Mehmet C. Vuran

Mohammad Mosiur Lunar, Mehmet C. Vuran

Dynamic spectrum access (DSA) has become a key pillar of next-generation wireless systems to address the spectrum scarcity due to the rapid growth of connected devices. Accurate short-term spectrum forecasting is critical for DSA, where data-driven approaches have proven most effective. Recent advances in and widespread adoption of large language model (LLM) architectures present new opportunities for spectrum prediction. In this paper, foundational large spectrum models (LSMs) are presented. A novel RF tokenizer is introduced to convert raw IQ measurements into token sequences by mapping each power-spectral density value to a fixed vocabulary along with embedding gain, frequency, FFT bin, and timestamp information. Five established open-source LLM architectures (Gemma-2B, GPT-2, LLaMA-7B, Mistral-7B, and Phi-1) are trained on this tokenized spectrum data for the task of spectrum forecasting, yielding LSMs. To leverage the scaling gains of LSMs, a fully automated outdoor wireless testbed is employed to collect over 22 TB of raw spectrum data across 33 sub-GHz frequency bands, yielding 8.4B tokens in total. Across all 33 bands, the best model (LSM-Mistral) achieves a root-mean-square error of 3.25 dB and 97% of predictions have a mean absolute error below 5 dB. Generalization of LSMs is illustrated by fine-tuning the models on data collected in different locations, where RMSE is maintained below 3.7 dB. These results demonstrate that widespread decoder-only transformer architectures can serve as effective predictive models for large-scale RF spectrum forecasting.

Avhishek Biswas, Apala Pramanik, Eylem Ekici et al.

Millimeter-wave (mmWave) frequencies promise multi-gigabit connectivity for vehicle-to-everything (V2X) networks, but face challenges in terms of severe path loss and mobility-related beam misalignment. Reliable V2X connectivity requires fast, double-directional beam alignment. However, existing methods suffer from high training overhead and limited generalization to unseen scenarios. This paper presents VIsion-based BEamforming(VIBE), a hybrid model-based, closed-loop, learning architecture for real-time double-directional mmWave beam management primed by camera sensing. VIBE fuses machine learning, model-based reasoning, and closed-loop RF feedback to balance beam-pair establishment latency with link quality. VIBE bypasses exhaustive training overhead and accelerates link establishment by leveraging camera observations to reduce the beam-search space. Lightweight beam refinement and offset tracking mechanisms adaptively refine beams in response to dynamic application requirements. VIBE is implemented and evaluated across online indoor/outdoor testbeds, public datasets, and real-time vehicular experiments, demonstrating strong generalization capabilities, making it suitable for real-time V2X communication. Comparisons with 5G NR hierarchical beamforming show that VIBE consistently maintains lower outage rates. Furthermore, VIBE outperforms state-of-the-art end-to-end ML models for beam selection when evaluated on public datasets and achieves outage rates as low as 1.1-1.4 %. The results show that a hybrid model-based, closed-loop learning architecture is better suited for real-world mmWave vehicular connectivity than end-to-end trained ML models. For reproducibility, we publish our code to https://github.com/UNL-CPN-Lab/Look-Once-Beam-Twice.

Sangwon Shin, Mehmet C. Vuran

The increasing congestion of the radio frequency spectrum presents challenges for efficient spectrum utilization. Cognitive radio systems enable dynamic spectrum access with the aid of recent innovations in neural networks. However, traditional real-valued neural networks (RVNNs) face difficulties in low signal-to-noise ratio (SNR) environments, as they were not specifically developed to capture essential wireless signal properties such as phase and amplitude. This work presents CMuSeNet, a complex-valued multi-signal segmentation network for wideband spectrum sensing, to address these limitations. Extensive hyperparameter analysis shows that a naive conversion of existing RVNNs into their complex-valued counterparts is ineffective. Built on complex-valued neural networks (CVNNs) with a residual architecture, CMuSeNet introduces a complexvalued Fourier spectrum focal loss (CFL) and a complex plane intersection over union (CIoU) similarity metric to enhance training performance. Extensive evaluations on synthetic, indoor overthe-air, and real-world datasets show that CMuSeNet achieves an average accuracy of 98.98%-99.90%, improving by up to 9.2 percentage points over its real-valued counterpart and consistently outperforms state of the art. Strikingly, CMuSeNet achieves the accuracy level of its RVNN counterpart in just two epochs, compared to the 27 epochs required for RVNN, while reducing training time by up to a 92.2% over the state of the art. The results highlight the effectiveness of complex-valued architectures in improving weak signal detection and training efficiency for spectrum sensing in challenging low-SNR environments. The dataset is available at: https://dx.doi.org/10.21227/hcc1-6p22