arXiv subjects

Jianchi Zhu

Publications and source records attributed to Jianchi Zhu.

9 recordsLinked to original sources

arXiv2026

Simultaneous Indoor and Outdoor Coverage with Conformal Intelligent Omni-Surface

Seamless indoor--outdoor coverage conventionally relies on coordinated outdoor macro cells and indoor small cells, incurring high deployment and backhaul costs. Intelligent omni-surfaces (IOSs) provide a promising alternative by enabling simultaneous reflection and transmission across building boundaries. Enabled by recent advances in conformal antennas, conformal IOSs (CIOSs) mounted on building facades offer a flexible and deployment-friendly solution for integrated indoor--outdoor coverage. This paper develops a stochastic-geometry framework to evaluate the downlink performance of CIOS-assisted networks serving both indoor and outdoor users. Specifically, a building-dependent spatial model is established where randomly located buildings are modeled via a Boolean scheme with CIOSs mounted on their facades. Unlike planar IOS deployment, conformal integration introduces geometry dependent visibility constraints, which are captured through the effective numbers of participating elements defined by visible arc metrics for reflection and transmission. Incorporating these constraints, coverage probability expressions are derived under three CIOS operating protocols. Numerical results show that, compared with planar IOS, CIOS improves the successful connection probabilities of the reflected and transmitted links by up to 75% and 125%, respectively, while the overall coverage gain remains dependent on blockage conditions because curvature reduces the effective serving aperture.

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arXiv2026

Semantic Forwarding and Codebook-Enhanced Model Division Multiple Access for Satellite-Terrestrial Networks

Satellite-terrestrial communications are severely constrained by high path loss, limited spectrum resources, and time-varying channel conditions, rendering conventional bit-level transmission schemes inefficient and fragile, particularly in low signal-to-noise ratio (SNR) regimes. Semantic communication has emerged as a promising paradigm to address these challenges by prioritizing task-relevant information over exact bit recovery. In this paper, we propose a semantic forwarding-based semantic communication (SFSC) framework optimized for satellite-terrestrial networks. Specifically, we develop a vector-quantized joint semantic coding and modulation scheme, in which the semantic encoder and semantic codebook are jointly optimized to shape the constellation symbol distribution, improving channel adaptability and semantic compression efficiency. To mitigate noise accumulation and reduce on-board computational burden, we introduce a satellite semantic forwarding mechanism, enabling relay satellites to forward signals directly at the semantic level without full decoding and re-encoding. Furthermore, we design a channel-aware semantic reconstruction scheme based on feature-wise linear modulation (FiLM) to fuse the received SNR with semantic features, enhancing robustness under dynamic channel conditions. To support multi-user access, we further propose a codebook split-enhanced model division multiple access (CS-MDMA) method to improve spectral efficiency. Simulation results show that the proposed SFSC framework achieves a peak signal-to-noise ratio (PSNR) gain of approximately 7.9 dB over existing benchmarks in the low-SNR regime, demonstrating its effectiveness for robust and spectrum-efficient semantic transmission in satellite-terrestrial networks.

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arXiv2026

Spatiotemporal Tracking in Cooperative ISAC Networks: A Stochastic Geometry Framework

We adopt a stochastic-geometry framework to study continuous target tracking in integrated sensing and communication (ISAC) networks, with base-station locations modelled as a Poisson point process. The single-BS analysis shows that the antenna energy-conservation identity forces the mean inter-BS coupling gain to unity, making densification an antenna-irreducible liability for monostatic sensing, while a first-passage-time analysis reveals a target-distance-dependent beamwidth trap. These findings rule out single-BS tracking under densification, motivating a multi-BS cooperative treatment. The static-cluster cooperative mean tracking lifetime is then shown to exhibit a sharp percolation phase transition, with the resulting sensing-capacity ceiling saturating above a critical macro density. Yet the static-cluster idealisation itself misrepresents modern network deployments, where the cooperating cluster is dynamically re-selected as the target drifts; we therefore lift this assumption with a dynamic clustering model that maps the $K$-nearest-neighbour handover onto a 2D Brownian motion with stochastic resetting, and obtain a Bessel-function closed form for the dynamic mean tracking lifetime that dissolves the phase transition under any positive handover rate. With a per-link reliability floor, the dynamic clustering framework preserves classical linear density scaling throughout the realistic 6G regime and delivers an order-of-magnitude capacity lift at small-cell densities. Monte-Carlo simulations corroborate all theoretical predictions.

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arXiv2025

Movable Antennas-Assisted Over-the-Air Computation: Dynamic and Static Design

A novel over-the-air computation (AirComp) framework empowered by movable antennas (MAs) is proposed to significantly enhance computation accuracy. Within this framework, the joint optimization of transmit power control, antenna positioning, and receive beamforming is investigated. Two design strategies are developed: (i) a dynamic design, where MA positions are optimized based on fast-varying instantaneous channel state information (CSI); and (ii) a static design, where antenna positions are optimized using only slow-varying statistical CSI. Numerical results validate the superior MSE performance of the proposed MA-enabled AirComp framework and demonstrate its clear advantage over benchmark systems employing conventional fixed-position antennas (FPAs).

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arXiv2025

Exploiting Movable Antennas in Multicast Communications

This article investigates the integration of movable antennas (MAs) into multicast communication systems. By discretizing the motion of the MAs, a novel MA-assisted multicast transmission architecture is formulated. An alternating optimization (AO) algorithm based on successive convex approximation is proposed to optimize the transmit beamforming and antenna positions. To gain further insights, the two-user case is examined, and a closed-form expression for the optimal beamformer is derived. On this basis, a low-complexity greedy search algorithm is developed to optimize the placement of the MAs. Furthermore, under the assumption of a line-of-sight propagation environment, a branch-and-bound algorithm is designed to determine the globally optimal antenna configuration with reduced complexity compared to exhaustive search. Numerical simulations confirm that the proposed methods effectively enhance the achievable multicast rate.

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arXiv2024

Secure Antenna Selection and Beamforming in MIMO Systems

This work proposes a novel joint design for multiuser multiple-input multiple-output wiretap channels. The base station exploits a switching network to connect a subset of its antennas to the available radio frequency chains. The switching network and transmit beamformers are jointly designed to maximize the weighted secrecy sum-rate for this setting. The principal design problem reduces to an NP-hard mixed-integer non-linear programming. We invoke the fractional programming technique and the penalty dual decomposition method to develop a tractable iterative algorithm that effectively approximates the optimal design. Our numerical investigations validate the effectiveness of the proposed algorithm and its superior performance compared with the benchmark.

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arXiv2023

Enabling Secure Wireless Communications via Movable Antennas

A pioneering secure transmission scheme is proposed, which harnesses movable antennas (MAs) to optimize antenna positions for augmenting the physical layer security. Particularly, an MA-enabled secure wireless system is considered, where a multi-antenna transmitter communicates with a single-antenna receiver in the presence of an eavesdropper. The beamformer and antenna positions at the transmitter are jointly optimized under two criteria: power consumption minimization and secrecy rate maximization. For each scenario, a novel suboptimal algorithm was proposed to tackle the resulting nonconvex optimization problem, capitalizing on the approaches of alternating optimization and gradient descent. Numerical results demonstrate that the proposed MA systems significantly improve physical layer security compared to various benchmark schemes relying on conventional fixed-position antennas (FPAs).

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arXiv2023

Sum-Rate Maximization for Movable Antenna Enabled Multiuser Communications

A novel multiuser communication system with movable antennas (MAs) is proposed, where the antenna position optimization is exploited to enhance the downlink sum-rate. The joint optimization of the transmit beamforming vector and transmit MA positions is studied for a multiuser multiple-input single-input system. An efficient algorithm is proposed to tackle the formulated non-convex problem via capitalizing on fractional programming, alternating optimization, and gradient descent methods. To strike a better performance-complexity trade-off, a zero-forcing beamforming-based design is also proposed as an alternative. Numerical investigations are presented to verify the efficiency of the proposed algorithms and their superior performance compared with the benchmark relying on conventional fixed-position antennas (FPAs).

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arXiv2023

Cramer-Rao Lower Bound Analysis for OTFS and OFDM Modulation Systems

The orthogonal time frequency space (OTFS) modulation as a promising signal representation attracts growingcinterest for integrated sensing and communication (ISAC), yet its merits over orthogonal frequency division multiplexing (OFDM) remain controversial. This paper devotes to a comprehensive comparison of OTFS and OFDM for sensing from the perspective of Cramer-Rao lower bounds (CRLB) analysis. To this end, we develop the cyclic prefix (CP)-Free and CP-added model for OFDM, while for OTFS, we consider the Zak transform based and the Two-Step conversion based models, respectively. Then we rephrase these four models into a unified matrix format to derive the CRLB of the delays and doppler shifts for multipath scenario. Numerical results demonstrate the superiority of OTFS modulation for sensing, and the effect of physical parameters for performance achievement.

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