01-02-2013, 09:45 AM
An Efficient FFT For OFDM Based Cognitive Radio On A Reconfigurable Architecture
1An Efficient FFT.pdf (Size: 257.31 KB / Downloads: 29)
Abstract
Cognitive Radio is a promising technology to utilize
non-used parts of the spectrum that actually are assigned to
licensed services. An adaptive OFDM based Cognitive Radio
system has the capacity to nullify individual carriers to avoid
interference to the licensed user. Therefore, there could be a
considerably large number of zero-valued inputs/outputs for
the IFFT/FFT in the OFDM transceiver. Due to the wasted
operations on zero values, the standard FFT is no longer efficient.
Based on this observation, we propose to use a computationally
efficient IFFT/FFT as an option for OFDM based Cognitive
Radio. Mapping this algorithm onto a reconfigurable architecture
is discussed.
INTRODUCTION
The increasing number of wireless multimedia applications
leads to a spectrum scarcity. However, recent studies show
that most of the assigned spectrum is underutilized. Cognitive
Radio ([1], [2]) is proposed as a promising technology to
solve the imbalance between spectrum scarcity and spectrum
under-utilization. In Cognitive Radio, spectrum sensing locates
the unused spectrum segments in a targeted spectrum pool
and the aim is to use these segments optimally without
harmful interference to the licensed user. This technology
is also mentioned in [3] as Spectrum Pooling. Our research
on Cognitive Radio is undertaken in the Adaptive Ad-hoc
Freeband (AAF) project [4]. The goal of the project is to
demonstrate an ad-hoc wireless communication network for
emergency situations based on Cognitive Radio principles. Our
work mainly focuses on mapping algorithms used in Cognitive
Radio onto a reconfigurable platform.
FFT Pruning
In [8], a DIF (Decimation-In-Frequency) FFT pruning algorithm
was suggested. Later the FFT pruning algorithm was
extended to DIT (Decimation-In-Time) FFT [9] and to both
input and output pruning [10]. In fact the basic idea of all these
pruning algorithms is to determine the index of the butterflies
to be chosen for calculations. The index is generated at runtime
by conditional statements. However, the effort to generate
the index can be considerable due to the execution of conditional
statements. The index shows irregularity because of
the irregular position of zero inputs/outputs. This irregularity
makes the hardware implementation of FFT pruning difficult.
The idea of FFT pruning was applied to a multichannel OFDM
system in [11], where a significant reduction of complexity
was suggested. However, the application on OFDM in [11]
assumes zero values with a considerable regularity. This is
not true for Cognitive Radio where subcarriers are switched
off at random positions based on the spectrum occupancy
information and the subchannel condition.
Transform Decomposition for Hardware Implementation
Transform decomposition shows considerable regularity
which facilitates its hardware implementation. Based on the
discussion in the previous section, we will show the computational
structure of transform decomposition followed by a
complexity analysis for our targeted Cognitive Radio system.
Although the algorithm in [5] applies to both the powerof-
two FFT and the prime factor algorithm, we will only
consider the power-of-two case because only power-of-two
FFTs are used in the proposed OFDM system.
CONCLUSION
In this paper, we present a computationally efficient
FFT/IFFT algorithm, namely transform decomposition, as an
option for OFDM based Cognitive Radio in case a large number
of subcarriers are nullified. A reconfigurable platform is
used to support this option. Mapping transform decomposition
onto a coarse-grain reconfigurable processor, the Montium, has
been discussed. The Montium architecture matches the computational
structure of the algorithm very well. The estimation
shows that this efficient algorithm on the Montium offers a
faster computation and a significant energy saving.