Abstract: In this paper we propose a family of algorithms based
on 3rd and 4th order cumulants for blind single-input single-output
(SISO) Non-Minimum Phase (NMP) Finite Impulse Response (FIR)
channel estimation driven by non-Gaussian signal. The input signal
represents the signal used in 10GBASE-T (or IEEE 802.3an-2006)
as a Tomlinson-Harashima Precoded (THP) version of random
Pulse-Amplitude Modulation with 16 discrete levels (PAM-16). The
proposed algorithms are tested using three non-minimum phase
channel for different Signal-to-Noise Ratios (SNR) and for different
data input length. Numerical simulation results are presented to
illustrate the performance of the proposed algorithms.
Abstract: All the available algorithms for blind estimation namely constant modulus algorithm (CMA), Decision-Directed Algorithm (DDA/DFE) suffer from the problem of convergence to local minima. Also, if the channel drifts considerably, any DDA looses track of the channel. So, their usage is limited in varying channel conditions. The primary limitation in such cases is the requirement of certain overhead bits in the transmit framework which leads to wasteful use of the bandwidth. Also such arrangements fail to use channel state information (CSI) which is an important aid in improving the quality of reception. In this work, the main objective is to reduce the overhead imposed by the pilot symbols, which in effect reduces the system throughput. Also we formulate an arrangement based on certain dynamic Artificial Neural Network (ANN) topologies which not only contributes towards the lowering of the overhead but also facilitates the use of the CSI. A 2×2 Multiple Input Multiple Output (MIMO) system is simulated and the performance variation with different channel estimation schemes are evaluated. A new semi blind approach based on dynamic ANN is proposed for channel tracking in varying channel conditions and the performance is compared with perfectly known CSI and least square (LS) based estimation.
Abstract: Multicarrier code-division multiple-access is one of the
effective techniques to gain its multiple access capability, robustness
against fading, and to mitigate the ISI. In this paper, we propose an
improved mulcarrier CDMA system with adaptive subchannel
allocation. We analyzed the performance of our proposed system in
frequency selective fading environment with narrowband interference
existing and compared it with that of parallel transmission over many
subchannels (namely, conventional MC-CDMA scheme) and
DS-CDMA system. Simulation results show that adaptive subchannel
allocation scheme, when used in conventional multicarrier CDMA
system, the performance will be greatly improved.
Abstract: The Carrier Frequency Offset (CFO) due to timevarying
fading channel is the main cause of the loss of orthogonality
among OFDM subcarriers which is linked to inter-carrier interference
(ICI). Hence, it is necessary to precisely estimate and compensate the
CFO. Especially for mobile broadband communications, CFO and
channel gain also have to be estimated and tracked to maintain the
system performance. Thus, synchronization pilots are embedded in
every OFDM symbol to track the variations. In this paper, we present
the pilot scheme for both channel and CFO estimation where channel
estimation process can be carried out with only one OFDM symbol.
Additional, the proposed pilot scheme also provides better
performance in CFO estimation comparing with the conventional
orthogonal pilot scheme due to the increasing of signal-tointerference
ratio.
Abstract: This paper reports on investigations into capacity of a
Multiple Input Multiple Output (MIMO) wireless communication
system employing a uniform linear array (ULA) at the transmitter and
either a uniform linear array (ULA) or a uniform circular array (UCA)
antenna at the receiver. The transmitter is assumed to be surrounded by
scattering objects while the receiver is postulated to be free from
scattering objects. The Laplacian distribution of angle of arrival
(AOA) of a signal reaching the receiver is postulated. Calculations of
the MIMO system capacity are performed for two cases without and
with the channel estimation errors. For estimating the MIMO channel,
the scaled least square (SLS) and minimum mean square error
(MMSE) methods are considered.