The sixth-generation (6G) wireless networks are expected to enable the deep integration of communication, sensing, computing, control, and intelligence in highly dynamic environments. This evolution drives a fundamental transition from conventional passive channel adaptation to proactive channel cognition and reconfiguration, wherein wireless channels are no longer regarded as uncontrollable propagation media but as network resources that can be learned, predicted, and actively reconfigured. This paper presents a comprehensive overview of this emerging paradigm. We first review channel cognition through the channel knowledge map (CKM) as a systematic framework for learning and exploiting channel characteristics across space, time, and frequency domain. The definitions, construction methods, and applications in wireless networks of CKMs are comprehensively reviewed. Building upon channel cognition, we then review channel reconfiguration technologies from two complementary perspectives: transceiver-side reconfiguration enabled by movable antennas (MAs) and environment-side reconfiguration enabled by intelligent reflecting surfaces (IRSs). For both MA- and IRS-enabled wireless systems, we review their architectures, performance advantages, and key design challenges. Finally, we discuss several promising research directions to inspire further innovations in this burgeoning field.
Wenyan Ma, Zixiang Ren, Weitong Zhai et al.· 0 citations
In this paper, we investigate a movable antenna (MA)-assisted uncrewed aerial vehicle (UAV) swarm communication system. Unlike conventional fixed-position antenna (FPA) systems, each UAV is equipped with an MA array distributed on two hemispherical surfaces at the head and tail, significantly expanding the spatial degrees of freedom (DoFs) in three-dimensional (3-D) seamless coverage. A far-field line-of-sight (LoS) channel model is adopted to characterize the UAV-to-UAV (U2U) communication links, incorporating both antenna positioning and radiation patterns. We formulate an achievable sum rate maximization problem by jointly optimizing the antenna position vectors (APVs) and transmit/receive beamforming vectors, subject to constraints on maximum transmit power, limited antenna moving region, and minimum inter-antenna spacing. To tackle this non-convex and highly coupled problem, we propose a two-loop iterative optimization algorithm that effectively combines the Spider Wasp Optimizer (SWO) for APV optimization and alternative optimization (AO) for beamforming design. Extensive simulation results demonstrate that the proposed MA-assisted scheme outperforms traditional FPA systems and other benchmark algorithms under various settings. The performance gains are attributed to the efficient optimization of antenna positions within the hemispherical moving region for interference suppression and coverage enhancement.
Fansheng Song, Lipeng Zhu, Xiangyu Pi et al.· IEEE Transactions on Communi...· 0 citations
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