U. Mitra et Hv. Poor, NEURAL-NETWORK TECHNIQUES FOR ADAPTIVE MULTIUSER DEMODULATION, IEEE journal on selected areas in communications, 12(9), 1994, pp. 1460-1470
Adaptive methods for performing multiuser demodulation in a direct-seq
uence spread-spectrum multiple-access (DS/SSMA) communication environm
ent are investigated, In this scenario, the noise is characterized as
being the sum of the interfering users' signals and additive Gaussian
noise, The optimal receiver for DS/SSMA systems has a complexity that
is exponential in the number of users, This prohibitive complexity has
spawned the area of research on suboptimal receivers with moderate co
mplexity, Adaptive algorithms for detection allow for reception when t
he communication environment is either unknown or changing, Motivated
by previous work with radial basis functions (RBF's) for performing eq
ualization, RBF networks that operate with knowledge of only a subset
of the system parameters are studied, Although this form of detection
has been previously studied (group detection) when the system paramete
rs are known, in this work, neural network techniques are employed to
adaptively determine unknown system parameters, This approach is furth
er bolstered by the fact that the optimal detector in the synchronous
case can be implemented by a RBF network when all of the system parame
ters are known, The RBF network's performance (with estimated paramete
rs) is compared with the optimal synchronous detector, the decorrelati
ng detector and the single layer perceptron detector, Clustering techn
iques and adaptive least mean squares methods are investigated to dete
rmine the unknown system parameters, This work shows that the adaptive
radial basis function network attains near optimal performance and is
robust in realistic communication environments,