近年來,正交分頻多工 (Orthogonal frequency division multiplexing, OFDM)在無線通訊中已成為最受歡迎的傳輸技術之一,其優點包括高頻寬效益以及對於頻率選擇性通道有良好的對抗性。然而其亦存在若干缺點,例如高峰均功率值比(Peak-to-average power ratio, PAPR)以及對於載波頻率偏移(Carrier frequency offset, CFO)極為敏感。其中載波頻率偏移使得OFDM系統之子通道失去正交性而降低系統效能。對於多重存取上傳系統(Orthogonal frequency division multiple access, OFDMA)而言,基地台同時接收多個使用者之上傳訊號,會同時存在多載波頻率偏移成份,相較於偵測單一頻率成份,多載波頻率偏移偵測是更大的挑戰。最大概似(Maximum likelihood, ML)偵測法是其中一種最佳化演算法,但過高的複雜度使其難以被實現。為降低計算複雜度,本論文應用粒子群(Particle swarm optimization, PSO)演算法來偵測OFDMA上傳系統之載波頻率偏移,並提出一個2級突變機制來改善粒子群演算法之效能。實驗結果顯示,我們提出的PSO演算法可獲得與ML相當之效能,但其計算複雜度遠低於ML。
Orthogonal frequency division multiplexing (OFDM) has attracted much attention in wireless communications due to its many advantages. However, OFDM is very sensitive to carrier frequency offset (CFO) which can destroy the orthogonality among subchannels. For OFDMA uplink systems with generalized carrier assignment scheme (GCAS), there are multiple CFOs need to be estimated simultaneously. While ML approach is recognized as one possible solution to this problem, its computational complexity is too high to be implemented for practical applications. To overcome this difficulty, this paper employs particle swarm optimization (PSO) algorithms to estimate CFOs for OFDMA uplink systems with GCAS. Furthermore, a two-stage mutation scheme is proposed to improve the performance of conventional PSO algorithm. Experimental result indicates that the proposed PSO algorithm can achieve the same performance as ML scheme but with much less computational complexity.