This letter investigates the performance enhancement by the concept of multi-carrier index keying in orthogonal frequency division multiplexing (OFDM) systems. For the performance evaluation, a tight closedform approximation of the bit error rate (BER) is derived introducing the expression for the number of bit errors occurring in both the index domain and the complex domain, in the presence of both imperfect and perfect detection of active multi-carrier indices. The accuracy of the derived BER results for various cases are validated using simulations, which can provide the accuracy within 1 dB at favorable channels.Index Terms-Multi-carrier index keying, orthogonal frequency division multiplexing, bit error rate.

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In this work, we investigate the performance of multicarrier index keying (MCIK) orthogonal frequency division multiplexing (OFDM) proposing a hybrid low complexity detection and diversity reception. For performance analysis, we derive novel exact closed-form expressions for the average pairwise error probability (PEP) and symbol error probability (SEP) of three detection methods: a greedy detector (GD), and a GD with maximal ratio combining (MRC) or selection combining (SC). Approximate and accurate expressions for the average PEP and SEP are also analyzed in closed-form. The derived expressions provide a useful insight into the error performance of MCIK-OFDM with the hybrid detector in low, moderate and extreme rate of sparse sub-carrier indices activation. The effects of multiple antennas and sparse sub-carriers activation on the SEP are addressed in several extreme cases; decreasing the average SEP exponentially with proper choice of both sub-carrier activation rate and number of antennas. In comparison with the benchmark detection, the numerical results and simulations clearly show that the proposed schemes can benefit from diversity gain, at substantially reduced complexity. The derived SEP expressions and analyses will be useful to evaluate various concepts of MCIK OFDM in low-power device applications. Index Terms-Greedy detection (GD), multicarrier index keying (MCIK), orthogonal frequency division multiplexing (OFDM), symbol error probability (SEP).

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This letter presents the first attempt of exploiting deep learning (DL) in the signal detection of orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems. Particularly, we propose a novel DL-based detector termed as DeepIM, which employs a deep neural network with fully-connected layers to recover data bits in an OFDM-IM system. To enhance the performance of DeepIM, the received signal and channel vectors are pre-processed based on the domain knowledge before entering the network. Using datasets collected by simulations, DeepIM is first trained offline to minimize the bit error rate (BER) and then the trained model is deployed for the online signal detection of OFDM-IM. Simulation results show that DeepIM can achieve a near-optimal BER with a lower runtime than existing hand-crafted detectors.