Abstract: Wavelet analysis has some strong advantages over Fourier analysis, as it allows a time-frequency domain analysis, allowing optimal resolution and flexibility. As a result, they have been satisfactorily applied in almost all the fields of communication systems including OFDM which is a strong candidate for next generation of wireless technology. In this paper, the performances of wavelet based Multiuser Multiple Input and Multiple Output Orthogonal Frequency Division Multiplexing (MU-MIMO OFDM) systems are analyzed in terms of BER. It has been shown that the wavelet based systems outperform the classical FFT based systems. This analysis also unfolds an interesting result, where wavelet based OFDM system will have a constant error performance using Regularized Channel Inversion (RCI) beamforming for any number of users, and outperforms in all possible scenario in a multiuser environment. An extensive computer simulations show that a PAPR reduction of up to 6.8dB can be obtained with M=64.
Abstract: We study the performance of compressed beamforming
weights feedback technique in generalized triangular decomposition
(GTD) based MIMO system. GTD is a beamforming technique that
enjoys QoS flexibility. The technique, however, will perform at its
optimum only when the full knowledge of channel state information
(CSI) is available at the transmitter. This would be impossible in
the real system, where there are channel estimation error and limited
feedback. We suggest a way to implement the quantized beamforming
weights feedback, which can significantly reduce the feedback data,
on GTD-based MIMO system and investigate the performance of
the system. Interestingly, we found that compressed beamforming
weights feedback does not degrade the BER performance of the
system at low input power, while the channel estimation error
and quantization do. For comparison, GTD is more sensitive to
compression and quantization, while SVD is more sensitive to the
channel estimation error. We also explore the performance of GTDbased
MU-MIMO system, and find that the BER performance starts
to degrade largely at around -20 dB channel estimation error.
Abstract: In this paper, we consider a multi user multiple input
multiple output (MU-MIMO) based cooperative reporting system for
cognitive radio network. In the reporting network, the secondary
users forward the primary user data to the common fusion center
(FC). The FC is equipped with linear equalizers and an energy
detector to make the decision about the spectrum. The primary user
data are considered to be a digital video broadcasting - terrestrial
(DVB-T) signal. The sensing channel and the reporting channel are
assumed to be an additive white Gaussian noise and an independent
identically distributed Raleigh fading respectively. We analyzed the
detection probability of MU-MIMO system with linear equalizers and
arrived at the closed form expression for average detection
probability. Also the system performance is investigated under
various MIMO scenarios through Monte Carlo simulations.