Abstract:
End-to-end learning communication provides an exciting new approach to physical layer design, which inspired us to design the Non-orthogonal multiple access (NOMA) system...Show MoreMetadata
Abstract:
End-to-end learning communication provides an exciting new approach to physical layer design, which inspired us to design the Non-orthogonal multiple access (NOMA) system with superposition coding guided by information theory. Unlike assuming continuous Gaussian inputs in most NOMA systems, we consider actual finite-alphabet inputs at two transmitters. Due to the lack of capacity region under finite cardinality, optimizing design is challenging. In this work, we first give a closed-form expression for the conditional mutual information to define the capacity region of NOMA with finite-alphabet inputs. To simplify the conditional mutual information, we then derive a tight lower bound on mutual information, which is necessary to achieve an accurate and consistent mutual information estimator. Finally, we propose an end-to-end learning model for NOMA that considers maximizing mutual information and bit error rate (BER) constraints to achieve optimal encoders and decoders. Simulation results show that the proposed scheme achieves the capacity region of NOMA with finite-alphabet inputs. It also significantly improves the bit error performance of the system than prior work.
Date of Conference: 26-29 March 2023
Date Added to IEEE Xplore: 12 May 2023
ISBN Information: