Orthogonal Frequency Division Multiplexing Systems Research Articles

A Bayesian Tensor Decomposition Method for Joint Estimation of Channel and Interference Parameters.

Bayesian tensor decomposition has been widely applied in channel parameter estimations, particularly in cases with the presence of interference. However, the types of interference are not considered in Bayesian tensor decomposition, making it difficult to accurately estimate the interference parameters. In this paper, we present a robust tensor variational method using a CANDECOMP/PARAFAC (CP)-based additive interference model for multiple input-multiple output (MIMO) with orthogonal frequency division multiplexing (OFDM) systems. A more realistic interference model compared to traditional colored noise is considered in terms of co-channel interference (CCI) and front-end interference (FEI). In contrast to conventional algorithms that filter out interference, the proposed method jointly estimates the channel and interference parameters in the time-frequency domain. Simulation results validate the correctness of the proposed method by the evidence lower bound (ELBO) and reveal the fact that the proposed method outperforms traditional information-theoretic methods, tensor decomposition models, and robust model based on CP (RCP) in terms of estimation accuracy. Further, the interference parameter estimation technique has profound implications for anti-interference applications and dynamic spectrum allocation.

Comparative study of DCT-and DHT-based OFDM systems over doubly dispersive fading channels

Abstract In high-mobility operating scenarios orthogonal frequency division multiplexing (OFDM) system lacks its optimality, intercarrier interference (ICI) occurs and the Doppler shifts deteriorate the orthogonality of the subcarriers. However, this problem can be overcome by utilizing complicated equalizers at the receiver. Discrete cosine transform (DCT) and discrete Hartley transform (DHT) have been used instead of discrete Fourier transform (DFT) in the standard OFDM system. The performance results achieved enhancement over dispersive selective channels that cannot be accomplished with a standard OFDM system, even with utilizing complex equalizers. In this paper, the performance of the DCT-based OFDM system and DHT-based OFDM system is compared and analyzed with respect to DFT-based OFDM system performance over doubly dispersive fading channel at various Doppler shifts, the block diagrams of the proposed systems are provided to simplify the theoretical analysis by making it easier to follow. Simulation results emphasized that the performance of DCT and DHT-based OFDM systems under doubly dispersive fading conditions scenario outperforms DFT-based OFDM system of order 3 dB energy per bit to noise power spectral density ratio (Eb/N0).

Non-pilot based carrier frequency offset estimation and reflection coefficient optimization for RIS-assisted OFDM systems

Carrier frequency synchronization between the transmitter and receiver is challenging due to oscillator inaccuracies and Doppler shifts, which lead to inter-carrier interference. To address this synchronization error, i.e., carrier frequency offset (CFO), we introduce a novel method for estimating the CFO in reconfigurable intelligent surfaces (RIS)-assisted orthogonal frequency division multiplexing (OFDM) systems. Additionally, we present a method to optimize reflection coefficients, as adjusting the reflection coefficient of RIS can effectively manipulate signal propagation and amplitude, thereby enhancing system performance. In the proposed method, a randomized set of reflection coefficients is applied for the first OFDM symbol, and CFO is estimated using the oversampling method within that symbol over the Rayleigh fading environment. A deterministic approach is adopted to reduce grid search complexity. Simulation results demonstrate the superior performance of the proposed CFO estimation method, particularly in high signal-to-noise ratio scenarios, compared to the existing Zadoff Chu sequence-based estimator in terms of mean square error (MSE). Subsequently, the CFO is compensated in the second OFDM symbol using the estimated CFO, and the reflection coefficient is optimized by identifying the channel tap associated with the maximum channel impulse gain technique. The optimized reflection coefficient enhances system performance in terms of bit error rate (BER) for RIS-assisted OFDM systems.

Generalized Filter Bank Orthogonal Frequency Division Multiplexing Systems for 6G

In this study, generalized filter bank orthogonal frequency division multiplexing (GFB-OFDM) is proposed for 6G. In order to meet the different requirements of various scenarios in 6G, a unified structure of GFB-OFDM is designed by adopting the flexible capabilities of suitable transmission modules. In the proposed GFB-OFDM system, the coexistence of different numerologies in different sub-bands and/or the coexistence of single-carrier and multi-carrier waveforms are achieved for adjusting different scenarios in 6G. Finally, simulation results are provided to validate the BER performance of GFB-OFDM, showing that it is capable of achieving a comparable BER performance and much more flexible transmissions compared with the classic OFDM systems.

Physical layer security enhanced scheme based on joint encryption of DNA and RNA data perturbation in CO-OFDM system

—In the work, we propose a new chaotic encryption algorithm for optical physical layer security enhanced in coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. The proposed algorithm adopts the six-dimensional (6D) hyperchaotic to achieve the generation of key sequences, which can effectively improve the unpredictability and security of the system. The encryption scheme includes two steps, first, the binary sequence is disturbed by DNA and RNA two-stage encoding and RNA mutation, and then the initial encrypted data is injected into the algorithm module with two-dimensional continuous chaotic mapping (2D-SCL) and Brownian motion to scramble successively. Simulations in a 16QAM CO-OFDM system at a symbol rate of 30 Gbaud over 80 km of standard single-mode fiber (SSMF) show that a legitimate user is able to successfully decrypt the received signals, and an illegal eavesdropper obtains a useless signal with a bit error rate (BER) of about 0.5. The scheme can tolerate very small deviations and has a key space of approximately 10294, which can effectively withstand illegal attacks. And the peak-to-average power ratio (PAPR) can be reduced by about 1.01 dB.

W-band photonics-aided OFDM system integrating sensing and communication with phase noise suppression scheme

Photonic-aided technology offers many advantages in millimeter-wave (MMW) communication and sensing, such as low equipment cost and wide bandwidth. The combination of photonics-aided systems with orthogonal frequency division multiplexing (OFDM) waveforms enables high-performance integration of sensing and communication. In this paper, we propose and experimentally demonstrate a W-band photonic-aided OFDM system integrating sensing and communication (ISAC). To address the destruction of signal coherence by phase noise of the system, this paper proposes an OFDM sensing scheme combined with frequency-domain filtering to reduce the sidelobe interference caused by phase noise. Combined with offline digital signal processing algorithms, the system achieves high-precision ranging-Doppler imaging with a ranging resolution of 0.96 cm at a detection distance of 2 m. In terms of communication, after 10 m of wireless transmission, the net rate reaches 47.06 Gbit/s. The proposed system makes full use of the system's resources by using OFDM signals as both the sensing and communication signals. This may be beneficial for some potential applications of 6G in the future.

Parametric channel estimation for RIS-assisted mmWave MIMO-OFDM systems with low pilot overhead

We consider the channel estimation problem in a millimeter-wave (mmWave) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system aided by a passive reconfigurable intelligent surface (RIS). Initially, leveraging the inherent sparsity of mmWave channels, we decouple the channel estimation into the recovery of multipath parameters, including spatial frequencies of angles, delays, and complex gains. Subsequently, we employ two training schemes: the first involves a full row rank constrained RIS phase shift pattern, and the second incorporates a Kronecker structure constrained RIS phase shift pattern. These schemes reform channel estimation into estimating parameters from two fifth-order tensor signals, which admit constrained Canonical Polyadic decomposition. Moreover, both tensor signals share Vandermonde-constrained factor matrices, enabling algebraic algorithms for tensor decomposition problems and recovering channel multipath parameters. Theoretical analyses show that the first training scheme has a lower signal processing complexity for channel estimation, while the second attains a higher estimation accuracy. Also, we prove that our proposed two schemes have the same minimum pilot overhead, which is as low as proportional to the product of the number of paths in the two MIMO channels (transmitter-RIS and RIS-receiver). Numerical results validate the effectiveness of our proposed methods.

Cooperative transmission of UAV swarm using orthogonal time–frequency space modulation

Unmanned aerial vehicles (UAVs) emerge as versatile aerial nodes capable of dynamically filling coverage gaps and enabling mission-critical communication based on their mobile nature. UAV swarms, characterized by the coordinated operation of multiple drones, are employed for a wide range of applications such as surveillance, environmental monitoring, precision farming, and autonomous delivery. While individual UAVs suffer from limited power, group transmissions of UAV swarms can enhance the data rate, reliability, and energy efficiency. However, their mobility may cause severe Doppler spread. The existing orthogonal frequency division multiplexing (OFDM) system exhibits limitations such as susceptibility to the Doppler effect in high-speed mobile environments. To address this issue, orthogonal time–frequency space (OTFS)-aided cooperative transmission (CT) for UAV swarms is considered, which can surmount the aforementioned limitations. The results show the BER improvement of the order of 103 at the SNR value of 10 dB when OTFS modulation is utilized and even better at the higher SNR values. Furthermore, the analytical framework of OTFS-based CT is presented by identifying the key design considerations on the subcarrier spacing, the number of symbols, and the number of subcarriers, which are subject to the maximum speed of the UAV and the cluster size of the UAV swarm. These results provide a significant platform for advancing research in the field of UAV-based CT, especially with the Doppler-resilient OTFS modulation.

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.

Sparse Ramanujan Sequences Transform based OFDM for PAPR reduction

Sparse Ramanujan Sequences (SRS) are periodic series derived from Ramanujan Sums (RS). RS is an exponential summation used to derive infinite series expansions for arithmetic functions, whose application pervades different fields of study from classical mathematics to digital signal processing. SRS has properties similar to RS such as periodicity and orthogonality. SRS transforms allow the sparse representation of signals, hence they have many potential applications, such as denoising and compression. The application of SRS transform to reduce the Peak to Average Power Ratio (PAPR) in Orthogonal Frequency Division Multiplexing (OFDM) is proposed in this paper. The Complementary Cumulative Distribution Function (CCDF) and Bit Error Rate (BER) are used to assess the PAPR reduction performance of the proposed system. The results show that the proposed SRS transform-based OFDM system significantly reduces PAPR and BER with reduced computational complexity in comparison with the existing systems.

Physical Layer Encryption for CO-OFDM Systems Enabled by Camera Projection Scrambler

In this paper, we propose a camera projection approach to enhance the physical layer security of coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. The data are converted to the new location by the camera projection module in the encryption system, where the 5D hyperchaotic system provides the keys for the camera projection module. The simulated 16QAM CO-OFDM security system over 80 km SSMF is shown to provide a key space of about 9 × 1090 through the five-dimensional (5D) hyperchaotic system, making it impossible for eavesdroppers to obtain valid information, and the peak-to-average power ratio (PAPR) is reduced by about 0.8 dB.

Development of Communication System between TPMS and Server using Combination of OFDM and Convolutional Code Technique Based on SDR

The Tire Pressure Monitoring System (TPMS) has evolved into an essential element of contemporary vehicles, playing a pivotal role in enhancing road safety and the overall driving experience. Traditionally, TPMS systems rely on dedicated hardware components for the collection and transmission of tire pressure data to the vehicle's onboard computer and the data is visible only to the driver. In this research, we have developed a wireless communication system between TPMS and a server, enabling tire pressure data to be accessible not only to the driver but also remotely traceable by others. To build a reliable communication system, we utilized a combination of Orthogonal Frequency Division Multiplexing (OFDM) and Convolutional Code technologies. This system is implemented using Software-Defined Radio (SDR) technology. This communication method employs OFDM to enhance data throughput and integrates Convolutional Code to mitigate errors in received data. Consequently, this approach achieves a maximum throughput of 119.19MBps when utilizing the OFDM system alongside 16QAM modulation. The bit error rate for received data without coding stands at 5.77%, but the application of Convolutional Code with a 1/2 code rate effectively reduces this error rate to 3.85%. This system improves the reliability of TPMS communication with the server while also ensuring a consistently high throughput. It enhances road safety and remote monitoring capabilities.

Time-Varying Channel Estimation Based on Distributed Compressed Sensing for OFDM Systems.

For orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, the estimation of time-varying multipath channels not only has a large error, which affects system performance, but also requires plenty of pilots, resulting in low spectral efficiency. To address these issues, we propose a time-varying multipath channel estimation method based on distributed compressed sensing and a multi-symbol complex exponential basis expansion model (MS-CE-BEM) by exploiting the temporal correlation and the joint delay sparsity of wideband wireless channels within the duration of multiple OFDM symbols. Furthermore, in the proposed method, a sparse pilot pattern with the self-cancellation of pilot intercarrier interference (ICI) is adopted to reduce the input parameter error of the MS-CE-BEM, and a symmetrical extension technique is introduced to reduce the modeling error. Simulation results show that, compared with existing methods, this proposed method has superior performances in channel estimation and spectrum utilization for sparse time-varying channels.

Performance enhancement with geometrically shaped 4/8D-SP-50QAM for DDO-OFDM system

We design the 4-dimensional set-partitioning 50-ary quadrature amplitude modulation (4D-SP-50QAM) and 8D-SP-50QAM with 7 bit/symbol in direct detection optical orthogonal frequency division multiplexing (DDO-OFDM) systems. Quadrant multiplexing (QM) and orthant symmetry (OS) are respectively used to reduce the number of constellation points and the complexity of constellation mapping, while improving system bandwidth utilization. The proposed 8D-SP-50QAM scheme outperforms 4D-SP-50QAM, 4D-OS-64QAM and 4D 128-ary polarization-ring-switching (4D-128PRS) in terms of generalized mutual information (GMI) and bit error rate (BER) performance at various fiber length. In addition, the GMI gain increases gradually with the increase of fiber length, proving that this scheme can effectively improve system capacity and nonlinear tolerance.

A comparative study of deep learning and iterative algorithms for joint channel estimation and signal detection in OFDM systems

Joint channel estimation and signal detection (JCESD) is crucial in orthogonal frequency division multiplexing (OFDM) systems, but traditional algorithms perform poorly in low signal-to-noise ratio (SNR) scenarios. Deep learning (DL) methods have been investigated, but concerns regarding computational expense and lack of validation in low-SNR settings remain. Hence, the development of a robust and low-complexity model that can deliver excellent performance across a wide range of SNRs is highly desirable. In this paper, we aim to establish a benchmark where traditional algorithms and DL methods are validated on different channel models, Doppler, and SNR settings, particularly focusing on the semi-blind setting. In particular, we propose a new DL model where the backbone network is formed by unrolling the iterative algorithm, and the hyperparameters are estimated by hypernetworks. Additionally, we adapt a lightweight DenseNet to the task of JCESD for comparison. We evaluate different methods in three aspects: generalization in terms of bit error rate (BER), robustness, and complexity. Our results indicate that DL approaches outperform traditional algorithms in the challenging low-SNR setting, while the iterative algorithm performs better in high-SNR settings. Furthermore, the iterative algorithm is more robust in the presence of carrier frequency offset, whereas DL methods excel when signals are corrupted by asymmetric Gaussian noise.

Sub-System Architecture for millimeter-wave massive MIMO systems

In this paper, we study the hybrid beamforming design for millimeter-wave (mmWave) massive multiple-input multiple-output (mMIMO) systems. The designing of hybrid beamforming for orthogonal frequency-division multiplexing (OFDM) systems is tasking since its analog beamforming is shared among all subcarriers. We adopt a two-step technique for designing the analog and digital beamforming separately in order to maximize the average achievable energy and spectral efficiency of frequency-selective mmWave mMIMO-OFDM systems. Firstly, the analog beamforming design is based on the viewpoint of sub-systems (SS) and the goal is to optimize the array gain and radio frequency chains. Secondly, the digital beamforming design is carried out by using the regularized channel diagonalization (RCD) and block diagonalization (BD) solutions. On the other hand, the BD solution is modified for single-user. Thus, we propose the use of SS-RCD for multi-user and SS-BD for single-user hybrid beamforming designs. The solutions provide interference suppression but differ in low-SNR performance when communicating to many mobile users via data streams. Simulation results present that our hybrid beamforming design outperforms several other designs.

Doppler spread analysis for suppressing channel time variation in high‐mobility massive MIMO V2V communications

AbstractHere, fast time‐varying channels of high‐mobility vehicle‐to‐vehicle communications for massive multiple‐input multiple‐output orthogonal frequency division multiplexing systems are considered. Large‐scale uniform linear arrays are configured at the transmitter and receiver to separate multiple angle domain Doppler frequency offsets based on transmit and receive beamforming with high spatial resolution. Then, each beamforming branch comprises only one dominant Doppler frequency offset. Next, the conventional channel estimation method is performed for each beamforming branch, and carry out maximum‐ratio‐combining for data detection. Power spectrum density and Doppler spread of the equivalent link between the transmitter and receiver are derived and regarded as the criterion for assessing the residual channel time variation caused by limited antennas in practice. Interestingly, a scaling law between the asymptotic Doppler spread and the number of transceiver antennas shows that asymptotic Doppler spread is proportional to the maximum Doppler frequency offset and decreases at the rate of , where and are the number of transmit and receive antennas, respectively. Simulation results confirm the validity of the proposed Doppler suppression framework in high‐mobility vehicle‐to‐vehicle communications.

Exploring and Analyzing a Hybrid PAPR Reduction Method for OFDM Optimization

Orthogonal Frequency Division Multiplexing (OFDM) is a effective modulation technique widely used in modern communication systems, including 5G and wireless networks because of its high spectral efficiency and robustness towards multipath fading. However, OFDM signals are characterized by a high Peak-to-Average Power Ratio (PAPR), which can severely impact the power efficiency and operational cost of these systems. This research investigates and analyse effectiveness of two conventional PAPR reduction methods that is Selective Mapping (SLM), and Partial Transmit Sequence (PTS) with incorporating their approach of PAPR reduction to develop a hybrid variant: SLM-PTS. This study utilized MATLAB simulations to implement and test each PAPR reduction technique on a standard OFDM system model. Parameters such as the number of subcarriers, oversampling factor, and modulation types were systematically varied to analyze the impact on PAPR, BER, and computational complexity. The effectiveness of each technique was measured using the Complementary Cumulative Distribution Function (CCDF) of PAPR values and BER performance under different channel conditions. Key findings reveal that hybrid techniques, particularly SLM & PTS, offer significant improvements in PAPR reduction compared to traditional methods alone, without a corresponding increase in BER or computational overhead. The research underscores the potential of hybrid PAPR reduction techniques to significantly enhance the efficiency and reliability of OFDM systems. These findings have important implications for the design and optimization of next-generation wireless communication systems, suggesting that hybrid approaches can provide a balanced solution to the longstanding issue of PAPR in OFDM transmissions. This study contributes valuable insights into the practical application of combined PAPR reduction strategies, offering a pathway toward more power-efficient and robust OFDM technologies. Keywords : OFDM, PAPR, CCDF, SLM and PTS

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.

Comparative analysis of coding schemes for effective wireless communication

Communication systems have recently focused on sending information efficiently and effectively from one sending point to another across a communication channel in the shortest amount of time. The main objective of this work is to compare the high-range coding scheme types, such as low density parity check (LDPC), turbo, and convolution, to see which works better and is more efficient. to establish a coding system with quadrature amplitude modulation (QAM) modulation and an additive white gaussian noise (AWGN) noisy channel to find which is more reliable and resilient for encoding and decoding. Because of this, digital media has to be sent over wireless channels and through satellites, requiring a connected network all the time, which has become a major concern over time. Furthermore, the high amount of data and efficiency are the focus points. After running the simulation, it was found that 64 QAM with a rate of 0.455 and an efficiency of 2.731 has a bit error rate (BER) of 0.001 and a 7.08 dB energy per bit Eb/No, and the 256 QAM simulation revealed that it has a BER of 0.001 and 11.88 dB Eb/No with a rate of 0.736 and an efficiency of 5.891. Over the AWGN channel noise, the simulation built a standard orthogonal frequency division multiplexing (OFDM) system, which used MATLAB.