arXiv subjects

Huayan Guo

Publications and source records attributed to Huayan Guo.

13 recordsLinked to original sources

arXiv2025

A Novel Pilot Scheme for Uplink Channel Estimation for Sub-array Structured ELAA in XL-MIMO systems

This paper proposes a novel pilot scheme for multi-user uplink channel estimation in extra-large-scale massive MIMO (XL-MIMO) systems with extremely large aperture arrays (ELAA). The large aperture of ELAA introduces spatial non-stationarity, where far-apart users have significantly distinct visibility at the antennas, thereby reducing inter-user interference. This insight motivates our novel pilot scheme to group users with distinct visibility regions to share the same frequency subcarriers for channel estimation, so that more users can be served with reduced pilot overhead. Specifically, the proposed pilot scheme employs frequency-division multiplexing for inter-group channel estimation, while intra-group users -- benefiting from strong spatial orthogonality -- are distinguished by shifted cyclic codes, similar to code-division multiplexing. Additionally, we introduce a sub-array structured ELAA, where each sub-array is a traditional MIMO array and treated as spatial stationary, while the distances between sub-arrays can be significantly larger to achieve an expanded aperture. The channel support for sub-arrays features clustered sparsity in the antenna-delay domain and is modeled by a 2-dimensional (2-D) Markov random field (MRF). Based on this, we propose a low-complexity channel estimation algorithm within a turbo Bayesian inference framework that incorporates the 2-D MRF prior model. Simulations show that the proposed scheme and algorithm allow the XL-MIMO system to support more users, and deliver superior channel estimation performance.

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arXiv2025

Channel Estimation and Passive Beamforming for Pixel-based Reconfigurable Intelligent Surfaces with Non-Separable State Response

Pixel-based reconfigurable intelligent surfaces (RISs) employ a novel design to achieve high reflection gain at a lower hardware cost by eliminating the phase shifters used in traditional RIS. However, this design presents challenges for channel estimation and passive beamforming due to its non-separable state response, rendering existing solutions ineffective. To address this, we first approximate the non-separable RIS response functions using a kernel-based method and a deep neural network, achieving high accuracy while reducing computational and memory complexity. Next, we propose a simplified cascaded channel model that focuses on dominated scattering paths with limited unknown parameters, along with customized algorithms to estimate short-term and long-term parameters separately. Finally, we introduce a low-complexity passive beamforming algorithm to configure the discrete RIS state vector, maximizing the achievable rate. Our simulation results demonstrate that the proposed solution significantly outperforms various baselines across a wide SNR range.

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arXiv2025

Channel Estimation and Analog Precoding for Pixel-based Fluid-Antenna-Assisted Multiuser MIMO-OFDM Systems

Pixel-based fluid antennas provide enhanced multiplexing gains and quicker radiation pattern switching than traditional designs. However, this innovation introduces challenges for channel estimation and analog precoding due to the state-non-separable channel response problem. This paper explores a multiuser MIMO-OFDM system utilizing pixel-based fluid antennas, informed by measurements from a real-world prototype. We present a sparse channel recovery framework for uplink channel sounding, employing an approximate separable channel response model with DNN-based antenna radiation functions. We then propose two low-complexity channel estimation algorithms that leverage orthogonal matching pursuit and variational Bayesian inference to accurately recover channel responses across various scattering cluster angles. These estimations enable the prediction of composite channels for all fluid antenna states, leading to an analog precoding scheme that optimally selects switching states for different antennas. Our simulation results indicate that the proposed approach significantly outperforms several baseline methods, especially in high signal-to-noise ratio environments with numerous users.

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arXiv2025

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder

This paper introduces a novel precoder design aimed at reducing pilot overhead for effective channel estimation in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) applications utilizing high-order modulation. We propose an innovative demodulation reference signal scheme that achieves up to an 8x reduction in overhead by implementing a delay-domain sparsity constraint on the precoder. Furthermore, we present a deep neural network (DNN)-based end-to-end architecture that integrates a propagation channel estimation module, a precoder design module, and an effective channel estimation module. Additionally, we propose a Bayesian model-assisted training framework that incorporates domain knowledge, resulting in an interpretable datapath design. Simulation results demonstrate that our proposed solution significantly outperforms various baseline schemes while exhibiting substantially lower computational complexity.

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arXiv2021

Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validation

In this paper, we develop an orthogonal-frequency-division-multiplexing (OFDM)-based over-the-air (OTA) aggregation solution for wireless federated learning (FL). In particular, the local gradients in massive IoT devices are modulated by an analog waveform and are then transmitted using the same wireless resources. To this end, achieving perfect waveform superposition is the key challenge, which is difficult due to the existence of frame timing offset (TO) and carrier frequency offset (CFO). In order to address these issues, we propose a two-stage waveform pre-equalization technique with a customized multiple access protocol that can estimate and then mitigate the TO and CFO for the OTA aggregation. Based on the proposed solution, we develop a hardware transceiver and application software to train a real-world FL task, which learns a deep neural network to predict the received signal strength with global positioning system information. Experiments verify that the proposed OTA aggregation solution can achieve comparable performance to offline learning procedures with high prediction accuracy.

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arXiv2021

Cascaded Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser MISO Systems

This paper investigates the uplink cascaded channel estimation for intelligent-reflecting-surface (IRS)-assisted multi-user multiple-input-single-output systems. We focus on a sub-6 GHz scenario where the channel propagation is not sparse and the number of IRS elements can be larger than the number of BS antennas. A novel channel estimation protocol without the need of on-off amplitude control to avoid the reflection power loss is proposed. In addition, the pilot overhead is substantially reduced by exploiting the common-link structure to decompose the cascaded channel coefficients by the multiplication of the common-link variables and the user-specific variables. However, these two types of variables are highly coupled, which makes them difficult to estimate. To address this issue, we formulate an optimization-based joint channel estimation problem, which only utilizes the covariance of the cascaded channel. Then, we design a low-complexity alternating optimization algorithm with efficient initialization for the non-convex optimization problem, which achieves a local optimum solution. To further enhance the estimation accuracy, we propose a new formulation to optimize the training phase shifting configuration for the proposed protocol, and then solve it using the successive convex approximation algorithm. Comprehensive simulations verify that the proposed algorithm has supreme performance compared to various state-of-the-art baseline schemes.

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arXiv2019

Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks

Reconfigurable intelligent surfaces (RIS) is a promising solution to build a programmable wireless environment via steering the incident signal in fully customizable ways with reconfigurable passive elements. In this paper, we consider a RIS-aided multiuser multiple-input single-output (MISO) downlink communication system. Our objective is to maximize the weighted sum-rate (WSR) of all users by joint designing the beamforming at the access point (AP) and the phase vector of the RIS elements, while both the perfect channel state information (CSI) setup and the imperfect CSI setup are investigated. For perfect CSI setup, a low-complexity algorithm is proposed to obtain the stationary solution for the joint design problem by utilizing the fractional programming technique. Then, we resort to the stochastic successive convex approximation technique and extend the proposed algorithm to the scenario wherein the CSI is imperfect. The validity of the proposed methods is confirmed by numerical results. In particular, the proposed algorithm performs quite well when the channel uncertainty is smaller than 10%.

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arXiv2019

Model-Free Optimization for Reconfigurable Intelligent Surface with Statistical CSI

In this paper, a single user multiple input single output downlink wireless communication system is investigated, in which multiple reconfigurable intelligent surfaces (RISs) are deployed to improve the propagation condition. Our objective is to optimize the phase shift matrices of all the RISs by exploiting the statistical channel state information (CSI). In particular, two model-free algorithms are proposed, which are applicable for any channel statistical assumptions. Numerical results show that the proposed algorithms significantly outperform the random phase shift scheme, especially when the channel randomness is low.

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arXiv2019

Large Intelligent Surface/Antennas (LISA): Making Reflective Radios Smart

Large intelligent surface/antennas (LISA), a two-dimensional artificial structure with a large number of reflective-surface/antenna elements, is a promising reflective radio technology to construct programmable wireless environments in a smart way. Specifically, each element of the LISA adjusts the reflection of the incident electromagnetic waves with unnatural properties, such as negative refraction, perfect absorption, and anomalous reflection, thus the wireless environments can be software-defined according to various design objectives. In this paper, we introduce the reflective radio basics, including backscattering principles, backscatter communication, and reflective relay, and the fundamentals and implementations of LISA technology. Then, we present an overview of the state-of-the-art research on emerging applications of LISA-aided wireless networks. Finally, the limitations, challenges, and open issues associated with LISA for future wireless applications are discussed.

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arXiv2019

Weighted Sum-Rate Optimization for Intelligent Reflecting Surface Enhanced Wireless Networks

Intelligent reflecting surface (IRS) is a promising solution to build a programmable wireless environment for future communication systems. In practice, an IRS consists of massive low-cost elements, which can steer the incident signal in fully customizable ways by passive beamforming. In this paper, we consider an IRS-aided multiuser multiple-input single-output (MISO) downlink communication system. In particular, the weighted sum-rate of all users is maximized by joint optimizing the active beamforming at the base-station (BS) and the passive beamforming at the IRS. In addition, we consider a practical IRS assumption, in which the passive elements can only shift the incident signal to discrete phase levels. This non-convex problem is firstly decoupled via Lagrangian dual transform, and then the active and passive beamforming can be optimized alternatingly. The active beamforming at BS is optimized based on the fractional programming method. Then, three efficient algorithms with closed-form expressions are proposed for the passive beamforming at IRS. Simulation results have verified the effectiveness of the proposed algorithms as compared to different benchmark schemes.

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arXiv2019

Intelligent Reflecting Surface: A Programmable Wireless Environment for Physical Layer Security

In this paper, we introduce an intelligent reflecting surface (IRS) to provide a programmable wireless environment for physical layer security. By adjusting the reflecting coefficients, the IRS can change the attenuation and scattering of the incident electromagnetic wave so that it can propagate in a desired way toward the intended receiver. Specifically, we consider a downlink multiple-input single-output (MISO) broadcast system where the base station (BS) transmits independent data streams to multiple legitimate receivers and keeps them secret from multiple eavesdroppers. By jointly optimizing the beamformers at the BS and reflecting coefficients at the IRS, we formulate a minimum-secrecy-rate maximization problem under various practical constraints on the reflecting coefficients. The constraints capture the scenarios of both continuous and discrete reflecting coefficients of the reflecting elements. Due to the non-convexity of the formulated problem, we propose an efficient algorithm based on the alternating optimization and the path-following algorithm to solve it in an iterative manner. Besides, we show that the proposed algorithm can converge to a local (global) optimum. Furthermore, we develop two suboptimal algorithms with some forms of closed-form solutions to reduce the computational complexity. Finally, the simulation results validate the advantages of the introduced IRS and the effectiveness of the proposed algorithms

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arXiv2018

Symbiotic Radio: A New Communication Paradigm for Passive Internet-of-Things

In this paper, a novel technique, called symbiotic radio (SR), is proposed for passive Internet-of-Things (IoT), in which a backscatter device (BD) is integrated with a primary transmission. The primary transmitter is designed to assist the primary and BD transmissions, and the primary receiver decodes the information from the primary transmitter as well as the BD. We consider a multiple-input single-output (MISO) SR and the symbol period for BD transmission is designed to be either the same as or much longer than that of the primary system, resulting in parasitic or commensal relationship between the primary and BD transmissions. We first derive the achievable rates for the primary system and the BD transmission. Then, we formulate two transmit beamforming optimization problems, i.e., the weighted sum-rate maximization problem and the transmit power minimization problem, and solve these non-convex problems by applying semi-definite relaxation technique. In addition, a novel transmit beamforming structure is proposed to reduce the computational complexity of the solutions. Simulation results show that when the BD transmission rate is properly designed, the proposed SR not only enables the opportunistic transmission for the BD via energy-efficient passive backscattering, but also enhances the achievable rate of the primary system by properly exploiting the additional signal path from the BD.

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arXiv2018

Noncoherent Multiantenna Receivers for Cognitive Backscatter System with Multiple RF Sources

Cognitive backscattering, an integration of cognitive radio and backsatter modulation, is emerging as a potential candidate for green Internet of Things (IoT). In cognitive backscatter systems, the backscatter device (BD) shares not only the same spectrum, but also the same radio-frequency (RF) source with the legacy system. In this paper, we investigate the signal transmission problem, in which a basic transmission model is considered which consists of K RF sources, one BD and one reader equipped with M antennas. A non-cooperative scenario is considered, where there is no cooperation between the legacy systems and the backscatter system, and no pilots are transmitted from the RF sources or BD to the reader. The on-off keying differential modulation is adopted to achieve noncoherent transmission. Firstly, through the capacity analyses, we point out that high-throughput backscatter transmission can be achieved when the number of the receive antennas satisfies M>K. The Chernoff Information (CI) is also derived to predict the detection performance. Next, we address the signal detection problem at the reader. The optimal soft decision (SD) and suboptimal hard decision (HD) detectors are designed based on the maximum likelihood criterion. To tackle the non-cooperation challenge, a fully blind channel estimation method is proposed to learn the detection-required parameters based on clustering. Extensive numerical results verify the effectiveness of the proposed detectors and the channel estimation method. In addition, it is illustrated that the increase of K may not necessarily lead to performance degradation when multiple receive antennas are exploited.

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