arXiv subjects

Xiuhong Wei

Publications and source records attributed to Xiuhong Wei.

7 recordsLinked to original sources

arXiv2025

The Optimal Tradeoff Between PAPR and Ambiguity Functions for Generalized OFDM Waveform Set in ISAC Systems

Integrated sensing and communications (ISAC) has been identified as one of the six usage scenarios for IMT-2030. Compared with communication performance, sensing performance is much more vulnerable to interference, and the received backscattered sensing signal with target information is usually too weak to be detected. It is interesting to understand the optimal tradeoff between interference rejection and signal strength improvement for the best sensing performance, but unfortunately it still remains unknown. In this paper, the trinity of auto-ambiguity function (AF), cross-AF and peak-to-average-power ratio (PAPR) is proposed to describe the interference and coverage related aspects for ISAC systems where multi-carrier waveform is usually assumed. We extend the existing orthogonal frequency division multiplexing (OFDM) waveforms in 5G to a generalized OFDM waveform set with some new members and a unified parametric representation. Then the optimal Pareto tradeoff between PAPR, auto-AF and cross-AF (i.e., the union bound) is developed for the generalized OFDM waveform set. To achieve the optimal Pareto union bound with reasonable computational complexity, we further propose a framework to optimize waveform parameters and sequences jointly. Finally, some practical design examples are provided and numerical results reveal that significant improvements can be achieved compared to the state-of-the-art 5G waveforms and sequences.

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arXiv2022

Near-Field Communications for 6G: Fundamentals, Challenges, Potentials, and Future Directions

Extremely large antenna array (ELAA) is a common feature of several key candidate technologies for sixth-generation mobile networks (6G), such as ultra-massive multiple-input-multiple-output (UM-MIMO), cell-free massive MIMO, reconfigurable intelligent surface (RIS), and terahertz communications. Since the number of antennas is very large for ELAA, the electromagnetic radiation field needs to be modeled by near-field spherical waves, which is opposed to the conventional planar-wave-based radiation model of 5G massive MIMO. As a result, near-field communications will become essential in 6G wireless networks. In this article, we systematically investigate the emerging near-field communication techniques. Firstly, we present the fundamentals of near-field communications and the metric to determine the near-field ranges in typical communication scenarios. Then, we investigate recent studies specific to near-field communications by classifying them into two categories, i.e., techniques addressing the challenges and those exploiting the potentials in near-field regions. Their principles, recent progress, pros and cons are discussed. More importantly, several open problems and future research directions for near-field communications are pointed out. We believe that this article would inspire more innovations for this important research topic of near-field communications for 6G.

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arXiv2021

Channel Estimation for Extremely Large-Scale Massive MIMO: Far-Field, Near-Field, or Hybrid-Field?

Extremely large-scale massive MIMO (XL-MIMO) is a promising technique for future 6G communications.However, existing far-field or near-field channel model mismatches the hybrid-field channel feature in the practical XL-MIMO system.Thus,existing far-field and near-field channel estimation schemes cannot be directly used to accurately estimate the hybrid-field XL-MIMO channel. To solve this problem, we propose an efficient hybrid-field channel estimation scheme by accurately modeling the XL-MIMO channel.Specifically,we firstly reveal the hybrid-field channel feature of the XL-MIMO channel, where different scatters may be in far-field or near-field region.Then, we propose a hybrid-field channel model to capture this feature, which contains both the far-field and near-field path components. Finally, we propose a hybrid-field channel estimation scheme, where the far-field and near-field path components are respectively estimated. Simulation results show that the proposed scheme performs better than existing schemes.

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arXiv2021

Codebook Design and Beam Training for Extremely Large-Scale RIS: Far-Field or Near-Field?

Reconfigurable intelligent surface (RIS) can improve the capacity of the wireless communication system by providing the extra link between the base station (BS) and the user. In order to resist the "multiplicative fading" effect, RIS is more likely to develop into extremely large-scale RIS (XL-RIS) for future 6G communications. Beam training is an effective way to acquire channel state information (CSI) for the XL-RIS assisted system. Existing beam training schemes rely on the far-field codebook, which is designed based on the far-field channel model. However, due to the large aperture of XL-RIS, the user is more likely to be in the near-field region of XL-RIS. The far-field codebook mismatches the near-field channel model. Thus, the existing far-field beam training scheme will cause severe performance loss in the XL-RIS assisted near-field communications. To solve this problem, we propose the efficient near-field beam training schemes by designing the near-field codebook to match the near-field channel model. Specifically, we firstly design the near-field codebook by considering the near-field cascaded array steering vector of XL-RIS. Then, the optimal codeword for XL-RIS is obtained by the exhausted training procedure between the XL-RIS and the user. In order to reduce the beam training overhead, we further design a hierarchical near-field codebook and propose the corresponding hierarchical near-field beam training scheme, where different levels of sub-codebooks are searched in turn with reduced codebook size. Simulation results show the two proposed near-field beam training schemes both perform better than the existing far-field beam training scheme. Particulary, the hierarchical near-field beam training scheme can greatly reduce the beam training overhead with acceptable performance loss.

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arXiv2021

Channel Estimation for RIS Assisted Wireless Communications: Part I -- Fundamentals, Solutions, and Future Opportunities

The reconfigurable intelligent surface (RIS) with low hardware cost and energy consumption has been recognized as a potential technique for future 6G communications to enhance coverage and capacity. To achieve this goal, accurate channel state information (CSI) in RIS assisted wireless communication system is essential for the joint beamforming at the base station (BS) and the RIS. However, channel estimation is challenging, since a large number of passive RIS elements cannot transmit, receive, or process signals. In the first part of this invited paper, we provide an overview of the fundamentals, solutions, and future opportunities of channel estimation in the RIS assisted wireless communication system. It is noted that a new channel estimation scheme with low pilot overhead will be provided in the second part of this paper.

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arXiv2021

Channel Estimation for RIS Assisted Wireless Communications: Part II -- An Improved Solution Based on Double-Structured Sparsity

Reconfigurable intelligent surface (RIS) can manipulate the wireless communication environment by controlling the coefficients of RIS elements. However, due to the large number of passive RIS elements without signal processing capability, channel estimation in RIS assisted wireless communication system requires high pilot overhead. In the second part of this invited paper, we propose to exploit the double-structured sparsity of the angular cascaded channels among users to reduce the pilot overhead. Specifically, we first reveal the double-structured sparsity, i.e., different angular cascaded channels for different users enjoy the completely common non-zero rows and the partially common non-zero columns. By exploiting this double-structured sparsity, we further propose the double-structured orthogonal matching pursuit (DS-OMP) algorithm, where the completely common non-zero rows and the partially common non-zero columns are jointly estimated for all users. Simulation results show that the pilot overhead required by the proposed scheme is lower than existing schemes.

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arXiv2020

Deep Learning for Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systems

Millimeter-wave massive multiple-input multiple-output (MIMO) can use a lens antenna array to considerably reduce the number of radio frequency (RF) chains, but channel estimation is challenging due to the number of RF chains is much smaller than that of antennas. By exploiting the sparsity of beamspace channels, the beamspace channel estimation can be formulated as a sparse signal recovery problem, which can be solved by the classical iterative algorithm named approximate message passing (AMP), and its corresponding version learned AMP (LAMP) realized by a deep neural network (DNN). However, these existing schemes cannot achieve satisfactory estimation accuracy. To improve the channel estimation performance, we propose a prior-aided Gaussian mixture LAMP (GM-LAMP) based beamspace channel estimation scheme in this paper. Specifically, based on the prior information that beamspace channel elements can be modeled by the Gaussian mixture distribution, we first derive a new shrinkage function to refine the AMP algorithm. Then, by replacing the original shrinkage function in the LAMP network with the derived Gaussian mixture shrinkage function, a prior-aided GM-LAMP network is developed to estimate the beamspace channel more accurately. Simulation results by using both the theoretical channel model and the ray-tracing based channel dataset show that, the proposed GM-LAMP network can achieve better channel estimation accuracy.

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