arXiv subjects

Weijie Yuan

Publications and source records attributed to Weijie Yuan.

At least 127 records · Page 7Linked to original sources

arXiv2022

Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks: A Deep Learning Approach

The implementation of integrated sensing and communication (ISAC) highly depends on the effective beamforming design exploiting accurate instantaneous channel state information (ICSI). However, channel tracking in ISAC requires large amount of training overhead and prohibitively large computational complexity. To address this problem, in this paper, we focus on ISAC-assisted vehicular networks and exploit a deep learning approach to implicitly learn the features of historical channels and directly predict the beamforming matrix for the next time slot to maximize the average achievable sum-rate of system, thus bypassing the need of explicit channel tracking for reducing the system signaling overhead. To this end, a general sum-rate maximization problem with Cramer-Rao lower bounds-based sensing constraints is first formulated for the considered ISAC system. Then, a historical channels-based convolutional long short-term memory network is designed for predictive beamforming that can exploit the spatial and temporal dependencies of communication channels to further improve the learning performance. Finally, simulation results show that the proposed method can satisfy the requirement of sensing performance, while its achievable sum-rate can approach the upper bound obtained by a genie-aided scheme with perfect ICSI available.

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arXiv2021

Faster-than-Nyquist Asynchronous NOMA Outperforms Synchronous NOMA

Faster-than-Nyquist (FTN) signaling aided non-orthogonal multiple access (NOMA) is conceived and its achievable rate is quantified in the presence of random link delays of the different users. We reveal that exploiting the link delays may potentially lead to a signal-to-interference-plus-noise ratio (SINR) gain, while transmitting the data symbols at FTN rates has the potential of increasing the degree-of-freedom (DoF). We then unveil the fundamental trade-off between the SINR and DoF. In particular, at a sufficiently high symbol rate, the SINR gain vanishes while the DoF gain achieves its maximum, where the achievable rate is almost $(1+β)$ times higher than that of the conventional synchronous NOMA transmission in the high signal-to-noise ratio (SNR) regime, with $β$ being the roll-off factor of the signaling pulse. Our simulation results verify our analysis and demonstrate considerable rate improvements over the conventional power-domain NOMA scheme.

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arXiv2021

Integrated Sensing and Communications for V2I Networks: Dynamic Predictive Beamforming for Extended Vehicle Targets

We investigate sensing-assisted predictive beamforming schemes for vehicle-to-infrastructure (V2I) communication by exploiting the integrated sensing and communication (ISAC) functionalities at the roadside unit (RSU). The RSU deploys a massive multi-input-multi-output (mMIMO) array and operates at millimeter wave (mmWave) frequencies. The pencil-sharp mMIMO beams and fine range resolution achieved at mmWave, implicates that the point target assumption is impractical in such V2I networks, as the volume and shape of the vehicles become essential for beamforming. Simply pointing a beam to the vehicle may result in the communication receiver (CR) never lying in the beam, even when the vehicle's trajectory is accurately tracked. To tackle this problem, we consider the extended vehicle target with two novel beam tracking schemes. For the first scheme, the beamwidth is adjusted in real-time to cover the entire vehicle, followed by an extended Kalman filtering (EKF) algorithm to predict and track the position of CR according to the resolved high-resolution scatterers. An upgraded scheme is further proposed by splitting each transmission block into two stages. The first stage is exploited for ISAC transmission, where a wide beam is adopted for both communication and sensing. Based on the sensed results at the first stage, the second stage is dedicated to communication by adopting a pencil-sharp beam, yielding a significant improvement of the achievable rate. We further reveal the inherent tradeoff between the two stages in terms of their durations, and develop an optimal time allocation strategy that maximizes the average achievable rate. Finally, numerical results are provided to verify the superiorities of proposed schemes over the state-of-the-art methods.

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arXiv2021

A Novel ISAC Transmission Framework based on Spatially-Spread Orthogonal Time Frequency Space Modulation

In this paper, we propose a novel integrated sensing and communication (ISAC) transmission framework based on the spatially-spread orthogonal time frequency space (SS-OTFS) modulation by considering the fact that communication channel strengths cannot be directly obtained from radar sensing. We first propose the concept of SS-OTFS modulation, where the key novelty is the angular domain discretization enabled by the spatial-spreading/de-spreading. This discretization gives rise to simple and insightful effective models for both radar sensing and communication, which result in simplified designs for the related estimation and detection problems. In particular, we design simple beam tracking, angle estimation, and power allocation schemes for radar sensing, by utilizing the special structure of the effective radar sensing matrix. Meanwhile, we provide a detailed analysis on the pair-wise error probability (PEP) for communication, which unveils the key conditions for both precoding and power allocation designs. Based on those conditions, we design a symbol-wise precoding scheme for communication based only on the delay, Doppler, and angle estimates from radar sensing, without the a priori knowledge of the communication channel fading coefficients, and also introduce the power allocation for communication. Furthermore, we notice that radar sensing and communication requires different power allocations. Therefore, we discuss the performances of both the radar sensing and communication with different power allocations and show that the power allocation should be designed leaning towards radar sensing in practical scenarios. The effectiveness of the proposed ISAC transmission framework is verified by our numerical results, which also agree with our analysis and discussions.

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arXiv2021

Integrated Sensing and Communication-assisted Orthogonal Time Frequency Space Transmission for Vehicular Networks

Orthogonal time frequency space (OTFS) modulation is a promising candidate for supporting reliable information transmission in high-mobility vehicular networks. In this paper, we consider the employment of the integrated (radar) sensing and communication (ISAC) technique for assisting OTFS transmission in both uplink and downlink vehicular communication systems. Benefiting from the OTFS-ISAC signals, the roadside unit (RSU) is capable of simultaneously transmitting downlink information to the vehicles and estimating the sensing parameters of vehicles, e.g., locations and speeds, based on the reflected echoes. Then, relying on the estimated kinematic parameters of vehicles, the RSU can construct the topology of the vehicular network that enables the prediction of the vehicle states in the following time instant. Consequently, the RSU can effectively formulate the transmit downlink beamformers according to the predicted parameters to counteract the channel adversity such that the vehicles can directly detect the information without the need of performing channel estimation. As for the uplink transmission, the RSU can infer the delays and Dopplers associated with different channel paths based on the aforementioned dynamic topology of the vehicular network. Thus, inserting guard space as in conventional methods are not needed for uplink channel estimation which removes the required training overhead. Finally, an efficient uplink detector is proposed by taking into account the channel estimation uncertainty. Through numerical simulations, we demonstrate the benefits of the proposed ISAC-assisted OTFS transmission scheme.

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arXiv2021

Iterative Detection for Orthogonal Time Frequency Space Modulation with Unitary Approximate Message Passing

The orthogonal-time-frequency-space (OTFS) modulation has emerged as a promising modulation scheme for high mobility wireless communications. To harvest the time and frequency diversity promised by OTFS, some promising detectors, especially message passing based ones, have been developed by taking advantage of the sparsity of the channel in the delay-Doppler domain. However, when the number of channel paths is relatively large or fractional Doppler {shifts have} to be considered, the complexity of existing detectors is a concern, and the message passing based detectors may suffer from performance loss due to the short loops involved in message passing. In this work, we investigate the design of OTFS detectors based on the approximate message passing (AMP). In particular, {leveraging the unitary AMP (UAMP), we design new detectors that enjoy} the structure of the channel matrix and allow efficient implementation. In addition, the estimation of noise variance is incorporated into the UAMP-based detectors. Thanks to the robustness of UAMP relative to AMP, the UAMP-based detectors deliver superior performance, and outperform state-of-the-art detectors significantly. We also investigate iterative joint detection and decoding in a coded OTFS system, where the OTFS detectors are integrated into a powerful turbo receiver, leading to considerable performance gains.

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arXiv2021

Orthogonal Time-Frequency Space Modulation: A Promising Next-Generation Waveform

The sixth-generation (6G) wireless networks are envisioned to provide a global coverage for the intelligent digital society of the near future, ranging from traditional terrestrial to non-terrestrial networks, where reliable communications in high-mobility scenarios at high carrier frequencies would play a vital role. In such scenarios, the conventional orthogonal frequency division multiplexing (OFDM) modulation, that has been widely used in both the fourth-generation (4G) and the emerging fifth-generation (5G) cellular systems as well as in WiFi networks, is vulnerable to severe Doppler spread. In this context, this article aims to introduce a recently proposed two-dimensional modulation scheme referred to as orthogonal time-frequency space (OTFS) modulation, which conveniently accommodates the channel dynamics via modulating information in the delay-Doppler domain. This article provides an easy-reading overview of OTFS, highlighting its underlying motivation and specific features. The critical challenges of OTFS and our preliminary results are presented. We also discuss a range of promising research opportunities and potential applications of OTFS in 6G wireless networks.

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arXiv2021

Performance Analysis and Window Design for Channel Estimation of OTFS Modulation

In this paper, we investigate the impacts of transmitter and receiver windows on orthogonal time-frequency space (OTFS) modulation and propose a window design to improve the OTFS channel estimation performance. Assuming ideal pulse shaping filters at the transceiver, we first identify the role of window in effective channel and the reduced channel sparsity with conventional rectangular window. Then, we characterize the impacts of windowing on the effective channel estimation performance for OTFS modulation. Based on the revealed insights, we propose to apply a Dolph-Chebyshev (DC) window at either the transmitter or the receiver to effectively enhance the sparsity of the effective channel. As such, the channel spread due to the fractional Doppler is significantly reduced, which leads to a lower error floor in channel estimation compared with that of the rectangular window. Simulation results verify the accuracy of the obtained analytical results and confirm the superiority of the proposed window designs in improving the channel estimation performance over the conventional rectangular or Sine windows.

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arXiv2021

Off-grid Channel Estimation with Sparse Bayesian Learning for OTFS Systems

This paper proposes an off-grid channel estimation scheme for orthogonal time-frequency space (OTFS) systems adopting the sparse Bayesian learning (SBL) framework. To avoid channel spreading caused by the fractional delay and Doppler shifts and to fully exploit the channel sparsity in the delay-Doppler (DD) domain, we estimate the original DD domain channel response rather than the effective DD domain channel response as commonly adopted in the literature. OTFS channel estimation is first formulated as a one-dimensional (1D) off-grid sparse signal recovery (SSR) problem based on a virtual sampling grid defined in the DD space, where the on-grid and off-grid components of the delay and Doppler shifts are separated for estimation. In particular, the on-grid components of the delay and Doppler shifts are jointly determined by the entry indices with significant values in the recovered sparse vector. Then, the corresponding off-grid components are modeled as hyper-parameters in the proposed SBL framework, which can be estimated via the expectation-maximization method. To strike a balance between channel estimation performance and computational complexity, we further propose a two-dimensional (2D) off-grid SSR problem via decoupling the delay and Doppler shift estimations. In our developed 1D and 2D off-grid SBL-based channel estimation algorithms, the hyper-parameters are updated alternatively for computing the conditional posterior distribution of channels, which can be exploited to reconstruct the effective DD domain channel. Compared with the 1D method, the proposed 2D method enjoys a much lower computational complexity while only suffers slight performance degradation. Simulation results verify the superior performance of the proposed channel estimation schemes over state-of-the-art schemes.

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arXiv2021

Cross Domain Iterative Detection for Orthogonal Time Frequency Space Modulation

Recently proposed orthogonal time frequency space (OTFS) modulation has been considered as a promising candidate for accommodating various emerging communication and sensing applications in high-mobility environments. In this paper, we propose a novel cross domain iterative detection algorithm to enhance the error performance of OTFS modulation. Different from conventional OTFS detection methods, the proposed algorithm applies basic estimation/detection approaches to both the time domain and delay-Doppler (DD) domain and iteratively updates the extrinsic information from two domains with the unitary transformation. In doing so, the proposed algorithm exploits the time domain channel sparsity and the DD domain symbol constellation constraints. We evaluate the estimation/detection error variance in each domain for each iteration and derive the state evolution to investigate the detection error performance. We show that the performance gain due to iterations comes from the non-Gaussian constellation constraint in the DD domain. More importantly, we prove the proposed algorithm can indeed converge and, in the convergence, the proposed algorithm can achieve almost the same error performance as the maximum-likelihood sequence detection even in the presence of fractional Doppler shifts. Furthermore, the computational complexity associated with the domain transformation is low, thanks to the structure of the discrete Fourier transform (DFT) kernel. Simulation results are consistent with our analysis and demonstrate a significant performance improvement compared to conventional OTFS detection methods.

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arXiv2020

Performance Analysis of Coded OTFS Systems over High-Mobility Channels

Orthogonal time frequency space (OTFS) modulation is a recently developed multi-carrier multi-slot transmission scheme for wireless communications in high-mobility environments. In this paper, the error performance of coded OTFS modulation over high-mobility channels is investigated. We start from the study of conditional pairwise-error probability (PEP) of the OTFS scheme, based on which its performance upper bound of the coded OTFS system is derived. Then, we show that the coding improvement for OTFS systems depends on the squared Euclidean distance among codeword pairs and the number of independent resolvable paths of the channel. More importantly, we show that there exists a fundamental trade-off between the coding gain and the diversity gain for OTFS systems, i.e., the diversity gain of OTFS systems improves with the number of resolvable paths, while the coding gain declines. Furthermore, based on our analysis, the impact of channel coding parameters on the performance of the coded OTFS systems is unveiled. The error performance of various coded OTFS systems over high-mobility channels is then evaluated. Simulation results demonstrate a significant performance improvement for OTFS modulation over the conventional orthogonal frequency division multiplexing (OFDM) modulation over high-mobility channels. Analytical results and the effectiveness of the proposed code design are also verified by simulations with the application of both classical and modern codes for OTFS systems.

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arXiv2020

Hybrid MAP and PIC Detection for OTFS Modulation

Orthogonal time frequency space (OTFS) modulation has attracted substantial attention recently due to its great potential of providing reliable communications in high-mobility scenarios. In this paper, we propose a novel hybrid signal detection algorithm for OTFS modulation. By characterizing the input-output relationship of OTFS modulation, we derive the near-optimal symbol-wise maximum a posteriori (MAP) detection algorithm for OTFS modulation, which aims to extract the information of each transmitted symbol based on the corresponding related received symbols. Furthermore, in order to reduce the detection complexity, we propose a partitioning rule that separates the related received symbols into two subsets for detecting each transmitted symbol, according to the corresponding path gains. We then introduce a hybrid detection algorithm to exploit the power discrepancy of each subset, where the MAP detection is applied to the subset with larger channel gains, while the parallel interference cancellation (PIC) detection is applied to the subset with smaller channel gains. Simulation results show that the proposed algorithms can not only approach the performance of the near-optimal symbol-wise MAP algorithms, but also offer a substantial performance gain compared with existing algorithms.

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arXiv2020

Transmitter and Receiver Window Designs for Orthogonal Time Frequency Space Modulation

In this paper, we investigate the impacts of transmitter and receiver windows on the performance of orthogonal time-frequency space (OTFS) modulation and propose window designs to improve the OTFS channel estimation and data detection performance. In particular, assuming ideal pulse shaping filters at the transceiver, we derive the impacts of windowing on the effective channel and its estimation performance in the delay-Doppler (DD) domain, the total average transmit power and the effective noise covariance matrix. When the channel state information (CSI) is available at the transceiver, we analyze the minimum squared error (MSE) of data detection and propose an optimal transmitter window to minimize the detection MSE. The proposed optimal transmitter window is interpreted as a mercury/water-filling power allocation scheme, where the mercury is firstly filled before pouring water to pre-equalize the TF domain channels. When the CSI is not available at the transmitter but can be estimated at the receiver, we propose to apply a Dolph-Chebyshev (DC) window at either the transmitter or the receiver, which can effectively enhance the sparsity of the effective channel in the DD domain. Thanks to the enhanced DD domain channel sparsity, the channel spread due to the fractional Doppler is significantly reduced, which leads to a lower error floor in both channel estimation and data detection compared with that of rectangular window. Simulation results verify the accuracy of the obtained analytical results and confirm the superiority of the proposed window designs in improving the channel estimation and data detection performance over the conventional rectangular window design.

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arXiv2020

Location-aware Predictive Beamforming for UAV Communications: A Deep Learning Approach

Unmanned aerial vehicle (UAV)-assisted communication becomes a promising technique to realize the beyond fifth generation (5G) wireless networks, due to the high mobility and maneuverability of UAVs which can adapt to heterogeneous requirements of different applications. However, the movement of UAVs impose challenge for accurate beam alignment between the UAV and the ground user equipment (UE). In this letter, we propose a deep learning-based location-aware predictive beamforming scheme to track the beam for UAV communications in a dynamic scenario. Specifically, a long short-term memory (LSTM)-based recurrent neural network (LRNet) is designed for UAV location prediction. Based on the predicted location, a predicted angle between the UAV and the UE can be determined for effective and fast beam alignment in the next time slot, which enables reliable communications between the UAV and the UE. Simulation results demonstrate that the proposed scheme can achieve a satisfactory UAV-to-UE communication rate, which is close to the upper bound of communication rate obtained by the perfect genie-aided alignment scheme.

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arXiv2020

Bayesian Predictive Beamforming for Vehicular Networks: A Low-overhead Joint Radar-Communication Approach

The development of dual-functional radar-communication (DFRC) systems, where vehicle localization and tracking can be combined with vehicular communication, will lead to more efficient future vehicular networks. In this paper, we develop a predictive beamforming scheme in the context of DFRC systems. We consider a system model where the road-side units estimates and predicts the motion parameters of vehicles based on the echoes of the DFRC signal. Compared to the conventional feedback-based beam tracking approaches, the proposed method can reduce the signaling overhead and improve the accuracy. To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields near optimal solution to the maximum a posteriori estimation. The beamformers are then designed based on the predicted angles for establishing the communication links.}With the employment of appropriate approximations, all messages on the factor graph can be derived in a closed-form, thus reduce the complexity. Simulation results show that the proposed DFRC based beamforming scheme is superior to the feedback-based approach in terms of both estimation and communication performance. Moreover, the proposed message passing algorithm achieves a similar performance of the high-complexity particle-based methods.

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arXiv2020

Joint Radar-Communication-Based Bayesian Predictive Beamforming for Vehicular Networks

In this paper, we develop a predictive beamforming scheme based on the dual-functional radar-communication (DFRC) technique, where the road-side units estimates the motion parameters of vehicles exploiting the echoes of the DFRC signals. Compared to the conventional feedback-based beam tracking approaches, the proposed method can reduce the signaling overhead and improve the tracking performance. A novel message passing algorithm is proposed, which yields a near optimal performance achieved by the maximum a posteriori estimation. Simulation results have shown the effectiveness of the proposed DFRC based scheme.

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arXiv2020

Radar-assisted Predictive Beamforming for Vehicle-to-Infrastructure Links

In this paper, we propose a radar-assisted predictive beamforming design for vehicle-to-infrastructure (V2I) communication by relying on the joint sensing and communication functionalities at road side units (RSUs). We present a novel extended Kalman filtering (EKF) framework to track and predict kinematic parameters of the vehicle. By exploiting the radar functionality of the RSU we show that the communication beam tracking overheads can be drastically reduced. Numerical results have demonstrated that the proposed radar-assisted approach significantly outperforms the communication-only feedback based technique in both the angle tracking and the downlink communication.

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arXiv2020

Joint Data and Active User Detection for Grant-free FTN-NOMA in Dynamic Networks

Both faster than Nyquist (FTN) signaling and non-orthogonal multiple access (NOMA) are promising next generation wireless communications techniques as a benefit of their capability of improving the system's spectral efficiency. This paper considers an uplink system that combines the advantages of FTN and NOMA. Consequently, an improved spectral efficiency is achieved by deliberately introducing both inter-symbol interference (ISI) and inter-user interference (IUI). More specifically, we propose a grant-free transmission scheme to reduce the signaling overhead and transmission latency of the considered NOMA system. To distinguish the active and inactive users, we develop a novel message passing receiver that jointly estimates the channel state, detects the user activity, and performs decoding. We conclude by quantifying the significant spectral efficiency gain achieved by our amalgamated FTN-NOMA scheme compared to the orthogonal transmission system, which is up to 87.5%.

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