Declaration 2 Publications
2.5 Conclusion
In this chapter, a survey/overview of the research paradigm called cognitive radio networks was presented. In the same vein, what makes it cognitive is embedded in its capabilities to: (1) intelligently sense the primary user’s channel to avoid collision (interference) using appropriate sensing techniques;
(2) dynamically and opportunistically access the primary user’s spectrum before the PU arrives (overlay) or coexist with the PU with limited interference (underlay); (3) make decisions based on both its radio environment and neighboring users; (4) centrally or otherwise share spectrum resources among each other to avoid starvation of weaker users; (5) hop to another free channel/spectrum if it cannot coexist with the PU and (6) assemble/aggregate spectrum resources (white space) across frequency and time domain to improve SU transmissions as shown in Figure 2.7. The essences of cognitive radios are to identify and use PU’s resources optimally. But, this identification and reuse of the PU resources is made possible by capitalizing on its erratic ON/OFF behaviours across the spectrum. The next chapter
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investigates the various assumed or commonly characterized ON/OFF behaviours and it impacts on SU performance in cognitive radio networks.
Figure 2.7 Aggregating of TV white space (spectrum holes) for SU services
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CHAPTER THREE
Primary Users ON/OFF Behaviour and Its Impact on Secondary User Channels in Cognitive Radio Networks
In the previous chapter, the PU behaviour has been characterized as ON/OFF. This behaviour has resulted in the inefficient usage of the scarce spectrum resources. In this chapter, two important aspects of cognitive radio network will be discussed and thus, this chapter is divided into two parts. The first aspect investigates the primary users ON/OFF behaviours while the second part focused on the interaction impact caused by the PU on the secondary channels in a centralized cognitive radio network using an overlay performance analysis. The first part investigates and compares the commonly assumed ON/OFF behaviours while in the latter part, an analytical approach was proposed to analyze the opportunistic spectrum access strategy (OSAS) with different occupancy statistics [34], [102], [103].
The average service time, throughput and time delay were the metrics used for the performance evaluation.
Part one is organized as follows: In section 3.1, introduction and related works are presented. System description and investigation of the PUs ON/OFF behaviours is described in section 3.2. Three primary users ON/OFF distribution are discussed section 3.3. The simplified flow chat/algorithm of the various primary users ON/OFF behaviours is presented in section 3.4 while numerical results and discussion is found in section 3.5. Part one of this chapter is summarized in section 3.6.
Part two of this chapter is organized as follows: introduction and closely related works are presented in section 3.7 and 3.8 respectively. System/network models and assumptions are presented in section 3.9.
System analytical model is found in section 3.10 while performance measures are discussed in section 3.11. System algorithm/flow chart is in section 3.12. Numerical results and discussions are presented in section 3.13 while the investigation is concluded in section 3.14.
3.1 Introduction
Recent research has established that part of the cause of spectrum scarcity is the underutilization of some spectrum bands (licensed band) [104] . However, this is ascribed to PUs ON/OFF behaviours, giving rise to TVWS. In this regard, cognitive radio technology (CRT) was recommended as a model for identifying and utilizing of TVWS across domain. This is made possible through the deployment of a dynamic spectrum access (DSA) scheme which is another promising access strategy for reclaiming some of the licensed spectrum that is not optimally unused [17], [25]. In addition, CRT has been
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proposed to deploy OFDMA system for spectrum access, while OFDM for resource distribution and interference cancellation/avoidance, sensing that the PU activities are imperative. This is to ensure good performance for both the PUs and the SUs. Thus, understanding and predicting the statistical behaviour of the PUs is also a key to a reliable and effective sensing process. The behaviour of PUs has been modelled as: ON/OFF Markovian, exponential and geometric respectively [37], [105, 106, 107, 108].
This part of this thesis focuses on investigating the PUs activity in relation to these three commonly assumed distributions. Also, to evaluate and establish which of these is/are relatively stable, so as to give the SUs the opportunity to utilize the OFF or idle channel- slots since this is of utmost concern to the SU. Since it has now been established that CRT is an efficient means to maximize bandwidth for next generation networks, coupled with the fact that radio resources are facing scarcity owing to speedy growth in multimedia application and service [105], [109]. Then, there is need to investigate and compare the commonly assumed PUs ON/OFF distributions which this part of the thesis has achieved.
The essence is to establish the level of available and stable of spectrum resources in terms of unused/idle channel-slots based on the distributions.
In Markovian process, two factors determine the ON/OFF states. They are: the state probability matrix and the state transition matrix [110]. While the exponential ON/OFF process is a random process which depends on ON/OFF mean time , the mean time determines the ON/OFF duration (how long), thus, serving as input to the function for generating random ON/OFF period thus, depicting a typical PU behaviour [37]. The geometric ON/OFF distribution is a stochastic process which depends on the probabilities of the ON/OFF epoch [108]. The contribution of this research work gives insight into spectrum availability pattern in different ON/OFF activity models (distribution).