With the development of high speed trains (HST) in many countries, providing broadband wireless services in HSTs is becoming crucial. Orthogonal frequency-division multiplexing (OFDM) has been widely adopted for broadband wireless communications due to its high spectral efficiency. However, OFDM is sensitive to the time selectivity caused by high-mobility channels, which costs large spectrum or time resources to obtain the accurate channel state information (CSI). Therefore, the channel estimation in high-mobility OFDM systems has been a long-standing challenge. In this paper, we first propose a new position-based high-mobility channel model,in which the HST's position information and Doppler shift are utilized to determine the positions of the dominant channel coefficients. %In this way, we can reduce the estimation complexity and to design the transmitted pilot.Then, we propose a joint pilot placement and pilot symbol design algorithm for compressed channel estimation. It aims to reduce the coherence between the pilot signal and the proposed channel model, and hence can improve the channel estimation accuracy. Simulation results demonstrate that the proposed method achieves better performances than existing channel estimation methods over high-mobility channels. Furthermore, we give an example of the designed pilot codebook to show the practical applicability of the proposed scheme.
Unique word-orthogonal frequency division multiplexing (UW-OFDM) is known to provide various performance benefits over conventional OFDM using cyclic prefixes (CP). Most important, UW-OFDM features excellent spectral sidelobe suppression properties and an outstanding bit error ratio performance. Current research has mainly focused on principle performance bounds of UW-OFDM, with less attention on challenges aside from idealized communication scenarios, such as system parameter estimation tasks. In this work we present an approach for including frequency pilots tones into the UW-OFDM signaling …
DBU-OFDM: A Trainable Deep Block-Unitary OFDM Waveform for Integrated Sensing and Communication
State Key Laboratory of Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）；National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院移动通信国家重点实验室）；Purple Mountain Laboratories（紫金山实验室）
We consider the problem of downlink channel estimation for intelligent reflecting surface (IRS)-assisted millimeter Wave (mmWave) orthogonal frequency division multiplexing (OFDM) systems. By exploring the inherent sparse scattering characteristics of mmWave channels, we show that the received signals can be expressed as a low-rank third-order tensor that admits a tensor rank decomposition, also known as canonical polyadic decomposition (CPD). A structured CPD-based method is then developed to estimate the channel parameters. Our analysis reveals that the training overhead required by our prop…
Path-Level Radio Map-Aided Fast and Robust Channel Estimation for Pilot-Starved MIMO-OFDM Systems
State Key Laboratory of ISN and School of Telecommunications Engineering, Xidian University（西安电子科技大学信息与通信工程学院）；School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；School of Information and Communication Engineering, Xi’an Jiaotong University（西安交通大学信息通信工程学院）
Due to the publicly-known deterministic character- istic of pilot tones, pilot-aware attack, by jamming, nulling and spoofing pilot tones, can significantly paralyze the uplink channel training in large-scale MISO-OFDM systems. To solve this, we in this paper develop an independence-checking coding based (ICCB) uplink training architecture for one-ring scattering scenarios allowing for uniform linear arrays (ULA) deployment. Here, we not only insert randomized pilots on subcarriers for channel impulse response (CIR) estimation, but also diversify and encode subcarrier activation patterns (SAPs…
Ambiguity Function Analysis of OFDM Signals With Pilots and Data Payloads
National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信重点实验室）；School of Automation and Intelligent Manufacturing, Southern University of Science and Technology（南方科技大学自动化与智能制造学院）；School of Information and Electronic Engineering, Beijing University of Posts and Telecommunications（北京邮电大学信息与电子工程学院）；Southeast University（东南大学）；Ulsan National Institute of Science and Technology (UNIST)（蔚山科学技术院）
Full-Frame OFDM-ISAC: Joint Constellation, Pilot, and Receive-Filter Design
Ulsan National Institute of Science and Technology (UNIST)（蔚山科学技术院）；University of Manchester（曼彻斯特大学）；National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院国家移动通信实验室）；University College London（伦敦大学学院）
A Computationally Efficient Joint Maximum Likelihood Estimator for Passive Localization in OFDM Distributed Antenna Systems with Pilots and Unknown Data Payloads
Amplitude-Phase-Frequency Block Modulation for OFDM-ISAC with SI-Free PAPR Reduction and Pilotless Sensing
Nanjing University of Posts and Telecommunications（南京邮电大学）；Jiangsu Key Laboratory of Wireless Communications（江苏省无线通信重点实验室）；Southeast University（东南大学）；State Key Laboratory of Millimeter Waves（毫米波国家重点实验室）；Purple Mountain Laboratories（紫金山实验室）
In this paper, we present a downlink pilot design scheme for Deep Learning (DL) based channel estimation (ChannelNet) in orthogonal frequency-division multiplexing (OFDM) systems. Specifically, in the proposed scheme, a feature selection method named Concrete Autoencoder (ConcreteAE) is used to find the most informative locations for pilot transmission. This autoencoder consists of a concrete layer as the encoder and a multilayer perceptron (MLP) as the decoder. During the training, the concrete layer selects the most informative pilot locations, and the decoder reconstructs an approximate est…
Orthogonal frequency division multiplexing (OFDM) is one of the dominant waveforms in wireless communication systems due to its efficient implementation. However, it suffers from a loss of spectral efficiency as it requires a cyclic prefix (CP) to mitigate inter-symbol interference (ISI) and pilots to estimate the channel. We propose in this work to address these drawbacks by learning a neural network (NN)-based receiver jointly with a constellation geometry and bit labeling at the transmitter, that allows CP-less and pilotless communication on top of OFDM without a significant loss in bit err…
Orthogonal frequency-division multiplexing (OFDM) is widely adopted for providing reliable and high data rate communication in high-speed train systems. However, with the increasing train mobility, the resulting large Doppler shift introduces intercarrier interference (ICI) in OFDM systems and greatly degrades the channel estimation accuracy. Therefore, it is necessary and important to investigate reliable channel estimation and ICI mitigation methods in high-mobility environments. In this paper, we consider a typical HST communication system and show that the ICI caused by the large Doppler s…
Orthogonal frequency-division multiplexing (OFD-M) and multi-cell architecture are widely adopted in current high speed train (HST) systems for providing high data rate wireless communications. In this paper, a typical multi-antenna OFDM HST communication system with multi-cell architecture is considered, where the inter-carrier interference (ICI) caused by high mobility and multi-cell interference (MCI) are both taken into consideration. By exploiting the train position information, a new position-based interference elimination method is proposed to eliminate both the MCI and ICI for a genera…
With the large number of antennas and subcarriers the overhead due to pilot transmission for channel estimation can be prohibitive in wideband massive multiple-input multiple-output (MIMO) systems. This can degrade the overall spectral efficiency significantly, and as a result, curtail the potential benefits of massive MIMO. In this paper, we propose a neural network (NN)-based joint pilot design and downlink channel estimation scheme for frequency division duplex (FDD) MIMO orthogonal frequency division multiplex (OFDM) systems. The proposed NN architecture uses fully connected layers for fre…
In this paper, we propose a novel estimator for pilot-aided orthogonal frequency division multiplexing (OFDM) channels in an additive Gaussian and impulsive perturbation environment. Due to sensor failure which might happen because of man-made noise, a number of measurements in high rate communication systems is often corrupted by impulsive noise. High power impulsive noise is generally an obstacle for OFDM systems as valuable information will be completely lost. To overcome this concern, an objective function based on a penalized atomic norm minimization (PANM) is provided in order to promote…
Previous studies have demonstrated that end-to-end learning enables significant shaping gains over additive white Gaussian noise (AWGN) channels. However, its benefits have not yet been quantified over realistic wireless channel models. This work aims to fill this gap by exploring the gains of end-to-end learning over a frequency- and time-selective fading channel using orthogonal frequency division multiplexing (OFDM). With imperfect channel knowledge at the receiver, the shaping gains observed on AWGN channels vanish. Nonetheless, we identify two other sources of performance improvements. Th…
Delay-Doppler Domain Signal Processing Aided OFDM (DD-a-OFDM) for 6G and Beyond
Beijing Jiaotong University（北京交通大学）；University of New South Wales（新南威尔士大学）；Technical University of Berlin（柏林工业大学）；Queensland University of Technology（昆士兰科技大学）；Southern University of Science and Technology（南方科技大学）；Xi’an Jiaotong University（西安交通大学）；Southeast University（东南大学）
Reservoir computing (RC) is a special recurrent neural network which consists of a fixed high dimensional feature mapping and trained readout weights. In this paper, we introduce a new RC structure for multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) symbol detection, namely windowed echo state network (WESN). The theoretical analysis shows that adding buffers in input layers can bring an enhanced short-term memory (STM) to the underlying neural network. Furthermore, a unified training framework is developed for the WESN MIMO-OFDM symbol detector using bot…
MIMO-OFDM AI Receiver Based on Incrementally Conditioned Diffusion with Soft Decision
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）；Zhejiang Key Laboratory of Multi-modal Commu. Netw. & Intell. Info. Proc.（浙江省多模通信网络与智能信息处理重点实验室）
Due to the publicly known and deterministic characteristic of pilot tones, pilot authentication (PA) in multi-user multi-antenna orthogonal frequency-division multiplexing systems is very susceptible to the jamming/nulling/spoofing behaviors. To solve this, in this paper, we develop a hierarchical 2-D feature (H2DF) coding theory that exploits the hidden pilot signal features, i.e., the energy feature and independence feature, to secure pilot information coding which is applied between legitimate parties through a well-designed five-layer hierarchical coding model to achieve secure multiuser P…
A pilot spoofer can paralyze the channel estimation in multi-user orthogonal frequency-division multiplexing (OFD- M) systems by using the same publicly-known pilot tones as legitimate nodes. This causes the problem of pilot authentication (PA). To solve this, we propose, for a two-user multi-antenna OFDM system, a code-frequency block group (CFBG) coding based PA mechanism. Here multi-user pilot information, after being randomized independently to avoid being spoofed, are converted into activation patterns of subcarrier-block groups on code-frequency domain. Those patterns, though overlapped …
In this paper, we devise a highly efficient machine learning-based channel estimation for orthogonal frequency division multiplexing (OFDM) systems, in which the training of the estimator is performed online. A simple learning module is employed for the proposed learning-based estimator. The training process is thus much faster and the required training data is reduced significantly. Besides, a training data construction approach utilizing least square (LS) estimation results is proposed so that the training data can be collected during the data transmission. The feasibility of this novel cons…
In cell-free massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, user equipments (UEs) are served by many distributed access points (APs), where channels are correlated due to finite angle-delay spread in realistic outdoor wireless propagation environments. Meanwhile, the number of UEs is growing rapidly for future fully networked society. In this paper, we focus on the uplink transmission design in crowded correlated cell-free massive MIMO-OFDM systems with limited number of orthogonal pilots. For the pilot transmission phase, we identify a…
Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation
University of Electronic Science and Technology of China（电子科技大学）；Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong-Shenzhen（香港中文大学（深圳）大数据研究院）
For an orthogonal frequency-division multiplexing (OFDM) system over a doubly selective (DS) channel, a large number of pilot subcarriers are needed to estimate the numerous channel parameters, resulting in low spectral efficiency. In this paper, by exploiting temporal correlation of practical wireless channels, we propose a highly efficient structured distributed compressive sensing (SDCS) based joint multi-symbol channel estimation scheme. Specifically, by using the complex exponential basis expansion model (CE-BEM) and exploiting the sparsity in the delay domain within multiple OFDM symbols…
