arXiv subjects

Haifan Yin

Publications and source records attributed to Haifan Yin.

At least 19 recordsLinked to original sources

arXiv2026

RRAM circuit-enabled nonlinear precoding and bit precision analysis

The rising number of users and antennas imposes exponentially growing computational loads on future communication systems. Yet conventional processors are facing a bottleneck for their nature of memory-computing separation. In-memory computing (IMC) emerges as a promising solution leveraging its intrinsic high parallelism. This work proposes an IMC architecture that employs resistive random access memory (RRAM) to reduce the computational complexity of the nonlinear Tomlinson-Harashima precoding (THP) to a linear scale. We present a computation-constraint principle for designing RRAM circuits to perform nonlinear matrix operations and construct an LQ decomposition RRAM circuit. Since the conductance of memristor is generally quantized, we perform the bit precision analysis and derive the lower bound of the Signal-to-Interference-plus-Noise Ratio (SINR) and achievable rate. Our analysis indicates that at a high Signal-to-Noise Ratio (SNR) or with a large number of antennas, each 1-bit precision increase yields 6 dB SINR gain and linear rate growth. For practical implementation, we derive the optimal bit precision to sustain SINR performance under varying system configurations. Simulation demonstrates the feasibility and accuracy of the RRAM-based circuit and our theoretical analysis. Our work proves that RRAM-based IMC holds significant potential for high-complexity nonlinear precoding, addressing the escalating computational demands for future communications.

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arXiv2026

Multi-target DoA estimation with a single Rydberg atomic receiver by spectral analysis of spatially-resolved fluorescence

Rydberg-based Direction-of-Arrival (DoA) estimation has been hampered by the complexity of receiver arrays and the single-target, narrow-band limitations of existing single-receiver methods. This paper introduces a novel approach that addresses these limitations. We demonstrate that by spatially resolving the fluorescence profile along the vapor cell, the multi-target problem can be effectively solved. Our approach hinges on the insight that by superimposing incoming signals with a strong local oscillator (LO), the complex atomic absorption pattern is linearized into a simple superposition of sinusoids. In this new representation, each spatial frequency uniquely and directly maps to the DoA of a target. This reduces the multi-target challenge into a spectral estimation problem, which we address using Prony's method. Our approach, termed Imaging-based Spectral Estimation (ISE), inherently supports multi-target detection and restores the full broadband capability of the sensor by removing the restrictive cell-length dependency. This development also shows potential for realizing multi-channel Rydberg receivers and the continuous-aperture sensing required for holographic multiple-input multiple-output (MIMO). We develop a comprehensive theoretical model, derive the Cramer-Rao Lower Bound (CRLB) as a performance benchmark, and present simulations validating the effectiveness of the approach to resolve multiple targets.

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arXiv2026

Precoding Matrix Indicator in the 5G NR Protocol: A Tutorial on 3GPP Beamforming Codebooks

This paper bridges this critical gap by providing a systematic examination of the beamforming codebook technology, i.e., precoding matrix indicator (PMI), in the 5G NR from theoretical, standardization, and implementation perspectives. We begin by introducing the background of beamforming in multiple-input multiple-output (MIMO) systems and the signaling procedures for codebook-based beamforming in practical 5G systems. Then, we establish the fundamentals of regular codebooks and port-selection codebooks in 3GPP standards. Next, we provide rigorous technical analysis of 3GPP codebook evolution spanning Releases 15-18, with particular focus on: 1) We elucidate the core principles underlying codebook design, 2) provide clear physical interpretations for each symbolic variable in the codebook formulas, summarized in tabular form, and 3) offer intuitive visual illustrations to explain how codebook parameters convey information. These essential pedagogical elements are almost entirely absent in the often-obscure standardization documents. Through mathematical modeling, performance benchmarking, feedback comparisons, and scenario-dependent applicability analysis, we provide researchers and engineers with a unified understanding of beamforming codebooks in real-world systems. Furthermore, we identify future directions and other beamforming scenarios for ongoing research and development efforts. This work serves as both an informative tutorial and a guidance for future research, facilitating more effective collaboration between academia and industry in advancing wireless communication technologies.

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arXiv2025

Wideband channel sensing with holographic interference surfaces

The Holographic Interference Surface (HIS) opens up a new prospect for building a more cost-effective wireless communication architecture by performing Radio Frequency (RF) domain signal processing. In this paper, we establish a wideband channel sensing architecture for electromagnetic wave reception and channel estimation based on the principle of holographic interference theory. Dute to the nonlinear structure of holograms, interferential fringes composed of wideband RF signals exhibit severe self-interference effects in the time-frequency domain, which are inherently resistant to the classical signal processing tools. To overcome the self-interference, we propose a holographic channel recovery method, which analyzes the time-domain variation of holograms from a geometrical perspective and constructs an inverse mapping from wideband holograms to object waves. Based on the Wirtinger partial derivative and Armijo condition, we then develop a wideband hologram-based maximum likelihood (WH-ML) estimation method for estimating the channel state information (CSI) from holograms. We also propose a geometric rotation-based object wave sensing (GROWS) algorithm to address the complicated computation of ML estimation. Furthermore, we derive the Cramér-Rao lower bound (CRLB) for investigating the achievable performance of wideband holographic channel estimation. Simulation results show that under the wideband channel sensing architecture, our proposed algorithm can accurately estimate the CSI in wideband scenarios.

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arXiv2025

Holographic interference surface: A proof of concept based on the principle of interferometry

Revolutionizing communication architectures to achieve a balance between enhanced performance and improved efficiency is becoming increasingly critical for wireless communications as the era of ultra-large-scale arrays approaches. In traditional communication architectures, radio frequency (RF) signals are typically converted to baseband for subsequent processing through operations such as filtering, analog-to-digital conversion and down-conversion, all of which depend on expensive and power-intensive RF chains. The increased hardware complexity and escalated power consumption resulting from this dependency significantly limit the practical deployment of ultra-large-scale arrays. To address these limitations, we propose a holographic communication system based on the principle of interferometry, designated as holographic interference surfaces (HIS). Utilizing the interference effect of electromagnetic waves, HIS estimates the channel state information (CSI) by dealing solely with power information, which enables the replacement of RF chains with power sensors and completes the signal processing in radio frequency. As proof-of-concept demonstrations, we implemented a prototype system based on principles of holographic interference. Experimental results align well with theoretical predictions, confirming the practical viability and effectiveness of the proposed HIS. This work provides a new paradigm for building a more cost-effective wireless communication architecture.

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arXiv2025

Port-LLM: A Port Prediction Method for Fluid Antenna based on Large Language Models

The objective of this study is to address the mobility challenges faced by user equipment (UE) through the implementation of fluid antenna (FA) on the UE side. This approach aims to maintain the time-varying channel in a relatively stable state by strategically relocating the FA to an appropriate port. To the best of our knowledge, this paper introduces, for the first time, the application of large language models (LLMs) in the prediction of FA ports, presenting a novel model termed Port-LLM. Our proposed method for predicting the moving port of the FA is a two-step prediction method. To enhance the learning efficacy of our proposed Port-LLM model, we integrate low-rank adaptation (LoRA) fine-tuning technology. Additionally, to further exploit the natural language processing capabilities of pre-trained LLMs, we propose a framework named Prompt-Port-LLM, which is constructed upon the Port-LLM architecture and incorporates prompt fine-tuning techniques along with a specialized prompt encoder module. The simulation results show that our proposed models all exhibit strong generalization ability and robustness under different numbers of base station antennas and medium-to-high mobility speeds of UE. In comparison to existing methods, the performance of the port predicted by our models demonstrates superior efficacy. Moreover, both of our proposed models achieve millimeter-level inference speed.

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arXiv2025

Fluid Antenna Port Prediction based on Large Language Models

This study seeks to utilize large language models (LLMs) to forecast the moving ports of fluid antenna (FA). By repositioning the antenna to the locations identified by our proposed model, we intend to address the mobility challenges faced by user equipment (UE). To the best of our knowledge, this paper introduces, for the first time, the application of LLMs in the prediction of FA ports, presenting a novel model termed Port-LLM. The architecture of our model is based on the pre-trained GPT-2 framework. We designed specialized data preprocessing, input embedding, and output projection modules to effectively bridge the disparities between the wireless communication data and the data format utilized by the pre-trained LLM. Simulation results demonstrate that our model exhibits superior predictive performance under different numbers of base station (BS) antennas and varying UE speeds, indicating strong generalization and robustness ability. Furthermore, the spectral efficiency (SE) attained by our model surpasses that achieved by traditional methods in both medium and high-speed mobile environments.

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arXiv2025

Design and Prototyping of Filtering Active STAR-RIS with Adjustable Power Splitting

Reconfigurable Intelligent Surfaces (RISs) have emerged as a transformative technology for next-generation wireless communication systems, offering unprecedented control over electromagnetic wave propagation. In particular, Simultaneously Transmitting and Reflecting RISs (STAR-RISs) have garnered significant attention due to their full-space coverage. This paper presents an active STAR-RIS, which enables independent control of both transmission and reflection phases and features out-of-band harmonic suppression. Unlike the traditional passive RIS, the proposed design integrates active amplification to overcome the inherent passive losses, significantly enhancing signal strength and system performance. Additionally, the system supports dynamic power allocation between transmission and reflection modes, providing greater flexibility to meet diverse communication demands in complex propagation environments. The versatility of the design is further validated by extending the Radar Cross Section (RCS)-based path loss model to the STAR-RIS. This design improves efficiency, flexibility, and adaptability, offering a promising solution for future wireless communication systems, particularly in scenarios requiring simultaneous control of transmission and reflection signals.

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arXiv2024

Movable Superdirective Pairs: A Phase Shifter-Free Approach to mmWave Communications

In this letter, we propose a novel Movable Superdirective Pairs (MSP) approach that combines movable antennas with superdirective pair arrays to enhance the performance of millimeter-wave (mmWave) communications on the user side. By controlling the rotation angles and positions of superdirective antenna pairs, the proposed MSP approach maximizes the received signal-to-noise ratio (SNR) of multipath signals without relying on phase shifters or attenuators. This approach addresses the limitations of traditional superdirective antennas, which are typically restricted to the endfire direction and suffer from reduced scanning bandwidth and increased complexity. An efficient algorithm based on alternating optimization and the gradient projection method is developed to solve the non-convex optimization problem of antennas' joint rotating positioning. Simulation results demonstrate that the MSP approach achieves significant performance gains over fixed-position array (FPA) employing Maximum Ratio Combining (MRC), while reducing system complexity and hardware costs.

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arXiv2024

A Superdirective Beamforming Approach with Impedance Coupling and Field Coupling for Compact Antenna Arrays

In most multiple-input multiple-output (MIMO) communication systems, antennas are spaced at least half a wavelength apart to reduce mutual coupling. In this configuration, the maximum array gain is equal to the number of antennas. However, when the antenna spacing is significantly reduced, the array gain of a compact array can become proportional to the square of the number of antennas, greatly exceeding that of traditional MIMO systems. Achieving this "superdirectivity" requires complex calculations of the excitation coefficients (beamforming vector), which is a challenging task. In this paper, we address this problem with a novel double coupling-based superdirective beamforming method. In particular, we categorize the antenna coupling effects to impedance coupling and field coupling. By characterizing these two coupling in model, we derive the beamforming vector for superdirective arrays. We prove that the field coupling matrix has the unique solution for an antenna array, and itself has the ability to fully characterize the distorted coupling field. Based on this proven theorem, we propose a method that accurately calculates the coupling matrix using only a number of angle sampling points on the order of the number of antennas. Moreover, a prototype of an independently-controlled superdirective antenna array is developed. Full-wave electromagnetic simulations and real-world experiments validate the effectiveness of our proposed approaches, and superdirectivity is achieved in reality by a compact array with 4 and 8 dipole antennas.

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arXiv2024

A Block Quantum Genetic Interference Mitigation Algorithm for Dynamic Metasurface Antennas and Field Trials

This paper proposes a quantum algorithm for Dynamic Metasurface Antennas (DMA) beamforming to suppress interference for an amplify-and-forward relay system in multi-base station environments. This algorithm introduces an efficient dynamic block initialization and overarching block update strategy, which can enhance the Signal-to-Interference-plus-Noise Ratio (SINR) of the target base station (BS) signal without any channel information. Furthermore, we built a relay system with DMA as the receiving antenna and conducted outdoor 5G BS interference suppression tests. To the best of our knowledge, this is the first paper to experiment DMA in commercial 5G networks. The field trial results indicate an SINR improvement of over 10 dB for the signal of the desired BS.

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arXiv2024

Modeling, Design, and Verification of An Active Transmissive RIS

Reconfigurable Intelligent Surface (RIS) is a promising technology that may effectively improve the quality of signals in wireless communications. In practice, however, the ``double fading'' effect undermines the application of RIS and constitutes a significant challenge to its commercialization. To address this problem, we present a novel 2-bit programmable amplifying transmissive RIS with a power amplification function to enhance the transmission of electromagnetic signals. The transmissive function is achieved through a pair of radiation patches located on the upper and lower surfaces, respectively, while a microstrip line connects two patches. A power amplifier, SP4T switch, and directional coupler provide signal amplification and a 2-bit phase shift. To characterize the signal enhancement of active transmissive RIS, we propose a dual radar cross section (RCS)-based path loss model to predict the power of the received signal for active transmissive RIS-aided wireless communication systems. Simulation and experimental results verify the reliability of the RIS design, and the proposed path loss model is validated by measurements. Compared with the traditional passive RIS, the signal power gain in this design achieves 11.9 dB.

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arXiv2024

Partial reciprocity-based precoding matrix prediction in FDD massive MIMO with mobility

The timely precoding of frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems is a substantial challenge in practice, especially in mobile environments. In order to improve the precoding performance and reduce the precoding complexity, we propose a partial reciprocity-based precoding matrix prediction scheme and further reduce its complexity by exploiting the channel gram matrix. We prove that the precoders can be predicted through a closed-form eigenvector interpolation which was based on the periodic eigenvector samples. Numerical results validate the performance improvements of our schemes over the conventional schemes from 30 km/h to 500 km/h of moving speed.

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arXiv2024

A Superdirective Beamforming Approach based on MultiTransUNet-GAN

In traditional multiple-input multiple-output (MIMO) communication systems, the antenna spacing is often no smaller than half a wavelength. However, by exploiting the coupling between more closely-spaced antennas, a superdirective array may achieve a much higher beamforming gain than traditional MIMO. In this paper, we present a novel utilization of neural networks in the context of superdirective arrays. Specifically, a new model called MultiTransUNet-GAN is proposed, which aims to forecast the excitation coefficients to achieve ``superdirectivity" or ``super-gain" in the compact uniform linear or planar antenna arrays. In this model, we integrate a multi-level guided attention and a multi-scale skip connection. Furthermore, generative adversarial networks are integrated into our model. To improve the prediction accuracy and convergence speed of our model, we introduce the warm up aided cosine learning rate (LR) schedule during the model training, and the objective function is improved by incorporating the normalized mean squared error (NMSE) between the generated value and the actual value. Simulations demonstrate that the array directivity and array gain achieved by our model exhibit a strong agreement with the theoretical values. Overall, it shows the advantage of enhanced precision over the existing models, and a reduced requirement for measurement and the computation of the excitation coefficients.

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arXiv2024

A manifold learning-based CSI feedback framework for FDD massive MIMO

Massive multi-input multi-output (MIMO) in Frequency Division Duplex (FDD) mode suffers from heavy feedback overhead for Channel State Information (CSI). In this paper, a novel manifold learning-based CSI feedback framework (MLCF) is proposed to reduce the feedback and improve the spectral efficiency for FDD massive MIMO. Manifold learning (ML) is an effective method for dimensionality reduction. However, most ML algorithms focus only on data compression, and lack the corresponding recovery methods. Moreover, the computational complexity is high when dealing with incremental data. Considering to utilize the intrinsic manifold structure where the CSI samples reside, we propose a landmark selection algorithm to describe the topological skeleton of this manifold. Based on the learned skeleton, the local patch of the incremental CSI on the manifold can be easily determined by its nearest landmarks. This motivates us to propose an incremental CSI compression and reconstruction scheme by keeping the local geometric relationships with landmarks invariant. We theoretically prove the convergence of the proposed landmark selection algorithm. Meanwhile, the upper bound on the error of approximating CSI with landmarks is derived. Simulation results under an industrial channel model of 3GPP demonstrate that the proposed MLCF outperforms existing deep learning based algorithms.

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arXiv2024

Transforming Time-Varying to Static Channels: The Power of Fluid Antenna Mobility

This paper addresses the mobility problem with the assistance of fluid antenna (FA) on the user equipment (UE) side. We propose a matrix pencil-based moving port (MPMP) prediction method, which may transform the time-varying channel to a static channel by timely sliding the liquid. Different from the existing channel prediction method, we design a moving port selection method, which is the first attempt to transform the channel prediction to the port prediction by exploiting the movability of FA. Theoretical analysis shows that for the line-ofsight (LoS) channel, the prediction error of our proposed MPMP method may converge to zero, as the number of BS antennas and the port density of the FA are large enough. For a multi-path channel, we also derive the upper and lower bounds of the prediction error when the number of paths is large enough. When the UEs move at a speed of 60 or 120 km/h, simulation results show that, with the assistance of FA, our proposed MPMP method performs better than the existing channel prediction method.

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arXiv2024

Wavefront Transformation-based Near-field Channel Prediction for Extremely Large Antenna Array with Mobility

This paper addresses the mobility problem in extremely large antenna array (ELAA) communication systems. In order to account for the performance loss caused by the spherical wavefront of ELAA in the mobility scenario, we propose a wavefront transformation-based matrix pencil (WTMP) channel prediction method. In particular, we design a matrix to transform the spherical wavefront into a new wavefront, which is closer to the plane wave. We also design a time-frequency projection matrix to capture the time-varying path delay. Furthermore, we adopt the matrix pencil (MP) method to estimate channel parameters. Our proposed WTMP method can mitigate the effect of near-field radiation when predicting future channels. Theoretical analysis shows that the designed matrix is asymptotically determined by the angles and distance between the base station (BS) antenna array and the scatterers or the user when the number of BS antennas is large enough. For an ELAA communication system in the mobility scenario, we prove that the prediction error converges to zero with the increasing number of BS antennas. Simulation results demonstrate that our designed transform matrix efficiently mitigates the near-field effect, and that our proposed WTMP method can overcome the ELAA mobility challenge and approach the performance in stationary setting.

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arXiv2024

Low-complexity eigenvector prediction-based precoding matrix prediction in massive MIMO with mobility

In practical massive multiple-input multiple-output (MIMO) systems, the precoding matrix is often obtained from the eigenvectors of channel matrices and is challenging to update in time due to finite computation resources at the base station, especially in mobile scenarios. In order to reduce the precoding complexity while enhancing the spectral efficiency (SE), a novel precoding matrix prediction method based on the eigenvector prediction (EGVP) is proposed. The basic idea is to decompose the periodic uplink channel eigenvector samples into a linear combination of the channel state information (CSI) and channel weights. We further prove that the channel weights can be interpolated by an exponential model corresponding to the Doppler characteristics of the CSI. A fast matrix pencil prediction (FMPP) method is also devised to predict the CSI. We also prove that our scheme achieves asymptotically error-free precoder prediction with a distinct complexity advantage. Simulation results show that under the perfect non-delayed CSI, the proposed EGVP method reduces floating point operations by 80\% without losing SE performance compared to the traditional full-time precoding scheme. In more realistic cases with CSI delays, the proposed EGVP-FMPP scheme has clear SE performance gains compared to the precoding scheme widely used in current communication systems.

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