As spectrum scarcity is becoming a serious problem, the worth of finding a general solution
for such issue has become even serious due to the rapid development of wireless communications.
The main objective of this thesis is to investigate the optimal power allocation
procedure that maximizes the capacity in OFDM based Cognitive Radio Systems.
The main purpose of the search is to modify the conventional water-filling algorithm applied
in general OFDM based Cognitive Radio systems due to the per subchannel power
constraints and individual peak power constraints. For Radio Resource Allocation (RRA),
one of the most typical problems is to solve power allocation using the Conventional Water-
filling. As communication system develops, the structures of the system models and the
corresponding RRA problems evolve to more advanced and more complicated ones.
In this thesis Iterative Partitioned Weighted Geometric Water-filling with Individual Peak
Power Constraints (IGPP), a simple and elegant approach is proposed to solve the weighted
radio resource allocation problem with peak power constraint and total subchannel power
constraint with channel partitions. The proposed IGPP algorithm requires less computation
than the Conventional Water-filling algorithm (CWF).
Dynamic Channel Sensing Iterative (DCSI) approach is another algorithm proposed to optimally
allocate power for OFDM based Cognitive Radio Systems. DCSI is a innovative
concept which will allow us to solve the same problem intelligently with less complexity. It
provides straight forward power allocation analysis, solutions and insights with reduced computation
over other approaches under the same memory requirement and sorted parameters.
Transceiver designs with matrix-version water-filling architecture under mixed power constraints
