Explainable Beam Alignment in mmWave Systems

• Explainable beam alignment is a framework that uses XAI methods to render mmWave beam selection transparent and interpretable.
• It leverages deep learning, digital twin simulations, and transfer learning to drastically reduce alignment overhead while achieving near-optimal spectral efficiency.
• Utilizing SHAP and DkNN, the approach quantifies uncertainty and robustly flags out-of-distribution inputs, enhancing security against adversarial conditions.

Explainable beam alignment refers to a class of algorithms and frameworks for mmWave multiple-input multiple-output (MIMO) systems in which the process of selecting optimal transmit/receive beams is made transparent, auditable, and robust via methods from explainable artificial intelligence (XAI) and uncertainty quantification. The emphasis is not only on high spectral efficiency and reduced alignment overhead, but also on delivering human-interpretable rationales for beam decisions and quantifiable robustness guarantees, particularly in AI-native and adversarially perturbed environments (Khan et al., 12 Jul 2025, Khan et al., 23 Jan 2025).

1. System Model and Classical Beam Alignment

mmWave MIMO systems rely on highly directional beams formed by large antenna arrays. Consider a downlink scenario with a base station (BS) equipped with a uniform linear array (ULA) of $N_{\mathrm{BS}}$ antennas and single-RF chain, serving $N_{\mathrm{U}}$ single-antenna UEs. The narrowband channel for user $u$ is expressed as

$$\mathbf{h}_u = \sum_{l=1}^{L} \alpha_{u,l}\mathbf{b}(\phi_{u,l}),$$

where each path $l$ has complex gain $\alpha_{u,l}$ and departure angle $\phi_{u,l}$, and $\mathbf{b}(\phi)$ is the array response (Khan et al., 12 Jul 2025, Khan et al., 23 Jan 2025). To align beams, the BS must select a beamforming vector $\mathbf{w}$ from a finite codebook to maximize $|\mathbf{h}_u^{H}\mathbf{w}|^2$. Exhaustive search across all possible beams achieves optimal SNR but entails excessive pilot transmission overhead, motivating the need for intelligent, low-overhead alignment procedures.

2. Deep Learning-Based Beam Alignment Engines

Recent advancements employ deep neural networks (DNNs) and convolutional neural networks (CNNs) to map received signal strength indicator (RSSI) measurements, obtained from wide-beam pilots, to the optimal narrow-beam index. Given a feature vector $N_{\mathrm{U}}$0, with each entry corresponding to an RSSI from a distinct wide beam, a classification model

$N_{\mathrm{U}}$1

predicts the best beam out of a narrow-beam codebook. Architectures typically use an input layer of size $N_{\mathrm{U}}$2, two or more hidden layers (e.g., 64–128 ReLU units), and a $N_{\mathrm{U}}$3-way output (Khan et al., 12 Jul 2025, Khan et al., 23 Jan 2025). Training is performed with cross-entropy loss on a dataset of measured or simulated pairs $N_{\mathrm{U}}$4, and the softmax output provides a predicted probability distribution over beams.

3. Data Generation via Digital Twin and Transfer Learning

A central challenge is curating sufficient labeled data for training. To address this, a site-specific digital twin (DT) is constructed using an electromagnetic (EM) ray-tracing model of the deployment environment. The DT generates synthetic channel realizations and RSSI vectors, yielding a large corpus $N_{\mathrm{U}}$5 for pretraining the beam alignment model. This model is then refined via transfer learning on a smaller real-world dataset $N_{\mathrm{U}}$6, with as little as 20–30% of the real data needed to achieve near-optimal accuracy—cutting practical data requirements by approximately 70% (Khan et al., 12 Jul 2025). This approach bridges the simulation-to-reality gap and ensures data efficiency.

4. Explainability Through SHAP and DkNN

To provide transparency into the beam selection process, explainability modules are layered onto the DNN/CNN engine by leveraging Shapley additive explanation (SHAP) values and Deep $N_{\mathrm{U}}$7-Nearest Neighbors (DkNN):

Deep SHAP

SHAP values decompose the model's prediction into contributions from each RSSI feature. For feature $N_{\mathrm{U}}$8 and input $N_{\mathrm{U}}$9, the (deep) SHAP value

$u$0

quantifies the marginal value of including feature $u$1. Deep SHAP (using DeepLIFT) efficiently approximates these values. By averaging absolute values across a test set, features are globally ranked, enabling the reduction of input dimensionality by selecting only those beams whose cumulative importance exceeds a threshold $u$2 (e.g., 95%), resulting in up to 62% lower measurement overhead with negligible accuracy loss (Khan et al., 12 Jul 2025).

DkNN

The DkNN module enhances interpretability and robustness by examining how many $u$3-nearest neighbor representations at each network layer agree with the predicted output class. The nonconformity score and a calibration set yield empirical $u$4-values, from which credibility and confidence metrics are derived. Low-credibility predictions—those poorly supported by training data—are flagged as outliers or adversarial instances, enabling intrinsic rejection and fallback strategies (Khan et al., 12 Jul 2025, Khan et al., 23 Jan 2025).

5. Robustness Against Out-of-Distribution and Adversarial Inputs

Standard softmax-based DNN classifiers are prone to overconfidence on out-of-distribution (OOD) or adversarially perturbed RSSI data. By introducing DkNN, the explainable beam alignment engine achieves up to 8.5-fold improvement in OOD/adversarial rejection at low credibility thresholds relative to softmax—DkNN credibility collapses toward zero for OOD cases whereas softmax remains spuriously confident (Khan et al., 12 Jul 2025, Khan et al., 23 Jan 2025). The model maintains high top-$u$5 accuracy even for measurement SNR as low as –35 dB in NLOS scenarios and provides calibrated reliability measures.

6. Quantitative Performance

Explainable beam alignment frameworks achieve near-optimal spectral efficiency (within 1–2% of the SVD upper bound) with significant reductions in both pilot and labeled data overhead:

Method	Measurement Overhead (% of exhaustive)	SE (% of exhaustive)	Outlier or Top-5@–30dB
DFT scan	100	100	45
CNN + softmax	25	97	75
CNN + DkNN	25	98.5	92

A SHAP-pruned engine requires only $u$6 wide-beam measurements, cutting beam alignment time by an order of magnitude (e.g., 10 ms to 0.98 ms), while DkNN-based OOD filtering robustly protects against adversarial manipulation (Khan et al., 12 Jul 2025, Khan et al., 23 Jan 2025).

7. Broader Context, Extensions, and Position-Aided Approaches

Explainable beam alignment mechanisms integrate seamlessly with more classical geometric and control-theoretic alignment approaches. For example, in mmWave backhaul with position-aided alignment, exchanging accurate node locations enables the system to predict LOS directions and recover nearly optimal beamforming gain using only two position messages, dramatically reducing alignment control overhead compared to exhaustive codebook search (Alexandropoulos, 2017). While no explicit SHAP or DkNN explainability layer is used in position-aided methods, both paradigms exemplify the trend toward interpretable, low-overhead, and robust mmWave beam alignment.

A plausible implication is that combining position-awareness, digital twinning, and XAI methods can yield systems that are both quantitatively efficient and operationally transparent—key for AI-native 6G deployments where trust, auditability, and reliability are paramount. Extensions to massive MIMO, hybrid analog/digital beamforming, multi-user MIMO, and wideband OFDM require adapting input features and retraining models (with noise augmentation as needed), but the explainability modules remain applicable (Khan et al., 23 Jan 2025, Khan et al., 12 Jul 2025).