A joint typicality approach to compute-forward
Por:
Lim, SH, Feng, C, Pastore, A, Nazer, B, Gastpar, M
Publicada:
1 ene 2018
Resumen:
This paper presents a joint typicality framework for encoding and decoding nested linear codes in multi-user networks. This framework provides a new perspective on compute-forward within the context of discrete memoryless networks. In particular, it establishes an achievable rate region for computing a linear combination over a discrete memoryless multiple-access channel (MAC). When specialized to the Gaussian MAC, this rate region recovers and improves upon the lattice-based compute-forward rate region of Nazer and Gastpar, thus providing a unified approach for discrete memoryless and Gaussian networks. Furthermore, our framework provides some valuable insights on establishing the optimal decoding rate region for compute-forward by considering joint decoders, progressing beyond most previous works that consider successive cancellation decoding. Specifically, this paper establishes an achievable rate region for simultaneously decoding two linear combinations of nested linear codewords from $K$ senders. © 2018 IEEE.
Filiaciones:
Lim, SH:
Korea Inst Ocean Sci & Technol, Busan 49111, South Korea
Feng, C:
Univ British Columbia, Sch Engn, Kelowna, BC V1V 1V7, Canada
Pastore, A:
CTTC CERCA, Ctr Tecnol Telecomunicac Catalunya, Castelldefels 08860, Spain
Nazer, B:
Boston Univ, Dept Elect & Comp Engn, Boston, MA 02215 USA
Gastpar, M:
Ecole Polytech Fed, Sch Comp & Commun Sci, CH-1015 Lausanne, Switzerland
hybrid, Green Submitted, All Open Access; Bronze Open Access; Green Open Access
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