## 4 Media Access Control
### Cooperative Sensing
#### 4.2.3 Decision Formulation  
Since the concept of cooperative spectrum sensing is ultimately reflected by the fact that adaptive decisions regarding spectrum are made by considering information of all cooperative peers, formulating final decisions upon the acquisition of collective knowledge is another critical aspect of MAC cooperative adaptation. Decision-making schemes can be broadly classified as soft fusion or hard combination according to the type of data involved [20]. In networks using soft fusion, cooperative nodes report their raw spectrum-sensing data to the decision-making entities such as cluster-heads (CHs), where adaptive decisions are formulated. The exchange of complete raw data, however, incurs overhead in terms of bandwidth, as well as computation. Thus, briefly processed data is often exchanged instead [20], which can be seen as a semi-soft approach. For example, [78] deploys a model where local sensing results are converted into integer values. In [59], quantized probability density function (PDF), rather than raw sensing data, are reported to the CHs. With a hard combination [52, 57, 79, 80, 81], contrary to soft fusion, only 1-bit binary information is utilized. Specifically, individual nodes generate tentative local values in the form of \(0\) or \(1\) to indicate decisions such as the detection of a major interference source or the need to change operating frequency. Similar to soft fusion, these local decisions are then reported to the CHs or fusion center, where they are analyzed to yield the final judgments [82]. A hard combination scheme is sometimes described as decision counting and generalized as _n-out-of-M_ rules, where \(M\) is the total number of local decisions and \(n\) denotes the final decision-making threshold [83]. From this model derive the two most straightforward and commonly adopted strategies, namely the OR-rule and the AND-rule. The OR-rule [57, 79, 84] arbitrates the final decision of \(1\) if any of the received local sensing decisions indicate that value. Hence, the OR-rule corresponds to the situation where \(n=1\) in \(n\)_-out-of-\(M\)_. As a contrast to the OR-rule, the AND-rule produces \(1\) only if all \(M\)_out of \(M\)_ local decisions agree on such a discovery [85]. It is argued in [86] that the choice between the two rules depends on the threshold used by individual nodes. According to the findings, the OR-rule provides good performance if the threshold is considerably high, whilst the AND-rule tends to be optimal in the case of a sufficiently low threshold. There are also a multitude of alternative combination techniques. For instance, analysis presented in [83, 86] suggests that for most of the cases, the combination criterion of \((M/2)\)_-out-of-\(M\)_ provides the optimal outcome. For either of the two approaches, the decision-making entities are able to apply weights to individual local results rather than giving them uniform significance to make refined decisions [54]. A number of criteria for determining weights were reported in the literature. Device locations [87] and signal-to-noise ratio (SNR) observed by the users [88] are among the basic attributes that can be associated with the weights. Additionally, more complex statistics such as the historical consistency between local sensing results and final decisions [58, 89] can help determine the reliability of certain nodes. Based on these records, local decisions from reputable users are granted greater weight, whilst users that tend to produce inconsistent results are assigned less significance. Both soft fusion and hard combination have their advantages and disadvantages. With soft fusion, the most comprehensive spectral information at each node is exposed to the fusion process. This inherent richness of information associated with the raw data enables more sophisticated analysis to be accomplished and thus may lead to a greater chance of more accurate detection and advantageous performance over hard combination [52, 85].