This article introduces a massive multiple-input—multiple-output (mMIMO) system that utilizes spectrally efficient frequency division multiplexing (SEFDM) and incorporates a deep neural network (DNN) for enhanced SEFDM channel estimation. Unlike existing studies on DNN-based channel estimation, this research employs estimated channel feedback to dynamically adjust SEFDM signal characteristics at the transmitter, thereby improving the system’s adaptability. This adaptive mechanism optimizes the SEFDM compression value and modulation order based on real-time channel conditions, significantly enhancing the symbol error rate (SER). Detailed simulations demonstrate that higher modulation techniques experience substantial performance degradation with increased subcarrier compression in SEFDM. The proposed DNN-based channel estimation and adaptive parameter selection outperform traditional linear schemes, utilizing a more stable SEFDM system to achieve significant spectral efficiency (SE) compared to conventional orthogonal frequency division multiplexing (OFDM).

Research has shown that data-driven robots deployed in social settings are likely to unconsciously perpetuate systemic social biases. Despite this, robots can also be deployed to promote fair behaviour in humans. These phenomena have led to the development of two broad sub-disciplines in HRI concerning ‘fairness’: a data-centric approach to ensuring robots operate fairly and a human-centric approach which aims to use robots as interventions to promote fairness in society. To date, these two fields have developed independently, thus it is unknown how data-driven robots can be used to suppress biases in human-human interactions. In this paper, we present a conceptual framework and hypothetical example of how robots might deploy data-driven fairness interventions, to actively suppress social biases in human-human interactions.

This paper presents dual-mode index modulation for spectral efficient frequency division multiplexing (SEFDM-DM). SEFDM is a non-orthogonal multicarrier technique created by compressing the subcarrier spacing of classical orthogonal frequency-division multiplexing (OFDM). SEFDM provides high spectrum efficiency (SE) at the expense of increased inter-carrier interference (ICI). SEFDM with index modulation (SEFDM-IM) has two information-bearing units, that is, a subcarrier activation pattern and modulated symbols, which reduce ICI at the expense of an SE deficit owing to inactive subcarriers. The proposed SEFDM-DM uses all available subcarriers while retaining the diversity gain of index modulation (IM). It enhances SE by transmitting distinct constellation modes through a subcarrier index selection mechanism. This study also presents SEFDM-DM with coordinate interleaving (SEFDM-CDM), which introduces space-time block codes with coordinate interleaving to enhance transmission diversity by sending real and imaginary parts of the constellation symbols over different subcarriers. Analytical and simulation results corroborate the benefits of the suggested work in terms of SE and bit error rate.

The fourth Industrial Revolution is expected to lead to an era of technological innovation and digitization that would require connectivity by the users, anywhere and anytime. The fifth generation of wireless communication systems and the technologies therein are being explored to cater to high connectivity needs that encompass high data rates, very low latencies, energy‐efficient systems, etc. A multiuser environment is anticipated that would require multiple access techniques, such as Nonorthogonal Multiple Access (NOMA). The user data in the power domain NOMA is superimposed, at the transmitter base station, which is in turn subjected to Successive Interference Cancellation at the user end. In the multiuser downlink, the desired user’s signal is subjected to imperfect SIC due to incomplete cancellation of the undesired user’s signal. Pulse‐shaping of NOMA symbols using wavelet transform is proposed to mitigate the multiuser interference due to imperfect SIC. Closed‐form symbol error rate (SER) expression is derived for the wavelet NOMA system for a three‐user scenario. Analytical results show that wavelet transform pulse‐shaped NOMA performs better compared to Fourier transform pulse‐shaped NOMA symbols in mitigating SIC and thereby minimize the residual error due to imperfect SIC.