Angel Lozano

Andrea Pizzo, Angel Lozano

The conventional linear scaling of beamforming gain with the number of antennas is not a fundamental physical limitation, but rather a consequence of the half-wavelength spacings that minimize mutual coupling. Relaxing this constraint facilitates beamforming gains exceeding those of uncoupled arrays along specific directions. This paper shows that, when antenna losses remain sufficiently small, mutual coupling enables the synthesis of super-beams whose endfire gain scales quadratically with the number of antennas. Notably, this quadratic scaling does not necessarily require vanishing spacings, but emerges for spacings slightly below half wavelength as the array aperture increases.

Sina Beyraghi, Masoud Sadeghian, Firdous Bin Ismail et al.

This paper explores the use of generative models to synthesize high-quality, site-specific multiple-input multiple-output (MIMO) channel data, addressing the high cost of the extensive measurement campaigns required to acquire real-world data for AI-native wireless networks. Two location-conditioned generative paradigms are compared: a conditional denoising diffusion implicit model (cDDIM), and a conditional flow matching model (cFMM). Both these models generate MIMO channel matrices conditioned on user coordinates, to preserve the spatial structure of the deployment site. The approaches are evaluated across three dimensions: statistical fidelity (including beam consistency and effective rank), generation efficiency, and utility in downstream tasks such as channel-state information compression and beam alignment. Results across diverse propagation scenarios (28 GHz and 3.5 GHz, both line-of-sight and non-line-of-sight) demonstrate that both models accurately capture site-specific characteristics, even when trained on scarce ground-truth data. Notably, cFMM achieves a quality comparable to cDDIM with roughly an order of magnitude less inference time. Augmenting scarce site-specific datasets with these synthetic channels yields hefty performance gains in downstream physical layer tasks compared to using scarce data alone or stochastic channels.

Hao Xu, Angel Lozano, Hongwen Yang

Dynamic metasurface antennas (DMAs) promise to relieve massive multiple-input multiple-output architectures from their high energy consumption and hardware costs. This paper proposes a two-timescale design for downlink multiuser transmission via DMAs, a design that balances pilot overhead, complexity, and spectral efficiency. At the onset of each frame, the DMA coefficients are configured based only on statistical channel-state information (CSI), a process for which the paper introduces an optimization framework that is shown to outperform the widely used stochastic successive convex approximation method. Then, within each frame, the digital precoder is updated at each slot, based on the optimized DMA coefficients and the effective lower-dimensional instantaneous CSI. The weighted minimum mean-squared error method is applied for this short-term optimization and, for the special case of single-user transmission, a closed-form solution for the digital precoder is provided. Performance evaluations demonstrate that the proposed two-timescale design can be an attractive ingredient for future wireless networks.