arXiv subjects

Xinrong Guan

Publications and source records attributed to Xinrong Guan.

16 recordsLinked to original sources

arXiv2026

Tensor Decomposition-Based Wireless Sensing for MIMO-OFDM ISAC via Flexible Spatial-Temporal-Spectral Optimization

Integrated sensing and communication (ISAC) is regarded as a key enabling technique in future 6th-generation (6G) mobile communication systems. However, existing multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) ISAC designs generally rely on the fixed-position antennas and fixed allocation of time-frequency resources, thereby limiting the degrees of freedom of wireless sensing along the spatial-temporal-spectral dimensions. In this paper, we propose a novel wireless sensing framework for MIMO-OFDM ISAC systems with flexible spatial-temporal-spectral optimization and propose a tensor decomposition-based approach to estimate target parameters, including azimuth/elevation angles, ranges, and velocities. Specifically, we first establish a monostatic wireless sensing model for MIMO-OFDM ISAC systems, where the positions of antenna elements, the allocation of OFDM symbols and subcarriers can be flexibly configured. Then, we formulate the problem of estimating target parameters as a tensor decomposition problem admitting to the canonical polyadic format, which enables the parallel target parameters estimation process from corresponding factor matrices along the spatial, temporal, and spectral dimensions, respectively. Based on the decomposed factor matrices, we derive the Cramer-Rao Bound (CRB) for the unknown target parameters and reveal that the estimation accuracy of azimuth/elevation angles, velocities and ranges is fundamentally determined by the array geometry, the distribution of OFDM symbols and subcarriers. Building on this insight, we obtain an optimized solution for the positions of antenna elements, and optimal solutions for the subcarrier allocation and OFDM symbol allocation to minimize the CRB, as well as the mean square error of target parameters estimation.

eess.SP↗

arXiv2026

Joint Beamforming and Position Optimization for IRS-Aided SWIPT with Movable Antennas

Simultaneous wireless information and power transfer (SWIPT) has been envisioned as a promising technology to support ubiquitous connectivity and reliable sustainability in Internet-of-Things (IoT) networks, which, however, generally suffers from severe attenuation caused by long distance propagation, leading to inefficient wireless power transfer (WPT) for energy harvesting receivers (EHRs). This paper proposes to introduce emerging intelligent reflecting surface (IRS) and movable antenna (MA) technologies into SWIPT systems aiming at enhancing information transmission for information decoding receivers (IDRs) and improving receive power of EHRs. We consider to maximize the weighted sum-rate of IDRs via jointly optimizing the active and passive beamforming at the base station (BS) and IRS, respectively, as well as the positions of MAs, while guaranteeing the requirements of all EHRs. To tackle this challenging task due to the non-convexity of associated optimization, we develop an efficient algorithm combining weighted minimal mean square error (WMMSE), block coordinate descent (BCD), majorization-minimization (MM), and penalty duality decomposition (PDD) frameworks. Besides, we present a feasibility characterization method to examine the achievability of EHRs' requirements. Simulation results demonstrate the significant benefits of our proposed solutions. Particularly, the optimized IRS configuration may exhibit higher performance gain than MA counterpart under our considered scenario.

cs.IT↗

arXiv2025

Channel Estimation for Movable-Antenna MIMO Systems Via Tensor Decomposition

In this letter, we investigate the channel estimation problem for MIMO wireless communication systems with movable antennas (MAs) at both the transmitter (Tx) and receiver (Rx). To achieve high channel estimation accuracy with low pilot training overhead, we propose a tensor decomposition-based method for estimating the parameters of multi-path channel components, including their azimuth and elevation angles, as well as complex gain coefficients, thereby reconstructing the wireless channel between any pair of Tx and Rx MA positions in the Tx and Rx regions. First, we introduce a two-stage Tx-Rx successive antenna movement pattern for pilot training, such that the received pilot signals in both stages can be expressed as a third-order tensor. Then, we obtain the factor matrices of the tensor via the canonical polyadic decomposition, and thereby estimate the angle/gain parameters for enabling the channel reconstruction between arbitrary Tx/Rx MA positions. In addition, we analyze the uniqueness condition of the tensor decomposition, which ensures the complete channel reconstruction between the whole Tx and Rx regions based on the channel measurements at only a finite number of Tx/Rx MA positions. Finally, simulation results are presented to evaluate the proposed tensor decomposition-based method as compared to existing methods, in terms of channel estimation accuracy and pilot overhead.

eess.SP↗

arXiv2023

Resource Allocation and Passive Beamforming for IRS-assisted URLLC Systems

In this correspondence, we investigate an intelligent reflective surface (IRS) assisted downlink ultra-reliable and low-latency communication (URLLC) system, where an access point (AP) sends short packets to multiple devices with the help of an IRS. Specifically, a performance comparison between the frequency division multiple access (FDMA) and time division multiple access (TDMA) is conducted for the considered system, from the perspective of average age of information (AoI). Aiming to minimize the maximum average AoI among all devices by jointly optimizing the resource allocation and passive beamforming. However, the formulated problem is difficult to solve due to the non-convex objective function and coupled variables. Thus, we propose an alternating optimization based algorithm by dividing the original problem into two sub-problems which can be efficiently solved. Simulation results show that TDMA can achieve lower AoI by exploiting the time-selective passive beamforming of IRS for maximizing the signal to noise ratio (SNR) of each device consecutively. Moreover, it also shows that as the length of information bits becomes sufficiently large as compared to the available bandwidth, the proposed FDMA transmission scheme becomes more favorable instead, due to the more effective utilization of bandwidth.

cs.IT↗

arXiv2022

Energy Efficient Design in IRS-Assisted UAV Data Collection System under Malicious Jamming

In this paper, we study an unmanned aerial vehicle (UAV) enabled data collection system, where an intelligent reflecting surface (IRS) is deployed to assist in the communication from a cluster of Internet-of-Things (IoT) devices to a UAV in the presence of a jammer. We aim to improve the energy efficiency (EE) via the joint design of UAV trajectory, IRS passive beamforming, device power allocation, and communication scheduling. However, the formulated non-linear fractional programming problem is challenging to solve due to its non-convexity and coupled variables. To overcome the difficulty, we propose an alternating optimization based algorithm to solve it sub-optimally by leveraging Dinkelbach's algorithm, successive convex approximation (SCA) technique, and block coordinate descent (BCD) method. Extensive simulation results show that the proposed design can significantly improve the anti-jamming performance. In particular, for the remote jammer case, the proposed design can largely shorten the flight path and thus decrease the energy consumption via the signal enhancement; while for the local jammer case, which is deemed highly challenging in conventional systems without IRS since the retreating away strategy becomes ineffective, our proposed design even achieves a higher performance gain owing to the efficient jamming signal mitigation.

cs.IT↗

arXiv2022

Pilot Optimization and Channel Estimation for Two-way Relaying Network Aided by IRS with Finite Discrete Phase Shifters

In this paper, we investigate the problem of pilot optimization and channel estimation of two-way relaying network (TWRN) aided by an intelligent reflecting surface (IRS) with finite discrete phase shifters. In a TWRN, there exists a challenging problem that the two cascading channels from source-to-IRS-to-Relay and destination-to-IRS-to-relay interfere with each other. Via designing the initial phase shifts of IRS and pilot pattern, the two cascading channels are separated by using simple arithmetic operations like addition and subtraction. Then, the least-squares estimator is adopted to estimate the two cascading channels and two direct channels from source to relay and destination to relay. The corresponding mean square errors (MSE) of channel estimators are derived. By minimizing MSE, the optimal phase shift matrix of IRS is proved. Then, two special matrices Hadamard and discrete Fourier transform (DFT) matrix is shown to be two optimal training matrices for IRS. Furthermore, the IRS with discrete finite phase shifters is taken into account. Using theoretical derivation and numerical simulations, we find that 3-4 bits phase shifters are sufficient for IRS to achieve a negligible MSE performance loss. More importantly, the Hadamard matrix requires only one-bit phase shifters to achieve the optimal MSE performance while the DFT matrix requires at least three or four bits to achieve the same performance. Thus, the Hadamard matrix is a perfect choice for channel estimation using low-resolution phase-shifting IRS.

cs.IT↗

arXiv2022

Robust Trajectory and Communication Design in IRS-Assisted UAV Communication under Malicious Jamming

In this paper, we study an unmanned aerial vehicle (UAV) communication system, where a ground node (GN) communicate with a UAV assisted by intelligent reflecting surface (IRS) in the presence of a jammer with imperfect location information. We aim to improve the achievable average rate via the joint robust design of UAV trajectory, IRS passive beamforming and GN's power allocation. However, the formulated optimization problem is challenging to solve due to its non-convexity and coupled variables. To overcome the difficulty, we propose an alternating optimization (AO) based algorithm to solve it sub-optimally by leveraging semidefinite relaxation (SDR), successive convex approximation (SCA), and S-procedure methods. Simulation results show that by deploying the IRS near the GN, our proposed algorithm always improves the uplink achievable average rate significantly compared with the benchmark algorithms, while deploying the IRS nearby the jammer is effective only when the jammer's location is perfectly known.

cs.IT↗

arXiv2022

Trajectory and Transmit Power Optimization for IRS-Assisted UAV Communication under Malicious Jamming

In this letter, we investigate an unmanned aerial vehicle (UAV) communication system, where an intelligent reflecting surface (IRS) is deployed to assist in the transmission from a ground node (GN) to the UAV in the presence of a jammer. We aim to maximize the average rate of the UAV communication by jointly optimizing the GN's transmit power, the IRS's passive beamforming and the UAV's trajectory. However, the formulated problem is difficult to solve due to the non-convex objective function and the coupled optimization variables. Thus, to tackle it, we propose an alternating optimization (AO) based algorithm by exploiting the successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques. Simulation results show that the proposed algorithm can significantly improve the average rate compared with the benchmark algorithms. Moreover, it also shows that when the jamming power is large and the number of IRS elements is relatively small, deploying the IRS near the jammer outperforms deploying it near the GN, and vice versa.

eess.SP↗

arXiv2021

Anchor-Assisted Channel Estimation for Intelligent Reflecting Surface Aided Multiuser Communication

Channel estimation is a practical challenge for intelligent reflecting surface (IRS) aided wireless communication. As the number of IRS reflecting elements or IRS-aided users increases, the channel training overhead becomes excessively high, which results in long delay and low throughput in data transmission. To tackle this challenge, we propose in this paper a new anchor-assisted channel estimation approach, where two anchor nodes, namely A1 and A2, are deployed near the IRS for facilitating its aided base station (BS) in acquiring the cascaded BS-IRS-user channels required for data transmission. Specifically, in the first scheme, the partial channel state information (CSI) on the element-wise channel gain square of the common BS-IRS link for all users is first obtained at the BS via the anchor-assisted training and feedback. Then, by leveraging such partial CSI, the cascaded BS-IRS-user channels are efficiently resolved at the BS with additional training by the users. While in the second scheme, the BS-IRS-A1 and A1-IRS-A2 channels are first estimated via the training by A1. Then, with additional training by A2, all users estimate their individual cascaded A2-IRS-user channels simultaneously. Based on the CSI fed back from A2 and all users, the BS resolves the cascaded BS-IRS-user channels efficiently. In both schemes, the quasi-static channels among the fixed BS, IRS, and two anchors are estimated off-line only, which greatly reduces the real-time training overhead. Simulation results demonstrate that our proposed anchor-assisted channel estimation schemes achieve superior performance as compared to existing IRS channel estimation schemes, under various practical setups. In addition, the first proposed scheme outperforms the second one when the number of antennas at the BS is sufficiently large, and vice versa.

cs.IT↗

arXiv2021

Intelligent Reflecting Surface Aided Wireless Energy and Information Transmission: An Overview

Intelligent reflecting surface (IRS) is a promising technology for achieving spectrum and energy efficient wireless networks cost-effectively. Most existing works on IRS have focused on exploiting IRS to enhance the performance of wireless communication or wireless information transmission (WIT), while its potential for boosting the efficiency of radio-frequency (RF) wireless energy transmission (WET) still remains largely open. Although IRS-aided WET shares similar characteristics with IRS-aided WIT, they differ fundamentally in terms of design objective, receiver architecture, and practical constraints. In this paper, we provide a tutorial overview on how to efficiently design IRS-aided WET systems as well as IRS-aided systems with both WIT and WET, namely IRS-aided simultaneous wireless information and power transfer (SWIPT) and IRS-aided wireless powered communication network (WPCN), mainly from a communication and signal processing perspective. In particular, we present state-of-the-art solutions to tackle the unique challenges in operating these systems, such as IRS passive reflection optimization, channel estimation and deployment. In addition, we also propose new solution approaches and point out important directions for future research and investigation.

cs.IT↗

arXiv2020

Energy-Efficient Trajectory Design for UAV-Enabled Communication Under Malicious Jamming

In this letter, we investigate a UAV-enabled communication system, where a UAV is deployed to communicate with the ground node (GN) in the presence of multiple jammers. We aim to maximize the energy efficiency (EE) of the UAV by optimizing its trajectory, subject to the UAV's mobility constraints. However, the formulated problem is difficult to solve due to the non-convex and fractional form of the objective function. Thus, we propose an iterative algorithm based on successive convex approximation (SCA) technique and Dinkelbach's algorithm to solve it. Numerical results show that the proposed algorithm can strike a better balance between the throughput and energy consumption by the optimized trajectory and thus improve the EE significantly as compared to the benchmark algorithms.

cs.IT↗

arXiv2020

Anchor-Assisted Intelligent Reflecting Surface Channel Estimation for Multiuser Communications

Due to the passive nature of Intelligent Reflecting Surface (IRS), channel estimation is a fundamental challenge in IRS-aided wireless networks. Particularly, as the number of IRS reflecting elements and/or that of IRS-served users increase, the channel training overhead becomes excessively high. To tackle this challenge, we propose in this paper a new anchor-assisted two-phase channel estimation scheme, where two anchor nodes, namely A1 and A2, are deployed near the IRS for helping the base station (BS) to acquire the cascaded BS-IRS-user channels. Specifically, in the first phase, the partial channel state information (CSI), i.e., the element-wise channel gain square, of the BS-IRS link is obtained by estimating the BS-IRS-A1/A2 channels and the A1-IRS-A2 channel, separately. Then, in the second phase, by leveraging such partial knowledge of the BS-IRS channel that is common to all users, the individual cascaded BS-IRS-user channels are efficiently estimated. Simulation results demonstrate that the proposed anchor-assisted channel estimation scheme is able to achieve comparable mean-squared error (MSE) performance as compared to the conventional scheme, but with significantly reduced channel training time.

cs.IT↗

arXiv2020

Robust and Secure Beamforming for Intelligent Reflecting Surface Aided mmWave MISO Systems

In this letter, we investigate the robust and secure beamforming (RSBF) in an intelligent reflecting surface (IRS) aided millimeter wave (mmWave) multiple input single output (MISO) system, where multiple single antenna eavesdroppers (Eves) are arbitrarily distributed nearby the legitimate receiver. Considering the channel state information (CSI) of Eves' channels is imperfectly known at the legitimate transmitter, the RSBF design problems to maximize the worst case of achievable secrecy rate (ASR) are formulated under the total transmission power and unit-modulus constraints. Since the problems are difficult to solve optimally due to their nonconvexity and coupled variables, we substitute the wiretap channels by a weighted combination of discrete samples and propose a RSBF scheme based on alternating optimization and semidefinite relaxation (SDR) techniques, for both colluding and noncolluding eavesdropping scenarios. Simulation results show that the proposed RSBF scheme can effectively improve the ASR and also outperforms other benchmark schemes.

cs.IT↗

arXiv2020

Joint Power Control and Passive Beamforming in IRS-Assisted Spectrum Sharing

In cognitive radio (CR) communication systems, achieving high secondary user (SU) rate in the presence of strong cross-link interference with the primary user (PU) is challenging. In this letter, we exploit the emerging intelligent reflecting surface (IRS) technology to tackle this problem. Specifically, we investigate an IRS-assisted CR communication system where an IRS is deployed to assist in the spectrum sharing between a PU link and an SU link. We aim to maximize the achievable SU rate subject to a given signal-to-interference-plus-noise ratio target for the PU link, by jointly optimizing the SU transmit power and IRS reflect beamforming. Since the formulated problem is difficult to solve due to its non-convexity and coupled variables, we propose an efficient algorithm based on alternating optimization and successive convex approximation techniques to solve it sub-optimally, along with some heuristic designs for lower complexity. Simulation results show that IRS is able to significantly improve the SU rate, even for the scenarios deemed most challenging in conventional CR systems without using IRS.

cs.IT↗

arXiv2019

Intelligent Reflecting Surface Assisted Secrecy Communication: Is Artificial Noise Helpful or Not?

In this letter, we investigate whether the use of artificial noise (AN) is helpful to enhance the secrecy rate of an intelligent reflecting surface (IRS) assisted wireless communication system. Specifically, an IRS is deployed nearby a single-antenna receiver to assist in the transmission from a multi-antenna transmitter, in the presence of multiple single-antenna eavesdroppers. Aiming to maximize the achievable secrecy rate, a design problem for jointly optimizing transmit beamforming with AN or jamming and IRS reflect beamforming is formulated, which is however difficult to solve due to its non-convexity and coupled variables. We thus propose an efficient algorithm based on alternating optimization to solve the problem sub-optimally. Simulation results show that incorporating AN in transmit beamforming is beneficial under the new setup with IRS reflect beamforming. In particular, it is unveiled that the IRS-aided design without AN even performs worse than the AN-aided design without IRS as the number of eavesdroppers near the IRS increases.

cs.IT↗

arXiv2019

Wireless Key Generation from Imperfect Channel State Information: Performance Analysis and Improvements

The basis of generating secret key from the common wireless channel at two communication parties is reciprocity. However, due to non-simultaneous measurements and unavoidable estimation error, bias always exists and key disagreement occurs. In this paper, performance analysis and improvements for key generation exploiting imperfect channel statement information (CSI) is studied. Firstly, the closed-form expression for key disagreement rate (KDR) is derived to evaluate the key mismatch caused by time delay and estimation error. Secondly, the energy efficiency involving power allocation between key generation and private data transmission is presented. After that, a neural network based prediction (NNBP) algorithm is proposed to improve the key consistency. Simulation results verify the theoretical analysis and show that NNBP achieves significant improvements. And it's interesting to find that NNBP algorithm achieves a higher performance gain in low signal-to-noise ration (SNR) and high delay condition, which is different from other methods, like guard band based quantizer (GBBQ).

cs.IT↗