The radio-over-fiber (RoF) technology which was first introduced in the late eighties has evolved over time and is now considered as a possible solution for future wireless transport. The exponential growth in mobile user applications and their diverse requirements demand the future wireless and its transport network to be more intelligent, software-defined, and ubiquitous to provide immersive, high bandwidth, ultra-low latency, and hyper reliable communication. These evolutions mandate rethinking of the design and development of physical layer and upper layer of RoF technologies. This paper reviews the past and current developments of RoF technologies and summarizes the challenges that the technologies can potentially face in the future to support beyond 5G networks and their requirements.

The benefits of 2×1 multiple-inputs-single-output scheme for transmitter diversity in the infrared indoor optical wireless communication link are theoretically investigated. The performance of repetition-coding (RC) and Alamouti-type real-valued space-time-block-coding (STBC) as effective transmitter diversity schemes is systematically compared under conditions of channel gain variation caused by the degradation in the received optical power due to the blocking of one optical beam of the optical wireless channel. It is shown that the linear addition of channel gains in the RC scheme outperforms the root-sum-square of channel gains in the STBC scheme with regards to the bit-error-rate (BER) performance. Proof-of-concept experiments are carried out with both schemes under emulated scenarios of channel blockage. The RC scheme exhibits better BER performance when observed experimentally, validating the proposed theoretical model for the two spatial diversity schemes. To understand the performance of RC and STBC schemes against the optical delay caused by the two optical channel path difference within one-bit interval, both schemes were experimentally investigated using on-off-keying modulation, and results show that RC still outperforms STBC. Both theoretical and experimental results indicate that RC has better robustness to channel blockage and differential channel paths induced optical delay.

In allusion to the privacy and security problems in 3D point cloud classification, a novel privacy protection method for 3D point cloud classification based on optical chaotic encryption scheme is proposed and implemented in this paper for the first time. The mutually coupled spin-polarized vertical-cavity surface-emitting lasers (MC-SPVCSELs) subject to double optical feedback (DOF) are studied to generate optical chaos for permutation and diffusion encryption process of 3D point cloud. The nonlinear dynamics and complexity results demonstrate that the MC-SPVCSELs with DOF have high chaotic complexity and can provide tremendously large key space. All the test-sets of ModelNet40 dataset containing 40 object categories are encrypted and decrypted by the proposed scheme, and then the classification results of 40 object categories for original, encrypted, and decrypted 3D point cloud are entirely enumerated through the PointNet++. Intriguingly, the class accuracies of the encrypted point cloud are nearly all equal to 0.0000% except for the plant class with 100.0000%, indicating the encrypted point cloud cannot be classified and identified. The decryption class accuracies are very close to the original class accuracies. Therefore, the classification results verify that the proposed privacy protection scheme is practically feasible and remarkably effective. Additionally, the encryption and decryption results show that the encrypted point cloud images are ambiguous and unrecognizable, while the decrypted point cloud images are identical to original images. Moreover, this paper improves the security analysis via analyzing 3D point cloud geometric features. Eventually, various security analysis results validate that the proposed privacy protection scheme has high security level and good privacy protection effect for 3D point cloud classification.

Ferroptosis, a type of programmed cell death induced by the iron-dependent lipid hydroperoxide pathway, has attracted widespread attention. However, Fenton response-dependent ferroptosis has many limitations, such as insufficient reaction conditions in the tumor micro-environment. Here, we propose an all-in-one phototherapy nanoplatform consisting of iron-polydopamine (Fe-PDA), a folic acid-modified red blood cell membrane (FA-RBCm), and epirubicin (EPI), namely, Fe-PDA-EPI@FA-RBCm NPs, to achieve enhanced photothermal-ferroptosis effects via overcoming the limitations of the Fenton-like reaction. The results showed that the synthesized biomimetic nanoparticles could decompose hydrogen peroxide (H₂O₂) to generate hydroxyl radicals (˙OH), and further induce the non-apoptotic ferroptosis pathway. After irradiation with near-infrared (NIR) light, the uptake of Fe-PDA-EPI@FA-RBCm NPs by cells could be effectively promoted, and it presented impressive in vitro and in vivo photothermal properties. In vitro and in vivo results showed that laser irradiation could enhance ferroptosis by promoting the production of reactive oxygen species (ROS) and lipid peroxides, down-regulating the expression of glutathione peroxidase 4 (GPX4), and reducing the mitochondrial membrane potential. Furthermore, the photothermal-promoted ferroptosis and apoptosis pathways (photothermal therapy and chemotherapy) exhibited outstanding synergistic antitumor efficacy in vitro and in vivo, with an in vivo tumor inhibition rate as high as 76.95%. In conclusion, the construction of tumor-targeted biomimetic nanocarriers utilizing the advantageous properties of RBCm has been investigated as a potential anticancer strategy.

The optical wireless communication (OWC) system has been widely studied as a promising candidate for indoor high-speed wireless communications. In particular, the spatial diversity scheme has shown to be effective to improve the performance of OWC systems. However, the delay amongst multiple channels in this scheme may result in severe inter-symbol-interference (ISI) and degradations of the system performance. Recent studies have shown that the symbol decision schemes based on recurrent neural networks (RNNs) can mitigate the impact of delays. However, their performances are limited when the channel delays are long. In this paper, we propose a delay-tolerant indoor OWC system, which utilizes an attention-augmented long-short term memory (ALSTM) RNN decision scheme to handle long channel delays. A 10 Gb/s repetition-coded indoor OWC system with the proposed ALSTM RNN decision scheme is experimentally demonstrated. Compared with traditional OWC systems which add cyclic prefix (CP) /zero-postfix (ZP) to combat the impact of channel delays, the proposed system does not reduce the effective data rate or throughput. Compared with the previous study which adopts the vanilla RNN decision scheme, the proposed ALSTM RNN decision scheme can better learn dependencies amongst received symbols with the help of attention mechanism, especially amongst non-neighboring symbols. Hence, it is more robust against the ISI induced by long channel delays. Experimental results show that compared with the previously studied vanilla RNN, over one-order-of-magnitude bit-error-rate improvement is achieved when the channel delay is more than 4.7-symbol-period.

The traditional Pignistic transformation is limited in the context of “betting”, which faces information loss and is inconvenient for multi-source information fusion. To tackle this challenge, an Enhanced Pignistic transformation is proposed for the first time. New divergence and information volume measures are tailor-made for the enhanced Pignistic probability, and a novel information fusion algorithm is developed. To further prove the fusion algorithm’s advantages in conflict management, it is applied in a new semi-automatic image segmentation scheme. Two uncertain decision-support techniques named adaptive belief assignment and scalable information extraction are raised, and a fuzzy heuristic refinement algorithm is conducted, fulfilling the gap between evidential decision-making and segmentation refinement. Experimental analysis shows the proposed segmentation algorithm is superior on four metrics each and can enhance the robustness of foreground segmentation, indicating the effectiveness of the proposal in solving the decision inaccuracy of evidential segmentation schemes.