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IEEE

Publications and source records attributed to IEEE.

2 recordsLinked to original sources

arXiv2026

RF Chain Count to Mitigate Beam Split in Wideband Hybrid Arrays: From Far-Field to Near-Field

Deploying large antenna arrays transitions wireless communication into the radiative near-field (NF), where spherical wavefronts enable beamfocusing in the joint angle-distance domain. However, wideband beamforming with large antenna arrays suffers from beam split across both spatial dimensions. Although fully digital arrays can eliminate this impairment, their prohibitive hardware cost and power consumption motivate the adoption of hybrid array architectures equipped with a limited number of radio-frequency (RF) chains. This paper investigates the minimum number of RF chains required to mitigate the beam split. We derive closed-form expressions for the required number of RF chains in fully-connected (FC) architectures and propose an algorithm for sub-connected (SC) architectures, considering both uniform linear arrays (ULAs) and uniform circular arrays (UCAs). Our analysis reveals that beyond a focal distance of 10L, where L denotes the aperture length, the NF RF chain count converges to its far-field counterpart and becomes a function of the angle alone. Moreover, the FC hybrid architecture mitigates the beam split by using fewer RF chains than the SC architecture, albeit at the cost of increased complexity in the phase shifter network.

eess.SP↗

arXiv2026

Multi-Agent Spectrum Sharing

This project explores how multiple cognitive radars can learn to share limited wireless spectrum with other radio users without interfering with one another. Using machine learning (ML), each device independently decides where and how widely to transmit within a fixed 100 MHz band. The system analyzes real or simulated signal activity to detect which parts of the spectrum are currently in use and which are open. Based on this information, the devices adapt their transmission choices to avoid crowded frequencies while making efficient use of available space. The goal is to develop a flexible, scalable approach to spectrum sharing that could support future wireless communication systems. Experimental results using both over-the-air software-defined radio (SDR) recordings and simulated environments demonstrate that the proposed meta-learning approach consistently balances competing objectives better than conventional reinforcement learning (RL) methods in multi-agent spectrum-sharing scenarios. Across five multi-agent benchmark environments, our proposed method achieved the highest average reward among the primary baseline algorithms while simultaneously maintaining low collision rates and stable transmission behavior.

cs.LG↗