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A New Hardware-Efficient Spectrum-Sensor VLSI Architecture for Data-Fusion-Based Cooperative Cognitive-Radio Network

This article presents a hardware-friendly algorithm and architecture for cooperative spectrum sensing (CSS) in the data-fusion-based cognitive-radio (CR) network. The proposed VLSI-algorithm is based on the iterative power method and… Click to show full abstract

This article presents a hardware-friendly algorithm and architecture for cooperative spectrum sensing (CSS) in the data-fusion-based cognitive-radio (CR) network. The proposed VLSI-algorithm is based on the iterative power method and deflation technique that alleviate the computational complexity of conventional CSS algorithm with minimal performance degradation. In this work, a new hardware-efficient VLSI architecture of cooperative spectrum sensor (CSR) for the data-fusion center is presented, which supports up to six secondary users in the cooperative CR network. Its performance analysis under fading channel environment has been carried out where it delivers 0.8 detection probability ( $P_{d}$ ) at −8 dB of channel SNR with a false alarm rate of 0.1. It shows the minimum performance degradation of 0.057 dB at $P_{d} = 0.88$ compared to the conventional algorithm. The suggested CSR architecture has been application-specific integrated circuit (ASIC)-synthesized and postlayout simulated in UMC 90 nm-CMOS process. Thus, it occupies 2.4 mm2 of the core area, consumes 36 mW of total power, and delivers a low sensing time of $60.41~\mu \text{s}$ while operating at a maximum clock frequency of 87.7 MHz. Comparison with the reported works indicates that the proposed design requires 40.3% lesser area, and it is 41% hardware efficient than the conventional implementation. Eventually, this design has been field-programmable gate array (FPGA) prototyped, and its functionality is verified in the real-world test environment.

Keywords: tex math; data fusion; hardware efficient; architecture; inline formula

Journal Title: IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Year Published: 2021

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