A base station controls antenna transmission directivity using known signals from communication terminals for downlink resource allocation.
Machine learning models process environment measurements to suggest optimal beam options, reducing reference signal sweeps and transmission delays.
A Layer 1 channel state based conditional handover mechanism uses current and past CSI reports to trigger cell switching.
Dynamic antenna grouping coordinates user equipment and base station configurations to reduce uplink interference while maintaining throughput.
Precoder cycling sets use separated precoders to reduce interference and power consumption during high mobility channel adaptation.
A compact ring antenna uses strategically arranged air gaps and auxiliary conductors to resonate at multiple frequencies within a single integrated element.
Extracting the signal space from covariance matrices reduces computational complexity for real time DOA estimation of coherent RF signals.
Bit-adaptive precoding matrix indicator feedback reduces signaling overhead by dynamically configuring precoder candidate sets based on channel statistics.
Segmenting analog RF steering with digital zero-forcing precoding reduces hardware complexity while maximizing sum-rate in multi-user environments.
Terminal devices send link failure recovery requests via primary cell uplink resources to assist secondary cells.
User equipment reports antenna-panel switching indication information to the base station, reducing system overhead from frequent uplink beam sweeping.
A low-complexity linear minimum mean squared error receiver design for MIMO-OTFS systems uses matrix reordering and banded inversion.
A base station segments physical sectors into beam areas to multiplex time-frequency resources for user terminals.
Network node selects mobility reference signal patterns based on channel diversity to reduce interference and power consumption from continuous transmission.
A wireless device determines channel sounding frequency based on movement characteristics to generate a request frame for dynamic beamforming.
Third-party RIC controller uses AI/ML models to predict optimal beamforming configurations for multiple MIMO modes in O-RAN systems.
A reconfigurable intelligent surface uses time-varying control parameters to suppress side-lobe interference.