Delay Doppler Research Articles

Delay Doppler Division Multiple Access Resource Allocation in Aircraft Network

Delay Doppler Division Multiple Access Resource Allocation in Aircraft Network

Low complexity stationary iteration based approximate inversion for signal detection in OTFS system

Orthogonal time frequency space (OTFS) system offers a high data rate and effectively exploits full diversity compared to orthogonal frequency division multiplexing (OFDM), achieving a seamless trade-off between data rate and processing gain. However, detecting OTFS signals is challenging due to the complex conversion between delay-Doppler (DD) and time domains. In this article, we propose a stationary iteration-based approximate inversion (SIAI) technique for low-complexity detection in uplink OTFS systems.The proposed SIAI detection technique features square-order computational complexity and delivers performance close to that of a linear minimum mean square error (LMMSE) detector. Simulation results demonstrate that the SIAI technique outperforms several state-of-the-art detection methods in terms of both error performance and computational complexity. Additionally, the robustness of the SIAI technique is validated in scenarios with imperfect channel state information at the receiver.

Accurate soil moisture retrieval using Cyclone Global Navigation Satellite System delay Doppler maps by random forest regression algorithm based on multi-characteristic variable combination

Accurate soil moisture retrieval using Cyclone Global Navigation Satellite System delay Doppler maps by random forest regression algorithm based on multi-characteristic variable combination

Progressive Inter-Path Interference Cancellation Algorithm for Channel Estimation Using Orthogonal Time-Frequency Space.

Fractional delay-Doppler (DD) channel estimation in orthogonal time-frequency space (OTFS) systems poses a significant challenge considering the severe effects of inter-path interference (IPI). To this end, several algorithms have been extensively explored in the literature for accurate low-complexity channel estimation in both integer and fractional DD scenarios. In this work, we develop a variant of the state-of-the-art delay-Doppler inter-path interference cancellation (DDIPIC) algorithm that progressively cancels the IPI as estimates are obtained. The key advantage of the proposed approach is that it requires only a final refinement procedure reducing the complexity of the algorithm. Specifically, the time difference in latency between the proposed approach and the DDIPIC algorithm is almost proportional to the square of the number of estimated paths. Numerical results show that the proposed algorithm outperforms the other channel estimation schemes achieving lower normalized mean square error (NMSE) and bit error rate (BER).

A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature

With the aim of solving the problem of identifying complex underwater acoustic targets using a single signal feature in the Time–Frequency(TF) feature, this paper designs a method that recognizes the underwater targets based on the Delay-Doppler joint feature. First, this method uses symplectic finite Fourier transform (SFFT) to extract the Delay-Doppler features of underwater acoustic signals, analyzes the Time–Frequency features at the same time, and combines the Delay-Doppler (DD) feature and Time–Frequency feature to form a joint feature (TF-DD). This paper uses three types of convolutional neural networks to verify that TF-DD can effectively improve the accuracy of target recognition. Secondly, this paper designs an object recognition model (TF-DD-CNN) based on joint features as input, which simplifies the neural network’s overall structure and improves the model’s training efficiency. This research employs ship-radiated noise to validate the efficacy of TF-DD-CNN for target identification. The results demonstrate that the combined characteristic and the TF-DD-CNN model introduced in this study can proficiently detect ships, and the model notably enhances the precision of detection.

Characteristics Analysis of Influence of Multiple Parameters of Mixed Sea Waves on Delay–Doppler Map in Global Navigation Satellite System Reflectometry

Feature capture and recognition of sea wave components in radar systems especially in global navigation satellite system reflectometry (GNSS-R) using signal processing approaches or computer simulative methods has become a research hotspot in recent years. At the same time, parameter inversion of marine phenomena from the discovered characteristics plays a significant role in monitoring and forewarning the different components of sea waves. This paper aims to investigate the impact of multiple parameters, such as the wind speed, directionality variable, wave amplitude, wave length, and directions of sea wave components, on the delay waveform of the delay–Doppler map (DDM). Two types of wind waves and the 2-D sinusoidal sea surface are chosen to be analyzed. By comparing and analyzing the discrepancy of delay waveforms under different conditions, it can be concluded that the increased MSS which arises from the increase in the roughness of the sea surface can lead to the difference in the peak value or trial edges exhibited in delay waveforms. The values of delay waveforms at zero chip along the increasing direction of long-crest wind waves exhibit the periodic spikes shape, which is the opposite of the short-crest wind waves, and the fluctuation of the periodic profiles decreases with the increase in the amplitude of waves. The results and conclusions can provide a foundation for the parameter inversion, tracking, and early warning of anomalous formations of waves in bistatic radar configuration.

Channel Estimation for Underwater Acoustic Communications in Impulsive Noise Environments: A Sparse, Robust, and Efficient Alternating Direction Method of Multipliers-Based Approach

Channel estimation in Underwater Acoustic Communication (UAC) faces significant challenges due to the non-Gaussian, impulsive noise in ocean environments and the inherent high dimensionality of the estimation task. This paper introduces a robust channel estimation algorithm by solving an l1−l1 optimization problem via the Alternating Direction Method of Multipliers (ADMM), effectively exploiting channel sparsity and addressing impulsive noise outliers. A non-monotone backtracking line search strategy is also developed to improve the convergence behavior. The proposed algorithm is low in complexity and has robust performance. Simulation results show that it exhibits a small performance deterioration of less than 1 dB for Channel Impulse Response (CIR) estimation in impulsive noise environments, nearly matching its performance under Additive White Gaussian Noise (AWGN) conditions. For Delay-Doppler (DD) doubly spread channel estimation, it maintains Bit Error Rate (BER) performance comparable to using ground truth channel information in both AWGN and impulsive noise environments. At-sea experimental validations for channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems further underscore the fast convergence speed and high estimation accuracy of the proposed method.

A review of the BuFeng-1 GNSS-R mission: calibration and validation results of sea surface and land surface

ABSTRACT In this paper, we will conclude the results of Bufeng-1 (BF-1) A/B data processing, calibration workflow, and validation of the calibrated sea surface winds, land surface soil moisture, and sea surface height measurements. Since 2019, the BF-1 mission has operated in-orbit for over 4 years. The Earth reflected delay Doppler maps (DDMs) are continuously collected to perform global sea surface and land observations. At the same time, the intermediate frequency (IF) raw data are also obtained for 12 seconds every pass in diagnostic mode. To begin with, a brief description of the spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) technique will be provided in the introduction. Next, we will present the overview of Chinese BF-1 mission and the data specifications used in our research. In the next section, the BF-1 mission-related spaceborne power calibration and validation are presented to show the support to power DDM observable production for sea surface and land surface applications. Then, the status of Chinese Beidou System (BDS) Equivalent Isotropic Radiated Power (EIRP) acquisition programme is then introduced. Furthermore, the latest sea surface height (SSH) measurements results including two modes (group delay and carrier phase) and wind speed derivation based on machine learning (ML) method will be spatial-temporal aligned and validated with auxiliary datasets including Denmark Technology University (DTU) mean sea surface (MSS) products and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis. The previous published results of sea surface winds retrieval under Hurricane conditions and soil moisture retrieval are also reviewed for the BF-1 mission applications. Finally, the conclusion of BF-1 derived results will be discussed to draw out ongoing/future works.

OTFS narrowband interference suppression based on energy concentration

Orthogonal Time Frequency Space (OTFS) modulation has significant Doppler resistance in non-geostationary orbit (NGSO) satellite communications, but is extremely affected by single-tone interference (STI). In this paper, a Fast Harris Hawk Optimization-based Energy Concentration Interference Suppression (FHHO-ECIS) algorithm is proposed to remove STI. First, the algorithm designs the translation matrices in the Delay-Doppler (DD) domain based on the interference center. The STI energy is concentrated by the translation matrices to a single point in the OTFS data block and removed by a zero-setting operation. Subsequently, an interference center pre-estimation module is proposed to improve the accuracy and speed of the HHO algorithm for interference center estimation. Finally, the theoretical analysis of the algorithm's interference suppression gain and the effect of the zero-setting operation on the data is performed. Simulation results show that the FHHO-ECIS algorithm improves the signal to interference plus noise ratio (SINR) by more than 1.7 dB and the bit error rate (BER) performance is improved by more than 0.3 dB.

Time-Frequency Domain Channel Estimation for OTFS Systems

Orthogonal-time-frequency-space (OTFS) has been developed as a promising modulation scheme for high Doppler channels. In OTFS systems, channel estimation (CE) plays a key role. Most existing CE schemes put pilots in delay-Doppler (DD)-domain, often resulting in low spectrum efficiency and high peak-to-average-power-ratio (PAPR). In this paper, we propose three schemes to solve the problems. Proposed methods have distinct features being able to estimate fractional delays and fractional Dopplers. Different from existing methods, our approach places pilots in time-frequency (TF)-domain. The first scheme overlaps pilots and data in both TF- and DD-domain, while Scheme II overlaps them in TF- but not in DD-domain. To further enhance the performance, the third scheme makes pilots and data non-overlapped in both TF- and DD-domain. To conduct CE, time-domain pilot signals are extracted with a filtering operation and a two-dimensional frequency estimation problem is formulated consequently. Delays are first estimated with root-MUSIC. Then, Dopplers are estimated using a simple line-fitting method. Finally, channel gains are estimated by a least-squares method. To solve the interference between pilots and data, successive-interference-cancellation (SIC) is also applied. With the estimated channel, a minimum-mean-square-error (MMSE) equalizer is used for the compensation of channel effect. Simulation results show that the bit-error-rate (BER) performance with the channel estimated by proposed CE methods can approach to that with known channel. Also, spectrum efficiency can be improved by at most 25% and PAPR can be reduced by at least 19 dB.

OTFS Enabled LEO Satellite Communications: A Promising Solution to Severe Doppler Effects

With the advantages of low cost and flexible deployment, low earth orbit satellite (LEO-Sat) communications have received widespread attentions, such as providing global wireless access with enhanced data rates in future 6G networks. However, due to the feature of high-mobility, LEO-Sat communications are facing severe Doppler effects, which heavily degrades the link reliability. The existing approaches for coping with Doppler effects, such as phase locked loop, mainly carry out the prediction and compensation for Doppler frequency shifts, which demands high implementation complexity, as well as accurate channel estimation. Against this background, this article portrays a new paradigm of reliable LEO-Sat communications with the aid of orthogonal time frequency space (OTFS) modulation scheme, which can average out the channel dynamics through modulating information in the delay-Doppler (DD) domain, thereby overcoming severe Doppler effects. In particular, we start with studying the Doppler effect characteristics of LEO-Sat communications, which serves as the motivations of using OTFS. Then, we conceive a case study to show the feasibility of OTFS enabled LEO-Sat communication by evaluating the reliability performance. Finally, we discuss a range of promising research opportunities and potential challenges.

The Orthogonal Time Frequency Space (OTFS) Technique In 6G Wireless Communications

The orthogonal time-frequency space (OTFS) technique is a potential waveform modulation method that modulates data in the delaydoppler (DD) domain. OTFS differs from traditional multiplexing techniques by utilizing two-dimensional modulation to switch between the time-frequency (TF) domain and the delay-Doppler domain. This allows for handling Doppler shifts caused by fast-moving objects, a capability lacking in traditional modulation techniques like orthogonal frequency division multiplexing (OFDM). The primary goal of this paper is to offer an overview and short survey of this new topic, highlighting its system model. We also examine key aspects of OTFS modulation such as data detection methods, channel estimation, MIMO, and multiuser systems.

A Low‐Complexity Expectation Propagation Detector for OTFS

In this paper, we propose a low‐complexity expectation propagation (EP) detector for orthogonal time frequency space (OTFS) system with practical rectangular waveforms. In the high‐mobility scenario, OTFS is becoming a potential scheme for the sixth‐generation (6G) wireless communication system. However, the large size of the effective delay‐Doppler (DD) domain channel matrix brings unbearable computational complexity to the signal detection algorithm based on the matrix inversion. We propose a low‐complexity EP detector based on the sparsity and the block circulant structure of the effective channel covariance matrix in the DD domain. The proposed algorithm only requires log‐linear complexity. In addition, simulation results show that the proposed algorithm not only has the advantage of low complexity but also has good performance, which achieves a tradeoff between performance and complexity.

From OTFS to DD-ISAC: Integrating Sensing and Communications in the Delay Doppler Domain

From OTFS to DD-ISAC: Integrating Sensing and Communications in the Delay Doppler Domain

OTFS-Based ISAC: How Delay Doppler Channel Estimation Assists Environment Sensing?

OTFS-Based ISAC: How Delay Doppler Channel Estimation Assists Environment Sensing?

A New Micro-Subcarrier OFDM-Based Waveform for Delay Doppler Domain Communication

A New Micro-Subcarrier OFDM-Based Waveform for Delay Doppler Domain Communication

Performance Evaluation of OTFS-NOMA Scheme for High Mobility Users

Orthogonal Time Frequency Space (OTFS) is a promising approach which is widely employed in sixth generation (6G) wireless network systems. Because of its superior performance in high-mobility environments, OTFS modulation has received a lot of attention lately. Due to OTFS modulation works in the delay-Doppler (DD) domain rather than the conventional time-frequency (TF) domain, it works effectively in such circumstances. The idea of non-orthogonal multiple access (NOMA) is integrated into OTFS as an important approach to improve the spectral efficiency (SE) to investigate the efficiency potential and performance. In this research, we study OTFS modulated NOMA system for two destination users in the high mobility environment. The message passing detection algorithm is utilized to examine bit error rate (BER) performance for both near and far users in the proposed OTFS modulated NOMA system. The BER simulation results demonstrate that the power allocation (PA) coefficient, delays, and Doppler effects significantly impact the performance of the system.

Integrated Sensing and Communication via Orthogonal Time Frequency Space Signaling with Hybrid Message Passing Detection and Fractional Parameter Estimation.

For the orthogonal time frequency space (OTFS) modulation, we generally multiplex symbols on a new type of carrier waveform in the delay-Doppler (DD) domain. These two parameters can be used to infer the range (R) and velocity (V) of the communication user and sensing target; thus, it is natural for the OTFS to be implemented in integrated sensing and communication (ISAC). A framework for ISAC based on OTFS modulation is proposed in this paper, in which the matched filter scheme with fractional parameter estimation is implemented for radar sensing. In addition, the hybrid message passing (MP) detection algorithm is developed for OTFS symbol demodulation. According to the simulation results, fractional DD shifts associated with multiple targets can be accurately obtained through the proposed framework. Meanwhile, the bit error rate under the proposed detector is less than 10-4 when the signal-to-noise ratio is high enough.

Detection of inland water bodies under dense biomass by CYGNSS

The reliable detection of inland water bodies under dense biomass is not possible with traditional spaceborne remote sensing systems. In this investigation, we demonstrate the capability of the National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission to detect and quantify the extent of small water bodies under dense biomass up to 400 ton/ha in complex heterogenous scenes such as tropical rainforests. A novel inland water body product based on L1 CYGNSS data is developed using a combination of peak surface reflectivity Γp and its spread with respect to time delay and Doppler shift, as characterized by the power ratio Pratio. Only L1 CYGNSS mission data has the potential to study Earth's surface water dynamics because raw Intermediate Frequency (IF) data availability is limited. Several coherence indices are used to verify that the L-band Global Positioning System (GPS) signals are being coherently reflected by surface water bodies and attenuated by the intervening vegetation canopy. These indices include full and computationally fast versions of the entropy associated with Delay Doppler Maps (DDMs), Efull and Efast, as well as the time derivative of the phase φpeak of the reflected GPS signal. The capability to accurately resolve surface water extent under dense vegetation has been shown to improve the estimation by current state-of-art surface water extension datasets used by global methane CH4 emission models in wetlands.

GAMP-Based Low-Complexity Sparse Bayesian Learning Channel Estimation for OTFS Systems in V2X Scenarios

Vehicle to everything (V2X) is widely regarded as a critical application for future wireless communication networks. In V2X, large relative speeds between vehicles may severely deteriorate the performance of communication between vehicles. Orthogonal time frequency space (OTFS) modulation, which converts time- and frequency-selective channels into non-selective channels in the delay-Doppler (DD) domain, provides a solution for establishing reliable wireless communications in V2X scenarios. However, in the complex multi-scattering scenarios, the channel also suffers from a serious inter-Doppler interference (IDI) problem, which poses a great challenge to the accurate demodulation of OTFS receiver signals. To address the above problems, this paper considers the variation of Doppler sampling points within one symbol when deriving the channel model, which effectively overcomes the IDI problem, and employs a basis expansion model (BEM) to convert the channel estimation into a sparse recovery problem for the basis coefficients. In addition, to better utilize the sparse nature of the OTFS channel, a generalized approximate message passing-sparse Bayesian learning (GAMP-SBL)-based algorithm is employed to estimate the basis coefficients of the channel. The complexity of this algorithm is greatly reduced compared to the conventional SBL algorithm. Finally, system simulation results are reported to verify the superiority of the proposed scheme.