Dynamic Spectrum Access Decisions. George F. Elmasry
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The chapters of this book are organized to help the reader make design decisions that can optimize the performance of DSA services and avoid the pitfalls mentioned above. For example, if you are designing a distributed cooperative DSA system, there could be room for local decisions and optimum local fusion and optimization of DSA control traffic volume. If you are designing a centralized DSA system, optimum hierarchical decision making can lead to more efficient spectrum management of heterogeneous networks than relying mainly on centralized decisions.
1.6 Concluding Remarks
DSA solutions can differ drastically from one system to another. However, there are common foundations that can be used in any DSA design approach. The goal is always the same: optimize the use of a given pool of spectrum resources dynamically and react to changes in environments. With this book laying some theoretical foundations of DSA design, addressing the most generic model of DSA, and then showing with case studies how this generic model can be applied to different cases, the reader should be able to obtain knowledge on how to approach DSA and how to create design concepts for any system under consideration. The separation of the physical layer aspects of DSA from the cognitive engine aspects in this book should help the reader address the design aspects of the physical layer separate from the design aspects of cognitive engines such that DSA can be applied to noncognitive systems and cognitive systems and the design can address any system requirements and boundaries.
As the reader goes through the rest of this book, the many facets of DSA will become clearer and the reader will see how a large system of heterogeneous networks may require using heuristic approaches, overlaying of decisions‐making processes, and considering many aspects and tradeoffs to design an effective system.
Exercises
1 Consider a hybrid DSA system you are designing with 10 mins minimum elapsed time before changing frequency assignment for a large‐scale set of heterogeneous networks. One of these heterogeneous networks has distributed agents in its nodes that make DSA decisions for the network and these decisions can take up to 200 ms due to spectrum sensing information propagation time and processing time. If you are asked to come up with a minimum time interval for a local agent to make a local DSA decision (time elapse after making a local DSA decision before you can consider making another one), which of the following time intervals would you choose?200 ms100 ms20 msExplain the reason for your choice.
2 In a hybrid DSA system, would you want a local DSA decision (e.g., power increase) to be propagated to peer agents that make distributed decisions? Explain your reason.
3 You are given a set of heterogeneous wireless networks with one network that operates at a narrowband of 100 kbps. Your analysis of DSA shows that DSA control traffic (e.g., spectrum sensing information and other DSA configuration parameters) would use about 30 kbps over this narrowband network. Would you proceed with your DSA design or consider assigning the narrowband network a fixed frequency band to operate at? Explain the reason for your choice.
Bibliography
1 Gandetto, M. and Regazzoni, C., Spectrum sensing: A distributed approach for cognitive terminals. IEEE Journal on Selected Areas of Communications, vol. 25, no. 3, pp. 546–557, April 2007.
2 Haykins, S., Cognitive radio: brain empowered wireless communications. IEEE Journal on Selected Areas in Communications, pp. 201–220, February 2005.
3 Hoffman, H., Ramachandra, H.P., Kovács, I.Z. et al., Potential of dynamic spectrum allocation in LTE macro networks. Advances in Radio Science Open Access Proceedings, vol. 13, pp. 95–102, 2015.
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5 Hu, R. and Qian, Y., An energy efficient and spectrum efficient wireless heterogeneous network framework for 5G systems. Proceedings of the IEEE Communications Magazine, vol. 52, issue 5, May 2014.
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7 Mitola, J., Cognitive Radio Architecture, in Cognitive Networks: Towards Self‐Aware Networks (ed. Q.H. Mahmoud). John Wiley & Sons, Chichester, 2007. doi: 10.1002/9780470515143.
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9 http://grouper.ieee.org/groups/dyspan/ .
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Notes
1 1 Part 4 of this book contains the DySPAN standards. DySPAN is selected over SAS in this book because it contains API design approaches that complement the goals of this book.
2 2 There are different types of DSA services that can be offered in a set of DSA cloud services. Part 2 of this book shows that co‐site interference avoidance can be a collection of DSA services that incrementally increases the efficiency of frequencies assignments. The consideration of blanking signal, which ensures the accuracy of sensing information, can be another type of DSA service.
3 3 DSA decisions are often needed when the radio frequency signal is compromised. The DSA control plane conditions may or may not be compromised at that time. When the DSA control plane is compromised, DSA services should be available. The response time between requesting a DSA service and granting the service should not be dependent on the DSA control plane conditions.
4 4 Some military networks can have an antijamming mode. A DSA decision can be switching from a high bandwidth normal mode to a lower bandwidth antijamming mode instead of changing the operating frequencies.
5 5 Order of time here means response time and the gap of time between two consecutive DSA service requests.
6 6 Abstracting spectrum sensing information is covered in detail in Chapter 4.
Chapter 2 Spectrum Sensing Techniques
In order for a DSA wireless system to observe, orient, decide, and act as explained in the previous chapter, it must be aware of the spectrum sensing parameters and how they relate to the sensed frequency band characteristics. This chapter addresses the different spectrum sensing techniques that can be utilized. The spectrum sensing techniques covered in this chapter are presented in a generic way while pointing to which techniques can be implemented on specialized hardware and which techniques can be implemented as same‐channel for in‐band sensing. Notice that the focus of this chapter is not limited to spectrum sensing performed for a secondary user to use some spectrum bands opportunistically. As the previous chapter explained, there are many DSA cases in defense and commercial applications that go beyond the secondary user scope and are not necessarily in the cognitive radio domain. The focus of this chapter is also not limited to spectrum sensing techniques that can be developed for distributed cooperative MANETs. The spectrum sensing techniques explained here are generic and can be used for local decisions, distributed cooperative decisions, centralized decisions, or hybrid decisions,