This paper describes a crest factor reduction （CFR） method that reduces peaks in the time domain by modifying selected data subcarriers within an OFDM signal. The data subcarriers selected for modification vary with...This paper describes a crest factor reduction （CFR） method that reduces peaks in the time domain by modifying selected data subcarriers within an OFDM signal. The data subcarriers selected for modification vary with each symbol interval and are limited to those subcarriers whose aata elements are mapped onto the outer boundary of the constellation. In the proposed method, a set of peaks are identified within an OFDM symbol interval. Data subcarriers whose data element has a positive or negative correlation with the set peak are selected. For a subcarrier with an outer element and a significant positive correlation, a bit error （reversal） is intentionally introduced. This moves the data element to the opposite side of the constellation. Outer elements on negatively-correlatea subcarriers are increased in magnitude along the real or imaginary axis. Experimental results show that selecting the correct subcarriers for bit reversals and outward enhancements reduces the peak-to-average power ratio （PAPR） of the OFDM signal to a target value and limits in-band degradation measured by bit error rate （BER） and error vector magnitude （EVM）.展开更多
Matrix factorization(MF) has been proved to be a very effective technique for collaborative filtering(CF),and hence has been widely adopted in today's recommender systems.Yet due to its lack of consideration of th...Matrix factorization(MF) has been proved to be a very effective technique for collaborative filtering(CF),and hence has been widely adopted in today's recommender systems.Yet due to its lack of consideration of the users' and items' local structures,the recommendation accuracy is not fully satisfied.By taking the trusts among users' and between items' effect on rating information into consideration,trust-aware recommendation systems(TARS) made a relatively good performance.In this paper,a method of incorporating trust into MF was proposed by building user-based and item-based implicit trust network under different contexts and implementing two implicit trust-based context-aware MF(ITMF)models.Experimental results proved the effectiveness of the methods.展开更多
文摘This paper describes a crest factor reduction （CFR） method that reduces peaks in the time domain by modifying selected data subcarriers within an OFDM signal. The data subcarriers selected for modification vary with each symbol interval and are limited to those subcarriers whose aata elements are mapped onto the outer boundary of the constellation. In the proposed method, a set of peaks are identified within an OFDM symbol interval. Data subcarriers whose data element has a positive or negative correlation with the set peak are selected. For a subcarrier with an outer element and a significant positive correlation, a bit error （reversal） is intentionally introduced. This moves the data element to the opposite side of the constellation. Outer elements on negatively-correlatea subcarriers are increased in magnitude along the real or imaginary axis. Experimental results show that selecting the correct subcarriers for bit reversals and outward enhancements reduces the peak-to-average power ratio （PAPR） of the OFDM signal to a target value and limits in-band degradation measured by bit error rate （BER） and error vector magnitude （EVM）.
文摘Matrix factorization(MF) has been proved to be a very effective technique for collaborative filtering(CF),and hence has been widely adopted in today's recommender systems.Yet due to its lack of consideration of the users' and items' local structures,the recommendation accuracy is not fully satisfied.By taking the trusts among users' and between items' effect on rating information into consideration,trust-aware recommendation systems(TARS) made a relatively good performance.In this paper,a method of incorporating trust into MF was proposed by building user-based and item-based implicit trust network under different contexts and implementing two implicit trust-based context-aware MF(ITMF)models.Experimental results proved the effectiveness of the methods.
