Iterative multi-code-set channel estimation extracts maximum interference taps to cancel signals from neighboring cells.
A channel assessment mode activates aggressive transmission schemes based on detected interference triggers.
Stations estimate physical channel conditions to determine required cyclic prefix values, allowing access points to select suitable parameters for each client.
A base station reconstructs channel status information using a canonical model with selected kernels and basis sets.
Transmit end adds prefix and suffix to frequency offset estimation sequence for improved anti-multipath interference performance.
A layered detection method segments QAM-FBMC equalization into one-tap recovery and interference cancellation layers to maintain symbol-level structure.
A preamble-based reference signal design leverages intrinsic interference in FBMC modulation to enable efficient channel estimation.
Pre-trained neural network parameters classified by onsite channel characteristics reduce computational complexity and training effort for massive MIMO systems.
A base station accumulates channel values across tones to create a phase change matrix for multi-user interference cancellation.