On Fuzzy Logic-Based Channel Selection in Cognitive Radio Networks

On Fuzzy Logic-Based Channel Selection in Cognitive Radio Networks

Yong Yao (Blekinge Institute of Technology, Sweden), Alexandru Popescu (Blekinge Institute of Technology, Sweden) and Adrian Popescu (Blekinge Institute of Technology, Sweden)
DOI: 10.4018/978-1-4666-6571-2.ch010
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Abstract

Cognitive radio networks are a new technology based on which unlicensed users are allowed access to licensed spectrum under the condition that the interference perceived by licensed users is minimal. That means unlicensed users need to learn from environmental changes and to make appropriate decisions regarding the access to the radio channel. This is a process that can be done by unlicensed users in a cooperative or non-cooperative way. Whereas the non-cooperative algorithms are risky with regard to performance, the cooperative algorithms have the capability to provide better performance. This chapter shows a new fuzzy logic-based decision-making algorithm for channel selection. The underlying decision criterion considers statistics of licensed user channel occupancy as well as information about the competition level of unlicensed users. The theoretical studies indicate that the unlicensed users can obtain an efficient sharing of the available channels. Simulation results are reported to demonstrate the performance and effectiveness of the suggested algorithm.
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Background And Motivation

In CR networks, the licensed channels are either exclusively reserved for PUs or temporarily used by SUs. Extensive research has been done to develop the concept of CR, based on which the SUs are allowed to access the available channels (also known as spectrum holes) not being occupied by PUs. Moreover, when the PU occupies a channel, the SU in the same channel must leave. Otherwise, the PU transmission would be impaired.

Since PUs do not need to notify SUs of their activities, a time-slotted transmission scheme is suggested for SUs to communicate in CR networks. In this scheme, the SU's transmission is divided into identical slots over time (Zhao, Tong, Swami, & Chen, 2007). During each slot, the SU first performs spectrum sensing to detect channel availability. The SU may then transmit data via an available channel (if it exists) within the remaining slot duration. Further, to alleviate the interruption from PUs, SUs need to learn from the statistical information about PUs' activity and, based on that, to select the most available channels to use. An existing solution along with this line is given by using the idle-time-based statistics. For a single channel, being idle indicates the PU absence and the idle time indicates how long this absence is. Yang et al. (2007) consider in their work that the longer an available channel remains idle in the near future, the higher the channel availability becomes. Further, by predicting the idle time, the most available channel is attributed to the characteristic of having the longest remaining idle time.

Key Terms in this Chapter

Spectrum Decision: Since multiple spectrum opportunities may be obtained by a particular SU at a time, this SU needs to decide which channel should be selected for the use in the near future. The selection process is often referred to as spectrum decision.

Secondary User (SU): The unlicensed mobile user who is allowed to use spectrum opportunities as long as not harmfully interfering with PUs.

Primary User (PU): The licensed mobile user who is authorized to exclusively use the licensed spectrum.

Spectrum Opportunity: If and when a spectrum band is not used by PUs, it becomes available for the use by SUs, so-called a spectrum opportunity for SUs.

Cognitive Radio Networks (CRNs): The novel communication paradigm of Cognitive Radio leads to an enabling framework “CR Networks”, which is first suggested by the Federal Communication Commission (FCC).

Competition Problem: The competition problem is created by that multiple SUs select and use the same spectrum opportunity during the same time period.

Cognitive Radio (CR): It is envisioned to act as a highly intelligent radio unit where transmission parameters like frequency range, transmit power and modulation type are altered by learning the radio environment.

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