3.8 Proceedings Paper

Hybrid Orthogonal Frequency Division Multiplexing with Subcarrier Number Modulation

Publisher

IEEE
DOI: 10.1109/ICC42927.2021.9500748

Keywords

Orthogonal frequency division multiplexing (OFDM); subcarrier number modulation; bit error rate (BER); low-complexity detection

Funding

  1. International Collaborative Research Program of Guangdong Science and Technology Department [2020A0505100061]
  2. National Nature Science Foundation of China [61872102, 61871190]

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The paper proposes a hybrid OFDM-SNM scheme JM-OFDM-SNM to avoid transmitting variable lengths of information bits by jointly considering subcarrier activation patterns and constellation symbols. A low-complexity detection method is designed to relieve the high computational complexity of optimal ML detection, and an upper bound on the bit error rate of IM-OFDM-SNM is analyzed. Additionally, a more general adaptive scheme named AJM-OFDM-SNM is proposed to enhance the utilization of frequency resource, achieving better performance than existing schemes.
In this paper. we propose a hybrid OFDM-SNM scheme, named joint-mapping OFDM-SNM (JM-OFDM-SNNI), to avoid transmitting variable lengths of information bits. In JM-OFDM-SNM, the signal vectors are generated by jointly considering subcarrier activation patterns and constellation symbols. To relieve the high computational complexity of the optimal maximum-likelihood (ML) detection, we design a low-complexity detection method via resorting to the log-likelihood ratio criterion. We also analyze the upper bound on the bit error rate of IM-OFDM-SNM. To further enhance the utilization of frequency resource, we propose a more general scheme, named adaptive JM-OFDM-SNM (AJM-OFDM-SNM), to accommodate the constellation orders for different numbers of activated subcarriers. Simulation results show that AJM-OFDM-SNM achieves better performance than both JM-OFDM-SNM and OFDM-SNM at the same spectral efficiency. The low-complexity detection method of JM-OFDM-SNM achieves very close performance to the optimal ML detection, and the theoretical curves well match the simulation curves in the high signal-to-noise ratio region.

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