A review on spectrum sensing for cognitive radio: challenges and solutions <s> Decision Fusion. <s> 1 Introduction.- 1.1 Distributed Detection Systems.- 1.2 Outline of the Book.- 2 Elements of Detection Theory.- 2.1 Introduction.- 2.2 Bayesian Detection Theory.- 2.3 Minimax Detection.- 2.4 Neyman-Pearson Test.- 2.5 Sequential Detection.- 2.6 Constant False Alarm Rate (CFAR) Detection.- 2.7 Locally Optimum Detection.- 3 Distributed Bayesian Detection: Parallel Fusion Network.- 3.1 Introduction.- 3.2 Distributed Detection Without Fusion.- 3.3 Design of Fusion Rules.- 3.4 Detection with Parallel Fusion Network.- 4 Distributed Bayesian Detection: Other Network Topologies.- 4.1 Introduction.- 4.2 The Serial Network.- 4.3 Tree Networks.- 4.4 Detection Networks with Feedback.- 4.5 Generalized Formulation for Detection Networks.- 5 Distributed Detection with False Alarm Rate Constraints.- 5.1 Introduction.- 5.2 Distributed Neyman-Pearson Detection.- 5.3 Distributed CFAR Detection.- 5.4 Distributed Detection of Weak Signals.- 6 Distributed Sequential Detection.- 6.1 Introduction.- 6.2 Sequential Test Performed at the Sensors.- 6.3 Sequential Test Performed at the Fusion Center.- 7 Information Theory and Distributed Hypothesis Testing.- 7.1 Introduction.- 7.2 Distributed Detection Based on Information Theoretic Criterion.- 7.3 Multiterminal Detection with Data Compression.- Selected Bibliography. <s> BIB001 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Decision Fusion. <s> In cognitive radio networks, the secondary users can use the frequency bands when the primary users are not present. Hence secondary users need to constantly sense the presence of the primary users. When the primary users are detected, the secondary users have to vacate that channel. This makes the probability of detection important to the primary users as it indicates their protection level from secondary users. When the secondary users detect the presence of a primary user which is in fact not there, it is referred to as false alarm. The probability of false alarm is important to the secondary users as it determines their usage of an unoccupied channel. Depending on whose interest is of priority, either a targeted probability of detection or false alarm shall be set. After setting one of the probabilities, the other can be optimized through cooperative sensing. In this paper, we show that cooperating all secondary users in the network does not necessary achieve the optimum performance, but instead, it is achieved by cooperating a certain number of users with the highest primary user's signal to noise ratio. Computer simulations have shown that the Pd can increase from 92.03% to 99.88% and Pf can decrease from 6.02% to 0.06% in a network with 200 users. <s> BIB002 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Decision Fusion. <s> In this letter, we consider cooperative spectrum sensing based on energy detection in cognitive radio networks. Soft combination of the observed energies from different cognitive radio users is investigated. Based on the Neyman-Pearson criterion, we obtain an optimal soft combination scheme that maximizes the detection probability for a given false alarm probability. Encouraged by the performance gain of soft combination, we further propose a new softened hard combination scheme with two-bit overhead for each user and achieve a good tradeoff between detection performance and complexity. <s> BIB003
In decision fusion, each user sends its one-bit or multiple-bit decision to a central processor, which deploys a fusion rule to make the final decision. Specifically, if each user only sends one-bit decision ("1" for signal present and "0" for signal absent) and no other information is available at the central processor, some commonly adopted decision fusion rules are described as follows BIB002 . (1) "Logical-OR (LO)" Rule: If one of the decisions is "1," the final decision is "1." Assuming that all decisions are independent, then the probability of detection and probability of false alarm of the final decision are , respectively, where P d,i and P f a,i are the probability of detection and probability of false alarm for user i, respectively. BIB001 . The probability of detection and probability of false alarm of the final decision are respectively. Alternatively, each user can send multiple-bit decision such that the central processor gets more information to make a more reliable decision. A fusion scheme based on multiple-bit decisions is shown in BIB003 . In general, there is a tradeoff between the number of decision bits and the fusion 8 EURASIP Journal on Advances in Signal Processing reliability. There are also other fusion rules that may require additional information BIB001  . Although cooperative sensing can achieve better performance, there are some issues associated with it. First, reliable information exchanges among the cooperating users must be guaranteed. In an ad hoc network, this is by no means a simple task. Second, most data fusion methods in literature are based on the simple energy detection and flat-fading channel model, while more advanced data fusion algorithms such as cyclostationary detection, space-time combining, and eigenvalue-based detection, over more practical propagation channels need to be further investigated. Third, existing decision fusions have mostly assumed that decisions of different users are independent, which may not be true because all users actually receive signals from some common sources. At last, practical fusion algorithms should be robust to data errors due to channel impairment, interference, and noise.