The chapter discusses the evolution of neurosurgical visualization techniques, drawing an analogy to Plato’s “Allegory of the Cave.” Traditional medical imaging provides an incomplete view of reality, similar to shadows on a cave wall. Neurosurgeons, however, can “escape the cave” by directly observing the body’s internal structures during surgery. The chapter highlights the advancements in magnification and visual augmentation tools, such as operating microscopes, endoscopes, and exoscopes. These tools have significantly improved surgical precision and outcomes. Fluorescent molecules such as fluorescein and 5-ALA enhance the surgeon’s ability to distinguish between normal and abnormal tissues. The chapter also explores the future potential of augmented reality (AR) and virtual reality (VR) in neurosurgery, which could further revolutionize surgical practices by providing enhanced visualization and planning capabilities.

The Channel Alliance participants use a wide variety of channel sounder architectures. Most fall into three major categories: VNA-based, correlation-based, and FMCW (chirp). Each is described briefly here. As well, because many channel sounder architectures rely on precise timing between the transmitter and the receiver or between antenna positions, there is a section devoted to synchronization techniques used by participants.

Path-loss models are the most widely used channel propagation models. This stems both from their simplicity and their direct application to link-layer analysis. This section provides an overview of path-loss models with concentration on models specific to mmWave systems.

When designing a wireless communication system, it is essential to have a channel model that can quickly and accurately generate channel impulse response needed for system simulations. Deterministic models such as ray-tracing offer an accurate model of the propagation environment, but their high computational complexity prohibits the intensive link or system-level simulations required during system design. Hence, models with lower computational complexity that could emulate a large class of radio-propagation environments are preferred. These requirements have led to stochastic channel models, which are often classified into geometry-based stochastic models (GSCMs) and nongeometrical stochastic models. In this chapter we focus on the GSCM models.

In this chapter we present introductions and some recent progress of clustering and tracking algorithm designs for use in radio channels, which have been widely used in cluster-based channel modeling for 4G and 5G communications.

Human blockage causes temporal variations to radio channels when a mobile device is in motion and some plane waves constituting the radio channels are blocked by a human body. Even when two sides of communications are static, moving human bodies often shadow some plane waves, leading to time-varying radio channel responses. Shadowing of plane waves due to human bodies makes the shapes of the Doppler spectrum significantly different for the stationary and mobile links. The main task of modeling human blockages is therefore to choose reasonable properties of the blocking objects. This chapter covers human blockage models with different shapes and material properties of the blocking objects, with mathematical representations to estimate the shadowing losses in addition to free-space losses of a plane wave.

The purpose of any propagation channel model is to represent the essential physical propagation effects that influence system design and performance, without getting swamped by irrelevant details. Thus, while the propagation channel itself is independent of any system that operates in it, channel models do depend on the system. The focus of this part of the book is to give a survey of the state of the art in channel modeling, covering the main modeling methods that have been presented in the literature.

The parameters of channel models are traditionally determined using measurements. Thus, it is critical to understand the connection between channel measurements and channel models. This will address two important questions: (1) How to use the channel measurements in estimating model parameters? Or how to fit the models to the measurements? And (2) what kind of measurements we need to make to measure the various model parameters. Finally, understanding this relationship will also help in propagating the uncertainties in channel parameters obtained from the measurements in the models. This chapter starts with a general physical model which is a specialization of the general model introduced in Chapter 2 to the practical case of uniform linear arrays. A sampled representation of the physical model serves as a starting point for connecting measurements and models, followed by techniques for high-resolution parameter estimation that can improve on the sampled representation.

Device-to-device (D2D) radio channels have fundamentally different properties compared to those of conventional cellular (device-to-infrastructure, D2I) channels. The main reason for this is that most often both the receive antenna and the transmit antenna are located at low heights, and hence there is more interaction with objects in the close neighborhood of the devices. Also, UE mobility, human presence, and finite multipath persistence are the principal factors that degrade link availability. Such models are the focus of this chapter.

Frequencies from 100 GHz to 3 THz are promising bands for the next generation of wireless communication systems because of the wide swaths of unused and unexplored spectrum. Terahertz wireless communications have two key advantages that can be combined to achieve very high data rates. First, the usable frequency band around each frequency is much larger, so each channel can have a much higher data rate. This alone can increase data rates to several hundreds of Gbit/s, but spatial multiplexing is still needed to reach Tbit/s data rates. Fortunately, THz frequencies allow smaller antennas and antenna spacing, which provides for more communication channels within the same array aperture within a chip package. However, to unlock THz wireless communications potential, several challenges in channel measurements and modeling need to be addressed, including antenna design, diffraction, reflection, and scattering. This chapter covers what is known to date in this new area.

Channel sounder verification ensures that participants measure and report channel characteristics that are due to the environment as opposed to measurement artifacts arising from the use of a suboptimal configuration, from nonidealities in the sounder hardware, or from errors in analysis and/or postprocessing. The participants in the 5G mmWave Channel Model Alliance have established a channel sounder verification program. The program allows labs to compare their measured, processed data to theory or to an artifact having known characteristics. Three types of verification are illustrated: “in-situ,” “controlled condition,” and “comparison-to-reference” verification.