Unsourced Random Access With Tensor-Based and Coherent Modulations
원문 초록
Unsourced random access (URA) is a particular form of grant-free uncoordinated random access wherein the users' identities are not associated to specific waveforms at the physical layer. Tensor-based modulation (TBM) has been recently advocated as a promising technique for URA due to its ability to support a large number of active users transmitting simultaneously by exploiting tensor decomposition for user separation. We propose a novel URA scheme that builds upon TBM by splitting the transmit message into two sub-messages. This first part is modulated according to a TBM scheme, while the second is encoded using a coherent non-orthogonal multiple access (NOMA) modulation. At the receiver side, we exploit the advantages of forward error correction (FEC) coding and interference cancellation techniques. We compare the performances of the introduced scheme with state-of-the-art URA schemes under a quasi-static Rayleigh fading model, proving the energy efficiency and the fading robustness of the proposed solution.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Revisit CP Tensor Decomposition: Statistical Optimality and Fast Convergence
D-Tracker: Modeling Interest Diffusion in Social Activity Tensor Data Streams
Multiscale Tensor Summation Factorization as a New Neural Network Layer (MTS Layer) for Multidimensional Data Processing
Knowledge Graph Completion with Mixed Geometry Tensor Factorization
Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models
Guaranteed Multidimensional Time Series Prediction via Deterministic Tensor Completion Theory
토론
이 논문과 연결된 포럼 글이 아직 없습니다.