In this paper, a supervised-learning based blind detection of interference parameters for network-assisted interference cancellation and suppression (NAICS) system is proposed. In order to enable joint detection or interference cancellation at a user equipment in NAICS system, we consider detection of the interference parameters including traffic-to-pilot power ratio (TPR), rank indicator (RI), and precoding matrix indicator (PMI). We divide overall process for detection of these interference parameters into two steps, and propose supervised learning based neural network architecture for detection of corresponding parameters in each steps. Link-level simulation results are provided to validate detection performance of the proposed neural network architecture and performance of NAICS system which uses the proposed learning based detection method with respect to block error rate (BLER).

The uplink performance of synchronous and asynchronous slow frequency-hop spread-spectrum multiple-access (SFHSS-MA) networks transmitting L bits per hop using binary differential phase shift keying (BDPSK) is analyzed under the additive white Gaussian noise (AWGN) and Rayleigh fading channels. Analytic expressions for the average conditional bit error probabilities given a hop is hit by K' interfering users are derived. Results show that SFHSS-MA networks using BDPSK achieve nearly twice the maximum normalized network throughput compared to networks using BFSK under both AWGN and Rayleigh fading channels.