research is based on Multiband Random Chaotic Key Generator, and the proposed provable authenticated methodology is based on symmetric authenticated encryption data (MBRCKG). In the proposed proven Orthogonal Frequency Division Multiplexing (OFDM) communication system, the Authenticated Chaotic Encrypted Biomedical Image (ACE-BI) is utilized. This study uses discrete wavelet transformation (DWT) and discrete cosine transformation (DCT) to mask patient data and hospital watermarks in biological images. With various statistical and OFDM settings, channel analysis and statistical analysis have been examined for their effects on the collected hospital logo and patient data. The simulation studies demonstrate how resistant to communication signal processing the proposed ACE-BI method is. Additionally, the proposed algorithm is able to reduce encryption time to one quarter because the partial encryption based in one level DWT scheme.

A Polar Coding With a Q-Section NBC Property for Managing Phase Ambiguity and PAPR in Multi-Carrier Communications

For an orthogonal frequency division multiplexing (OFDM) communication system which requires channel estimation for each sub-carrier without using pilot signals, there may exist the problem of phase ambiguity. By extending the conventional concept of noncoherent block coding (NBC), we propose a $Q$ -section polar code that has the NBC property in such a way that each codeword is divided into $Q$ sections and reversing the code bits in some sections results in another codeword which is encoded by the same desired message. The polar coded OFDM with a $Q$ -section NBC property enables us to solve the problem of phase ambiguity that exists in each sub-carrier. In addition, the $Q$ -section NBC property also enables us to employ the multiple-candidate method for reducing the peak-to-average-power ratio (PAPR).