readjust resource allocations. These functions can include the following:
A cycle adaptation function that is responsible for managing coexistence with other transmit/receiver pairs. This function adopts the time‐domain utilization pattern.
A listen‐before‐talk (LBT) function, which is tied to the coexistence function through detecting the state of the frequency resource prior to the data transmission. This function manages energy detection, preamble detection, and duration. This function can rely on tunable thresholds to compare the sensing results with.
A frame format function, which is responsible for adapting the MAC frame according to the different active bearers (e.g., uplink versus downlink resource ratio, transmission time interval [TTI], or slot duration modification). This function can also react to the changes in the channel quality, enabling better coexistence without the need for intervention from higher hierarchy entity (e.g., cell).
A contention coordination function, which manages any perceived contention on the utilized frequency bands. This function can adapt the random access schemes and block resources on licensed bands or can use a tunable contention access algorithms on shared spectrum bands.
A multiple access (MA) function, which is responsible for the configuration and adaptation of the use of spectrum resources utilizing orthogonality and nonorthogonal multiple access schemes. Within the cell, this function can manage different active end users given the end‐user locations and QoS requirements.
A sensing function that coordinates between the different sensing mechanisms. This function can create sequential sensing patterns on the different bands while using configurable parameters such as sensing duration, minimum signal detection level, and sampling rate to create effective spectrum sensing information.
Within the cell to end‐user radio access technology (RAT), the above functions make short‐term decisions leaving longer‐term decisions to the centralized function.
Notice that there are other aspects of DSM that have to be considered by the developed DSA technique, including the following:
Traffic demand can be high in one area and low in another area. The centralized arbitrator allocation of spectrum resource blocks to the different RATs in different areas of the network it is managing can take into consideration traffic demand over time. The resource blocks (or the network infrastructure) are essentially a shared service between the different RATs.
The small cell has limitations in handling traffic loads at a required QoS. Throwing more resource blocks to a small cell may be a waste of resources given the small cell limitations.
Ultra‐dense urban deployments can force the DSM central arbitrator to create layered architecture of spectrum sharing. The DSM technique would need to consider the tradeoff between spectrum and network density to optimize the network spectral efficiency of multiple RATs sharing a spectrum resources pool.
Other aspects include the goals of the service provider. Some service providers may market guaranteed QoS for higher prices to attract high paying customers while others may market lower prices with less QoS guarantees to create a mass market. These revenue‐focused aspects will drive DSM implementation in the 5G infrastructure.
6.7 Concluding Remarks
Although the concept of local, distributed, and centralized spectrum sensing decision exists with 5G, one can see how it can differ from the military network examples used in Part 1 of this book. One can also see how the hybrid DSA design can be different in some aspects while others stay the same. Aspects that stay the same include sensing in geographically distributed locations, creating the framework for hierarchical fusions, and sharing of spectrum sensing information in a combined distributed and centralized manner when possible. The aspect of making DSA a cloud service with well‐studied metrics that can be used to evaluate DSA services in real time and in post processing is also common between the many DSA cases.
5G gives the service provider many flexibilities in managing the infrastructure. One can expect a diverse set of DSM techniques to be present in different services providers' networks. However, there is a common theme in these optimization approaches where metrics are used and cognitive techniques are used to approach optimality of spectrum assignment. Relying on a good model for spectrum sharing and using expressive metrics to measure performance is always needed. Also, all service providers are likely to approach DSM as an IaaS set of cloud services, as explained in Chapter 5. One can expect that any service provider approach will have to be hybrid in one way or another and will have to morph based on deployment constraints and real‐time measurements.
In some aspects, DSA for military networks may seem easier than commercial 5G, but the expected use of commercial technologies within military communications systems can complicate military network DSA decisions. The push for military networks to use unlicensed spectrum is another factor that can make military system DSA techniques more challenging.
This chapter presented a sample of DSM techniques for 5G. There is a wealth of literature sources for 5G DSM that further explain the developing of mathematical models for DSM, explain the implementation of these mathematical models, and present optimization techniques in the DSA cognitive engine implementation and MIMO antenna design that can aid DSM optimization techniques. This chapter focused on the core aspects of 5G DSM.
Exercises
1 There are many challenges with directional mm‐wave links. Fog, rain, and other particles can negatively affect the signal. Beam forming, interference management, and using MIMO antennas are a few of the techniques used with mm‐wave design to enhance over‐the‐air performance. The design of MIMO antennae can leverage multipath for further antenna gain. Consider the equation below used to calculate free space path loss (FSPL) in dB based on distance (d), frequency (f), transmitting antenna gain (Gt), and receiving antenna gain (Gr).For the mm‐wave frequency signal (chose any frequency in the mm‐wave range), a distance of 14 km, and a 20 dB gain at both transmitting and receiving antennas, calculate how much free space path loss the signal faces.State some of the options that could be considered to overcome this dB loss.
2 Microwave links are designed as point‐to‐point links between two stationary nodes. Some designs considered using a mechanical gimbal‐based antenna to allow mobile nodes to track each other with point‐to‐point microwave links. For 5G mm‐waves, would you consider using a mechanical gimbal‐based antenna or a massive MIMO antenna? State your reasons for your choice.
3 Assume you are designing a voice call acceptance/rejection algorithm for a 5G cell. Your limitations include the air‐interface (radio) resources limits and the backhaul link capacity limits.Will you consider prioritizing voice over data for both air‐interface and backhaul link resources? State your reasons for this prioritization.Will you control the data rate access for users to allow more users to join in? State some use case(s) where you would consider data limits to increase the number of users versus other use case(s) where you consider limiting the number of users and guarantee access rates.State two different practical scenarios where (i) air–interface resources are the limiting factor in accepting/rejecting a new calls and (ii) the backhauling link capacity is the limiting factor in accepting/rejecting new calls.The Erlang distribution can be used to create a call admission control algorithm in a cellular base station such that there is room for a call handover between two cells and there is room for emergency calls. Without going into too much detail on the Erlang distribution, use Table 6.2 and add columns at the end to give an intuitive estimation of the number of voice calls you would allocate for new calls, the
