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Leveraging the ultra‐wideband advantages of the terahertz band, Integrated sensing and communication (ISAC) facilitates high‐precision sensing demands in human smart home applications. ISAC channel characteristics are the basis for ISAC system design. Currently, the ISAC channel is divided into target and background channels. Existing researches primarily focus on the attributes of human target itself, e.g. radar cross‐section and micro‐Doppler effect. However, the impact of human target on neither the pathloss characteristic of background channel nor the multipath propagation characteristic of target channel is considered. To address the gap, we conduct indoor channel measurements at 105 GHz to investigate the ISAC channel characteristics with the impact of human target. Firstly, by analysing the power angular delay profiles with and without human target, the changes in quantity and power of multipath components (MPCs) are observed. Then, a parameter called power control factor is proposed to evaluate the human target impact on pathloss, thereby modifying the existing pathloss model of background channel. Eventually, the MPCs belonging to target channel are extracted within target‐oriented power delay profile to count the power proportion of each bounce MPCs of the target‐Rx link, which supports the necessity of multi‐bounce (indirect) paths modelling in target channel. This letter conduct indoor channel measurements at 105 GHz to investigate the ISAC channel characteristics with the impact of human target. A novel parameter called power control factor is proposed to evaluate the human target impact on pathloss, thereby modifying the existing pathloss model of background channel. Further, the power proportion of each bounce MPCs of the target‐Rx link is counted to support the necessity of multi‐bounce (indirect) paths modelling in target channel.

The increasing demand of unoccupied and unregulated bandwidth for wireless communication systems will inevitably lead to the extension of operation frequencies toward the lower THz frequency range. Higher carrier frequencies will allow for fast transmission of huge amounts of data as needed for new emerging applications. Despite the tremendous hurdles that have to be overcome with regard to sources and detectors, circuit and antenna technology and system architecture to realize ultrafast data transmission in a scenario with extensive transmission loss, a new area of research is beginning to form. In this article we give an overview of emerging technologies and system research that might lead to ubiquitous THz communication systems in the future.

A novel concept for integrating visible light communications (VLC) with three‐dimensional indoor positioning is presented. A VLC link based on transmitter and receiver characteristics using experimental measurements was modelled. Proposed is a three‐dimensional positioning algorithm using received signal strength indication, which changes based on the angle and distance of the location based service. To reduce inter‐cell interference, the transmitter's location code was sent using different subcarriers. A demonstration shows that the proposed algorithm can obtain a user's position, including height, accurately and without inter‐cell interference.

This study analyses the interference effect in a visible light‐non‐orthogonal multiple access (VL‐NOMA) network that considers the signal power parameters performance for near and far users. The light‐emitting diode (LED) as a carrier transmits signals, and we investigate the interference effect. The interference effect challenge is a result of unaligned signal power parameters, thereby producing noise or echo during the signal transmission. The signal power parameters are successfully aligned, and NOMA techniques are deployed, which improves the signal performance in terms of bit‐error rate (BER), achieved data rate, and signal‐to‐interference plus noise ratio (SINR). Furthermore, the deployed NOMA techniques, such as power allocations (PA) to assign the signals appropriately, then superposition coding (SC) encodes the entire signal, and successive interference cancellation (SIC) cancels the interference within the signals. The signal behavior of the aligned and the unaligned signal power parameters performance are used to investigate the interference effect. We observed that unaligned signal power parameters reduce the signal performance of achieved data rate, BER, and SINR. Further, the aligned signal power parameter with NOMA techniques improves the signal performance. Moreover, in the aligned signal power scenario of NOMA, the near user performed better than the far user. This study analyses the interference effect in a visible light‐non‐orthogonal multiple access (VL‐NOMA) network that considers the signal power parameters performance for near and far users. NOMA techniques are deployed such as the power allocations (PA) to assign the signals appropriately to the two users, then superposition coding (SC) to encode the entire signal, and successive interference cancellation (SIC) that cancels the interference within the signals.

This study investigates the integration of dimmable constant weight polar‐coded non‐orthogonal multiple access (NOMA) with orthogonal space‐time block coding (OSTBC) in visible light communication (VLC) systems over Nakagami‐m $m$fading environments. The proposed scheme aims to enhance the reliability of VLC systems by reducing the outage probability while accommodating dimming requirements. By allowing multiple users to utilize the same time‐frequency resources and adjust power levels based on individual channel conditions, the system optimizes resource utilization. Additionally, the OSTBC method provides effective communication among multiple users by leveraging transmit diversity to improve system performance. The efficacy of the proposed approach is demonstrated through analytical analysis and Monte Carlo simulations, showcasing superior outage probability performance compared to conventional Alamouti‐STBC schemes. The study integrates dimmable constant weight polar‐coded non‐orthogonal multiple access (CWPC‐NOMA) with orthogonal space‐time block coding (OSTBC) for visible light communication (VLC). The proposed scheme enhances spectral efficiency and reliability while optimizing resource utilization and leveraging transmit diversity.

The use of pulse-coded signals over each colour component on a colour shift keying (CSK) modulation is explored. This codification improves the time-synchronisation recovery capability on the IEEE 802.15.7 PHY III standard and reduces the overall system complexity. Different symbol codification rates are studied so as to improve the symbol error rate while minimising the effect over the throughput of the whole system.

Visible light communication (VLC) is a promising solution for future wireless communication systems due to its high data rate, wide bandwidth, and enhanced security features. However, challenges such as high peak‐to‐average power ratio (PAPR) and out‐of‐band (OOB) spectral leakage limit its performance. In this study, we propose the integration of discrete prolate spheroidal sequences (DPSS) with direct current optical generalised frequency division multiplexing (DCO‐GFDM) to enhance the performance of indoor VLC systems. A comparative analysis between traditional DCO‐OFDM and the proposed DCO‐GFDM scheme is conducted under both line‐of‐sight (LOS) and non‐line‐of‐sight (NLOS) channel conditions. Simulation results show that the proposed method achieves approximately 2.5 dB reduction in PAPR and 45% reduction in OOB leakage compared to conventional DCO‐OFDM, while maintaining a similar bit error rate (BER) performance. Moreover, the DCO‐GFDM scheme demonstrates higher spectral efficiency without significant degradation in BER, achieving a BER below 10−3 at a signal‐to‐noise ratio (SNR) of 20 dB in both LOS and NLOS scenarios. These improvements underline the effectiveness of the DPSS‐based approach in enhancing the reliability and spectral efficiency of indoor VLC systems. VLC is a promising solution for future wireless communication systems due to its high data rate, wide bandwidth, and enhanced security features. However, challenges such as high PAPR and OOB spectral leakage limit its performance. In this study, we propose the integration of DPSS with DCO‐GFDM to enhance the performance of indoor VLC systems. A comparative analysis between traditional DCO‐OFDM and the proposed DCO‐GFDM scheme is conducted under both LOS and NLOS channel conditions. Simulation results show that the proposed method achieves approximately 2.5 dB reduction in PAPR and 45% reduction in OOB leakage compared to conventional DCO‐OFDM, while maintaining a similar BER performance. Moreover, the DCO‐GFDM scheme demonstrates higher spectral efficiency without significant degradation in BER, achieving a BER below 10−3 at a (SNR of 20 dB in both LOS and NLOS scenarios. These improvements underline the effectiveness of the DPSS‐based approach in enhancing the reliability and spectral efficiency of indoor VLC systems.

Accurate channel estimation is essential for optimising intelligent reflecting surface‐assisted multi‐user communication systems, particularly in dynamic indoor environments. Conventional techniques such as least squares (LS), linear minimum mean square error (LMMSE), and orthogonal matching pursuit (OMP) suffer from noise sensitivity and fail to effectively capture spatial dependencies in high‐dimensional intelligent reflecting surface (IRS)‐assisted channels. To overcome these limitations, this work proposes a deep learning‐driven ResNet+UNet framework that refines initial LS estimates using residual learning and multi‐scale feature reconstruction. While UNet enhances channel estimation through hierarchical processing, efficiently decreasing noise and enhancing estimate accuracy, ResNet gathers spatial features. Simulation results show that the proposed method significantly outperforms existing methods across various performance metrics. In NMSE versus signal‐to‐noise ratio assessments, the proposed approach surpasses convolutional deep residual network (CDRN) by 59%, OMP by 81%, LMMSE by 114%, and LS by 115%. When IRS elements are modified, it overcomes CDRN by 60%, OMP by 78%, LS by 107%, and LMMSE by 110%. Along with this, recommended structure performs more effectively than CDRN by 39%, OMP by 44%, LS by 122%, and LMMSE by 129% across various antenna configurations. The proposed approach is particularly beneficial for augmented reality (AR) applications, where real‐time, high‐precision channel estimation ensures seamless data streaming and ultra‐low latency, enhancing immersive experiences in AR‐based communication and interactive environments. These results illustrate the proposed method's scalability and resilience, making it a suitable choice for next‐generation IRS‐assisted wireless communication networks. Effective channel estimation is vital for optimising intelligent reflecting surface‐assisted multi‐user communication systems, particularly in dynamic indoor environments. This study introduces a deep learning‐based ResNet+UNet framework that enhances initial least squares (LS) estimates by incorporating residual learning and multi‐scale feature reconstruction to reduce noise and improve spatial feature extraction. Simulation results show that the proposed approach outperforms traditional methods like LS, linear minimum mean square error, and orthogonal matching pursuit across multiple performance metrics, especially in NMSE versus signal‐to‐noise ratio evaluations.

The emergence of terahertz (THz) communication in ultra‐massive multiple‐input multiple‐output (UM‐MIMO) systems presents new challenges for accurate and efficient channel estimation, particularly under hybrid‐field propagation conditions. Conventional estimation techniques struggle to meet the demands of such high‐dimensional systems, especially in the presence of limited radio frequency (RF) chains and mixed near‐ and far‐field effects. To address these limitations, this paper proposes a deep learning‐based framework that combines a fully connected neural network (FCNN) for linear channel estimation with a convolutional neural network (CNN) for non‐linear refinement. The architecture is designed to adapt to diverse propagation environments while maintaining computational efficiency. Simulation studies based on realistic THz scenarios demonstrate that the proposed approach significantly improves estimation accuracy, achieving up to 90% reduction in normalized mean squared error (NMSE) compared to traditional and advanced estimation techniques. The robustness of the model under varying signal‐to‐noise ratios and noise power levels underscores its potential for deployment in future 6G THz communication networks. This paper introduces a deep learning‐based framework for channel estimation in terahertz (THz) ultra‐massive multiple‐input multiple‐output systems, combining fully connected neural network for linear estimation and connected neural network for non‐linear refinement. The proposed method significantly reduces normalized mean squared error by up to 90% compared to traditional techniques, showcasing its potential for 6G THz communication. Simulation results confirm its robustness across varying signal‐to‐noise ratios.

Channel estimation is a critical component of modern wireless communication systems, especially in massive multiple‐input multiple‐output (MIMO) architectures, where the accuracy of received signal decoding heavily depends on the quality of channel state information. As wireless networks evolve into fifth‐generation (5G) and beyond, they face increasingly complex propagation environments with rapid mobility, dense connectivity, and hardware constraints. Accurate and timely channel estimation is therefore essential for maintaining system performance, enabling reliable data transmission, and supporting techniques such as beamforming and interference management. Traditional estimation methods like least squares and minimum mean square error offer baseline performance but are often limited by their computational complexity, sensitivity to noise, and inefficiency in quantised systems—particularly those employing one‐bit analogue‐to‐digital converters. These limitations hinder their applicability in real‐time, low‐power, and bandwidth‐constrained scenarios. To address these challenges, this paper proposes a novel channel estimation framework based on conditional generative adversarial networks. The approach incorporates a U‐Net‐based generator and a sequential convolutional neural network discriminator to learn complex channel mappings from highly quantised received signals. Unlike existing methods, the proposed architecture dynamically adapts to various noise levels and system configurations, offering improved robustness and generalisation. Comprehensive experiments conducted on realistic indoor massive MIMO datasets demonstrate that the proposed method achieves substantial performance gains. The model improves estimation accuracy from 93% to 95.5% and significantly enhances normalised mean square error, consistently outperforming conventional and deep learning‐based techniques across diverse training conditions. These results confirm the effectiveness of the proposed scheme in delivering high‐accuracy channel estimation under extreme quantisation conditions, making it suitable for next‐generation wireless systems. This paper proposes a novel channel estimation framework for massive multiple‐input multiple‐output (MIMO) systems using conditional generative adversarial networks with a U‐Net‐based generator and sequential convolutional neural network discriminator. The method improves accuracy and robustness under severe quantisation conditions, outperforming traditional and deep learning‐based techniques, achieving significant performance gains in terms of normalised mean square error and estimation accuracy. Experiments on realistic massive MIMO datasets demonstrate its suitability for next‐generation wireless communication systems.