Fluid Antenna System-Enabled UAV Communications in the Finite Blocklength Regime

TL;DR

The paper tackles the challenge of ultra-reliable, low-latency UAV communications under finite blocklength by introducing a fluid antenna system (FAS) at the UE and an eigenvalue-based channel model to capture spatial diversity. It derives closed-form block error rate expressions for rural LoS-dominant and urban LoS/NLoS scenarios, including high-SNR asymptotics that reveal the diversity order as m2 N_eff and the existence of an end-to-end error floor. A realistic EE optimization incorporates FAS port-selection overhead and presents a hierarchical solution that jointly optimizes blocklength, UAV altitude, transmit power, and port count, revealing an optimal finite number of ports and scenario-dependent deployment strategies. The work provides foundational analytical tools and practical design guidelines for energy-efficient, FAS-enabled UAV communications in heterogeneous environments.

Abstract

This paper develops a comprehensive framework for the performance analysis of fluid antenna system (FAS)-enabled unmanned aerial vehicle (UAV) relaying networks operating in the finite blocklength regime. Our contribution lies in establishing a rigorous methodology for characterizing system reliability under diverse propagation environments. Closed-form expressions for the block error rate (BLER) are derived by employing a tractable eigenvalue-based approximation of the spatially correlated UAV-to-user link, whose underlying independent diversity components are modeled as Nakagami-$m$ fading. This approach addresses both line-of-sight (LoS) dominant rural and probabilistic non-line-of-sight (NLoS) urban scenarios. Furthermore, a high signal-to-noise ratio (SNR) asymptotic analysis is developed, revealing the fundamental diversity order of the UAV-to-user link. Based on this, we further address the practical issue of energy efficiency. A realistic energy efficiency maximization problem is formulated, which explicitly accounts for the time and energy overhead inherent in the FAS port selection process, a factor often omitted in idealized models. An efficient hierarchical algorithm is then proposed to jointly optimize the key system parameters. Extensive numerical results validate the analysis and illustrate that while FASs can yield substantial power gains, the operational overhead introduces a non-trivial trade-off. This trade-off leads to an optimal number of ports and fundamentally different UAV deployment strategies in rural versus urban environments. This work provides both foundational analysis and practical design guidelines for FAS-enabled UAV communications.

Figures (10)

• Figure 1: A FAS-enabled UAV cooperative short-packet system under the rural and urban scenarios.
• Figure 2: Validation of the analytical framework against Monte Carlo simulation of the analytical model. Parameters: $L=100, N=2, W=0.5\lambda$. (a) Rural scenario: $m_1=m_2=5, P_1=10$ dBm. (b) Urban scenario: $m_{\text{LoS}}=5, m_{\text{NLoS}}=1, P_1=40$ dBm.
• Figure 3: Performance comparison between FAS ($N=2$) and FPA ($N=1$). Parameters are identical to those in Fig. \ref{['fig:validation']}.
• Figure 4: End-to-end BLER versus FAS aperture size $W$ for a fixed $N=8$. (a) Rural scenario: $L=200, m_1=5, m_2=7, P_1=15$ dBm. (b) Urban scenario: $L=100, m_{\text{LoS}}=5, m_{\text{NLoS}}=1, P_1=40$ dBm.
• Figure 5: End-to-end BLER versus UAV transmit power $P_2$ for different numbers of FAS ports $N$. (a) Rural scenario: $L=100, m_1=5, m_2=5, P_1=20$ dBm. (b) Urban scenario: $L=100,m_{\text{LoS}}=5, m_{\text{NLoS}}=1, P_1=40$ dBm.

Theorems & Definitions (17)

• Lemma 1
• proof
• Lemma 2
• proof
• Theorem 1
• proof
• Remark 1
• Lemma 3
• proof
• Lemma 4