Dynamic Spectrum Access Decisions. George F. Elmasry

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      1 Avoid rippling effects. Rippling effects here means that the machine‐based decision‐making processes switches a user or a network from frequency f1 to frequency f2 only to decide quickly to switch back from f2 to f1. This rippling effect can reduce the throughput efficiency of the network and diminish the optimized use of the spectrum resources pool of frequency.

      2 Consider traffic demand. In a large‐scale set of heterogeneous networks, centralized DSA decisions can be more successful in allocating more frequency bands to networks where there is high traffic demand. This will increase the overall throughput of the managed networks. With DSA, spectrum is a commodity that can be distributed to overcome interference and resources should be increased where demand increases.

      3 Consider secondary user rules. If the system being designed is for an opportunistic use of spectrum as a secondary user, the design has to adhere to the secondary user rules. Secondary user rules give the primary user the first right to spectrum use and spectrum sensing has to always be on the lookout for primary user activities. Secondary user rules are discussed in more details in Chapter 2.

      4 Consider hidden node effects. The challenges associated with hidden nodes are discussed in the next chapter. Being able to sense spectrum use from different locations helps avoid this challenge. In a hybrid system with a centralized spectrum arbitrator and using a large set of spectrum sensors, the hidden node challenge can be mitigated effectively.

      5 Consider the hierarchy of response times mentioned above. Local decisions are fast, distributed decisions are slower and more insightful, centralized decisions are intended to be long lasting. A hybrid DSA decision system design has to consider making the different DSA hierarchal levels work in harmony.

      6 Avoid conflicts with other decision‐making processes. This challenge can surface as SDR and SDN allow the incremental addition of software modules that can be prone to making contradicting decisions. Consider this example: a software module makes adaptive power control decisions. This module can obtain SNIR information from the physical layer and decides to increase power to overcome low SNIR assuming that the peer node, from which it received a signal with a low SNIR, is suffering from the same degradation reciprocally. In the meantime, a DSA agent obtaining the same SNIR metric attempts to switch frequency to overcome the low SNIR. There are pros and cons for each of these decisions. Increasing spectrum emission power can quickly fix the low SNIR challenge but may reduce frequency reusability in a large‐scale set of networks. Switching to a new frequency can also address the low SNIR challenge radically but can be a greedy decision. The new frequency may have been more needed by a neighboring network. The main point here is for the designer to consider how SDN and SDR can have other software modules that address spectrum conflicts from different angles and make sure DSA decisions are made in harmony between all spectrum access techniques. Chapter 5 explains the value of using a generic cognitive engine skeleton at all entities of DSA decision making with a single information repository and a single decision maker. This skeleton design ensures that a single process makes all the DSA‐related decisions (i.e., offers all DSA services) at any given DSA service entity.