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Similar to the conventional orthogonal frequencydivision multiplexing (OFDM) system, an OFDM multiple access (OFDMA) system will have a carrier frequency offset (CFO) problem. Since CFOs of all users are different, CFO compensation in the OFDMA uplink system is much more involved. A simple, yet efficient, method is the zero-forcing (ZF) compensation method. However, it involves an inverse of an N × N CFO-induced ICI matrix, where N is the number of subcarriers. Thus, the complexity can become very high when N is large, a case commonly seen in OFDMA systems. In this work, we propose a low-complexity ZF method to overcome the problem. The main idea is to use Newton's method to solve matrix inversion iteratively. We explore the structure of the CFOinduced ICI matrix and develop a method that can implement Newton's method with fast Fourier transforms (FFTs). As a result, the required computational complexity is significantly reduced from O(N 3 ) to O(2N log 2 N ). Simulations show that, with only three iterations, the proposed method can have similar performance to the direct ZF method.

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In this thesis, we propose a power allocation strategy for overlay cognitive networks, where each node is equipped with single antenna. Owing to the overly strategy, the secondary user (SU) can help transmit the primary user's (PU) data and meanwhile conveys its own data with the superposition coding (SC). We first analyze the bit-error-rate (BER) for the PU and the SU. Then, the power allocation strategy is devised by minimizing total power, providing that the BER of the PU and that of SU are guaranteed. Since the BER formulations are not convex, the optimization is difficult to conduct. We then propose two new tractable BER approximations for the PU and the SU, which can sophisticatedly transfer the design into a convex problem. The optimum solution can thus be easily obtained. Simulations verify our power allocation design is validated for different channel environments.