arXiv subjects

Shi Jin

Publications and source records attributed to Shi Jin.

At least 325 records · Page 18Linked to original sources

arXiv2024

Two-Timescale Transmission Design for Wireless Communication Systems Aided by Active RIS

This paper considers an active reconfigurable intelligent surface (RIS)-aided communication system, where an M-antenna base station (BS) transmits data symbols to a single-antenna user via an N-element active RIS. We use two-timescale channel state information (CSI) in our system, so that the channel estimation overhead and feedback overhead can be decreased dramatically. A closed-form approximate expression of the achievable rate (AR) is derived and the phase shift at the active RIS is optimized. In addition, we compare the performance of the active RIS system with that of the passive RIS system. The conclusion shows that the active RIS system achieves a lager AR than the passive RIS system.

eess.SP↗

arXiv2024

Two-stage Synthetic Supervising and Multi-view Consistency Self-supervising based Animal 3D Reconstruction by Single Image

Pixel-aligned Implicit Function (PIFu) effectively captures subtle variations in body shape within a low-dimensional space through extensive training with human 3D scans, its application to live animals presents formidable challenges due to the difficulty of obtaining animal cooperation for 3D scanning. To address this challenge, we propose the combination of two-stage supervised and self-supervised training to address the challenge of obtaining animal cooperation for 3D scanning. In the first stage, we leverage synthetic animal models for supervised learning. This allows the model to learn from a diverse set of virtual animal instances. In the second stage, we use 2D multi-view consistency as a self-supervised training method. This further enhances the model's ability to reconstruct accurate and realistic 3D shape and texture from largely available single-view images of real animals. The results of our study demonstrate that our approach outperforms state-of-the-art methods in both quantitative and qualitative aspects of bird 3D digitization. The source code is available at https://github.com/kuangzijian/drifu-for-animals.

cs.CV↗

arXiv2024

Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients

Federated learning (FL) is an emerging distributed training paradigm that aims to learn a common global model without exchanging or transferring the data that are stored locally at different clients. The Federated Averaging (FedAvg)-based algorithms have gained substantial popularity in FL to reduce the communication overhead, where each client conducts multiple localized iterations before communicating with a central server. In this paper, we focus on FL where the clients have diverse computation and/or communication capabilities. Under this circumstance, FedAvg can be less efficient since it requires all clients that participate in the global aggregation in a round to initiate iterations from the latest global model, and thus the synchronization among fast clients and straggler clients can severely slow down the overall training process. To address this issue, we propose an efficient asynchronous federated learning (AFL) framework called Delayed Federated Averaging (DeFedAvg). In DeFedAvg, the clients are allowed to perform local training with different stale global models at their own paces. Theoretical analyses demonstrate that DeFedAvg achieves asymptotic convergence rates that are on par with the results of FedAvg for solving nonconvex problems. More importantly, DeFedAvg is the first AFL algorithm that provably achieves the desirable linear speedup property, which indicates its high scalability. Additionally, we carry out extensive numerical experiments using real datasets to validate the efficiency and scalability of our approach when training deep neural networks.

cs.LG↗

arXiv2024

Robust Symbol-Level Precoding for Massive MIMO Communication Under Channel Aging

This paper investigates the robust design of symbol-level precoding (SLP) for multiuser multiple-input multiple-output (MIMO) downlink transmission with imperfect channel state information (CSI) caused by channel aging. By utilizing the a posteriori channel model based on the widely adopted jointly correlated channel model, the imperfect CSI is modeled as the statistical CSI incorporating the channel mean and channel variance information with spatial correlation. With the signal model in the presence of channel aging, we formulate the signal-to-noise-plus-interference ratio (SINR) balancing and minimum mean square error (MMSE) problems for robust SLP design. The former targets to maximize the minimum SINR across users, while the latter minimizes the mean square error between the received signal and the target constellation point. When it comes to massive MIMO scenarios, the increment in the number of antennas poses a computational complexity challenge, limiting the deployment of SLP schemes. To address such a challenge, we simplify the objective function of the SINR balancing problem and further derive a closed-form SLP scheme. Besides, by approximating the matrix involved in the computation, we modify the proposed algorithm and develop an MMSE-based SLP scheme with lower computation complexity. Simulation results confirm the superiority of the proposed schemes over the state-of-the-art SLP schemes.

eess.SP↗

arXiv2024

Reconfigurable Intelligent Surface: Power Consumption Modeling and Practical Measurement Validation

The reconfigurable intelligent surface (RIS) has received a lot of interest because of its capacity to reconfigure the wireless communication environment in a cost- and energy-efficient way. However, the realistic power consumption modeling and measurement validation of RIS has received far too little attention. Therefore, in this work, we model the power consumption of RIS and conduct measurement validations using various RISs to fill this vacancy. Firstly, we propose a practical power consumption model of RIS. The RIS hardware is divided into three basic parts: the FPGA control board, the drive circuits, and the RIS unit cells. The power consumption of the first two parts is modeled as $P_{\text {static}}$ and that of the last part is modeled as $P_{\text {units}}$. Expressions of $P_{\text {static}}$ and $P_{\text {units}}$ vary amongst different types of RISs. Secondly, we conduct measurements on various RISs to validate the proposed model. Five different RISs including the PIN diode, varactor diode, and RF switch types are measured, and measurement results validate the generality and applicability of the proposed power consumption model of RIS. Finally, we summarize the measurement results and discuss the approaches to achieve the low-power-consumption design of RIS-assisted wireless communication systems.

cs.IT↗

arXiv2024

A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G

Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large antenna size, wide bandwidth, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both new challenges and opportunities for the practical design of 6G. On one hand, acquiring channel state information (CSI) in real time for all wireless links becomes quite challenging in 6G. On the other hand, there would be numerous data sources in 6G containing high-quality location-tagged channel data, e.g., the estimated channels or beams between base station (BS) and user equipment (UE), making it possible to better learn the local wireless environment. By exploiting this new opportunity and for tackling the CSI acquisition challenge, there is a promising paradigm shift from the conventional environment-unaware communications to the new environment-aware communications based on the novel approach of channel knowledge map (CKM). This article aims to provide a comprehensive overview on environment-aware communications enabled by CKM to fully harness its benefits for 6G. First, the basic concept of CKM is presented, followed by the comparison of CKM with various existing channel inference techniques. Next, the main techniques for CKM construction are discussed, including both environment model-free and environment model-assisted approaches. Furthermore, a general framework is presented for the utilization of CKM to achieve environment-aware communications, followed by some typical CKM-aided communication scenarios. Finally, important open problems in CKM research are highlighted and potential solutions are discussed to inspire future work.

cs.IT↗

arXiv2024

Sliding Secure Symmetric Multilevel Diversity Coding

Symmetric multilevel diversity coding (SMDC) is a source coding problem where the independent sources are ordered according to their importance. It was shown that separately encoding independent sources (referred to as ``\textit{superposition coding}") is optimal. In this paper, we consider an $(L,s)$ \textit{sliding secure} SMDC problem with security priority, where each source $X_α~(s\leq α\leq L)$ is kept perfectly secure if no more than $α-s$ encoders are accessible. The reconstruction requirements of the $L$ sources are the same as classical SMDC. A special case of an $(L,s)$ sliding secure SMDC problem that the first $s-1$ sources are constants is called the $(L,s)$ \textit{multilevel secret sharing} problem. For $s=1$, the two problems coincide, and we show that superposition coding is optimal. The rate regions for the $(3,2)$ problems are characterized. It is shown that superposition coding is suboptimal for both problems. The main idea that joint encoding can reduce coding rates is that we can use the previous source $X_{α-1}$ as the secret key of $X_α$. Based on this idea, we propose a coding scheme that achieves the minimum sum rate of the general $(L,s)$ multilevel secret sharing problem. Moreover, superposition coding of the $s$ sets of sources $X_1$, $X_2$, $\cdots$, $X_{s-1}$, $(X_s, X_{s+1}, \cdots, X_L)$ achieves the minimum sum rate of the general sliding secure SMDC problem.

cs.IT↗

arXiv2024

Electromagnetic Property Sensing: A New Paradigm of Integrated Sensing and Communication

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel scheme that utilizes orthogonal frequency division multiplexing (OFDM) pilot signals in ISAC systems to sense the electromagnetic (EM) property of the target and thus also identify the material of the target. Specifically, we first establish an end-to-end EM propagation model by means of Maxwell equations, where the EM property of the target is captured by a closed-form expression of the ISAC channel, incorporating the Lippmann-Schwinger equation and the method of moments (MOM) for discretization. We then model the relative permittivity and conductivity distribution (RPCD) within a specified detection region. Based on the sensing model, we introduce a multi-frequency-based EM property sensing method by which the RPCD can be reconstructed from compressive sensing techniques that exploits the joint sparsity structure of the EM property vector. To improve the sensing accuracy, we design a beamforming strategy from the communications transmitter based on the Born approximation that can minimize the mutual coherence of the sensing matrix. The optimization problem is cast in terms of the Gram matrix and is solved iteratively to obtain the optimal beamforming matrix. Simulation results demonstrate the efficacy of the proposed method in achieving high-quality RPCD reconstruction and accurate material classification. Furthermore, improvements in RPCD reconstruction quality and material classification accuracy are observed with increased signal-to-noise ratio (SNR) or reduced target-transmitter distance.

eess.SP↗

arXiv2024

Quantized RIS-aided mmWave Massive MIMO Channel Estimation with Uniform Planar Arrays

In this paper, we investigate a cascaded channel estimation method for a millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system aided by a reconfigurable intelligent surface (RIS) with the BS equipped with low-resolution analog-to-digital converters (ADCs), where the BS and the RIS are both equipped with a uniform planar array (UPA). Due to the sparse property of mmWave channel, the channel estimation can be solved as a compressed sensing (CS) problem. However, the low-resolution quantization cause severe information loss of signals, and traditional CS algorithms are unable to work well. To recovery the signal and the sparse angular domain channel from quantization, we introduce Bayesian inference and efficient vector approximate message passing (VAMP) algorithm to solve the quantize output CS problem. To further improve the efficiency of the VAMP algorithm, a Fast Fourier Transform (FFT) based fast computation method is derived. Simulation results demonstrate the effectiveness and the accuracy of the proposed cascaded channel estimation method for the RIS-aided mmWave massive MIMO system with few-bit ADCs. Furthermore, the proposed channel estimation method can reach an acceptable performance gap between the low-resolution ADCs and the infinite ADCs for the low signal-to-noise ratio (SNR), which implies the applicability of few-bit ADCs in practice.

cs.IT↗

arXiv2024

Channel Estimation for FAS-assisted Multiuser mmWave Systems

This letter investigates the challenge of channel estimation in a multiuser millimeter-wave (mmWave) time-division duplexing (TDD) system. In this system, the base station (BS) employs a multi-antenna uniform linear array (ULA), while each mobile user is equipped with a fluid antenna system (FAS). Accurate channel state information (CSI) plays a crucial role in the precise placement of antennas in FAS. Traditional channel estimation methods designed for fixed-antenna systems are inadequate due to the high dimensionality of FAS. To address this issue, we propose a low-sample-size sparse channel reconstruction (L3SCR) method, capitalizing on the sparse propagation paths characteristic of mmWave channels. In this approach, each fluid antenna only needs to switch and measure the channel at a few specific locations. By observing this reduced-dimensional data, we can effectively extract angular and gain information related to the sparse channel, enabling us to reconstruct the full CSI. Simulation results demonstrate that our proposed method allows us to obtain precise CSI with minimal hardware switching and pilot overhead. As a result, the system sum-rate approaches the upper bound achievable with perfect CSI.

cs.IT↗

arXiv2023

6D Radar Sensing and Tracking in Monostatic Integrated Sensing and Communications System

In this paper, we propose a novel scheme for sixdimensional (6D) radar sensing and tracking of dynamic target based on multiple input and multiple output (MIMO) array for monostatic integrated sensing and communications (ISAC) system. Unlike most existing ISAC studies believing that only the radial velocity of far-field dynamic target can be measured based on one single base station (BS), we find that the sensing echo channel of MIMO-ISAC system actually includes the distance, horizontal angle, pitch angle, radial velocity, horizontal angular velocity, and pitch angular velocity of the dynamic target. Thus we may fully rely on one single BS to estimate the dynamic target's 6D motion parameters from the sensing echo signals. Specifically, we first propose the long-term motion and short-term motion model of dynamic target, in which the short-term motion model serves the single-shot sensing of dynamic target, while the long-term motion model serves multiple-shots tracking of dynamic target. As a step further, we derive the sensing channel model corresponding to the short-term motion. Next, for singleshot sensing, we employ the array signal processing methods to estimate the dynamic target's horizontal angle, pitch angle, distance, and virtual velocity. By realizing that the virtual velocities observed by different antennas are different, we adopt plane fitting to estimate the radial velocity, horizontal angular velocity, and pitch angular velocity of dynamic target. Furthermore, we implement the multiple-shots tracking of dynamic target based on each single-shot sensing results and Kalman filtering. Simulation results demonstrate the effectiveness of the proposed 6D radar sensing and tracking scheme.

eess.SP↗

arXiv2023

Reshaping the ISAC Tradeoff Under OFDM Signaling: A Probabilistic Constellation Shaping Approach

Integrated sensing and communications is regarded as a key enabling technology in the sixth generation networks, where a unified waveform, such as orthogonal frequency division multiplexing (OFDM) signal, is adopted to facilitate both sensing and communications (S&C). However, the random communication data embedded in the OFDM signal results in severe variability in the sidelobes of its ambiguity function (AF), which leads to missed detection of weak targets and false detection of ghost targets, thereby impairing the sensing performance. Therefore, balancing between preserving communication capability (i.e., the randomness) while improving sensing performance remains a challenging task. To cope with this issue, we characterize the random AF of OFDM communication signals, and demonstrate that the AF variance is determined by the fourth-moment of the constellation amplitudes. Subsequently, we propose an optimal probabilistic constellation shaping (PCS) approach by maximizing the achievable information rate (AIR) under the fourth-moment, power and probability constraints, where the optimal input distribution may be numerically specified through a modified Blahut-Arimoto algorithm. To reduce the computational overheads, we further propose a heuristic PCS approach by actively controlling the value of the fourth-moment, without involving the communication metric in the optimization model, despite that the AIR is passively scaled with the variation of the input distribution. Numerical results show that both approaches strike a scalable performance tradeoff between S&C, where the superiority of the PCS-enabled constellations over conventional uniform constellations is also verified. Notably, the heuristic approach achieves very close performance to the optimal counterpart, at a much lower computational complexity.

eess.SP↗

arXiv2023

Beam Foreseeing in Millimeter-Wave Systems with Situational Awareness: Fundamental Limits via Cramér-Rao Lower Bound

Millimeter-wave (mmWave) networks offer the potential for high-speed data transfer and precise localization, leveraging large antenna arrays and extensive bandwidths. However, these networks are challenged by significant path loss and susceptibility to blockages. In this study, we delve into the use of situational awareness for beam prediction within the 5G NR beam management framework. We introduce an analytical framework based on the Cramér-Rao Lower Bound, enabling the quantification of 6D position-related information of geometric reflectors. This includes both 3D locations and 3D orientation biases, facilitating accurate determinations of the beamforming gain achievable by each reflector or candidate beam. This framework empowers us to predict beam alignment performance at any given location in the environment, ensuring uninterrupted wireless access. Our analysis offers critical insights for choosing the most effective beam and antenna module strategies, particularly in scenarios where communication stability is threatened by blockages. Simulation results show that our approach closely approximates the performance of an ideal, Oracle-based solution within the existing 5G NR beam management system.

cs.SI↗

arXiv2023

Beam-Delay Domain Channel Estimation for mmWave XL-MIMO Systems

This paper investigates the uplink channel estimation of the millimeter-wave (mmWave) extremely large-scale multiple-input-multiple-output (XL-MIMO) communication system in the beam-delay domain, taking into account the near-field and beam-squint effects due to the transmission bandwidth and array aperture growth. Specifically, we model the sparsity in the delay domain to explore inter-subcarrier correlations and propose the beam-delay domain sparse representation of spatial-frequency domain channels. The independent and non-identically distributed Bernoulli-Gaussian models with unknown prior hyperparameters are employed to capture the sparsity in the beam-delay domain, posing a challenge for channel estimation. Under the constrained Bethe free energy minimization framework, we design different structures on the beliefs to develop hybrid message passing (HMP) algorithms, thus achieving efficient joint estimation of beam-delay domain channel and prior hyperparameters. To further improve the model accuracy, the multidimensional grid point perturbation (MDGPP)-based representation is presented, which assigns individual perturbation parameters to each multidimensional discrete grid. By treating the MDGPP parameters as unknown hyperparameters, we propose the two-stage HMP algorithm for MDGPP-based channel estimation, where the output of the initial estimation stage is pruned for the refinement stage for the computational complexity reduction. Numerical simulations demonstrate the significant superiority of the proposed algorithms over benchmarks with both near-field and beam-squint effects.

eess.SP↗

arXiv2023

Asymptotic-Preserving Neural Networks for Multiscale Kinetic Equations

In this paper, we present two novel Asymptotic-Preserving Neural Networks (APNNs) for tackling multiscale time-dependent kinetic problems, encompassing the linear transport equation and Bhatnagar-Gross-Krook (BGK) equation with diffusive scaling. Our primary objective is to devise efficient and accurate APNN approaches for resolving multiscale kinetic equations. We have established a neural network based on even-odd decomposition and concluded that enforcing the initial condition for the linear transport equation with inflow boundary conditions is crucial. This APNN method based on even-odd parity relaxes the stringent conservation prerequisites while concurrently introducing an auxiliary deep neural network. Additionally, we have incorporated the conservation laws of mass, momentum, and energy for the Boltzmann-BGK equation into the APNN framework by enforcing exact boundary conditions. This is our second contribution. The most notable finding of this study is that approximating the zeroth, first and second moments of the particle density distribution is simpler than the distribution itself. Furthermore, a compelling phenomenon in the training process is that the convergence of density is swifter than that of momentum and energy. Finally, we investigate several benchmark problems to demonstrate the efficacy of our proposed APNN methods.

math.NA↗

arXiv2023

Rank Optimization for MIMO Channel with RIS: Simulation and Measurement

Reconfigurable intelligent surface (RIS) is a promising technology that can reshape the electromagnetic environment in wireless networks, offering various possibilities for enhancing wireless channels. Motivated by this, we investigate the channel optimization for multiple-input multiple-output (MIMO) systems assisted by RIS. In this paper, an efficient RIS optimization method is proposed to enhance the effective rank of the MIMO channel for achievable rate improvement. Numerical results are presented to verify the effectiveness of RIS in improving MIMO channels. Additionally, we construct a 2$\times$2 RIS-assisted MIMO prototype to perform experimental measurements and validate the performance of our proposed algorithm. The results reveal a significant increase in effective rank and achievable rate for the RIS-assisted MIMO channel compared to the MIMO channel without RIS.

cs.IT↗

arXiv2023

Analog quantum simulation of partial differential equations

Quantum simulators were originally proposed for simulating one partial differential equation (PDE) in particular - Schrodinger's equation. Can quantum simulators also efficiently simulate other PDEs? While most computational methods for PDEs - both classical and quantum - are digital (PDEs must be discretised first), PDEs have continuous degrees of freedom. This suggests that an analog representation can be more natural. While digital quantum degrees of freedom are usually described by qubits, the analog or continuous quantum degrees of freedom can be captured by qumodes. Based on a method called Schrodingerisation, we show how to directly map D-dimensional linear PDEs onto a (D+1)-qumode quantum system where analog or continuous-variable Hamiltonian simulation on D+1 qumodes can be used. This very simple methodology does not require one to discretise PDEs first, and it is not only applicable to linear PDEs but also to some nonlinear PDEs and systems of nonlinear ODEs. We show some examples using this method, including the Liouville equation, heat equation, Fokker-Planck equation, Black-Scholes equations, wave equation and Maxwell's equations. We also devise new protocols for linear PDEs with random coefficients, important in uncertainty quantification, where it is clear how the analog or continuous-variable framework is most natural. This also raises the possibility that some PDEs may be simulated directly on analog quantum systems by using Hamiltonians natural for those quantum systems.

quant-ph↗

arXiv2023

Gradient-Based Markov Chain Monte Carlo for MIMO Detection

Accurately detecting symbols transmitted over multiple-input multiple-output (MIMO) wireless channels is crucial in realizing the benefits of MIMO techniques. However, optimal MIMO detection is associated with a complexity that grows exponentially with the MIMO dimensions and quickly becomes impractical. Recently, stochastic sampling-based Bayesian inference techniques, such as Markov chain Monte Carlo (MCMC), have been combined with the gradient descent (GD) method to provide a promising framework for MIMO detection. In this work, we propose to efficiently approach optimal detection by exploring the discrete search space via MCMC random walk accelerated by Nesterov's gradient method. Nesterov's GD guides MCMC to make efficient searches without the computationally expensive matrix inversion and line search. Our proposed method operates using multiple GDs per random walk, achieving sufficient descent towards important regions of the search space before adding random perturbations, guaranteeing high sampling efficiency. To provide augmented exploration, extra samples are derived through the trajectory of Nesterov's GD by simple operations, effectively supplementing the sample list for statistical inference and boosting the overall MIMO detection performance. Furthermore, we design an early stopping tactic to terminate unnecessary further searches, remarkably reducing the complexity. Simulation results and complexity analysis reveal that the proposed method achieves exceptional performance in both uncoded and coded MIMO systems, adapts to realistic channel models, and scales well to large MIMO dimensions.

eess.SP↗