Constant envelope orthogonal frequency-division multiplexing (CE-OFDM) has attracted increasing attention as a power-efficient modulation scheme for Internet of Things (IoT) networks, particularly in energy-constrained scenarios such as space–air–ground integrated networks (SAGINs). Despite its inherently low peak-to-average power ratio (PAPR), CE-OFDM suffers from significant bit error rate (BER) degradation due to nonlinear phase wrapping caused by phase modulation. To address this limitation, we propose a selective mapping (SLM)-aided CE-OFDM scheme, in which the transmit sequence with the minimum number of phase jumps is selected from a pre-defined set of candidates. This strategy effectively mitigates the impact of phase wrapping and significantly improves BER performance. Furthermore, to eliminate the spectral overhead caused by conventional side information transmission, we propose an embedded side information (ESI) mechanism that seamlessly incorporates index data into the transmitted signal without requiring additional bandwidth. Simulation results verify that the proposed SLM-aided CE-OFDM scheme, equipped with ESI, achieves substantial BER improvements over conventional CE-OFDM systems, while maintaining the low-PAPR property essential for energy-constrained IoT devices.

The role of robot following is crucial for effective human-robot collaboration. Traditional methods often rely on maintaining a significant distance between the robot and the human, which limits interaction and responsiveness. In contrast, close-proximity front-following facilitates immediate engagement, enhancing user experience and improving human-robot interaction. Nonetheless, it presents challenges in accurately interpreting human walking intentions due to a restricted observational field. In our paper, we introduce an innovative Depth-Temporal Attention Network that takes lower-limb depth images and robot motor signals as input, to accurately predict human walking intentions. This network leverages a depth attention module to capture essential spatial features and integrates a temporal attention mechanism to analyze movement dynamics. To enhance generalization, we use a domain adversarial module that focuses on shared features across diverse walking data, ensuring consistent performance across users. Experimental results demonstrate that our approach achieves an impressive average intention prediction accuracy of 91.09%, significantly surpassing baseline models by 12.59% to 23.66%. Additionally, an ablation study reveals that the depth-attention module substantially improves the model's understanding of depth features, resulting in an 11.44% increase in accuracy. With this high prediction accuracy, smooth front-following is achieved at close-proximity.

In the field of constant envelope orthogonal frequency division multiplexing (CE-OFDM), the use of phase modulation heralds a paradigm with an extremely reduced peak-to-average power ratio (PAPR) of zero. However, the non-linear characteristics inherent to phase modulation pose a formidable challenge to the integration of modern channel coding strategies aimed at improved bit error rate (BER) results. To address this issue, we present an efficient low-complexity log-likelihood ratio (LLR) computation strategy based on the traditional phase receiver, anchored in an effective approximation to the phase noise variance. Furthermore, this foundation paves the way for an innovative receiver architecture, namely the near-maximum likelihood (NML) receiver, designed to improve decoding performance without incurring significant computational overhead. Comparative analyses of encoded CE-OFDM across different receiver models not only confirm the accuracy and superiority of the proposed LLR estimation, but also underscore the dual advantages of our proposed NML receiver in optimising BER and detection complexity, positioning CE-OFDM as an optimal candidate for scenarios requiring high-efficiency power amplifiers (PAs).