Varun S. Advani | Search arXiv

arXiv2026

Orientation-Aware Deep Learning Framework for RSSI-Based Indoor Localization in RIS-Aided mmWave Systems

Reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) systems provide directional measurements of received power for indoor localization, but device orientation changes the fingerprints. This work proposes an orientation-aware dual-stream gated (O-DSG) deep learning framework for received signal strength indicator (RSSI)-based indoor localization. An orientation classifier estimates device orientation from RSSI, so inference needs only received power. An embedding of the predicted angle and posterior entropy controls feature modulation and soft expert gating. Parallel convolutional and statistical streams are fused through bidirectional long short-term memory (BiLSTM) processing and temporal attention. Single-user measurements in an indoor laboratory at 28 GHz under non-line-of-sight (NLoS) conditions yield a median localization error of 0.141 m, a 25.8% improvement over the closest evaluated baseline (supplied with the measured orientation), and 92.3% orientation classification accuracy across four discrete classes. In spatial regions withheld from training, O-DSG has an average median error of 0.247 m, against 0.392 m for a validation-selected sequence model. A separate offline evaluation constructs non-orthogonal multiple access (NOMA)-style power mixtures from the single-user measurements and measures localization accuracy and computational scaling with up to eight emulated users whose power contributions overlap, under the specified mixture model.

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arXiv2026

Digital Twin-Conditioned RSS-Fingerprint Localization in Cell-Free Millimeter-Wave Networks

Digital twins (DTs) can generate dense synthetic fingerprints for localization via ray tracing, yet the sim-to-real domain gap limits their direct use, particularly in cell-free millimeter-wave (mmWave) deployments where distributed geometries and blockage-induced sparsity widen the mismatch. We propose DT-conditioned (DTCond), a framework that fuses real and DT beam-domain fingerprints as complementary inputs through per-domain encoders, learnable per-access point (AP) attention, and calibrated uncertainty. We derive oracle and pipeline Cramér-Rao bounds (CRBs) for DT-aided fingerprint localization, proving that DT information strictly tightens the positioning bound whenever spatial structure is preserved, with a heteroscedastic refinement that tightens the bound by a further 9.1%. On a real 28 GHz cell-free testbed, the deployable two-stage pipeline, which reads the DT fingerprint at a coarse k-nearest neighbor (k-NN) estimate, achieves 0.284 m mean error against 0.313 m for real-only k-NN. With the DT fingerprint read at the true position (oracle input), DT conditioning achieves 0.186 m mean error, validated through a ten-method ablation against domain-adaptation (DA) and transfer-learning baselines, and a 48% improvement on unseen regions. Multi-user self-attention provides 31-41% per-user gain for emulated simultaneous users, and progressive calibration reduces the measurement budget by 50%. As a use case, localization drives network configuration in closed loop. Between beam sweeps, a DT updated from measured RSS and read at the tracked position estimate selects the 3 of 81 beam pairs that each AP trains. With 49 calibration anchors in the update, the mean beam-selection loss stays below 1 dB over 2 m of motion, whereas reusing the last sweep loses 2.7 dB after 1 m.

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