# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 2 Cognitive Radio Networks
### Cognitive Radio Functions
#### 2.1.1 Spectrum Sensing  
Identifying available spectrum using spectrum sensing algorithms is a fundamental functionality in CR networks. By providing CR with the spectrum sensing information, CR becomes aware of the adjustments in its encompassing surroundings and can adapt itself to those changes. The research community has proposed different techniques for spectrum sensing to identify and detect a signal transmission. These techniques can be grouped into three categories; namely, matched filter coherent detection [33; 34], energy detection [35; 36; 37] and cyclostationary feature detection [38; 39]. Energy detection is the technique most used because it presents low computational and implementation complexities. Assume that in a communications channel, a transmitted signal from a PU, \(s(n)\), experiences Additive White Gaussian Noise (AWGN) \(w(n)\), where \(n\) is the sample index, then the received signal by an SU, \(y(n)\), has the following form:  
\[y(n)=s(n)+w(n) \tag{1}\]  
Note that when the PU is not transmitting, \(s(n)=0\). For accurate spectrum sensing, CR devices have to measure the power of the PUs signal alone [40]. Therefore, channel estimations must be obtained when other CR devices are not transferring, which requires coordination between CR users. Achieving this coordination is a difficult task due to the lack of synchronization in the network and the relative positions and separation distances among CR devices. In addition to the coordination, the definition of the times for sensing and transmission is an essential trade-off between guaranteeing protections to the PUs and expanding the information throughput at the MAC layer. Spectrum sensing is firmly identified with other range administration capacities and additional layering protocols (PHY and MAC) to give data on spectrum accessibility [41].  
Each individual node experiences many factors affecting its performance for the spectrum sensing functions, such as shadowing, fadings due to multipath effects and the receiver uncertainty problem [42; 43]. However, to overcome unreliable observations from individual nodes, cooperative decision making with observations collected distributively in space of each individual node can be pursued [44; 45]. This is usually referred to as the cooperative spectrum sensing method, which combines the spectrum information from local sensing of multiple CRs in order to minimize the sensing error. There are several works in the literature that show how cooperation can improve the performance of the overall spectrum sensing and maximize the probability of detecting the PU [46; 47]. Cooperative sensing has also been proven to overcome and mitigate the problem of shadowing, fading and the receiver uncertainty problem.  
Figure 2: Spectrum management process.  
In cooperative spectrum sensing, each node needs to sense the channel and later report the information to a central entity or share it with its neighbors. Considering the multipath propagation environment between the PU and CRs, fading in sensing channels is usually Rayleigh distributed with AWGN. Usually, the channel used by the nodes to report the sensing information is assumed to be error-free, [48; 49; 50]. However, in practice, as the reporting channel also has noise and interference, the common receiver receives the cooperative decisions with error.