arXiv subjects

Zhentian Zhang

Publications and source records attributed to Zhentian Zhang.

31 records · Page 2Linked to original sources

arXiv2026

Fundamental Tradeoffs for ISAC Multiple Access in Finite-Blocklength Regime

This paper investigates the fundamental communication--sensing tradeoffs of uplink dual-functional integrated sensing and communication (ISAC) multiple access under finite blocklength (FBL) constraints. Unlike conventional asymptotic analyses, we explicitly account for the limitations under FBL constraints imposed by short packets and low-latency transmission. By examining the unbiased channel state sensing estimator, we establish a geometric decomposition of the sensing error, indicating that it is jointly determined by the signal-to-noise ratio and the correlation structure of the information codebook. This insight reveals how cross-correlation among active users in the codebook geometry fundamentally constrains dual-functional ISAC performance. Consequently, we derive achievability and converse bounds that characterize the tradeoff between communication code rate and sensing accuracy in the FBL regime, with the converse further bounded by Shannon capacity. Moreover, by treating channel state sensing as a high-level sensing objective, a universal Cramér--Rao bound is derived to link channel estimation accuracy to practical sensing parameters. Examples of parameter sensing are also provided based on 3GPP standard. Numerical results validate the theoretical analysis and demonstrate the impact of blocklength, antenna dimensions, and sensing requirements.

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arXiv2025

Joint Activity Detection and Channel Estimation For Fluid Antenna System Exploiting Geographical and Angular Information

The fluid antenna system (FAS) refers to a family of reconfigurable antenna technologies that provide substantial spatial gains within a compact, predefined small space, thereby offering extensive degrees of freedom in the physical layer for future communication networks. The acquisition of channel state information (CSI) is critical, as it determines the placement of ports/antennas, which directly impacts FAS-based optimization. Although various channel estimation methods have been developed, significant flaws persist. For instance, the performance of greedy-based algorithms is heavily influenced by signal assumptions, and current model-free methods are infeasible due to prohibitively high computational complexity issue. Consequently, there is a pressing need for a well-balanced solution that exhibits flexibility, feasibility, and low complexity to support massive connectivity in FAS. In this work, we propose methods based on approximate message passing (AMP) integrated with adaptive expectation maximization (EM). The EM-AMP framework uniquely enables efficient large matrix computations with adaptive learning capabilities, independent of prior knowledge of the model or parameters within potential distributions, making it a robust candidate for FAS networks. We introduce two variants of the EM-AMP framework that leverage geographical and angular features in a FAS network. These proposed algorithms demonstrate improved estimation precision, fast convergence, and low computational complexity in large activity regions. Additionally, we analytically elucidate the reasons behind the inherent performance floor of greedy-based methods and highlight the critical role of angular information in algorithm design. Extensive numerical results validate the promising efficacy of the proposed algorithm designs and the derived analytical findings.

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arXiv2025

Finite-blocklength Fluid Antenna Systems With Spatial Block-Correlation Channel Model

Massive connectivity with ultra-low latency and high reliability necessitates fundamental advances in future communication networks operating under finite-blocklength (FBL) transmission. Fluid antenna systems (FAS) have emerged as a promising enabler, offering superior spectrum and energy efficiency in short-packet/FBL scenarios. In this work, by leveraging the simplicity and accuracy of block-correlation channel modeling, we rigorously bound the performance limits of FBL-FAS from a statistical perspective, focusing on two key performance metrics: block error rate (BLER) and outage probability (OP). Furthermore, we introduce a novel complex-integral simplification method based on Gauss-Laguerre quadrature, which achieves higher approximation accuracy compared to existing Taylor-expansion-based approaches. Numerical results validate the robustness of the proposed analysis and clearly demonstrate the superiority of FBL-FAS over conventional multiple-antenna systems with fixed antenna placement.

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arXiv2025

Fluid Antenna-Enhanced Flexible Beamforming

Fluid antenna systems encompass a broad class of reconfigurable antenna technologies that offer substantial spatial diversity for various optimization objectives and communication tasks. Their capability to enhance spatial resolution within a fixed physical aperture makes fluid antennas particularly attractive for next-generation wireless deployments. In this work, we focus on the beamforming problem using a two-dimensional planar fluid antenna array. Since both narrow-beam and broad-beam patterns are essential in practical communication networks, enabling flexible beamforming through fluid antennas becomes an important and interesting research direction. We establish a unified and flexible framework that connects arbitrary beam-pattern synthesis with fluid-antenna port selection. The resulting formulation transforms beam-pattern reconstruction into a sparse regression problem, which is addressed using a tailored compressive sensing algorithm designed to operate efficiently with the fast Fourier transform (FFT). Furthermore, to ensure physically consistent phase modeling in the desired beam, we introduce an iterative FFT-based phase retrieval method. Owing to its structure, the proposed phase-refinement procedure exhibits low computational complexity and rapid convergence, requiring only one FFT and one inverse FFT per iteration. Simulation results demonstrate the effectiveness of the proposed flexible beamforming framework. Compared with conventional fixed-array architectures, fluid antennas exhibit significantly improved beam-pattern reconstruction accuracy, highlighting their potential for high-resolution and adaptive beamforming in future wireless systems.

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arXiv2025

A Universal Block Error Rate Bound for Fluid Antenna Systems

Fluid antenna systems (FASs) offer genuine simplicity for communication network design by eliminating expensive hardware overhead and reducing the complexity of access protocol architectures. Through the discovery of significant spatial diversity within a compact antenna space, FASs enable the implementation of reconfigurable-antenna-based architectures. However, current state-of-the-art studies rarely investigate the impact of finite blocklength constraints on FAS-based designs, leaving a gap in both analytical modeling and the establishment of a solid, universally applicable performance metric for finite blocklength fluid antenna systems (FBL-FAS). In this work, we focus on the study of FBL-FAS and, more importantly, derive a block error rate (BLER) bound that serves as a general and practical performance benchmark across various FAS architectures. The proposed BLER bound is computable both with and without an explicit statistical model, meaning that the BLER performance can be characterized analytically or empirically under model-aware or model-free system scenarios. Moreover, when the statistical model is known, the analytical results derived from the proposed BLER bound exhibit strong alignment with the empirical findings, demonstrating the remarkable simplicity, accuracy, and universality of the proposed BLER bound.

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arXiv2025

Neural Networks-Enabled Channel Reconstruction for Fluid Antenna Systems: A Data-Driven Approach

Fluid antenna systems (FASs) offer substantial spatial diversity by exploiting the electromagnetic port correlation within compact array spaces, thereby generating favorable small-scale fading conditions with beneficial channel gain envelope fluctuations. This unique capability opens new opportunities for a wide range of communication applications and emerging technologies. However, accurate channel state information (CSI) must be acquired before a fluid antenna can be effectively utilized. Although several efforts have been made toward channel reconstruction in FASs, a generally applicable solution to both model-based or model-free scenario with both high precision and efficient computational flow remains lacking. In this work, we propose a data-driven channel reconstruction approach enabled by neural networks. The proposed framework not only achieves significantly enhanced reconstruction accuracy but also requires substantially lower computational complexity compared with existing model-free methods. Numerical results further demonstrate the rapid convergence and robust reconstruction capability of the proposed scheme, outperforming current state-of-the-art techniques.

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arXiv2025

Low-Complexity CSI Acquisition Exploiting Geographical Diversity in Fluid Antenna System

The fluid antenna system (FAS) employs reconfigurable antennas for high spatial gains in compact spaces, enhancing physical layer flexibility. Channel state information (CSI) acquisition is vital for port selection and FAS optimization. Greedy algorithms rely on signal assumptions, and model-free methods face high complexity. A flexible, low-complexity solution is needed for massive connectivity in FAS. Based on expectation maximization-approximate message passing (EM-AMP) framework, efficient matrix computations and adaptive learning without prior model knowledge naturally suit CSI acquisition for FAS. We propose a EM-AMP variant exploiting FAS geographical priors, improving estimation precision, accelerating convergence, and reducing complexity in large-scale deployment. Simulations validate the efficacy of the proposed algorithm.

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arXiv2025

Probabilistic ODMA Receiver with Low-Complexity Algorithm for MIMO Unsourced Random Access

In this work, we present the design for both pilot-uncoupled and pilot-free on-off multiple access (ODMA) receivers in unsourced random access (URA) for multiple-input multiple-output (MIMO) systems. Unlike pilot-coupled ODMA, where on-off patterns are linked to pilot selection, pilot-uncoupled and pilot-free ODMA reduce transmission redundancy but face challenges in processing complexity and capacity performance. The joint pattern and data detector (JPDD) design is critical for these schemes, but the current JPDD algorithm has high complexity with quadratic computational costs. To address this, we propose a low-complexity detector based on approximate message passing (AMP), which offers linear complexity, providing reduced cost and improved performance in the under-determined linear regression case. Decoding is initialized via pilot-free matrix factorization through alternating minimization, resolving phase and scalar ambiguities. Compared to existing pilot-free schemes, the proposed method achieves a 13 dB improvement and favorable trade-offs in complexity and capacity performance when compared to benchmarks.

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arXiv2025

Sparse Code Transceiver Design for Unsourced Random Access with Analytical Power Division in Gaussian MAC

In this work, we discuss the problem of unsourced random access (URA) over a Gaussian multiple access channel (GMAC). To address the challenges posed by emerging massive machine-type connectivity, URA reframes multiple access as a coding-theoretic problem. The sparse code-oriented schemes are highly valued because they are widely used in existing protocols, making their implementation require only minimal changes to current networks. However, drawbacks such as the heavy reliance on extrinsic feedback from powerful channel codes and the lack of transmission robustness pose obstacles to the development of sparse codes. To address these drawbacks, a novel sparse code structure based on a universally applicable power division strategy is proposed. Comprehensive numerical results validate the effectiveness of the proposed scheme. Specifically, by employing the proposed power division method, which is derived analytically and does not require extensive simulations, a performance improvement of approximately 2.8 dB is achieved compared to schemes with identical channel code setups.

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arXiv2025

Integrated Sensing and Communications for Unsourced Random Access: A Spectrum Sharing Compressive Sensing Approach

This paper addresses the unsourced/uncoordinated random access problem in an integrated sensing and communications (ISAC) system, with a focus on uplink multiple access code design. Recent theoretical advancements highlight that an ISAC system will be overwhelmed by the increasing number of active devices, driven by the growth of massive machine-type communication (mMTC). To meet the demands of future mMTC network, fundamental solutions are required that ensure robust capacity while maintaining favorable energy and spectral efficiency. One promising approach to support emerging massive connectivity is the development of systems based on the unsourced ISAC (UNISAC) framework. This paper proposes a spectrum-sharing compressive sensing-based UNISAC (SSCS-UNISAC) and offers insights into the practical design of UNISAC multiple access codes. In this framework, both communication signals (data transmission) and sensing signals (e.g., radar echoes) overlap within finite channel uses and are transmitted via the proposed UNISAC protocol. The proposed decoder exhibits robust performance, providing 20-30 dB capacity gains compared to conventional protocols such as TDMA and ALOHA. Numerical results validate the promising performance of the proposed scheme.

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arXiv2025

Sparsity-Exploiting Channel Estimation For Unsourced Random Access With Fluid Antenna

This work explores the channel estimation (CE) problem in uplink transmission for unsourced random access (URA) with a fluid antenna receiver. The additional spatial diversity in a fluid antenna system (FAS) addresses the needs of URA design in multiple-input and multiple-output (MIMO) systems. We present two CE strategies based on the activation of different FAS ports, namely alternate ports and partial ports CE. Both strategies facilitate the estimation of channel coefficients and angles of arrival (AoAs). Additionally, we discuss how to refine channel estimation by leveraging the sparsity of finite scatterers. Specifically, the proposed partial ports CE strategy is implemented using a regularized estimator, and we optimize the estimator's parameter to achieve the desired AoA precision and refinement. Extensive numerical results demonstrate the feasibility of the proposed strategies, and a comparison with a conventional receiver using half-wavelength antennas highlights the promising future of integrating URA and FAS.

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arXiv2025

On Fundamental Limits for Fluid Antenna-assisted Integrated Sensing and Communications for Unsourced Random Access

This paper investigates the unsourced random access (URA) problem for integrated sensing and commutations (ISAC). Recent results reveal that conventional multiple access strategies for ISAC such as treating interference as noise (TIN) and time-division multiple access (TDMA) can be easily overwhelmed and fail to support the increasingly surging number of active users. Hence, the unsourced ISAC (UNISAC) system model has emerged as a promising enabler for the future ISAC networks. To advance this work, we adopt a more realistic channel model and propose to utilize fluid antenna system (FAS) for UNISAC. The achievable performance bound and floor of the proposed FAS-UNISAC are derived to validate the great potential. Our results demonstrate that promising improvement on the available user volume and the sensing and communication capability can be obtained due to the spatial diversities inherent within fluid antenna.

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arXiv2023

Pilot-Free Unsourced Random Access Via Dictionary Learning and Error-Correcting Codes

Massive machine-type communications (mMTC) or massive access is a critical scenario in the fifth generation (5G) and the future cellular network. With the surging density of devices from millions to billions, unique pilot allocation becomes inapplicable in the user ID-incorporated grant-free random access protocol. Unsourced random access (URA) manifests itself by focusing only on unwrapping the received signals via a common codebook. In this paper, we propose a URA protocol for a massive access cellular system equipped with multiple antennas at the base station. The proposed scheme encompasses a codebook enabling construction of sparse transmission frame, a receiver equipped with dictionary learning and error-correcting codes and a collision resolution strategy for the collided codeword. Discrepant to the existing schemes with necessary overhead for preamble signals, no overhead or pre-defined pilot sequences are needed in the proposed scheme, which is favorable for energy-efficient transmission and latency reduction. Numerical results verify the viability of the proposed scheme in practical massive access scenario.

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