QoE-Aware and Secure UAV-Aided Rate-Splitting Multiple Access Based Communications

TL;DR

The paper addresses QoE-aware secrecy in UAV-aided multiuser RSMA networks by maximizing the sum MOS, where each user MOS is $MOS_l = \lambda_1 \ln(R_l^{\sec}) + \lambda_l$. It proposes an alternating optimization framework that decomposes the problem into a beamforming/rate allocation subproblem and a UAV trajectory subproblem; the beamforming subproblem is convexified using the epigraph method, norm-bounded CSI, SOC, and first-order Taylor expansions, while the trajectory subproblem is handled by unrolling constraints and applying a first-order Taylor expansion. The approach accommodates CSI uncertainties and yields a CVX-solvable formulation; simulations show convergence within around 20 iterations and performance gains with longer horizons and more antennas, at the cost of reduced MOS for larger data sizes. The work demonstrates how QoE metrics can be integrated with secrecy and mobility considerations to enhance UAV-based RSMA networks in practical scenarios.

Abstract

In this work, we address the issue of quality of experience (QoE) in unmanned aerial vehicle (UAV) aided multiuser rate-splitting multiple access (RSMA) networks under secrecy constraints. The problem is formulated as maximization of sum mean opinion scores (MOSs) of the users. The problem is decomposed into two subproblems, beamforming and rate allocation and UAV trajectory subproblem. For, beamforming and rate allocation subproblem, we use the epigraph method, property of polynomials, and the norm-bounded error of channels, we linearize the objective function. Then, applying second-order conic (SOC) and first Taylor expansion, we convexify the remaining nonconvex constraints. For the highly nonconvex UAV trajectory, we unroll the constraints and we apply first Taylor expansion on the unrolled constraints. The simulation results demonstrate the efficiency of the proposed framework.

Figures (4)

• Figure 1: Convergence of Algorithm 1.
• Figure 2: Sum MOS for different number of user and $T$.
• Figure 3: Sum MOS for different values of $P_{max}$ and $T$.
• Figure 4: Sum MOS for different values of $\Omega$ and $L$.