COGNITIVE RADIO-IMPROVED GROUPING TECHNIQUE USING MACHINE LEARNING AND COOPERATIVE SPECTRUM SENSING
DESIGN DETAILS
Cognitive networks can potentially solve the problems of spectrum scarcity by accommodating unlicensed (secondary) users in under-utilized segments of the spectrum. Spectrum sensing serves as the primary stimulus for a cognitive radio and is vitally important for ensuring that the unlicensed users do not offer intolerable levels of interference to licensed (primary) users. Cooperative spectrum sensing provides the capability to cognitive networks to overcome problems related to “hidden” primary users. Cooperative spectrum sensing (CSS) is an environment where two or more spectrum sensing nodes, that form part of a CR network, combine their spectrum sensing capabilities leading to centralized or decentralized decision fusion. CSS allows individual nodes to gain a more global degree of awareness about spectrum occupancy. It also has the inherent advantages of increased levels of agility as well as greater accuracy due to the ability to detect a primary user (PU) that is obscured to a sub-set of sensing nodes due to channel behaviour. CSS must be considered in the context of increased communication overhead. If the inherent advantages of CSS are more important as compared to the cost of overhead, then it is a viable trade-off.
In this Matlab design, two CSS grouping model are developed for performance analysis which are discussed below.
DESIGN-1 CSS GROUPING
The actual objective of grouping is to enable the employment of a tasking mechanism within groups. The grouping mechanism, itself, has ensured that maximum information gain is obtained through sharing of aggregated sensed spectra between groups. We therefore propose the following tasking mechanism for grouped CSS nodes:
(a) A group decision fusion centre (GDFC) is defined for each group.
(b) The GDFC is informed about the members of its group by the CDFC.
(c) Each GDFC divides the entire spectrum to be sensed into equal parts according to the number of group members thus creating equal sub-bands within the group.
(d) These non-overlapping sub-bands are sensed by the tasked SU according to the instructions of the GDFC.
(e) Each GDFC fuses the results obtained from each member SU and passes the aggregated result to the CDFC.
(f) The CDFC fuses the results from each GDFC to obtain a global picture of the sensed spectrum.
(g) Fusion of results is optimized by reduction of vector size through creation of sparse vectors.
DESIGN-2 IMPROVED CSS GROUPING USING MACHINE LEARNING
The primary contributions of this design are to introduce the concept of user grouping into the machine learning based CSS model and propose a new grouping optimisation model-based CSS framework which includes four modules (SVM training module, SVM classification module, user grouping module, and group scheduling module). Three different types grouping algorithms are used to achieve the functions without reducing sensing accuracy.
1. User grouping algorithm 1: Abnormal user grouping algorithm
2. User grouping algorithm 2: Redundant user grouping algorithm
3. User grouping algorithm 3: Optimised user grouping algorithm
The network used in this simulation is a centralised CSS model, composed of a fusion centre and 10 or 20 CR users, where N = 100, K = 10, 000, and σm = 1.
DOWNLOAD THE MATLAB(P-CODE) FOM THE BELOW URL,
https://drive.google.com/file/d/1a3Vm...
REFERENCES
Reference Paper-1: Grouping Technique for Cooperative Spectrum Sensing in Cognitive Radios
Author’s Name: Rizwan Akhtar, Adnan Rashdi and Abdul Ghafoor
Source: IEEE
Year: 2009
Reference Paper-2: Improved cooperative spectrum sensing model based on machine learning for cognitive radio networks
Author’s Name: Zan Li, Wen Wu, Xiangli Liu1 and, Peihan Qi1
Source: IET Journals
Year: 2018
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