Abstract
In recent years, cognitive radio has become a valid solution to the problem of descrimination in the spectrum resource allocation. Among the categories of CR related technique, spectrum sensing is one of the most identical issues. Hence in this thesis, we present an optimal detector applied for the Thomson's adaptive multitaper spectral estimation (AMTSE) in CR. This detector is optimized based on Neyman-Pearson Theorem and it can adjust the detection thresholds to the environment change. In this way, the detector will be more robust to the colored noise impairments and be compatible to multiband spectrum sensing applications. The proposed AMTSE detector can greatly reduce the number of observations and detect the primary users and spectrum holes compared with other conventional methods. It is shown that the detection rate of the proposed detector outperforms the one of energy detector by 40%. Besides, to achieve the PD = 0.999 the minimum required observation points of our proposed method are much fewer than the conventional one about 75%. Subsequently, the author also proposes a novel FFT architecture for the AMTSE detector. The 1024 points FFT processor adopts the radix-2 and radix-2/4/8/16 to efficiently reduce the number of nontrivial twiddle factor multipliers. And by using the timing sharing techniques, the trivial twiddle factor multiplier can be realized by using some adders and shifters. Moreover, we use delay buffer change technique to save power as only 0.06831(mw/MHz). The hardware complexity is significantly reduced that the gate count of FFT is 158.57 (gate/points) and it is more efficient to handle two data streams. Finally, the proposed AMTSE detector has been emulated on the FPGA board and implemented with the synthesizable RTL by cell-based ASIC design flow. This chip is implemented in UMC 90-nm 1P9M process process, and the power consumption of this detector operating in 100 MHz is 13.06 mw. The core area is 1585 × 1585(μm2) and the total area is 2210 × 2210(μm2).