for the best multihop path utilizing the r2 impact and SIR threshold shown in this model.
Equation (6.4) makes it possible to consider the transmission capacity problem starting from a simple model.
6.2.3.2 5G Cell Overlay
The model in the previous section and illustrated in Figure 6.7 considers transmission capacity in a given area in a general way. The model sheds light on the metrics a 5G DSM design would consider. However, 5G uses small cells that overlay on existing cellular networks to improve capacity and coverage. Indoor users specifically make use of small 5G cells such as femtocells. The best model for using 5G cells on top cellular networks is the two‐tier model where one tier is a base station and the other is the cells. This overlay model is represented in Figure 6.8 where end users are randomly located. In this model, the density of the 5G cells can be expressed as lC, while the density of the end users can be expressed as lEU. Naturally, lEU ≫ lC. One can extend the model in the previous section to consider other factors. For example, one can state the following:
1 The interference at an end user can be impacted by the interference from neighboring base stations and from the randomly placed small cells.
2 The interference at a small cell can be impacted by all the uplink connections from the end users within a certain vicinity of the small cell.
3 End users transmitting to a base station can use relatively higher power than end users transmitting to a small cell.
4 The aggregate calculated SIR may consider the impact of small cells, base stations, base station users, and cell users.
Figure 6.8 5G cell overlay over cellular base station.
Notice in Figure 6.8 that it is possible for an end user to be in the coverage of a 5G cell but use the base station not the cell because of the cell limited capacity or because of a required transmission rate and quality of service (QoS).
A DSM technique can consider the density of end users in a base station coverage area to create a metric for the transmission capacity needed and to point out the need for more cell deployment within the base station coverage area. Other factors that can be used are the transmission outage estimated from Equation (6.4) and actual measurements of events such as connection denial to an end user can be utilized to increase the cell density.14
Cellular infrastructure pre 5G is fixed. 5G has no fixed infrastructure since cells can be deployed anywhere where demand is needed. Spatial modeling, covered in Section 6.2, is essential to DSM for both the planning and runtime aspects. After deployment, the model can be used for finding out what new features and capabilities a 5G infrastructure needs in order to increase spectrum efficiency.
There is another layer of overlay that can complicate this spatial modeling. If and when 5G deploys nonterrestrial infrastructure, there will be a satellite or a high altitude platform (HAP) that will have a nonterrestrial base station overlaid on top of multiple ground base stations areas. This model, however, is far in the future and beyond the scope of DSM in this chapter.
6.3 Stages of 5G SI Cancellation
The previous sections focused on spatial modeling and evaluation metrics without considering some DSA techniques that can decrease the impact of SIR on the transmission capacity of 5G infrastructure. This section focuses on one important aspect of DSM in 5G, the maximization of SI cancellation. In order to increase the infrastructure capacity, 5G uses different stages of SI cancellation, as shown in Figure 6.9.
Figure 6.9 5G FD communications with different stages of noise cancellation.
Let us consider the following aspects of SI cancellation with FD communications that makes DSM more efficient and possible with the higher frequency bands 5G is utilizing:
1 Directionality. 5G beam forming relying on MIMO antenna technology means the signal is as narrow as possible where the spectrum is concentrated to the receiving node, with minimal spectrum leaks to other transmitting and receiving node pairs using the same frequency.
2 5G MIMO antennas implement SI cancellation using multipath fading analysis stages that reach up to 20 dB gain at both the transmitting and receiving antenna. This is shown in Figure 6.9 as the MIMO antenna cancellation.
3 After using a low noise amplifier, the receiver implements further analog noise cancellation of the RF signal.
4 After the analog to digital converter, the 5G receiver further implements other digital signal noise cancellation techniques.
The 5G protocol stack and the SDR concepts of 5G allow the service provider more flexibility in implementation. Figure 6.10 shows the 5G protocol stack at a higher level. The use of 5G in specific industrial applications has the flexibility of adding software modules that can allow for adapting 5G to the industrial application needs. For example, a service provider can explore using a form of network coding in the open wireless architecture (OWA) layer for further dB gain that can be achieved when using 5G mm‐wave links for long‐range reachability. This technique will trade some bandwidth for additional error control coding redundancy.
Figure 6.10 5G protocol stack.
The reader is encouraged to explore antenna design literature for more details about how 5G MIMO antenna interference cancellation is achieved in the mm‐wave range, which is beyond the scope of this book. However, the next chapter introduces some MIMO techniques that can be considered for adapting 5G for military communications systems.
6.4 5G and Cooperative Spectrum Sensing
Cooperative spectrum sensing was originally explored in the context of CR networks where the secondary user (SU) has the ability to detect spectrum utilization and collect spectrum sensing information either as a standalone radio terminal, cooperatively with peer nodes, or relying on an external spectrum sensing information. This was covered in Part 1 of this book, where energy detection and other sensing techniques that can be used by the SU are explained. Part 1 also covered DSA for heterogeneous MANET networks with some focus on military communications challenges and approaches to solving the challenges of
