arXiv subjects

Chongwen Huang

Publications and source records attributed to Chongwen Huang.

At least 127 records · Page 7Linked to original sources

arXiv2023

Energy Efficient Semantic Communication over Wireless Networks with Rate Splitting

In this paper, the problem of wireless resource allocation and semantic information extraction for energy efficient semantic communications over wireless networks with rate splitting is investigated. In the considered model, a base station (BS) first extracts semantic information from its large-scale data, and then transmits the small-sized semantic information to each user which recovers the original data based on its local common knowledge. At the BS side, the probability graph is used to extract multi-level semantic information. In the downlink transmission, a rate splitting scheme is adopted, while the private small-sized semantic information is transmitted through private message and the common knowledge is transmitted through common message. Due to limited wireless resource, both computation energy and transmission energy are considered. This joint computation and communication problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under computation, latency, and transmit power constraints. To solve this problem, an alternating algorithm is proposed where the closed-form solutions for semantic information extraction ratio and computation frequency are obtained at each step. Numerical results verify the effectiveness of the proposed algorithm.

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arXiv2022

Over-the-Air Split Machine Learning in Wireless MIMO Networks

In split machine learning (ML), different partitions of a neural network (NN) are executed by different computing nodes, requiring a large amount of communication cost. To ease communication burden, over-the-air computation (OAC) can efficiently implement all or part of the computation at the same time of communication. Based on the proposed system, the system implementation over wireless network is introduced and we provide the problem formulation. In particular, we show that the inter-layer connection in a NN of any size can be mathematically decomposed into a set of linear precoding and combining transformations over MIMO channels. Therefore, the precoding matrix at the transmitter and the combining matrix at the receiver of each MIMO link, as well as the channel matrix itself, can jointly serve as a fully connected layer of the NN. The generalization of the proposed scheme to the conventional NNs is also introduced. Finally, we extend the proposed scheme to the widely used convolutional neural networks and demonstrate its effectiveness under both the static and quasi-static memory channel conditions with comprehensive simulations. In such a split ML system, the precoding and combining matrices are regarded as trainable parameters, while MIMO channel matrix is regarded as unknown (implicit) parameters.

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arXiv2022

WAIR-D: Wireless AI Research Dataset

It is a common sense that datasets with high-quality data samples play an important role in artificial intelligence (AI), machine learning (ML) and related studies. However, although AI/ML has been introduced in wireless researches long time ago, few datasets are commonly used in the research community. Without a common dataset, AI-based methods proposed for wireless systems are hard to compare with both the traditional baselines and even each other. The existing wireless AI researches usually rely on datasets generated based on statistical models or ray-tracing simulations with limited environments. The statistical data hinder the trained AI models from further fine-tuning for a specific scenario, and ray-tracing data with limited environments lower down the generalization capability of the trained AI models. In this paper, we present the Wireless AI Research Dataset (WAIR-D)1, which consists of two scenarios. Scenario 1 contains 10,000 environments with sparsely dropped user equipments (UEs), and Scenario 2 contains 100 environments with densely dropped UEs. The environments are randomly picked up from more than 40 cities in the real world map. The large volume of the data guarantees that the trained AI models enjoy good generalization capability, while fine-tuning can be easily carried out on a specific chosen environment. Moreover, both the wireless channels and the corresponding environmental information are provided in WAIR-D, so that extra-information-aided communication mechanism can be designed and evaluated. WAIR-D provides the researchers benchmarks to compare their different designs or reproduce results of others. In this paper, we show the detailed construction of this dataset and examples of using it.

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arXiv2022

Robust Security Energy Efficiency Optimization for RIS-Aided Cell-Free Networks with Multiple Eavesdroppers

In this paper, we investigate the energy efficiency (EE) problem under reconfigurable intelligent surface (RIS)-aided secure cell-free networks, where multiple legitimate users and eavesdroppers (Eves) exist. We formulate a max-min secure EE optimization problem by jointly designing the distributed active beamforming and artificial noise at base stations as well as the passive beamforming at RISs under practical constraints. To deal with it, we first divide the original optimization problem into two sub-ones, and then propose an iterative optimization algorithm to solve each sub-problem based on the fractional programming, constrained convex-convex procedure (CCCP) and semi-definite programming (SDP) techniques. After that, these two sub-problems are alternatively solved until convergence, and the final solutions are obtained. Next, we extend to the imperfect channel state information of the Eves' links, and investigate the robust SEE beamforming optimization problem by bringing the outage probability constraints. Based on this, we first transform the uncertain outage probability constraints into the certain ones by the bernstein-type inequality and sphere boundary techniques, and then propose an alternatively iterative algorithm to obtain the solutions of the original problem based on the S-procedure, successive convex approximation, CCCP and SDP techniques. Finally, the simulation results are conducted to show the effectiveness of the proposed schemes.

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arXiv2022

Tri-Polarized Holographic MIMO Surface in Near-Field: Channel Modeling and Precoding Design

This paper investigates the utilization of triple polarization (TP) for multi-user (MU) holographic multiple-input multi-output surface (HMIMOS) wireless communication systems, targeting capacity boosting and diversity exploitation without enlarging the antenna array sizes. We specifically consider that both the transmitter and receiver are both equipped with an HMIMOS consisting of compact sub-wavelength TP patch antennas within the near-field (NF) regime. To characterize TP MU-HMIMOS systems, a TP NF channel model is constructed using the dyadic Green's function, whose characteristics are leveraged to design two precoding schemes for mitigating the cross-polarization and inter-user interference contributions. Specifically, a user-cluster-based precoding scheme assigns different users to one of three polarizations at the expense of the system's diversity, and a two-layer precoding scheme removes interference using the Gaussian elimination method at a high computational cost. The theoretical correlation analysis for HMIMOS in the NF region is also investigated, revealing that both the spacing of transmit patch antennas and user distance impact transmit correlation factors. Our numerical results show that the users far from transmitting HMIMOS experience higher correlation than those closer within the NF regime, resulting in a lower channel capacity. Meanwhile, in terms of channel capacity, TP HMIMOS can almost achieve 1.25 times gain compared with dual-polarized HMIMOS, and 3 times compared with conventional HMIMOS. In addition, the proposed two-layer precoding scheme combined with two-layer power allocation realizes a higher spectral efficiency than other schemes without sacrificing diversity.

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arXiv2022

Variational Bayesian Inference Clustering Based Joint User Activity and Data Detection for Grant-Free Random Access in mMTC

Tailor-made for massive connectivity and sporadic access, grant-free random access has become a promising candidate access protocol for massive machine-type communications (mMTC). Compared with conventional grant-based protocols, grant-free random access skips the exchange of scheduling information to reduce the signaling overhead, and facilitates sharing of access resources to enhance access efficiency. However, some challenges remain to be addressed in the receiver design, such as unknown identity of active users and multi-user interference (MUI) on shared access resources. In this work, we deal with the problem of joint user activity and data detection for grant-free random access. Specifically, the approximate message passing (AMP) algorithm is first employed to mitigate MUI and decouple the signals of different users. Then, we extend the data symbol alphabet to incorporate the null symbols from inactive users. In this way, the joint user activity and data detection problem is formulated as a clustering problem under the Gaussian mixture model. Furthermore, in conjunction with the AMP algorithm, a variational Bayesian inference based clustering (VBIC) algorithm is developed to solve this clustering problem. Simulation results show that, compared with state-of-art solutions, the proposed AMP-combined VBIC (AMP-VBIC) algorithm achieves a significant performance gain in detection accuracy.

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arXiv2022

Deep Learning-Based Channel Estimation for Double-RIS Aided Massive MIMO System

Reconfigurable Intelligent Surface (RIS) is considered as an energy-efficient solution for future wireless communication networks due to its fast and low-cost configuration. In this letter, we consider the estimation of cascaded channels in a double-RIS aided massive multiple-input multiple-output system, which is a critical challenge due to the large number of antennas equipped at the base station and passive RIS elements. To tackle this challenge, we propose a skip-connection attention (SC-attention) network that utilizes self-attention layers and skip-connection structure to improve the channel estimation performance from the noisy pilot-based observations. Simulation results compare the proposed SC-attention network with other benchmark methods and show that SC-attention network can effectively improve the accuracy performance on normalized mean square error (NMSE) for cascaded links in a double-RIS aided system.

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arXiv2022

Electromagnetic Effective-Degree-of-Freedom Limit of a MIMO System in 2-D Inhomogeneous Environment

Compared with a single-input-single-output (SISO) wireless communication system, the benefit of multiple-input-multiple-output (MIMO) technology originates from its extra degree of freedom (DOF), also referred as scattering channels or spatial electromagnetic (EM) modes, brought by spatial multiplexing. When the physical sizes of transmitting and receiving arrays are fixed, and there are sufficient antennas (typically with half-wavelength spacings), the DOF limit is only dependent on the propagating environment. Analytical methods can be used to estimate this limit in free space, and some approximate models are adopted in stochastic environments, such as Clarke's model and Ray-tracing methods. However, this DOF limit in an certain inhomogeneous environment has not been well discussed with rigorous full-wave numerical methods. In this work, volume integral equation (VIE) is implemented for investigating the limit of MIMO effective degree of freedom (EDOF) in three representative two-dimensional (2-D) inhomogeneous environments. Moreover, we clarify the relation between the performance of a MIMO system and the scattering characteristics of its propagating environment.

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arXiv2022

Reconfigurable Intelligent Computational Surfaces: When Wave Propagation Control Meets Computing

The envisioned sixth-generation (6G) of wireless networks will involve an intelligent integration of communications and computing, thereby meeting the urgent demands of diverse applications. To realize the concept of the smart radio environment, reconfigurable intelligent surfaces (RISs) are a promising technology for offering programmable propagation of impinging electromagnetic signals via external control. However, the purely reflective nature of conventional RISs induces significant challenges in supporting computation-based applications, e.g., wave-based calculation and signal processing. To fulfil future communication and computing requirements, new materials are needed to complement the existing technologies of metasurfaces, enabling further diversification of electronics and their applications. In this event, we introduce the concept of reconfigurable intelligent computational surface (RICS), which is composed of two reconfigurable multifunctional layers: the `reconfigurable beamforming layer' which is responsible for tunable signal reflection, absorption, and refraction, and the `intelligence computation layer' that concentrates on metamaterials-based computing. By exploring the recent trends on computational metamaterials, RICSs have the potential to make joint communication and computation a reality. We further demonstrate two typical applications of RICSs for performing wireless spectrum sensing and secrecy signal processing. Future research challenges arising from the design and operation of RICSs are finally highlighted.

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arXiv2022

Extra DoF of Near-Field Holographic MIMOCommunications Leveraging Evanescent Waves

In this letter, we consider transceivers with spatially-constrained antenna apertures of rectangular symmetry, and aim to improve of spatial degrees of freedom (DoF) and channel capacity leveraging evanescent waves for information transmission in near-field scenarios based on the Fourier plane-wave series expansion. The treatment is limited to an isotropic scattering environment but can be extended to the non-isotropic case through the linear-system theoretic interpretation of plane-wave propagation. Numerical results show that evanescent waves have the significant potential to provide additional DoF and capacity in the near-field region.

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arXiv2022

Reconfiguring Wireless Environment via Intelligent Surfaces for 6G: Reflection, Modulation, and Security

Reconfigurable intelligent surface (RIS) has been recognized as an essential enabling technique for the sixth-generation (6G) mobile communication network. Specifically, an RIS is comprised of a large number of small and low-cost reflecting elements whose parameters are dynamically adjustable with a programmable controller. Each of these elements can effectively reflect a phase-shifted version of the incident electromagnetic wave. By adjusting the wave phases in real time, the propagation environment of the reflected signals can be dynamically reconfigured to enhance communication reliability, boost transmission rate, expand cellular coverage, and strengthen communication security. In this paper, we provide an overview on RIS-assisted wireless communications. Specifically, we elaborate on the state-of-the-art enabling techniques of RISs as well as their corresponding substantial benefits from the perspectives of RIS reflection and RIS modulation. With these benefits, we envision the integration of RIS into emerging applications for 6G. In addition, communication security is of unprecedented importance in the 6G network with ubiquitous wireless services in multifarious verticals and areas. We highlight potential contributions of RIS to physical-layer security in terms of secrecy rate and secrecy outage probability, exemplified by a typical case study from both theoretical and numerical aspects. Finally, we discuss challenges and opportunities on the deployment of RISs in practice to motivate future research.

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arXiv2022

Multiple RISs Assisted Cell-Free Networks With Two-timescale CSI: Performance Analysis and System Design

Reconfigurable intelligent surface (RIS) can be employed in a cell-free system to create favorable propagation conditions from base stations (BSs) to users via configurable elements. However, prior works on RIS-aided cell-free system designs mainly rely on the instantaneous channel state information (CSI), which may incur substantial overhead due to extremely high dimensions of estimated channels. To mitigate this issue, a low-complexity algorithm via the two-timescale transmission protocol is proposed in this paper, where the joint beamforming at BSs and RISs is facilitated via alternating optimization framework to maximize the average weighted sum-rate. Specifically, the passive beamformers at RISs are optimized through the statistical CSI, and the transmit beamformers at BSs are based on the instantaneous CSI of effective channels. In this manner, a closed-form expression for the achievable weighted sum-rate is derived, which enables the evaluation of the impact of key parameters on system performance. To gain more insights, a special case without line-of-sight (LoS) components is further investigated, where a power gain on the order of $\mathcal{O}(M)$ is achieved, with $M$ being the BS antennas number. Numerical results validate the tightness of our derived analytical expression and show the fast convergence of the proposed algorithm. Findings illustrate that the performance of the proposed algorithm with two-timescale CSI is comparable to that with instantaneous CSI in low or moderate SNR regime. The impact of key system parameters such as the number of RIS elements, CSI settings and Rician factor is also evaluated. Moreover, the remarkable advantages from the adoption of the cell-free paradigm and the deployment of RISs are demonstrated intuitively.

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arXiv2022

Cooperative Beamforming for RIS-Aided Cell-Free Massive MIMO Networks

The combination of cell-free massive multiple-input multiple-output (CF-mMIMO) and reconfigurable intelligent surface (RIS) is envisioned as a promising paradigm to improve network capacity and enhance coverage capability. However, to reap full benefits of RIS-aided CF-mMIMO, the main challenge is to efficiently design cooperative beamforming (CBF) at base stations (BSs), RISs, and users. Firstly, we investigate the fractional programing to convert the weighted sum-rate (WSR) maximization problem into a tractable optimization problem. Then, the alternating optimization framework is employed to decompose the transformed problem into a sequence of subproblems, i.e., hybrid BF (HBF) at BSs, passive BF at RISs, and combining at users. In particular, the alternating direction method of multipliers algorithm is utilized to solve the HBF subproblem at BSs. Concretely, the analog BF design with unit-modulus constraints is solved by the manifold optimization (MO) while we obtain a closed-form solution to the digital BF design that is essentially a convex least-square problem. Additionally, the passive BF at RISs and the analog combining at users are designed by primal-dual subgradient and MO methods. Moreover, considering heavy communication costs in conventional CF-mMIMO systems, we propose a partially-connected CF-mMIMO (P-CF-mMIMO) framework to decrease the number of connections among BSs and users. To better compromise WSR performance and network costs, we formulate the BS selection problem in the P-CF-mMIMO system as a binary integer quadratic programming (BIQP) problem, and develop a relaxed linear approximation algorithm to handle this BIQP problem. Finally, numerical results demonstrate superiorities of our proposed algorithms over baseline counterparts.

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arXiv2022

Nanoscale Reconfigurable Intelligent Surface Design and Performance Analysis for Terahertz Communications

Terahertz (THz) communications have been envisioned as a promising enabler to provide ultra-high data transmission for sixth generation (6G) wireless networks. To tackle the blockage vulnerability brought by severe attenuation and poor diffraction of THz waves, a nanoscale reconfigurable intelligent surface (NRIS) is developed to smartly manipulate the propagation directions of incident THz waves. In this paper, the electric properties of the graphene are investigated by revealing the relationship between conductivity and applied voltages, and then an efficient hardware structure of electrically-controlled NRIS is designed based on Fabry-Perot resonance model. Particularly, the phase response of NRIS can be programmed up to 306.82 degrees. To analyze the hardware performance, we jointly design the passive and active beamforming for NRIS aided THz communication system. Particularly, an adaptive gradient descent (A-GD) algorithm is developed to optimize the phase shift matrix of NRIS by dynamically updating the step size during the iterative process. Finally, numerical results demonstrate the effectiveness of our designed hardware architecture as well as the developed algorithm.

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arXiv2022

Joint Channel Estimation and Signal Recovery for RIS-Empowered Multi-User Communications

Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and lowpower configuration enables coverage extension, massive connectivity, and low-latency communications. Due to a large number of unknown variables referring to the RIS unit elements and the transmitted signals, channel estimation and signal recovery in RIS-based systems are the ones of the most critical technical challenges. To address this problem, we focus on the RIS-assisted wireless communication system and present two joint channel estimation and signal recovery schemes based on message passing algorithms in this paper. Specifically, the proposed bidirectional scheme applies the Taylor series expansion and Gaussian approximation to simplify the sum-product procedure in the formulated problem. In addition, the inner iteration that adopts two variants of approximate message passing algorithms is incorporated to ensure robustness and convergence. Two ambiguities removal methods are also discussed in this paper. Our simulation results show that the proposed schemes show the superiority over the state-of-art benchmark method. We also provide insights on the impact of different RIS parameter settings on the proposed schemes.

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arXiv2022

Multi-User Holographic MIMO Surfaces: Channel Modeling and Spectral Efficiency Analysis

The multi-user Holographic Multiple-Input and Multiple-Output Surface (MU-HMIMOS) paradigm, which is capable of realizing large continuous apertures with minimal power consumption, has been recently considered as an energyefficient solution for future wireless networks, offering increased flexibility in impacting electromagnetic (EM) wave propagation according to the desired communication, localization, and sensing objectives. The tractable channel modeling in MU-HMIMOS wireless systems is one of the most critical research challenges, mainly due to the coupling effect induced by the excessively large number of closely spaced patch antennas. In this paper, we focus on this challenge for the downlink of multi-user MIMO communications and extend an EM-compliant channel model to multiuser case, which is expressed in the wavenumber domain using the Fourier plane wave approximation. Based on the presented channel model, we investigate the spectral efficiency of maximumratio transmission and Zero-Forcing (ZF) precoding schemes. We also introduce a novel hardware efficient ZF precoder, leveraging Neumann series (NS) expansion to replace the required matrix inversion operation, which is very hard to be computed in the conventional way due to the extremely large number of patch antennas in the envisioned MU-HMIMOS communication systems. In comparison with the conventional independent and identical Rayleigh fading channels that ignore antenna coupling effects, the proposed EM-compliant channel model captures the mutual couplings induced by the very small antenna spacing. Our extensive performance evaluation results demonstrate that our theoretical performance expressions approximate sufficiently well ...

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arXiv2022

Environment Sensing Considering the Occlusion Effect: A Multi-View Approach

In this paper, we consider the problem of sensing the environment within a wireless cellular framework. Specifically, multiple user equipments (UEs) send sounding signals to one or multiple base stations (BSs) and then a centralized processor retrieves the environmental information from all the channel information obtained at the BS(s). Taking into account the occlusion effect that is common in the wireless context, we make full use of the different views of the environment from different users and/or BS(s), and propose an effective sensing algorithm called GAMP-MVSVR (generalized-approximate-message-passing-based multi-view sparse vector reconstruction). In the proposed algorithm, a multi-layer factor graph is constructed to iteratively estimate the scattering coefficients of the cloud points and their occlusion relationship. In each iteration, the occlusion relationship between the cloud points of the sparse environment is recalculated according to a simple occlusion detection rule, and in turn, used to estimate the scattering coefficients of the cloud points. Our proposed algorithm can achieve improved sensing performance with multi-BS collaboration in addition to the multi-views from the UEs. The simulation results verify its convergence and effectiveness.

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arXiv2022

Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications

Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low-power configuration, which has increased potential in enabling massive connectivity and low-latency communications. Accurate and low-overhead channel estimation in RIS-based systems is one of the most critical challenges due to the usually large number of RIS unit elements and their distinctive hardware constraints. In this paper, we focus on the uplink of a RIS-empowered multi-user Multiple Input Single Output (MISO) uplink communication systems and propose a channel estimation framework based on the parallel factor decomposition to unfold the resulting cascaded channel model. We present two iterative estimation algorithms for the channels between the base station and RIS, as well as the channels between RIS and users. One is based on alternating least squares (ALS), while the other uses vector approximate message passing to iteratively reconstruct two unknown channels from the estimated vectors. To theoretically assess the performance of the ALS-based algorithm, we derived its estimation Cramér-Rao Bound (CRB). We also discuss the downlink achievable sum rate computation with estimated channels and different precoding schemes for the base station. Our extensive simulation results show that our algorithms outperform benchmark schemes and that the ALS technique achieves the CRB. It is also demonstrated that the sum rate using the estimated channels always reach that of perfect channels under various settings, thus, verifying the effectiveness and robustness of the proposed estimation algorithms.

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