The next generation (4G) wireless systems are expected to provide
universal personal and multimedia communications with seamless connection
and very high rate transmissions and without regard to the users’ mobility and
location. OFDM technique is recognized as one of the leading candidates to
provide the wireless signalling for 4G systems. The major challenges in
downlink multiuser OFDM based 4G systems include the wireless channel, the
synchronization and radio resource management. Thus algorithms are required
to achieve accurate timing and frequency offset estimation and the efficient
utilization of radio resources such as subcarrier, bit and power allocation.
The objectives of the thesis are of two fields. Firstly, we presented the
frequency offset estimation algorithms for OFDM systems. Building our work
upon the classic single user OFDM architecture, we proposed two FFT-based
frequency offset estimation algorithms with low computational complexity.
The computer simulation results and comparisons show that the proposed
algorithms provide smaller error variance than previous well-known algorithm.
Secondly, we presented the resource allocation algorithms for OFDM
systems. Building our work upon the downlink multiuser OFDM architecture,
we aimed to minimize the total transmit power by exploiting the system
diversity through the management of subcarrier allocation, adaptive
modulation and power allocation. Particularly, we focused on the dynamic
resource allocation algorithms for multiuser OFDM system and multiuser
MIMO-OFDM system. For the multiuser OFDM system, we proposed a lowiv
complexity channel gain difference based subcarrier allocation algorithm. For
the multiuser MIMO-OFDM system, we proposed a unit-power based
subcarrier allocation algorithm. These proposed algorithms are all combined
with the optimal bit allocation algorithm to achieve the minimal total transmit
power. The numerical results and comparisons with various conventional nonadaptive
and adaptive algorithmic approaches are provided to show that the
proposed resource allocation algorithms improve the system efficiencies and
performance given that the Quality of Service (QoS) for each user is
guaranteed.
The simulation work of this project is based on hand written codes in the
platform of the MATLAB R2007b
