IEEE Access (Jan 2017)

Zero-Padded Orthogonal Frequency Division Multiplexing with Index Modulation Using Multiple Constellation Alphabets

  • Tianqi Mao,
  • Qi Wang,
  • Jinguo Quan,
  • Zhaocheng Wang

DOI
https://doi.org/10.1109/ACCESS.2017.2756659
Journal volume & issue
Vol. 5
pp. 21168 – 21178

Abstract

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Orthogonal frequency-division multiplexing (OFDM) with index modulation has been emerged as a promising technique for next-generation networks, where the specific activation pattern of the OFDM subcarriers is capable of conveying additional information implicitly without additional energy consumption, hence improving the system energy efficiency. In this paper, zero-padded tri-mode OFDM with index modulation (ZTM-OFDM-IM) is proposed, which is capable of achieving high spectral and energy efficiency. In ZTM-OFDM-IM, subcarriers are partitioned into subblocks. In each OFDM subblock, only a fraction of subcarriers are modulated by two distinguishable constellation alphabets, while the others remain empty, which reduces the energy consumption. With such an arrangement, extra information bits can be carried by the subcarrier activation pattern. At the receiver, a maximum-likelihood (ML) detector as well as a reduced-complexity two-stage log-likelihood ratio (LLR) detector are developed for signal demodulation. Theoretical performance analysis based on Euclidean distance metric and pairwise error probability are conducted. Besides, in order to attain better BER performance, the design strategies for the two distinguishable constellation alphabets are investigated, which is followed by brief discussions on generalization methods for the proposed ZTM-OFDM-IM. It is demonstrated via Monte Carlo simulations that ZTM-OFDM-IM is capable of enhancing the BER performance compared with the conventional OFDM and other conventional index-modulated OFDM benchmarks, which provides high-rate data transmission with low energy consumption. Moreover, the LLR detection of ZTM-OFDM-IM only suffers from slight performance loss in comparison with the ML detection and achieves significant complexity reduction.

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