Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the uplink of multi-user multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, there are multiple carrier frequency offsets (CFO's) from the multiple users. ,  study algorithms to estimate these multiple CFO values.  first derive the maximum likelihood (ML) estimator and show that the complexity of the ML estimator increases exponentially with the number of users so that the estimator is not suitable for practical implementations. To reduce the complexity,  propose a suboptimal algorithm using constant amplitude zero autocorrelation (CAZAC) sequences. The complexity of the proposed method increases only linearly with the number of users. Using computer simulations,  compare the performance using CAZAC training sequences with that using the m sequence and the short training field (STF) of the IEEE 802.11n systems. The results show that in the low to medium SNR regions, the performance using CAZAC sequences is very close to the single-user Cramer-Rao bound. For high SNR regions, an error floor exists due to multiple access interference (MAI). The error floor using CAZAC sequences is more than 10 times smaller compared to the error floor using the other two sequences.
['Yan Wu', 'Samir Attallah', 'Jan W. M. Bergmans']
I am writing a research paper on a idea Carrier Frequency Offset Estimation for Multi-User MIMO OFDM Uplink Using CAZAC Sequences
[{"from": "human", "value": "I am writing a research paper on a idea Carrier Frequency Offset Estimation for Multi-User MIMO OFDM Uplink Using CAZAC Sequences"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the uplink of multi-user multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, there are multiple carrier frequency offsets (CFO's) from the multiple users. ,  study algorithms to estimate these multiple CFO values.  first derive the maximum likelihood (ML) estimator and show that the complexity of the ML estimator increases exponentially with the number of users so that the estimator is not suitable for practical implementations. To reduce the complexity,  propose a suboptimal algorithm using constant amplitude zero autocorrelation (CAZAC) sequences. The complexity of the proposed method increases only linearly with the number of users. Using computer simulations,  compare the performance using CAZAC training sequences with that using the m sequence and the short training field (STF) of the IEEE 802.11n systems. The results show that in the low to medium SNR regions, the performance using CAZAC sequences is very close to the single-user Cramer-Rao bound. For high SNR regions, an error floor exists due to multiple access interference (MAI). The error floor using CAZAC sequences is more than 10 times smaller compared to the error floor using the other two sequences."}]