We study spectrum sharing between two dense low-earth orbit (LEO) satellite constellations, an incumbent primary system and a secondary system that must respect interference protection constraints on the primary system. In particular, we propose a secondary satellite selection framework and algorithm that maximizes capacity while guaranteeing that the time-average interference and absolute interference inflicted upon each primary ground user never exceeds specified thresholds. We solve this NP-hard constrained, combinatorial satellite selection problem through Lagrangian relaxation to decompose it into simpler problems which can then be solved through subgradient methods. A high-fidelity simulation is developed based on public FCC filings and technical specifications of the Starlink and Kuiper systems. We use this case study to illustrate the effectiveness of our approach and that explicit protection is indeed necessary for healthy coexistence. We further demonstrate that deep learning models can be used to predict the primary satellite system associations, which helps the secondary system avoid inflicting excessive interference and maximize its own capacity.

Deep neural network (DNN)-based algorithms are emerging as an important tool for many physical and MAC layer functions in future wireless communication systems, including for large multi-antenna channels. However, training such models typically requires a large dataset of high-dimensional channel measurements, which are very difficult and expensive to obtain. This paper introduces a novel method for generating synthetic wireless channel data using diffusion-based models to produce user-specific channels that accurately reflect real-world wireless environments. Our approach employs a conditional denoising diffusion implicit model (cDDIM) framework, effectively capturing the relationship between user location and multi-antenna channel characteristics. We generate synthetic high fidelity channel samples using user positions as conditional inputs, creating larger augmented datasets to overcome measurement scarcity. The utility of this method is demonstrated through its efficacy in training various downstream tasks such as channel compression and beam alignment. Our diffusion-based augmentation approach achieves over a 1-2 dB gain in NMSE for channel compression, and an 11 dB SNR boost in beamforming compared to prior methods, such as noise addition or the use of generative adversarial networks (GANs).

While neural lossy compression techniques have markedly advanced the efficiency of Channel State Information (CSI) compression and reconstruction for feedback in MIMO communications, efficient algorithms for more challenging and practical tasks—such as CSI compression for future channel prediction and reconstruction with relevant side information—remain underexplored, often resulting in suboptimal performance when existing methods are extended to these scenarios. To that end, we propose a novel framework for compression with side information, featuring an encoding process with fixed-rate compression using a trainable codebook for codeword quantization, and a decoding procedure modeled as a backward diffusion process conditioned on both the codeword and the side information. Experimental results show that our method significantly outperforms existing CSI compression algorithms, often yielding over twofold performance improvement by achieving comparable distortion at less than half the data rate of competing methods in certain scenarios. These findings underscore the potential of diffusion-based compression for practical deployment in communication systems.

Risk-averse modeling is critical in safety-sensitive and high-stakes applications. Conditional Value-at-Risk (CVaR) quantifies such risk by measuring the expected loss in the tail of the loss distribution, and minimizing it provides a principled framework for training robust models. However, direct CVaR minimization remains challenging due to the difficulty of accurately estimating rare, high-loss events—particularly at extreme quantiles. In this work, we propose a novel training framework that synthesizes informative samples for CVaR optimization using score-based generative models. Specifically, we guide a diffusion-based generative model to sample from a reweighted distribution that emphasizes inputs likely to incur high loss under a pretrained reference model. These samples are then incorporated via a loss-weighted importance sampling scheme to reduce noise in stochastic optimization. We establish convergence guarantees and show that the synthesized, high-loss-emphasized dataset substantially contributes to the noise reduction. Empirically, we validate the effectiveness of our approach across multiple settings, including a real-world wireless channel compression task, where our method achieves significant improvements over standard risk minimization strategies.

For millimeter wave (mmWave) communication, fast and accurate beam alignment is essential but challenging. Site-specific beam adaptation using deep learning is a very promising paradigm for beam alignment, but such methods typically require a lot of clean channel measurements for training, which can be difficult or even impossible to achieve in practice. This paper introduces a novel method to learn beam alignment policies using only uplink (UL) pilot measurements. The proposed method integrates a generative adversarial network (GAN)-based channel estimation (CE) model with an unsupervised deep learning model beam alignment engine (BAE). We introduce an efficient form of dataset amplification for improved training that leverages the randomness of the deep generative model (DGM) and an early stopping mechanism. Our experiments show that the GAN-BAE method achieves a better signal-to-noise ratio (SNR) by nearly 3 dB compared to compressed sensing (CS) methods such as orthogonal matching pursuit (OMP) and EM-GM-AMP (an Approximate Message Passing algorithm), especially when there are limited pilot measurements from each mobile user.

Wireless use cases such as spectrum sharing and Massive Machine Type Communications (mMTC) can benefit from the detection of unknown signals, which includes estimating their received power as well as other key characteristics such as bandwidth, modulation type, and waveform. While conventional signal detection methods are susceptible to noise, deep learning (DL) models offer a more robust alternative. Previously, DL models were used for solving simpler problems, focusing mainly on modulation recognition. We propose an advanced DL neural network structure that extracts the parameters of 5G NR frequency range 2 (FR2) mmWave test model waveforms. We evaluate our framework on a state-of-the-art signal generator and vector signal analyzer (VSA) that mimics real-world detection. Our work shows that incorporating curriculum training (CT) on both additive white Gaussian noise (AWGN) and frequency shift error enhances the model's accuracy across all SNR and frequency shift ranges. We further enhance the accuracy by employing the error vector magnitude (EVM) function to prioritize the top five scored parameters and validate selected parameters. As a result, our method consistently achieves an accuracy rate exceeding 90% when extracting the key parameters from 5G NR FR2 mmWave waveforms at diverse noise levels.