**10. Future work**

Future work may include:

• AOD/AOA Measurement

The angular-delay spectrum is an important parameter in the modeling of the state-space-based MIMO-OFDM channels. In practice, how to measure the directional information will directly affect the results of a realistic channel correlation accuracy. On the other hand, the effective channel modeling largely relies on well-defined correlation functions.

• AOD/AOA Estimation

Extracting or estimating AOD/AOA from measurements is another issue. This is a hot research topic that attracts people. Many results have been published in the literature, for example, the multiple signal classification (MUSIC) algorithm [27, 28], the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm [28, 29], the expectation-maximization (EM) algorithm [30, 31], and the space-alternating generalized expectation-maximization (SAGE) algorithm [31, 32].

Several issues related to these algorithms need to be addressed, for example, how to estimate the number of signal sources and estimate the arbitrariness of the DOA. In addition, these algorithms do not work when the number of signal sources is larger than the number of antennas. The recurrent neural network (RNN) and convolutional neural network (CNN) may be suitable to solve this problem.

• Reduce Simulation Complexity

In simulations, the computation complexity depends on the size of the antenna arrays *Mr Mt*, the number of sub-carriers *Mf* , and the number of uncorrelated scattering clusters *K*.

For each *Mr Mt* block, we may assign a small number to *Mf* and use the interpolation technique to increase the size of the channels. This idea makes sense because the contributing channels have high coherence bandwidth, which renders close to flat fading.

In this way, the size of a spectral correlation matrix and the computational complexity in a simulation will be highly reduced. Hence, the problem of decomposition of the spectral correlation matrix using the Cholesky decomposition method may be avoided. For the large size of the matrix, the Cholesky decomposition method may lead to numerical problems.

• Massive MIMO

The massive MIMO technology uses a large number of antennas at the BS to serve multiple users simultaneously. It is proposed to improve the performance of wireless communication systems, such as higher data rates, improved spectral efficiency, and better link reliability. Due to the large number of antennas, the propagating wave will no longer be a plane wave. That is, the spherical wave model for near-field should be taken into account. In this case, a mathematical model describing the radio channel characteristics is needed.

• Channel Generators

The spatial channel model (SCM) [33] and the WINNER II [34] are channel models used in wireless communication systems. They are designed to simulate the propagation of radio waves in different environments and are used for evaluating and testing the performance of wireless communication systems.

They are good channel models and have been used in many radio propagation scenarios [35–37]. However, the scatterers in both SCM and WINNER II are limited and they cannot be used to describe situations such as the propagation of a large number of signal sources, i.e., the presence of a large number of scatterers in the propagation environments.

The channel model presented in this chapter can be employed to describe the situations of a large number of scattering objects in the radio wave propagation environment and to evaluate the performance of the designed wireless communication systems.
