Efficient architecture mapping of FFT/IFFT for cognitive radio networks

Cognitive radio networks require flexibility to support a variety of wireless communication system standards. Many modern systems utilize some form of orthogonal frequency division multiplexing (OFDM) and single-carrier frequency-division multiple access (SC-FDMA) often augmented with multiple input multiple output (MIMO) antenna schemes. A common module in these standards is the fast Fourier transform (FFT) and its inverse. Although many architectures exist for traditional power-of-two FFT lengths, the recent 3GPP LTE standards define non-power-of-two transform lengths. The various FFT and IFFT lengths for both the uplink and downlink processing require support for radix-2, radix-3, and radix-5 modules. In this paper, we propose a highly flexible FFT/IFFT architecture that can support a broad variety of transform sizes and efficient mapping to programmable testbed platforms for cognitive radio networks. This novel architecture will provide a range of transform sizes of the general form (2ⁿ 3^k 5^l), and for use in emerging algorithms for massive MIMO detectors.