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Cognitive Radio Networks offers solution of spectrum insufficiency, In CRNs licensed spectrum channels are used by licensed user (Primary users) and unlicensed users (Secondary users) such that secondary user (SU) does not harm and interfere activities of primary user (PU). Spectrum Decision System is required for intelligent spectrum sensing, access and distribution between PU and SU. Cooperative spectrum sensing (CSS) can reduce the overhead on signal processing techniques and enable the SUs for reliable detection of PUs activity. Spectrum availability in CSS approach node makes a binary decision based on its local observation and then forwards its decision to the fusion center (FC). At the FC, all these decisions are merged together according to some fusion rule. In this paper, a frameork for cooperative SUs is presented, which have to decide about the presence or absence of the PU in target spectrum. The performance of the framework is then evaluated and it is observed that it outperforms different commonly used routing scheme. It ensures to sufficiently increase the probability of correct detection and decrease in the probability of false alarm.

In this paper, we present an efficient hardware mapping methodology for realizing vector matrix multiplication (VMM) on resistive memory (RRAM) arrays. Using the proposed VMM computation technique, we experimentally demonstrate a binarized-ADALINE (Adaptive Linear) classifier on an OxRAM crossbar. An 8×8 OxRAM crossbar with Ni/3-nm HfO2/7 nm Al-doped-TiO2/TiN device stack is used. Weight training for the binarized-ADALINE classifier is performed ex-situ on UCI cancer dataset. Post weight generation the OxRAM array is carefully programmed to binary weight-states using the proposed weight mapping technique on a custom-built testbench. Our VMM powered binarized-ADALINE network achieves a classification accuracy of 78% in simulation and 67% in experiments. Experimental accuracy was found to drop mainly due to crossbar inherent sneak-path issues and RRAM device programming variability.