  Due to the limited isolation of duplexer's stopband transceivers operating in
frequency division duplex (FDD) encounter a leakage of the transmitted signal
onto the receiving path. Leakage signal with the combination of the
second-order nonlinearity of the low noise amplifier (LNA) and receiver
down-conversion mixer may lead to second-order intermodulation distortion
(IMD2) generation thus greatly reducing the receiver sensitivity. Cancellation
of undesirable interferences based on adaptation of traditional models such as
memoryless and memory polynomials, spline polynomial based Hammerstein and
Wiener-Hammerstein models proved its efficiency in case of well-known
nonlinearity nature. On the other hand, currently there is an intensive
research in the field of nonlinearity detection by means of neural network (NN)
structures. NN-based IMD cancellers are effective in the case of unknown
interference content due to their high generalization ability. Therefore, NN
approach can provide universal model, which is capable of IMD suppression even
in case it is hard to separate intermodulation products generated by LNA,
down-conversion mixer or even power amplifier in transmitter path.
Nevertheless, such structures suffer from high complexity and can`t be
implemented in hardware. Current paper presents low-complexity feed-forward
NN-based model, which successfully competes with traditional architectures in
terms of computational complexity. The testbench results demonstrate the
acceptable performance of provided model, which can be equal to the polynomial
nonlinear canceler's performance at a reduced computational cost. Current paper
provides performance and required resources comparison of traditional memory
polynomial-based scheme and NN-based model for IMD2 cancellation.
