As an emerging technique, index modulation (IM) can improve the system bit error rate (BER) performance due to the robustness of index bits over conventional modulated symbol bits. The existing IM aided orthogonal frequency division multiplexing (OFDM) schemes only employ one index modulator at the transmitter, which transmits a small amount of index bits in each transmission. In this paper, a novel IM technique, called cascade IM (CIM), is proposed to increase the proportion of the index bits in the transmission by combining the conventional IM with the multiple‐mode IM together. Subcarrier‐wise and subblock‐wise CIM schemes are proposed to achieve different spectral efficiency and diversity order for diverse scenarios in the next generation wireless communication networks. The optimal subcarrier‐wise maximum likelihood detector is proposed for OFDM‐CIM. To reduce the demodulation complexity, a novel tree search based and an iterative log‐likelihood ratio based detectors, which can avoid illegal index patterns in the search process, are developed for OFDM‐CIM. Monte Carlo simulations show that the proposed scheme achieves better BER performance than OFDM‐IM and appears as a competitive candidate of multi‐carrier transmission techniques for next generation wireless communication networks.
Calibration performed by a robotic manipulator is crucial in the field of industrial intelligent production, as it ensures precise and accurate measurements. In this paper, we present a new method for addressing the hand-to-eye calibration problem using deep reinforcement learning. Our proposed algorithm utilizes an actor-critic framework and incorporates neurodynamics adaptive reward and action functions, which allows for better convergence, reduces the dependence on the initial value, and overcomes the local convergence issues of traditional deep reinforcement learning method. Additionally, we introduce a step-wise mechanism under the guidance of the attention mechanism, and zero stability to handle the complexity of the calibration task in challenging environments. A number of experiments were conducted to demonstrate the validity of the proposed algorithm. The experimental results show that our proposed algorithm can achieve a nearly 100% success rate after training phase. Additionally, we compared our proposed algorithm with other widely used methods, such as deterministic deep policy gradient (DDPG) and soft actor-critic (SAC) to further demonstrate its effectiveness.
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