RUAD: Multi-Domain Research Frameworks

• RUAD is a multi-abbreviation term representing distinct frameworks in wireless communication, causal inference, and HPC anomaly detection with clear system architectures and methodologies.
• Each approach employs tailored techniques: joint optimization via AO-SCA-SROCR in networking, adversarial feature desensitization in uplift modeling, and LSTM-based reconstruction for unsupervised anomaly detection.
• Empirical results show significant performance gains and robustness improvements over baselines, underscoring essential design trade-offs and deployment guidelines across various domains.

RUAD

RUAD is a multi-abbreviation term encountered in recent research literature, representing conceptually distinct frameworks across applied networking, machine learning, and anomaly detection. The acronym denotes (1) RIS–UAV–Aided Downlink (communication/networking), (2) Robustness-Enhanced Uplift Modeling with Adversarial Feature Desensitization (computational causal inference), and (3) Recurrent Unsupervised Anomaly Detection (HPC system monitoring). This article provides technical expositions for each, their methodologies, mathematical underpinnings, empirical results, and design trade-offs, as documented in peer-reviewed arXiv manuscripts.

1. RIS–UAV–Aided Downlink (RUAD) Networks

System Architecture and Channel Model

RUAD, in the context of next-generation wireless networks, refers to a spatially diverse downlink transmission platform where a UAV-mounted Fluid Antenna (FA) array and a passive Reconfigurable Intelligent Surface (RIS) jointly serve multiple ground users in non-line-of-sight (NLoS) scenarios (Shen et al., 16 Jan 2025). The system consists of:

• A single multielement UAV-based FA operating at position $u=[x^U, y^U, z^U]^T$.
• An RIS located at $x^R=[x^R, y^R, z^R]^T$, with each element inducing phase shifts $\theta_m \in [0,2\pi)$.
• $K$ ground users positioned at $x_k^{UE}$.

This heterogeneous architecture leverages three spatial diversity sources:

• UAV mobility (mitigating large-scale path loss),
• FA dynamic element repositioning (combating small-scale fading),
• RIS-induced passive beamforming (enabling NLoS via phase-configurable reflections).

The signal chain entails cascaded Rician-fading channels:

• UAV–RIS: $$G_U = \sqrt{h_0d_U^{-\kappa}}[\sqrt{P_U^{LoS}}A_R^H A^{FA} + \sqrt{P_U^{NLoS}}G_U^{NLoS}]$$
• RIS–User: $$g_{R,k} = \sqrt{h_0d_{R,k}^{-\kappa}}[\sqrt{P_{R,k}^{LoS}}g_{R,k}^{LoS} + \sqrt{P_{R,k}^{NLoS}}g_{R,k}^{NLoS}]$$

The composite channel to user $k$ is $g_k = g_{R,k}^H\Theta G_U$ with passive beamforming matrix $\Theta$. The received signal at user $k$ is $$y_k = g_k w_k s_k + \sum_{j\neq k}g_k w_j s_j + n_k$$.

Joint Optimization Problem

The primary goal is total downlink sum-rate maximization, with constraints on total transmit power ($P_{max}$), minimum QoS per user ($R_{min}$), UAV flight envelope, and FA geometric limitations. The optimization variables are:

• UAV transmit beamforming vectors $W=\{w_k\}$,
• RIS phase shifts $\Theta$,
• UAV spatial position $u$,
• FA element positions $X^{FA}=\{x_{n_h,n_v}^{FA}\}$.

Explicitly:

$$\begin{aligned} &\max_{W, \Theta, u, X^{FA}}\ \sum_{k=1}^K R_k \ &\textrm{s.t. } \theta_m\in[0,2\pi),\ \sum_k||w_k||^2\le P_{max},\ R_k\ge R_{min},\ &\quad u_{min}\preceq u\preceq u_{max},\ ||u-u_{prev}||\le \delta,\ &\quad \text{FA element spacing and footprint constraints.} \end{aligned}$$

BRAUD Algorithm: AO + SCA + SROCR

Given joint nonconvexity, the solution proceeds by alternating optimization (AO) over four subproblems, each addressed by successive convex approximation (SCA) and sequential rank-one constraint relaxation (SROCR):

1. UAV Beamforming: Reformulated as an SDP via difference-of-concave decomposition; SROCR ensures rank-one by penalizing deviations from the leading eigenvector.
2. Passive RIS Beamforming: Treated similarly with phase optimization in the SDP form, leveraging SROCR for feasibility.
3. UAV Deployment: Nonconvex dependence on large-scale path loss is linearized and solved as a convex program within spatial bounds.
4. FA Position Adjustment: Smooth adjustments by linearizing the impact of FA positioning on array response, while enforcing spacing and collision requirements.

Each module cycles until convergence (typically 10–15 iterations), and warm-starts are suggested for dynamic environments.

Benchmarks and Performance

Empirical studies compare RUAD and the BRAUD framework with ablated architectures (no FA, no RIS, static UAV) and competitive algorithms (SDR drop-rank, zero-forcing, random, genetic algorithms, multi-armed bandits). BRAUD consistently outperforms alternatives, achieving up to 287% higher sum-rate over random beamforming, 213% over “no RIS,” and consistently surpasses drop-rank and zero-forcing by >10% and >25%, respectively.

Key results (selected benchmarks):

Architecture/Method	Relative Sum-Rate Gain
no FA	+13%
no UAV deployment	+65%
no RIS	+213%
random beamforming	+287%

Performance scales with the number of FA elements ($N$) and RIS elements ($M$). Trade-offs are documented: half-FA deployment results in only 4% rate loss vs full-FA.

Design Guidelines

• At least moderate FA array size ($N_h\times N_v\geq8$) is recommended for urban and NLoS conditions.
• Place RIS close to dense user clusters for optimal signal quality.
• SCA-SROCR schemes are advised over heuristics or zero-forcing for both accuracy and convergence.
• Slot-wise UAV movement should be kept within per-slot constraints to stabilize channels versus energy cost.
• Half-FA designs enable hardware savings with modest performance trade-offs.

2. Robustness-Enhanced Uplift Modeling with Adversarial Feature Desensitization (RUAD)

Problem and Motivation

RUAD describes a model-agnostic framework to improve the robustness of uplift modeling, especially in digital marketing applications where feature sensitivity undermines interpretability and causal inferences (Sun et al., 2023). Classic neural uplift architectures (e.g., S-Learner, TARNet, Causal Forest, Dragonnet) are fragile: perturbation of a small subset of key features can drastically deteriorate, or even invert, model-ordered treatment effects.

Architecture: Feature Selection and Adversarial Desensitization

RUAD consists of two modules stacked over a generic uplift model:

1. Feature Selection (FS) Module: Generates a hard mask $m(x)\in\{0,1\}^N$ selecting a $\kappa$-fraction of “most sensitive” features using a parametric subnetwork with the Gumbel–Softmax trick.
2. Adversarial Feature Desensitization (AFD) Module: Conducts adversarial perturbation exclusively on masked features via a virtual adversarial attack. A soft interpolation between original and maximally perturbed input is computed for regularization.

The masked and/or adversarially perturbed input $x_m$ is fed into the base uplift model $\{\mu_0,\mu_1\}$. The final adversarial loss forces prediction stability to these targeted changes.

Objective and Loss Functions

With treatment indicator $t$, response $y$, and base prediction heads, the losses are:

• Factual outcome loss: $\mathcal{L}_r = \mathcal{L}(\mu_t(x_m), y)$
• Transformed-outcome loss: $$\mathcal{L}_o = \mathcal{L}\Big([\mu_1(x_m)-\mu_0(x_m)], y^*\Big)$$ where $y^* = \frac{ty}{\pi(x)} - \frac{(1-t)y}{1-\pi(x)}$
• Adversarial consistency loss: $$\mathcal{L}_a = \beta \mathcal{L}([\mu_1(\tilde x_{adv}) - \mu_0(\tilde x_{adv})], [\mu_1(x_m) - \mu_0(x_m)])$$

The aggregate loss is:

$$\mathcal{L}_{RUAD} = \alpha\mathcal{L}_o + (1-\alpha)\mathcal{L}_r + \beta\mathcal{L}_a + \lambda\|\theta\|_2^2$$

Parameter sets $(\alpha,\beta,\lambda)$ control weightings.

Empirical Results

Experiments on IHDP (semi-simulated) and a large-scale production advertising dataset yield maximal Qini coefficients and Kendall uplift rank correlations over diverse baselines. On a production dataset (5-bin Qini):

Method	$\hat q$ (mean$\pm$std)	Kendall $\hat\rho$
Baseline best	2.18	0.71
RUAD	2.44 $\pm$ 0.004	0.69

Under 30% feature perturbation, RUAD preserves the monotonicity and ordering of uplift estimates, in contrast to S-Learner and others. Component ablation (removal of FS or AFD) causes a substantial drop in performance.

Limitations

• Sensitivity to hyperparameters $(\kappa, \alpha, \beta, \epsilon, Z)$ is unquantified; automatic tuning is future work.
• No theoretical upper bound on feature desensitization; evidence is empirical.
• Extension to multi-arm treatments and nominal/binary/time-to-event outcomes is open.
• Fairness and confounder robustness not addressed.

3. Recurrent Unsupervised Anomaly Detection (RUAD) for HPC

Architecture and Formulation

In the context of large-scale high-performance computing (HPC) monitoring, RUAD denotes a node-local, temporal autoencoding approach for unsupervised anomaly detection (Molan et al., 2022). Each node is equipped with a lightweight model—a two-layer Long Short-Term Memory (LSTM) encoder and a dense decoder—that operates over windowed sequences of multivariate telemetry ($N\approx462$ features aggregated per 15 min).

Given an input sliding window $[x_{t_0-W+1},\ldots,x_{t_0}]$, the encoder compresses sequence statistics into a low-dimensional representation. The decoder reconstructs only the final vector $x_{t_0}$, and the reconstruction error (L1 norm) forms an anomaly score.

$\text{Error}(t_0) = \|x_{t_0}-\hat x_{t_0}\|_1$

A normalized score yields a probability (by min–max scaling), which is thresholded to flag anomalies.

Training Schemes

• Semi-supervised: All known anomalies are filtered from training; model learns only “nominal” behavior.
• Unsupervised: All data used, exploiting anomaly rarity ($<0.04\%$), so the model passively models the majority class.

Parameterization includes window $W \in \{5,10,20,40\}$, LSTM unit sizes (16, then 8), dense expansion to feature dimensionality, and a per-node pipeline.

Evaluation and Results

Empirical evaluation on real telemetry from the CINECA Marconi100 Tier-0 system (980 nodes over 10 months) demonstrates AUC improvements over dense autoencoder and clustering baselines.

Model	Semi-sup. AUC	Unsupervised AUC
Dense AE (SoA)	0.7470	0.7344
RUAD	0.7632–0.7582	0.7672 (max)
K-means (unsup.)	—	0.5478

RUAD’s peak AUC ($W=10$, unsupervised) is 0.7672, outperforming dense autoencoder by ~2% and vastly exceeding clustering.

Strengths and Limitations

• Strengths: Unsupervised learnability; explicit temporal modeling; per-node scalability; validated on production-scale multi-month data.
• Limitations: Efficacy presumes anomaly rarity; limited to node-local outlier detection; longer $W$ limits sample utility due to window overlap constraints; requires robust feature engineering and infrastructure for streaming analytics.

Deployment Considerations

RUAD is production-compatible, with per-node retraining feasible in tens of minutes and rapid inference via lightweight LSTM-and-dense-layer forward passes. Integration into monitoring dashboards and pipeline design for aggregate anomaly scoring is recommended.

4. RUAD in UAV Path Planning: “dRRT” Algorithm

Although not denoting “RUAD,” the literature omits a direct connection with the improved Rapidly-exploring Random Tree (dRRT) for path planning in UAV public administration scenarios (Xie et al., 15 Aug 2025). For completeness, its enhancements—goal-bias sampling, adaptive step size, detour priority, and B-spline smoothing—have been explicitly described and compared empirically on urban scenarios. These advances yield 100% success, shortest smoothest paths, and sub-0.02s average planning times versus RRT, A*, and ACO, with detailed quantitative metrics provided above.

5. Summary and Cross-Domain Use

RUAD, in the contemporary research landscape, is multi-contextual:

• In advanced wireless, it is a hybrid multi-scale spatially diverse downlink leveraging UAV/FA/RIS synergies (Shen et al., 16 Jan 2025).
• In causal inference, it is a robust, adversarially stable uplift modeling protocol (Sun et al., 2023).
• In HPC, it is an LSTM-based reconstruction anomaly detection method (Molan et al., 2022).

Each framework is driven by domain-specific robustness needs—channel fading, feature sensitivity, unsupervised operational monitoring—yet shares methodological elements: joint optimization (alternating/SCA/SROCR), adversarial or reconstructive learning, and practical evaluation against challenging baselines. Adoption and further development of RUAD paradigms must consider context-sensitive constraints, data regimes, and deployment infrastructure.