arXiv subjects

Ibtissam Labriji

Publications and source records attributed to Ibtissam Labriji.

4 recordsLinked to original sources

arXiv2026

Masked Latent Prediction of CSI for Indoor Localization in Integrated Sensing and Communication Systems

Channel State Information (CSI)-based fingerprinting can enable accurate indoor localization but suffers from domain shift, limited labeled data, and degraded performance in multipath-rich environments. To address these challenges, we propose a self-supervised localization framework built on a Joint Embedding Predictive Architecture (JEPA). Each CSI time snapshot is treated as a token, and the encoder is pre-trained by predicting the latent embeddings of masked snapshots from the visible ones, learning robust channel representations without any labels. The encoder is then frozen, and only a lightweight regression head is trained on a small set of labeled positions to estimate the user location. We evaluate the framework on the measured DICHASUS-005x dataset, a single-antenna transmitter received by a 32-antenna array in a multipath-rich indoor environment. With a 50% masking ratio, the proposed method reduces the mean localization error from 0.90 m to 0.42 m (a 53% reduction, or 0.48 m) relative to a supervised Convolutional Neural Network (CNN) baseline trained on raw CSI. Owing to its label-efficient, frozen-encoder design, the framework aligns with integrated sensing and communication (ISAC) objectives, enabling reliable sensing with minimal labeling and compute overhead.

eess.SP↗

arXiv2026

Hierarchical JEPA Meets Predictive Remote Control in Beyond 5G Networks

In wireless networked control systems, ensuring timely and reliable state updates from distributed devices to remote controllers is essential for robust control performance. However, when multiple devices transmit high-dimensional states (e.g., images or video frames) over bandwidth-limited wireless networks, a critical trade-off emerges between communication efficiency and control performance. To address this challenge, we propose a Hierarchical Joint-Embedding Predictive Architecture (H-JEPA) for scalable predictive control. Instead of transmitting states, device observations are encoded into low-dimensional embeddings that preserve essential dynamics. The proposed architecture employs a three-level hierarchical prediction, with high-level, medium-level, and low-level predictors operating across different temporal resolutions, to achieve long-term prediction stability, intermediate interpolation, and fine-grained refinement, respectively. Control actions are derived within the embedding space, removing the need for state reconstruction. Simulation results on inverted cart-pole systems demonstrate that H-JEPA enables up to 42.83 % more devices to be supported under limited wireless capacity without compromising control performance.

eess.SY↗

arXiv2025

Energy Efficiency in AI for 5G and Beyond: A DeepRx Case Study

This study addresses the challenge of balancing energy efficiency with performance in AI/ML models, focusing on DeepRX, a deep learning receiver based on a fully convolutional ResNet architecture. We evaluate the energy consumption of DeepRX, considering factors including FLOPs/Watt and FLOPs/clock, and find consistency between estimated and actual energy usage, influenced by memory access patterns. The research extends to comparing energy dynamics during training and inference phases. A key contribution is the application of knowledge distillation (KD) to train a compact DeepRX student model that emulates the performance of the teacher model but with reduced energy consumption. We experiment with different student model sizes, optimal teacher sizes, and KD hyperparameters. Performance is measured by comparing the Bit Error Rate (BER) performance versus Signal-to-Interference & Noise Ratio (SINR) values of the distilled model and a model trained from scratch. The distilled models demonstrate a lower error floor across SINR levels, highlighting the effectiveness of KD in achieving energy-efficient AI solutions.

cs.LG↗

arXiv2022

Towards URLLC with Proactive HARQ Adaptation

In this work, we propose a dynamic decision maker algorithm to improve the proactive HARQ protocol for beyond 5G networks. Based on Lyapunov stochastic optimization, our adaptation control framework dynamically selects the number of proactive retransmissions for intermittent URLLC traffic scenarios under time-varying channel conditions without requiring any prior knowledge associated with this stochastic process. It then better exploits the trade-off between Radio Access Network (RAN) latency, reliability and resource efficiency, which is still limited in its realization on current HARQ designs. We then evaluate the performance of several HARQ strategies and show that our proposal further improves latency over the reactive regime without affecting the resource efficiency such as fixed proactive retransmission while maintaining target reliability.

cs.NI↗