MF-precoded RSMA offers improved energy efficiency and reduced complexity compared to conventional RSMA.
Optimizing remote estimation of multiple Markov sources under constraints through semantic-aware communication.
Feedback in communication schemes enhances error convergence rates with variable-length coding.
提案されたアルゴリズムは、ミリ波/テラヘルツ周波数のマルチユーザーダウンリンク通信システムにおいてエネルギー消費を最小化することを目的としています。
Optimizing beamforming for semantic-bit coexisting systems is crucial for maximizing performance and efficiency.
The author proposes a novel hashing multi-arm beam training scheme to reduce complexity and improve accuracy in multi-user millimeter wave communication systems.
The authors propose SICNNv1 and SICNNv2, NN-based equalizers inspired by iterative soft interference cancellation, to improve performance and reduce complexity in communication systems.
The author employs deep reinforcement learning to optimize precoders and power allocation in RSMA, addressing interference challenges in communication systems.
The author introduces a framework for using Syndrome-Based Neural Decoders (SBND) for high-order Bit-Interleaved Coded Modulations (BICM), extending previous results to different modulation schemes. The proposed SBND system is implemented and compared for two polar codes using different neural network architectures.
The study introduces RSMA in a multi-user multi-target ISAC system, optimizing the waveform to enhance communication users' MMF rate and minimize CRB for unbiased estimation.