arXiv subjects

Cunhua Pan

Publications and source records attributed to Cunhua Pan.

At least 109 records · Page 6Linked to original sources

arXiv2024

Near-Field Mobile Tracking: A Framework of Using XL-RIS Information

This paper introduces a novel mobile tracking framework leveraging the high-dimensional signal received from extremely large-scale (XL) reconfigurable intelligent surfaces (RIS). This received signal, named XL-RIS information, has a much larger data dimension and therefore offers a richer feature set compared to the traditional base station (BS) received signal, i.e., BS information, enabling more accurate tracking of mobile users (MUs). As the first step, we present an XL-RIS information reconstruction (XL-RIS-IR) algorithm to reconstruct the high-dimensional XL-RIS information from the low-dimensional BS information. Building on this, this paper proposes a comprehensive framework for mobile tracking, consisting of a Feature Extraction Module and a Mobile Tracking Module. The Feature Extraction Module incorporates a convolutional neural network (CNN) extractor for spatial features, a time and frequency (T$\&$F) extractor for domain features, and a near-field angles of arrival (AoAs) extractor for capturing AoA features within the XL-RIS. These features are combined into a comprehensive feature vector, forming a time-varying sequence fed into the Mobile Tracking Module, which employs an Auto-encoder (AE) with a stacked bidirectional long short-term memory (Bi-LSTM) encoder and a standard LSTM decoder to predict MUs' positions in the upcoming time slot. Simulation results confirm that the tracking accuracy of our proposed framework is significantly enhanced by using reconstructed XL-RIS information and exhibits substantial robustness to signal-to-noise ratio (SNR) variations.

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arXiv2024

Large AI Model Empowered Multimodal Semantic Communications

Multimodal signals, including text, audio, image, and video, can be integrated into Semantic Communication (SC) systems to provide an immersive experience with low latency and high quality at the semantic level. However, the multimodal SC has several challenges, including data heterogeneity, semantic ambiguity, and signal distortion during transmission. Recent advancements in large AI models, particularly in the Multimodal Language Model (MLM) and Large Language Model (LLM), offer potential solutions for addressing these issues. To this end, we propose a Large AI Model-based Multimodal SC (LAM-MSC) framework, where we first present the MLM-based Multimodal Alignment (MMA) that utilizes the MLM to enable the transformation between multimodal and unimodal data while preserving semantic consistency. Then, a personalized LLM-based Knowledge Base (LKB) is proposed, which allows users to perform personalized semantic extraction or recovery through the LLM. This effectively addresses the semantic ambiguity. Finally, we apply the Conditional Generative adversarial network-based channel Estimation (CGE) for estimating the wireless channel state information. This approach effectively mitigates the impact of fading channels in SC. Finally, we conduct simulations that demonstrate the superior performance of the LAM-MSC framework.

cs.AI↗

arXiv2024

Large AI Model-Based Semantic Communications

Semantic communication (SC) is an emerging intelligent paradigm, offering solutions for various future applications like metaverse, mixed reality, and the Internet of Everything. However, in current SC systems, the construction of the knowledge base (KB) faces several issues, including limited knowledge representation, frequent knowledge updates, and insecure knowledge sharing. Fortunately, the development of the large AI model (LAM) provides new solutions to overcome the above issues. Here, we propose a LAM-based SC framework (LAM-SC) specifically designed for image data, where we first apply the segment anything model (SAM)-based KB (SKB) that can split the original image into different semantic segments by universal semantic knowledge. Then, we present an attention-based semantic integration (ASI) to weigh the semantic segments generated by SKB without human participation and integrate them as the semantic aware image. Additionally, we propose an adaptive semantic compression (ASC) encoding to remove redundant information in semantic features, thereby reducing communication overhead. Finally, through simulations, we demonstrate the effectiveness of the LAM-SC framework and the possibility of applying the LAM-based KB in future SC paradigms.

cs.AI↗

arXiv2024

LAMBO: Large AI Model Empowered Edge Intelligence

Next-generation edge intelligence is anticipated to benefit various applications via offloading techniques. However, traditional offloading architectures face several issues, including heterogeneous constraints, partial perception, uncertain generalization, and lack of tractability. In this paper, we propose a Large AI Model-Based Offloading (LAMBO) framework with over one billion parameters for solving these problems. We first use input embedding (IE) to achieve normalized feature representation with heterogeneous constraints and task prompts. Then, we introduce a novel asymmetric encoder-decoder (AED) as the decision-making model, which is an improved transformer architecture consisting of a deep encoder and a shallow decoder for global perception and decision. Next, actor-critic learning (ACL) is used to pre-train the AED for different optimization tasks under corresponding prompts, enhancing the AED's generalization in multi-task scenarios. Finally, we propose an active learning from expert feedback (ALEF) method to fine-tune the decoder of the AED for tracking changes in dynamic environments. Our simulation results validate the advantages of the proposed LAMBO framework.

cs.AI↗

arXiv2024

A Framework of FAS-RIS Systems: Performance Analysis and Throughput Optimization

In this paper, we investigate reconfigurable intelligent surface (RIS)-assisted communication systems which involve a fixed-antenna base station (BS) and a mobile user (MU) that is equipped with fluid antenna system (FAS). Specifically, the RIS is utilized to enable communication for the user whose direct link from the base station is blocked by obstacles. We propose a comprehensive framework that provides transmission design for both static scenarios with the knowledge of channel state information (CSI) and harsh environments where CSI is hard to acquire. It leads to two approaches: a CSI-based scheme where CSI is available, and a CSI-free scheme when CSI is inaccessible. Given the complex spatial correlations in FAS, we employ block-diagonal matrix approximation and independent antenna equivalent models to simplify the derivation of outage probabilities in both cases. Based on the derived outage probabilities, we then optimize the throughput of the FAS-RIS system. For the CSI-based scheme, we first propose a gradient ascent-based algorithm to obtain a near-optimal solution. Then, to address the possible high computational complexity in the gradient algorithm, we approximate the objective function and confirm a unique optimal solution accessible through a bisection search method. For the CSI-free scheme, we apply the partial gradient ascent algorithm, reducing complexity further than full gradient algorithms. We also approximate the objective function and derive a locally optimal closed-form solution to maximize throughput. Simulation results validate the effectiveness of the proposed framework for the transmission design in FAS-RIS systems.

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arXiv2024

GAN Based Near-Field Channel Estimation for Extremely Large-Scale MIMO Systems

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is a promising technique to achieve ultra-high spectral efficiency for future 6G communications. The mixed line-of-sight (LoS) and non-line-of-sight (NLoS) XL-MIMO near-field channel model is adopted to describe the XL-MIMO near-field channel accurately. In this paper, a generative adversarial network (GAN) variant based channel estimation method is proposed for XL-MIMO systems. Specifically, the GAN variant is developed to simultaneously estimate the LoS and NLoS path components of the XL-MIMO channel. The initially estimated channels instead of the received signals are input into the GAN variant as the conditional input to generate the XL-MIMO channels more efficiently. The GAN variant not only learns the mapping from the initially estimated channels to the XL-MIMO channels but also learns an adversarial loss. Moreover, we combine the adversarial loss with a conventional loss function to ensure the correct direction of training the generator. To further enhance the estimation performance, we investigate the impact of the hyper-parameter of the loss function on the performance of our method. Simulation results show that the proposed method outperforms the existing channel estimation approaches in the adopted channel model. In addition, the proposed method surpasses the Cram$\acute{\mathbf{e}}$r-Rao lower bound (CRLB) under low pilot overhead.

eess.SP↗

arXiv2024

Exploit High-Dimensional RIS Information to Localization: What Is the Impact of Faulty Element?

This paper proposes a novel localization algorithm using the reconfigurable intelligent surface (RIS) received signal, i.e., RIS information. Compared with BS received signal, i.e., BS information, RIS information offers higher dimension and richer feature set, thereby providing an enhanced capacity to distinguish positions of the mobile users (MUs). Additionally, we address a practical scenario where RIS contains some unknown (number and places) faulty elements that cannot receive signals. Initially, we employ transfer learning to design a two-phase transfer learning (TPTL) algorithm, designed for accurate detection of faulty elements. Then our objective is to regain the information lost from the faulty elements and reconstruct the complete high-dimensional RIS information for localization. To this end, we propose a transfer-enhanced dual-stage (TEDS) algorithm. In \emph{Stage I}, we integrate the CNN and variational autoencoder (VAE) to obtain the RIS information, which in \emph{Stage II}, is input to the transferred DenseNet 121 to estimate the location of the MU. To gain more insight, we propose an alternative algorithm named transfer-enhanced direct fingerprint (TEDF) algorithm which only requires the BS information. The comparison between TEDS and TEDF reveals the effectiveness of faulty element detection and the benefits of utilizing the high-dimensional RIS information for localization. Besides, our empirical results demonstrate that the performance of the localization algorithm is dominated by the high-dimensional RIS information and is robust to unoptimized phase shifts and signal-to-noise ratio (SNR).

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arXiv2024

Deep Learning-Based CSI Feedback for XL-MIMO Systems in the Near-Field Domain

In this paper, we consider an extremely large-scale massive multiple-input-multiple-output (XL-MIMO) system. As the scale of antenna arrays increases, the range of near-field communications also expands. In this case, the signals no longer exhibit planar wave characteristics but spherical wave characteristics in the near-field channel, which makes the channel state information (CSI) highly complex. Additionally, the increase of the antenna arrays scale also makes the size of the CSI matrix significantly increase. Therefore, CSI feedback in the near-field channel becomes highly challenging. To solve this issue, we propose a deep-learning (DL)-based ExtendNLNet that can compress the CSI, and further reduce the overhead of CSI feedback. In addition, we have introduced the Non-Local block to obtain a larger area of CSI features. Simulation results show that the proposed ExtendNLNet can significantly improve the CSI recovery quality compared to other DL-based methods.

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arXiv2024

Transmission Design for XL-RIS-Aided Massive MIMO System with Visibility Regions

This paper proposes a two-timescale transmission scheme for extremely large-scale (XL)-reconfigurable intelligent surfaces (RIS)-aided massive multi-input multi-output (MIMO) systems considering visibility regions (VRs). The beamforming of base stations (BS) is designed based on rapidly changing instantaneous channel state information (CSI), while the phase shifts of RIS are configured based on slowly changing statistical CSI. Specifically, we first formulate a system model with spatially correlated Rician fading channels and introduce the concept of VRs. Then, we derive a closed-form approximate expression for the achievable rate applicable to any number of BS antennas and RIS elements, and analyze the impact of VRs on system performance and complexity. Next, we solve the problem of maximizing the minimum user rate by optimizing the phase shifts of RIS through an algorithm based on accelerated gradient ascent. Finally, we present numerical results to demonstrate the performance of the gradient algorithm from different aspects and reveal the low system complexity of deploying XL-RIS in massive MIMO systems with the help of VRs.

cs.IT↗

arXiv2024

Employing High-Dimensional RIS Information for RIS-aided Localization Systems

Reconfigurable intelligent surface (RIS)-aided localization systems have attracted extensive research attention due to their accuracy enhancement capabilities. However, most studies primarily utilized the base stations (BS) received signal, i.e., BS information, for localization algorithm design, neglecting the potential of RIS received signal, i.e., RIS information. Compared with BS information, RIS information offers higher dimension and richer feature set, thereby significantly improving the ability to extract positions of the mobile users (MUs). Addressing this oversight, this paper explores the algorithm design based on the high-dimensional RIS information. Specifically, we first propose a RIS information reconstruction (RIS-IR) algorithm to reconstruct the high-dimensional RIS information from the low-dimensional BS information. The proposed RIS-IR algorithm comprises a data processing module for preprocessing BS information, a convolution neural network (CNN) module for feature extraction, and an output module for outputting the reconstructed RIS information. Then, we propose a transfer learning based fingerprint (TFBF) algorithm that employs the reconstructed high-dimensional RIS information for MU localization. This involves adapting a pre-trained DenseNet-121 model to map the reconstructed RIS signal to the MU's three-dimensional (3D) position. Empirical results affirm that the localization performance is significantly influenced by the high-dimensional RIS information and maintains robustness against unoptimized phase shifts.

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arXiv2024

Two-Timescale Design for Reconfigurable Intelligent Surface-Aided URLLC

In this paper, to tackle the blockage issue in massive multiple-input-multiple-output (mMIMO) systems, a reconfigurable intelligent surface (RIS) is seamlessly deployed to support devices with ultra-reliable and low-latency communications (URLLC). The transmission power of the base station and the phase shifts of the RIS are jointly devised to maximize the weighted sum rate while considering the spatially correlation and channel estimation errors. Firstly, \textcolor{black}{the relationship between the channel estimation error and spatially correlated RIS's elements is revealed by using the linear minimum mean square error}. Secondly, based on the maximum-ratio transmission precoding, a tight lower bound of the rate under short packet transmission is derived. Finally, the NP-hard problem is decomposed into two optimization problems, where the transmission power is obtained by geometric programming and phase shifts are designed by using gradient ascent method. Besides, we have rigorously proved that the proposed algorithm can rapidly converge to a sub-optimal solution with low complexity. Simulation results confirm the tightness between the analytic results and Monte Carlo simulations. Furthermore, the two-timescale scheme provides a practical solution for the short packet transmission.

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arXiv2024

Active RIS-Aided Massive MIMO With Imperfect CSI and Phase Noise

Active reconfigurable intelligent surface (RIS) has attracted significant attention as a recently proposed RIS architecture. Owing to its capability to amplify the incident signals, active RIS can mitigate the multiplicative fading effect inherent in the passive RIS-aided system. In this paper, we consider an active RIS-aided uplink multi-user massive multiple-input multiple-output (MIMO) system in the presence of phase noise at the active RIS. Specifically, we employ a two-timescale scheme, where the beamforming at the base station (BS) is adjusted based on the instantaneous aggregated channel state information (CSI) and the statistical CSI serves as the basis for designing the phase shifts at the active RIS, so that the feedback overhead and computational complexity can be significantly reduced. The aggregated channel composed of the cascaded and direct channels is estimated by utilizing the linear minimum mean square error (LMMSE) technique. Based on the estimated channel, we derive the analytical closed-form expression of a lower bound of the achievable rate. The power scaling laws in the active RIS-aided system are investigated based on the theoretical expressions. When the transmit power of each user is scaled down by the number of BS antennas M or reflecting elements N, we find that the thermal noise will cause the lower bound of the achievable rate to approach zero, as the number of M or N increases to infinity. Moreover, an optimization approach based on genetic algorithms (GA) is introduced to tackle the phase shift optimization problem. Numerical results reveal that the active RIS can greatly enhance the performance of the considered system under various settings.

cs.IT↗

arXiv2024

Visual Language Model based Cross-modal Semantic Communication Systems

Semantic Communication (SC) has emerged as a novel communication paradigm in recent years, successfully transcending the Shannon physical capacity limits through innovative semantic transmission concepts. Nevertheless, extant Image Semantic Communication (ISC) systems face several challenges in dynamic environments, including low semantic density, catastrophic forgetting, and uncertain Signal-to-Noise Ratio (SNR). To address these challenges, we propose a novel Vision-Language Model-based Cross-modal Semantic Communication (VLM-CSC) system. The VLM-CSC comprises three novel components: (1) Cross-modal Knowledge Base (CKB) is used to extract high-density textual semantics from the semantically sparse image at the transmitter and reconstruct the original image based on textual semantics at the receiver. The transmission of high-density semantics contributes to alleviating bandwidth pressure. (2) Memory-assisted Encoder and Decoder (MED) employ a hybrid long/short-term memory mechanism, enabling the semantic encoder and decoder to overcome catastrophic forgetting in dynamic environments when there is a drift in the distribution of semantic features. (3) Noise Attention Module (NAM) employs attention mechanisms to adaptively adjust the semantic coding and the channel coding based on SNR, ensuring the robustness of the CSC system. The experimental simulations validate the effectiveness, adaptability, and robustness of the CSC system.

cs.CV↗

arXiv2024

NMBEnet: Efficient Near-field mmWave Beam Training for Multiuser OFDM Systems Using Sub-6 GHz Pilots

Combining millimetre-wave (mmWave) communications with an extremely large-scale antenna array (ELAA) presents a promising avenue for meeting the spectral efficiency demands of the future sixth generation (6G) mobile communications. However, beam training for mmWave ELAA systems is challenged by excessive pilot overheads as well as insufficient accuracy, as the huge near-field codebook has to be accounted for. In this paper, inspired by the similarity between far-field sub-6 GHz channels and near-field mmWave channels, we propose to leverage sub-6 GHz uplink pilot signals to directly estimate the optimal near-field mmWave codeword, which aims to reduce pilot overhead and bypass the channel estimation. Moreover, we adopt deep learning to perform this dual mapping function, i.e., sub-6 GHz to mmWave, far-field to near-field, and a novel neural network structure called NMBEnet is designed to enhance the precision of beam training. Specifically, when considering the orthogonal frequency division multiplexing (OFDM) communication scenarios with high user density, correlations arise both between signals from different users and between signals from different subcarriers. Accordingly, the convolutional neural network (CNN) module and graph neural network (GNN) module included in the proposed NMBEnet can leverage these two correlations to further enhance the precision of beam training.

cs.IT↗

arXiv2024

Active RIS-Aided Massive MIMO Uplink Systems with Low-Resolution ADCs

This letter considers an active reconfigurable intelligent surface (RIS)-aided multi-user uplink massive multipleinput multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). The letter derives the closedform approximate expression for the sum achievable rate (AR), where the maximum ratio combination (MRC) processing and low-resolution ADCs are applied at the base station. The system performance is analyzed, and a genetic algorithm (GA)-based method is proposed to optimize the RIS's phase shifts for enhancing the system performance. Numerical results verify the accuracy of the derivations, and demonstrate that the active RIS has an evident performance gain over the passive RIS.

cs.IT↗

arXiv2024

Near-Field Multiuser Beam-Training for Extremely Large-Scale MIMO Systems

Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are capable of improving spectral efficiency by employing far more antennas than conventional massive MIMO at the base station (BS). However, beam training in multiuser XL-MIMO systems is challenging. To tackle these issues, we conceive a three-phase graph neural network (GNN)-based beam training scheme for multiuser XL-MIMO systems. In the first phase, only far-field wide beams have to be tested for each user and the GNN is utilized to map the beamforming gain information of the far-field wide beams to the optimal near-field beam for each user. In addition, the proposed GNN-based scheme can exploit the position-correlation between adjacent users for further improvement of the accuracy of beam training. In the second phase, a beam allocation scheme based on the probability vectors produced at the outputs of GNNs is proposed to address the above beam-direction conflicts between users. In the third phase, the hybrid TBF is designed for further reducing the inter-user interference. Our simulation results show that the proposed scheme improves the beam training performance of the benchmarks. Moreover, the performance of the proposed beam training scheme approaches that of an exhaustive search, despite requiring only about 7% of the pilot overhead.

cs.IT↗

arXiv2024

RIS-assisted Cell-Free Massive MIMO Systems With Two-Timescale Design and Hardware Impairments

Integrating the reconfigurable intelligent surface (RIS) into a cell-free massive multiple-input multiple-output (CF-mMIMO) system is an effective solution to achieve high system capacity with low cost and power consumption. However, existing works of RIS-assisted systems mostly assumed perfect hardware, while the impact of hardware impairments (HWIs) is generally ignored. In this paper, we consider the general Rician fading channel and uplink transmission of the RIS-assisted CF-mMIMO system under transceiver impairments and RIS phase noise. To reduce the feedback overhead and power consumption, we propose a two-timescale transmission scheme to optimize the passive beamformers at RISs with statistical channel state information (CSI), while transmit beamformers at access points (APs) are designed based on instantaneous CSI. Also, the maximum ratio combining (MRC) detection is applied to the central processing unit (CPU). On this basis, we derive the closed-form approximate expression of the achievable rate, based on which the impact of HWIs and the power scaling laws are analyzed to draw useful theoretical insights. To maximize the users' sum rate or minimum rate, we first transform our rate expression into a tractable form, and then optimize the phase shifts of RISs based on an accelerated gradient ascent method. Finally, numerical results are presented to demonstrate the correctness of our derived expressions and validate the previous analysis, which provide some guidelines for the practical application of the imperfect RISs in the CF-mMIMO with transceiver HWIs.

cs.IT↗

arXiv2024

Deep Learning-Based CSI Feedback for RIS-Aided Massive MIMO Systems with Time Correlation

In this paper, we consider an reconfigurable intelligent surface (RIS)-aided frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) downlink system.In the FDD systems, the downlink channel state information (CSI) should be sent to the base station through the feedback link. However, the overhead of CSI feedback occupies substantial uplink bandwidth resources in RIS-aided communication systems. In this work, we propose a deep learning (DL)-based scheme to reduce the overhead of CSI feedback by compressing the cascaded CSI. In the practical RIS-aided communication systems, the cascaded channel at the adjacent slots inevitably has time correlation. We use long short-term memory to learn time correlation, which can help the neural network to improve the recovery quality of the compressed CSI. Moreover, the attention mechanism is introduced to further improve the CSI recovery quality. Simulation results demonstrate that our proposed DLbased scheme can significantly outperform other DL-based methods in terms of the CSI recovery quality

eess.SP↗