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The efficient representation, transmission, and reconstruction of three-dimensional (3D) contents are becoming increasingly important for sixth-generation (6G) networks that aim to merge virtual and physical worlds for offering immersive communication experiences. Neural radiance field (NeRF) and 3D Gaussian splatting (3D-GS) have recently emerged as two promising 3D representation techniques based on radiance field rendering, which are able to provide photorealistic rendering results for complex scenes. Therefore, embracing NeRF and 3D-GS in 6G networks is envisioned to be a prominent solution to support emerging 3D applications with enhanced quality of experience. This paper provides a comprehensive overview on the integration of NeRF and 3D-GS in 6G. First, we review the basics of the radiance field rendering techniques, and highlight their applications and implementation challenges over wireless networks. Next, we consider the over-the-air training of NeRF and 3D-GS models over wireless networks by presenting various learning techniques. We particularly focus on the federated learning design over a hierarchical device-edge-cloud architecture, which is suitable for exploiting distributed data and computing resources over 6G networks to train large models representing large-scale scenes. Then, we consider the over-the-air rendering of NeRF and 3D-GS models at wireless network edge. We present three practical rendering architectures, namely local, remote, and co-rendering, respectively, and provide model compression approaches to facilitate the transmission of radiance field models for rendering. We also present rendering acceleration approaches and joint computation and communication designs to enhance the rendering efficiency. In a case study, we propose a new semantic communication enabled 3D content transmission design, in which the radiance field models are exploited as the semantic knowledge base to reduce the communication overhead for distributed inference. In addition, we discuss the utilization of radiance field rendering in wireless applications like radio mapping and radio imaging, in which radiance field models are used to effectively represent complex radio environments to facilitate wireless network designs. It is our hope that this paper can provide new insights on the interesting wireless integration with radiance field rendering for future 6G networks with 3D contents.

We have studied the laser pulse shape dependence of the conversion efficiency of λ = 1.03 μm laser pulse energy into 13.5 nm extreme ultraviolet (EUV) emission from a Sn laser-produced plasma. Laser pulses of arbitrary temporal shape ranging from hundreds of picoseconds to several nanoseconds were generated using a programmable pulse synthesizer based on a diode-pumped chirped pulse amplification Yb: YAG laser. Measurements show that the conversion efficiency favors the use of nearly square pulses of duration longer than 2 ns, in agreement with hydrodynamic/atomic physics simulations. A 35% increase in conversion efficiency was obtained when Q-switched pulses were substituted by square pulses of a similar duration. Experiments conducted irradiating a Sn target with a sequence of two time-delayed 250 ps pulses showed a 30 percent increase in the EUV yield respect to a single pulse of the same total energy when the pulse separation was optimum at 2.1 ns. This suggests that re-heating of the plasma with delayed laser pulses could be used to improve the EUV yield. The spectroscopic characterization of EUV emission and in-band EUV images that characterize the source size are also presented.