Interference cancellation (IC) is the strategy for forming an estimate of incurred interference, like intersymbol interference (ISI), co-channel interference (CCI), adjacent channel interference (ACI), etc., and subtracting it from received signals before detection. Though it has intensively been investigated over the last decades, its commercial applications for mitigating multiuser access interference (MAI) in mobile network can only be found in the last serval years [1]. Several IC schemes, such as successive IC and subspacebased IC, have been reported to be implemented for 3G mobile network like cdma2000 and WCDMA [2]. The recent standardizing Femto cell further brought more interests and potential applications of IC techniques [3]. Compared with other detection strategies, IC focuses more on interference estimation. And different interference estimation methods may lead to different IC schemes [4], [5], e.g. successive IC, multistage detection, decision-feedback IC (DFIC) [6], [7], etc. DFIC, including minimum mean squared error (MMSE) DFIC [6] and decorrelating DFIC [7], belongs to the decisiondriven schemes that combines features of successive IC and multistage detection [4]. Conventional IC receivers are known to be able to solve the near-far problem with the knowledge of the signature information of all users [4]. However this assumption isn’t consistent with many practical situations where the receiver may only know the signatures of the expected signals not interfering signals. Recent research has been devoted to semiblind/blind implementation of IC for the practical applications in which adaptive filter techniques, e.g., Wiener filtering [8], Kalman filtering [9] and subspace-based implementations [10], are among the popular choices.
In the procedure of advanced multiuser receiver development, it is known that a proper received signal model can help us understand received signals as well as receiver design. There are two popular multiuser signal models which have been intensively discussed for multiuser receiver design. They are the conventional multiuser signal model and the subspace-based multiuser signal model. In the conventional signal model, each received signal is directly taken as a linear combination of actual signal signatures [4], [11], [9]. Most related blind multiuser receivers are developed either by explicitly estimating the signal signature [4] or by removing interfering signal components using adaptive filtering techniques, e.g., the blind receiver design with Wiener filter [11] and Kalman filter [9] techniques. Though the conventional signal model provides us a natural view of received signals, the involved signature waveforms or amplitudes information is unknown and it usually take the receiver lots of efforts to obtain it before detection.
For compensating the weakness of the conventional signal model, the subspace signal model is proposed with subspace-based signal processing techniques [10]. In the subspace signal model, each received signal is taken as a linear combination of signal subspace bases, which can be obtained by subspace signal processing techniques on the autocorrelation matrix of received signals. Subspace signal mode can be taken as a result of parametric signal modelling and provides a in-depth comprehension of received signals. Though subspacebased approaches don’t need explicitly estimate each user’s signal signature and the initialization and adaptive speed are improved with good performance, the signal subspace formation procedure still is not trivial.
It is known that the conventional signal model provides us the foundation for both optimal and conventional multiuser receiver design and subspace signal model helps us understand signal underneath structure. However, neither of them is easy enough for developing the blind multiuser receivers for high-speed CDMA systems [1]. In order to solve the near-far problem with minimum prior knowledge and computation complexity, we propose a new blind multiuser model with directly connecting the current received signal and several previous received signal while no explicitly signal structure estimation. With this blind signal model and widely employed signal estimation criteria including least squares (LS), minimum mean-squared errors (MMSE) and maximum likelihood (ML), several novel blind multiuser receivers are developed. There is no statistical signal estimation or subspace separation procedure required. Only a minimum number of previously received signals and the desired user’s signal signature waveform and timing are required. Hence the computation complexity and detection delay can be much reduced.
[1] J. G. Andrews. Interference cancellation for cellular systems: A contemporary overview. IEEE Wireless Communications, pages 19–29, April 2005.
[2] S. Wang, et al. Toward forward link interference cancellation. In CDG Technology Forum, Burlingame, CA, April 2006.
[3] Airvana. Enhancd cdma2000 support for femto/pico devices. In 3GPP2 TSG-C WG3 C30-20070514-022, San Diego, California, May 2007.
[4] S. Verdu. Multiuser Detection. Cambridge University Press, 1998.
[5] S. Wang and J. Caffery. On interference cancellation for synchronous cdma. In International Symposium on Wireless Communications (ISWC) 2002, September 2002.
[6] M. Kaveh and J. Salz. Cross-polarization cancellation and equalization in digital transmission over dually polarized multipath fading channels. AT&T Techn. Jour., 64:2211–2245, December 1985.
[7] A. Duel-Hallen. Decorrelating decision-feedback detector for asynchronous code-division multiple-access channels. IEEE Trans. On Communications, 43:421–434, February 1995.
[8] U. Madhow and M. Honig. Mmse interference suppression for directsquence spread spectrum cdma. IEEE Trans. on Communication, 42:3178–3188, December 1994.
[9] X. Zhang and W. Wei. Blind adaptive multiuser detection based on kalman filtering. IEEE Transactions on Signal Processing.
[10] X. Wang and H. V. Poor. Blind multiuser detection: A subspace approach. IEEE Trans. on Info. Theo., 44:677–691, March 1998.
[11] M. Honig, U. Madhow and S. Verdu. Blind adaptive multiuser detection. IEEE Trans. on Information Theory, 41:944–960, July 1995