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The synergy of fluid-based reconfigurable antenna (FA) technology and full-duplex (FD) communications can be jointly beneficial, as FD can enhance the spectral efficiency of a point-to-point link, while the new degree of freedom offered by the FA technology can be exploited to handle the overall interference. Hence, in this paper, an analytical framework based on stochastic geometry is developed, aiming to assess both the outage and average sum-rate performance of large-scale FA- aided FD cellular networks. In contrast to existing studies, where perfect channel state information is assumed, the developed framework accurately captures the impact of channel estimation (CE) on the performance of the considered network deployments, as well as the existence of residual loop-interference (LI) at the FD transceivers. Particularly, we focus on a limited coherence interval scenario, where a novel sequential linear minimum- mean-squared-error-based CE method is performed for all FA ports and LI links, followed by data reception from the port with the strongest estimated channel. By using stochastic geometry tools, analytical expressions for the outage and the average sum-rate performance are derived. Our results reveal that FA- aided FD communications experience an improved average sum- rate performance of around 45% compared to conventional FD communications.
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Signal Processing (eess.SP), FOS: Computer and information sciences, full-duplex, Computer Science - Information Theory, Information Theory (cs.IT), stochastic geometry, FOS: Electrical engineering, electronic engineering, information engineering, Fluid antenna, sum-rate, Electrical Engineering and Systems Science - Signal Processing, LMMSE channel estimation, outage probability
Signal Processing (eess.SP), FOS: Computer and information sciences, full-duplex, Computer Science - Information Theory, Information Theory (cs.IT), stochastic geometry, FOS: Electrical engineering, electronic engineering, information engineering, Fluid antenna, sum-rate, Electrical Engineering and Systems Science - Signal Processing, LMMSE channel estimation, outage probability
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