ML time delay estimation for 5G links with DSSS multi-carrier multipath MIMO radio access

Abstract

This paper presents two new implementations of the maximum likelihood (ML) time delay estimation (TDE) from multi-carrier (MC) Direct-Sequence Spread Spectrum (DSSS) in multipath MIMO transmissions that will characterize future 5G radio interface technologies (RITs). The first TDE, based on expectation maximization (EM), provides accurate estimates of the delays when a good initialisation of the parameters is available. The second TDE returns the global maximum of the compressed likelihood function (CLF) using the importance sampling (IS) technique without requiring any initialization. Interestingly, in the non-data-aided (NDA) case, temporal, spatial (transmit and receive), and frequency samples have the same impact on estimation accuracy and performance bound which depends on the product of these dimensions regardless of the channel correlation type. Furthermore, we cope with such channel correlations that arise in practice and, hence, become very challenging both in estimation and CRLB derivation in the data-aided (DA) case, but that have been so far overlooked in previous works.