Maximum likelihood channel parameter estimation from multidimensional channel sounding measurements

Abstract

We describe a multidimensional maximum likelihood estimator for radio channel parameters. We also derive a data model to describe the complete data, that is virtually applicable to every antenna array geometry. The proposed iterative gradient based algorithm has been developed, since algorithms using component-wise updates of the likelihood function shows a slow convergence, if at least two propagation paths with nearly the same parameters exist in the measured radio propagation scenario, that means if super-resolution is necessary. The algorithm provides furthermore a variance estimate of the estimated parameters, since the Fisher-information matrix is calculated throughout the alcorithm.

Performance limits of channel parameter estimation for joint communication and positioning

“…Thus, CPE enables positioning and data detection. Typically, only one aspect of CPE is considered: On the one hand, CPE is well known in the context of channel sounding [11][12][13]. In this case, the parameters of the physical channel are of interest, while the channel coefficients of the equivalent discrete-time channel model, which are available as well, are not further processed.…”

“…There are two equivalent approaches: On the one hand, the channel parameters can be estimated directly from the received samples as it is often the case for channel sounding [11][12][13]. Similar estimators have been investigated for example in the context of Rake receivers in code-division multiple access [17] or for pure navigation purposes [18].…”

Parameter estimation using the sliding-correlator’s output for wideband propagation channels

“…Reducing the algorithm complexity can be performed in different ways. For example, instead of estimating a large number of multipath components, we may determine an appropriate model order in advance by applying the Akaike Information Criterion [19], and furthermore, the complexity involved in the parameter estimate updating procedure can be reduced by using advanced searching methods [20,21]. When being implemented in reality, the proposed estimation method requires the SC's outputs that are the results of filtering the original received sequence by using a LPF of bandwidth B n , n > 1.…”

Joint communication and positioning based on soft channel parameter estimation

“…The second approach in (10) may seem more natural to some readers since the parameters are estimated directly from the received samples. But since both approaches are equivalent, as proven in the "Appendix", it seems more convenient to the authors to apply the first approach: Channel estimates are usually already available in communication systems and the metric derived from (9) is less complex than the metric derived from (10). Hence, only the first approach is considered in the following.…”

“…However, the information about the physical channel is neglected in these publications. On the other hand, channel sounding is performed in order to estimate the parameters of the physical channel [8][9][10]. But, to the authors best knowledge, the proposed parameter estimation methods are not applied for estimation of the equivalent discretetime channel model.…”