-1- ICA Based Blind Adaptive MAI Suppression in DS-CDMA Systems Malay Gupta and Balu Santhanam SPCOM Laboratory Department of E.C.E. The University of New Mexico DSP-WKSP-2004

-2-Motivation Conventional detector ignores MAI and is near far sensitive. Optimum detector requires complete knowledge of MAI and has exponential complexity. Decorrelator requires complete knowledge of MAI. MMSE detector requires training. MOE detector requires knowledge about the desired user only. ICA has been used in various source separation problems. DSP-WKSP-2004

-3- Blind Multiuser Detection Channel supports multiple users simultaneously. No separation between the users either in time or in frequency domain. Receiver observers superposition of signal from all the active users in the channel. Detection process needs to form a decision about the desired user (MISO model) or about all the active users (MIMO model), based only on the observed data. DSP-WKSP-2004

-4- Composite signal at time t can be expressed as User signature waveform is given as Matrix formulation of the chip synchronous signal with AWGN is b(i) is a bpsk signal CDMA Signal Model DSP-WKSP-2004

-5- Processing of biomedical signals, i.e. ECG, EEG, fMRI, and MEG. Algorithms for reducing noise in natural images, e.g. Nonlinear Principal Component Analysis (NLPCA). Finding hidden factors in financial data. Separation and enhancement of speech or music (few of them were applied to deal with real environments). Rotating machine vibration analysis, nuclear reactor monitoring and analyzing seismic signals. Traditional Applications of ICA DSP-WKSP-2004

-6- Mutual information between random vectors x and y is given as : Mutual information in terms of Kullback-Leibler distance : Kullback-Leibler distance of a random vector is defined as. Independent Component Analysis DSP-WKSP-2004

-7- ICA algorithms minimize mutual information (or it’s approximation) to restore independence at the output. ICA algorithms use SOS for preprocessing the data and HOS for independence. Fixed Point ICA algorithm is the cost function to be minimized. G(.) is any non quadratic function. ICA Algorithms DSP-WKSP-2004

-8- Correlation matrix corresponding to the interfering users data, based on snapshots Performing an eigen-decomposition on gives Interfering User subspace DSP-WKSP-2004

-9- U s =[u 1, u 2, …, u K-1 ] forms an orthonormal basis for the interfering users. U s ? denotes an orthogonal complement of U s Projection of a vector x on U s ? is given as Projection Operators DSP-WKSP-2004

-10- Unconstrained ICA algorithms lead to extraction of one user but there is no control over which user is extracted. Desired detector belongs to a subspace associated with the desired user’s code sequence. Eigen-structure can be obtained only from the knowledge of the received data. Indeterminacy can be removed by constraining the ICA detector to desired user’s subspace. Code Constrained ICA DSP-WKSP-2004

-11- Use the knowledge of the desired user’s code to estimated the interfering user signal subspace. Use fixed point ICA algorithm to compute the separating vector. Compute the projection of the separating vector onto the null space of the interfering user subspace. Apply norm constraint to converge to the desired solution. Proposed Algorithm DSP-WKSP-2004

-12- To demonstrate the efficacy of the present approach average symbol error probability measure is used. For binary modulation case this is given as :- Effect of increasing correlation between the users is quantified by the signal to noise and interference ratio (SINR). Performance Metric DSP-WKSP-2004

-13- Eigen-spread quantifies the correlation between active users. SINR is degrades when eigen-spread or correlation is high. BER performance depends on the extent of correlation. Effect of Correlation DSP-WKSP-2004

-14- Performance of CC-ICA better than MOE detector. Performance close to that of decorrelator. Perfect power control is assumed. Performance with two users DSP-WKSP-2004

-15- Performance better than MOE. Exhibits performance close to decorrelator. Five equal energy user channel. Performance with five users DSP-WKSP-2004

-16- Performance comparison in absence of power control. Number of users in the channel is 5. insensitive to near far problem. Performance again close to that of the decorrelator. No Power Control DSP-WKSP-2004

-17- Attempts to remove the inherent indeterminacy problem in ICA computations by constraining the ICA weight vector to lie in the null space of the interfering users. The detector performance is near-far resistant. Performance is close to that of decorrelator and better than MOE with significantly lesser side information.Conclusions DSP-WKSP-2004