This paper proposes a data-driven generative framework for learning ray-tracing behavior directly from base station and user equipment locations. The channel is represented in the angular-delay domain, and a conditional denoising diffusion probabilistic model (cDDPM) is trained to learn its distribution conditioned on spatial coordinates. To enhance the use of location information, the model employs a cross-attention mechanism. The proposed approach captures geometry-dependent channel behavior without requiring explicit 3D scene information. Experiments in a realistic urban environment show that it outperforms both a convolutional neural network baseline and a naive cDDPM in predicting dominant path characteristics. These results highlight the promise of cross-attention-enhanced diffusion models for location-conditioned and data-driven synthesis of ray-traced wireless channels.
Despite significant advancements in deep learning-based CSI compression, some key limitations remain unaddressed. Current approaches predominantly treat CSI compression as a source coding problem, neglecting transmission errors. In finite block length regimes, separate source and channel coding proves suboptimal, with reconstruction performance deteriorating significantly under challenging channel conditions. While autoencoder-based compression schemes can be adapted for joint source-channel coding, they struggle to accurately model increasingly complex channel distributions.We propose Residual-Diffusion Joint Source-Channel Coding (RD-JSCC), a novel framework that leverages diffusion models to learn robust CSI representations. Our architecture integrates a lightweight autoencoder with a residual diffusion module that iteratively refines CSI reconstruction, enabling: (i) graceful performance degradation across variable SNR conditions, and (ii) robust CSI estimation under realistic multipath fading scenarios. Our flexible decoding strategy balances computational efficiency and performance by dynamically selecting between low-complexity autoencoder decoding and sophisticated diffusion-based refinement based on channel conditions. Comprehensive simulations demonstrate that RD-JSCC significantly outperforms existing autoencoder-based approaches, particularly in challenging wireless environments. Furthermore, we demonstrate substantial decoding latency reduction through a single-step inference variant of our diffusion model, offering an efficient and practical solution for next-generation wireless communication systems.
Hybrid parasitic arrays are one approach to scale array aperture sizes with limited number of radio frequency (RF) chains and low power consumption. In this architecture, several active elements are connected to RF chains while parasitic elements are connected to digitally controlled reactances. Achieving good communication performance requires sufficient mutual coupling between active and parasitic elements. In this paper, we optimize the assignment of parasitic and active elements at the transmitter based on the signal-to-noise ratio (SNR) at the receiver. For a fixed fraction of parasitic antenna elements, this problem becomes a constrained assignment task, which is NP-hard due to its combinatorial nature. We employ a simulated annealing approach to find near-optimal antenna configurations that work well in a variety of channel conditions. The numerical results show how the optimized assignment patterns become beneficial for channels with stronger line-of-sight (LOS) component.
Passive correlation-based methods have revolutionized seismic imaging and temporal monitoring over the last several decades by implementing blind use of the complete source environment. Under the support of DARPA’s Defense Application of Innovative Remote Sensing program, we here present an initial study aimed at practical interpretation of array-based passive correlation radar methods with an emphasis on geometric untangling of range-Doppler images. Both isotropic and azimuthally biased source distributions are considered, and we describe through both real and synthetic examples how target information may be recovered in each scenario. We present results from single closely spaced linear arrays and show that interpretability, correlation function convergence, and an understanding of the source environment can lead to usable information. This precludes the need for the transmitter location and waveform modeling involved in classic passive radar approaches, and opens the possibility of using distributed receiver networks to broadly image and monitor environmental and anthropogenic targets.
Physical layer security (PLS) can be used to pro-vide keyless and innately secure communications by exploiting the propagation characteristics of the wireless channel at the physical layer (PHY) layer to co-generate cipher keys for symmetric encryption. While existing methods use channel state information as input for secret key generation, we consider a novel approach that derives features from frequency-time domain representation of in-phase and quadrature (IQ) samples to derive initial keys with high agreement rate. A recurrent neural network (RNN) with a quadruplet loss function is trained based on spectrograms collected from bi-directional time-division duplex channel probing between the two parties and on spectrograms collected from a passive eavesdropper. Information reconciliation is achieved using Reed Solomon codes. At the last step of the protocol, a 512-bit cryptographic key is generated by feeding the reconciled bit sequence to the Secure Hash Algorithm 3. Our experiments with software-defined radios in both indoor and outdoor environments evaluate the proposed key generation system under different channel propagation environments and hardware inaccuracies. We demonstrate that the proposed RNN-assisted channel feature extractor trained on indoor channel probe data operates successfully in two outdoor setups and produces up to 70 amplified keys out of 100 probes exchanged between two trusted parties i.e., Alice and Bob, while the passive eavesdropper fails to directly predict any of the generated keys.
