arXiv subjects

Cunhua Pan

Publications and source records attributed to Cunhua Pan.

At least 163 records · Page 9Linked to original sources

arXiv2023

Transmission Design for Active RIS-Aided Simultaneous Wireless Information and Power Transfer

Reconfigurable intelligent surface (RIS) is a revolutionary technology to enhance both the spectral efficiency and energy efficiency of wireless communication systems. However, most of the existing contributions mainly focused on the study of passive RIS, which suffers from the ``double fading'' effect. On the other hand, active RIS, which is equipped with amplifiers, can effectively address this issue. In this paper, we propose an active RIS-aided simultaneous wireless information and power transfer (SWIPT) system. Specifically, we maximize the weighted sum rate of the information receivers, subject to the minimum power received at all energy receivers, amplification power constraint at the active RIS, and the maximum transmit power constraint at the base station (BS). By adopting alternating optimization framework, suboptimal solutions are obtained. Simulation results show that the active RIS-aided SWIPT system has higher performance gain with the same power budget.

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arXiv2022

RIS-Aided Near-Field Localization and Channel Estimation for the Sub-Terahertz System

The low hardware cost makes ultra-large (XL) reconfigurable intelligent surface (RIS) an attractive solution for the performance enhancement of localization and communication systems, but it results in near-field propagation channels, especially for the high-frequency communication systems, e.g. mmWave and sub-terahertz (subTHz) systems. This makes localization and channel estimation in the near field of the RIS-aided subTHz system more challenging. In this paper, we consider the spherical wavefront propagation in the near field of the subTHz system with the assistance of a RIS. A near-field channel estimation and localization (NF-JCEL) algorithm is proposed based on the derived second-order Fresnel approximation of the near-field channel model. To be specific, we first decouple the user equipment (UE) distances and angles of arrival (AoAs) through a down-sampled Toeplitz covariance matrix, so that the vertical and azimuth AoAs in the array steering vectors can be estimated separately with low complexities. Then, the UE distance can be estimated by the simple one-dimensional search, and the channel attenuation coefficients are obtained through the orthogonal matching pursuit (OMP) method. Simulation results validate the superiority of the proposed NF-JCEL algorithm to the conventional far-field algorithm, and show that higher resolution accuracy can be obtained by the proposed algorithm.

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arXiv2022

Low-overhead Beam Training Scheme for Extremely Large-Scale RIS in Near-field

Extremely large-scale reconfigurable intelligent surface (XL-RIS) has recently been proposed and is recognized as a promising technology that can further enhance the capacity of communication systems and compensate for severe path loss . However, the pilot overhead of beam training in XL-RIS-assisted wireless communication systems is enormous because the near-field channel model needs to be taken into account, and the number of candidate codewords in the codebook increases dramatically accordingly. To tackle this problem, we propose two deep learning-based near-field beam training schemes in XL-RIS-assisted communication systems, where deep residual networks are employed to determine the optimal near-field RIS codeword. Specifically, we first propose a far-field beam-based beam training (FBT) scheme in which the received signals of all far-field RIS codewords are fed into the neural network to estimate the optimal near-field RIS codeword. In order to further reduce the pilot overhead, a partial near-field beam-based beam training (PNBT) scheme is proposed, where only the received signals corresponding to the partial near-field XL-RIS codewords are served as input to the neural network. Moreover, we further propose an improved PNBT scheme to enhance the performance of beam training by fully exploring the neural network's output. Finally, simulation results show that the proposed schemes outperform the existing beam training schemes and can reduce the beam sweeping overhead by approximately 95%.

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arXiv2022

Resource Allocation for Uplink Cell-Free Massive MIMO enabled URLLC in a Smart Factory

Smart factories need to support the simultaneous communication of multiple industrial Internet-of-Things (IIoT) devices with ultra-reliability and low-latency communication (URLLC). Meanwhile, short packet transmission for IIoT applications incurs performance loss compared to traditional long packet transmission for human-to-human communications. On the other hand, cell-free massive multiple-input and multiple-output (CF mMIMO) technology can provide uniform services for all devices by deploying distributed access points (APs). In this paper, we adopt CF mMIMO to support URLLC in a smart factory. Specifically, we first derive the lower bound (LB) on achievable uplink data rate under the finite blocklength (FBL) with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and full-pilot zero-forcing (FZF) decoders. \textcolor{black}{The derived LB rates based on the MRC case have the same trends as the ergodic rate, while LB rates using the FZF decoder tightly match the ergodic rates}, which means that resource allocation can be performed based on the LB data rate rather the exact ergodic data rate under FBL. The \textcolor{black}{log-function method} and successive convex approximation (SCA) are then used to approximately transform the non-convex weighted sum rate problem into a series of geometric program (GP) problems, and an iterative algorithm is proposed to jointly optimize the pilot and payload power allocation. Simulation results demonstrate that CF mMIMO significantly improves the average weighted sum rate (AWSR) compared to centralized mMIMO. An interesting observation is that increasing the number of devices improves the AWSR for CF mMIMO whilst the AWSR remains relatively constant for centralized mMIMO.

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arXiv2022

Joint Angle Estimation Error Analysis and 3D Positioning Algorithm Design for mmWave Positioning System

In this paper, we propose a comprehensive framework to jointly analyze the angle estimation error and design the three-dimensional (3D) positioning algorithm for a millimeter wave (mmWave) positioning system. First, we estimate the angles of arrival (AoAs) at the anchors by applying the two-dimensional discrete Fourier transform (2D-DFT) algorithm. Based on the property of the 2D-DFT algorithm, the angle estimation error is analyzed in terms of probability density functions (PDF). The analysis shows that the derived angle estimation error is non-Gaussian, which is different from the existing work. Second, the intricate expression of the PDF of the AoA estimation error is simplified by employing the first-order linear approximation of triangle functions. Then, we derive a complex expression for the variance based on the derived PDF. Specifically, for the azimuth estimation error, the variance is separately integrated according to the different non-zero intervals of the PDF. Finally, we apply the weighted least square (WLS) algorithm to estimate the 3D position of the MU by using the estimated AoAs and the obtained non-Gaussian variance. Extensive simulation results confirm that the derived angle estimation error is non-Gaussian, and also demonstrate the superiority of the proposed framework.

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arXiv2022

Device Scheduling for Over-the-Air Federated Learning with Differential Privacy

In this paper, we propose a device scheduling scheme for differentially private over-the-air federated learning (DP-OTA-FL) systems, referred to as S-DPOTAFL, where the privacy of the participants is guaranteed by channel noise. In S-DPOTAFL, the gradients are aligned by the alignment coefficient and aggregated via over-the-air computation (AirComp). The scheme schedules the devices with better channel conditions in the training to avoid the problem that the alignment coefficient is limited by the device with the worst channel condition in the system. We conduct the privacy and convergence analysis to theoretically demonstrate the impact of device scheduling on privacy protection and learning performance. To improve the learning accuracy, we formulate an optimization problem with the goal to minimize the training loss subjecting to privacy and transmit power constraints. Furthermore, we present the condition that the S-DPOTAFL performs better than the DP-OTA-FL without considering device scheduling (NoS-DPOTAFL). The effectiveness of the S-DPOTAFL is validated through simulations.

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arXiv2022

Fingerprint Based mmWave Positioning System Aided by Reconfigurable Intelligent Surface

Reconfigurable intelligent surface (RIS) is a promising technique for millimeter wave (mmWave) positioning systems. In this paper, we consider multiple mobile users (MUs) positioning problem in the multiple-input multiple-output (MIMO) time-division duplex (TDD) mmWave systems aided by the RIS. We derive the expression for the space-time channel response vector (STCRV) as a novel type of fingerprint. The STCRV consists of the multipath channel characteristics, e.g., time delay and angle of arrival (AOA), which is related to the position of the MU. By using the STCRV as input, we propose a novel residual convolution network regression (RCNR) learning algorithm to output the estimated three-dimensional (3D) position of the MU. Specifically, the RCNR learninng algorithm includes a data processing block to process the input STCRV, a normal convolution block to extract the features of STCRV, four residual convolution blocks to further extract the features and protect the integrity of the features, and a regression block to estimate the 3D position. Extensive simulation results are also presented to demonstrate that the proposed RCNR learning algorithm outperforms the traditional convolution neural network (CNN).

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arXiv2022

Two-Timescale Design for Reconfigurable Intelligent Surface-Aided Massive MIMO Systems with Imperfect CSI

This paper investigates the two-timescale transmission design for reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems, where the beamforming at the base station (BS) is adapted to the rapidly-changing instantaneous channel state information (CSI), while the passive beamforming at the RIS is adapted to the slowly-changing statistical CSI. Specifically, we first propose a linear minimum mean square error (LMMSE) estimator to obtain the aggregated channel from the users to the BS in each channel coherence interval. Based on the estimated channel, we apply the low-complexity maximal ratio combining (MRC) beamforming at the BS, and then derive the ergodic achievable rate in a closed form expression. To draw design insights, we perform a detailed theoretical analysis departing from the derived ergodic achievable rate. If the BS-RIS channel is Rician distributed, we prove that the transmit power can be scaled proportionally to $1/M$, as the number of BS antennas, $M$, grows to infinity while maintaining a non-zero rate. If the BS-RIS channel is Rayleigh distributed, the transmit power can be scaled either proportionally to $1/\sqrt{M}$ as $M$ grows large, or proportionally to $1/N$ as the number of reflecting elements, $N$, grows large, while still maintaining a non-zero rate. By capitalizing on the derived expression of the data rate under the statistical knowledge of the CSI, we maximize the minimum user rate by designing the passive beamforming at the RIS. Numerical results confirm that, even in the presence of imperfect CSI, the integration of an RIS in massive MIMO systems results in promising performance gains. In addition, the obtained results reveal that it is favorable to place the RIS close to the users rather than close to the BS.

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arXiv2022

Toward Secure and Private Over-the-Air Federated Learning

In this paper, a novel secure and private over-the-air federated learning (SP-OTA-FL) framework is studied where noise is employed to protect data privacy and system security. Specifically, the privacy leakage of user data and the security level of the system are measured by differential privacy (DP) and mean square error security (MSE-security), respectively. To mitigate the impact of noise on learning accuracy, we propose a channel-weighted post-processing (CWPP) mechanism, which assigns a smaller weight to the gradient of the device with poor channel conditions. Furthermore, employing CWPP can avoid the issue that the signal-to-noise ratio (SNR) of the overall system is limited by the device with the worst channel condition in aligned over-the-air federated learning (OTA-FL). We theoretically analyze the effect of noise on privacy and security protection and also illustrate the adverse impact of noise on learning performance by conducting convergence analysis. Based on these analytical results, we propose device scheduling policies considering privacy and security protection in different cases of channel noise. In particular, we formulate an integer nonlinear fractional programming problem aiming to minimize the negative impact of noise on the learning process. We obtain the closed-form solution to the optimization problem when the model is with high dimension. For the general case, we propose a secure and private algorithm (SPA) based on the branch-and-bound (BnB) method, which can obtain an optimal solution with low complexity. The effectiveness of the proposed CWPP mechanism and the policies for device selection are validated through simulations.

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arXiv2022

Robust Transmission Design for RIS-assisted Secure Multiuser Communication Systems in the Presence of Hardware Impairments

This paper investigates reconfigurable intelligent surface (RIS)-assisted secure multiuser communication systems subject to hardware impairments (HIs). We jointly optimize the beamforming vectors at the base station (BS) and the phase shifts of the reflecting elements at the RIS so as to maximize the weighted minimum secrecy rate (WMSR), subject to both transmission power constraints at the BS and unit-modulus constraints at the RIS. To address the formulated optimization problem, we first decouple it into two tractable subproblems and then use the block coordinate descent (BCD) method to alternately optimize the subproblems. Two different methods are proposed to solve the two obtained subproblems. The first method transforms each subproblem into a second order cone programming (SOCP) problem, which can be directly solved using CVX. The second method leverages the Minorization- Maximization (MM) algorithm. Specifically, we first derive a concave approximation function, which is a lower bound of the original objective function, and then the two subproblems are transformed into two simple surrogate problems with closedform solutions. Simulation results verify the performance gains of the proposed robust transmission method over existing nonrobust designs. In addition, the MM algorithm is shown to have much lower complexity than the SOCP-based algorithm.

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arXiv2022

Deep Learning Based Beam Training for Extremely Large-Scale Massive MIMO in Near-Field Domain

Extremely large-scale massive multiple-input-multiple-output (XL-MIMO) is regarded as a promising technology for next-generation communication systems. In order to enhance the beamforming gains, codebook-based beam training is widely adopted in XL-MIMO systems. However, in XL-MIMO systems, the near-field domain expands, and near-field codebook should be adopted for beam training, which significantly increases the pilot overhead. To tackle this problem, we propose a deep learning-based beam training scheme where the near-field channel model and the near-field codebook are considered. To be specific, we first utilize the received signals corresponding to the far-field wide beams to estimate the optimal near-field beam. Two training schemes are proposed, namely the proposed original and the improved neural networks. The original scheme estimates the optimal near-field codeword directly based on the output of the neural networks. By contrast, the improved scheme performs additional beam testing, which can significantly improve the performance of beam training. Finally, the simulation results show that our proposed schemes can significantly reduce the training overhead in the near-field domain and achieve beamforming gains.

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arXiv2022

Active Reconfigurable Intelligent Surface for Mobile Edge Computing

This paper investigates an active reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC) system. Compared with passive RIS, the active RIS is equipped with active reflective amplifier, which can effectively circumvent the "double path loss" attenuation. We propose a joint computing and communication design to minimize the maximum computational latency (MCL), subject to both the phase shift constraints and the edge computing capability constraints. Specifically, the original problem is decoupled into four subproblems, and then the block coordinate descent (BCD) method and the successive convex approximation (SCA) method are applied to alternately optimize the subproblems. The simulation results show that with the same power budget, the performance gain achieved by the active RIS is much larger than that by the passive RIS.

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arXiv2022

Joint Trajectory and Passive Beamforming Design for Intelligent Reflecting Surface-Aided UAV Communications: A Deep Reinforcement Learning Approach

In this paper, the intelligent reflecting surface (IRS)-aided unmanned aerial vehicle (UAV) communication system is studied, where the UAV is deployed to serve the user equipment (UE) with the assistance of multiple IRSs mounted on several buildings to enhance the communication quality between UAV and UE. We aim to maximize the energy efficiency of the system, including the data rate of UE and the energy consumption of UAV via jointly optimizing the UAV's trajectory and the phase shifts of reflecting elements of IRS, when the UE moves and the selection of IRSs is considered for the energy saving purpose. Since the system is complex and the environment is dynamic, it is challenging to derive low-complexity algorithms by using conventional optimization methods. To address this issue, we first propose a deep Q-network (DQN)-based algorithm by discretizing the trajectory, which has the advantage of training time. Furthermore, we propose a deep deterministic policy gradient (DDPG)-based algorithm to tackle the case with continuous trajectory for achieving better performance. The experimental results show that the proposed algorithms achieve considerable performance compared to other traditional solutions.

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arXiv2022

Error Propagation and Overhead Reduced Channel Estimation for RIS-Aided Multi-User mmWave Systems

In this paper, we propose a novel two-stage based uplink channel estimation strategy with reduced pilot overhead and error propagation for a reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) system. Specifically, in Stage I, with the carefully designed RIS phase shift matrix and introduced matching matrices, all users jointly estimate the correlation factors between different paths of the common RIS-base station (BS) channel, which achieves significant multi-user diversity gain. Then, the inherent scaling ambiguity and angle ambiguity of the mmWave cascaded channel are utilized to construct an ambiguous common RIS-BS channel composed of the estimated correlation factors. In Stage II, with the constructed ambiguous common RIS-BS channel, each user uses reduced pilots to estimate their specific user-RIS channel independently so as to obtain the entire cascaded channel. The theoretical number of pilots required for the proposed method is analyzed and the simulation results are presented to validate the effectiveness of this strategy.

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arXiv2022

Energy Minimization in RIS-Assisted UAV-Enabled Wireless Power Transfer Systems

Unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) systems offer significant advantages in coverage and deployment flexibility, but suffer from endurance limitations due to the limited onboard energy. This paper proposes to improve the energy efficiency of UAV-enabled WPT systems with multiple ground sensors by utilizing reconfigurable intelligent surface (RIS). Specifically, the total energy consumption of the UAV is minimized, while meeting the energy requirement of each sensor. Firstly, we consider a fly-hover-broadcast (FHB) protocol, in which the UAV radiates radio frequency (RF) signals only at several hovering locations. The energy minimization problem is formulated to jointly optimize the UAV's trajectory, hovering time and the RIS's reflection coefficients. To solve this complex non-convex problem, we propose an efficient algorithm. Specifically, the successive convex approximation (SCA) framework is adopted to jointly optimize the UAV's trajectory and hovering time, in which a minorization-maximization (MM) algorithm that maximizes the minimum charged energy of all sensors is provided to update the reflection coefficients. Then, we investigate the general scenario in which the RF signals are radiated during the flight, aiming to minimize the total energy consumption of the UAV by jointly optimizing the UAV's trajectory, flight time and the RIS's reflection coefficients. By applying the path discretization (PD) protocol, the optimization problem is formulated with a finite number of variables. A high-quality solution for this more challenging problem is obtained. Finally, our simulation results demonstrate the effectiveness of the proposed algorithm and the benefits of RIS in energy saving.

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arXiv2022

Performance Analysis and Optimization for RIS-Assisted Multi-User Massive MIMO Systems with Imperfect Hardware

The paper studies a reconfigurable intelligent surface (RIS)-assisted multi-user uplink massive multiple-input multiple-output (MIMO) system with imperfect hardware. At the RIS, the paper considers phase noise, while at the base station, the paper takes into consideration the radio frequency impairments and low-resolution analog-to-digital converters. The paper derives approximate expressions for the ergodic achievable rate in closed forms under Rician fading channels. For the cases of infinite numbers of antennas and infinite numbers of reflecting elements, asymptotic data rates are derived to provide new design insights. The derived power scaling laws indicate that while guaranteeing a required system performance, the transmit power of the users can be scaled down at most by the factor 1/M when M goes infinite, or by the factor 1/(MN) when M and N go infinite, where M is the number of antennas and N is the number of the reflecting units. Furthermore, an optimization algorithm is proposed based on the genetic algorithm to solve the phase shift optimization problem with the aim of maximizing the sum rate of the system. Additionally, the optimization problem with discrete phase shifts is considered. Finally, numerical results are provided to validate the correctness of the analytical results.

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arXiv2022

Secrecy Performance Analysis of RIS-aided Communication System with Randomly Flying Eavesdroppers

In this letter, we analyze the secrecy performance of a reconfigurable intelligent surface (RIS)-aided communication system with spatially random unmanned aerial vehicles (UAVs) acting as eavesdroppers. We consider the scenarios where the base station (BS) is equipped with single and multiple antennas.The signal-to-noise ratios (SNRs) of the legitimate user and the eavesdroppers are derived analytically and approximated through a computationally effective method. The ergodic secrecy capacity is approximated and derived in closed-form expressions.Simulation results validate the accuracy of the analytical and approximate expressions and show the security-enhanced effect of the deployment of the RIS.

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arXiv2022

Deep Reinforcement Learning for RIS-aided Multiuser Full-Duplex Secure Communications with Hardware Impairments

In this paper, we investigate a reconfigurable intelligent surface (RIS)-aided multiuser full-duplex secure communication system with hardware impairments at transceivers and RIS, where multiple eavesdroppers overhear the two-way transmitted signals simultaneously, and an RIS is applied to enhance the secrecy performance. Aiming at maximizing the sum secrecy rate (SSR), a joint optimization problem of the transmit beamforming at the base station (BS) and the reflecting beamforming at the RIS is formulated under the transmit power constraint of the BS and the unit modulus constraint of the phase shifters. As the environment is time-varying and the system is high-dimensional, this non-convex optimization problem is mathematically intractable. A deep reinforcement learning (DRL)-based algorithm is explored to obtain the satisfactory solution by repeatedly interacting with and learning from the dynamic environment. Extensive simulation results illustrate that the DRL-based secure beamforming algorithm is proved to be significantly effective in improving the SSR. It is also found that the performance of the DRL-based method can be greatly improved and the convergence speed of neural network can be accelerated with appropriate neural network parameters.

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