A Variable Step Sizes Frequency Offsets-Compensated Least Mean Squares Algorithm

Frequency offsets-compensated least mean squares (FO-LMS) algorithm is a generic method for estimating a wireless channel under carrier and sampling frequency offsets when the transmitted signal is beforehand known to the receiver. The algorithm iteratively and explicitly adjusts its estimates of the channel and frequency offsets using stochastic gradient descent-based rules and the step sizes of these rules determine the learning rate and stability of the algorithm. Within the stability conditions, the choice of step sizes reflects a trade-off between the algorithm's ability to react to changes in the channel and the ability to minimize misadjustments caused by noise. This paper provides theoretical expressions to predict and optimize the tracking and misadjusment errors of FO-LMS when estimating channels and frequency offsets with known time-varying characteristics. This work also proposes a method to adjust the FO-LMS's step sizes based on the algorithm's performance when the time-varying characteristics are not known, which is more often the case in practice. Accuracy of the expressions and performance of the proposed variable step sizes algorithm are studied through simulations.

Decision Feedback-Aided Known-Interference Cancellation

Known-interference cancellation (KIC) in combination with cooperative jamming can be used to provide covertness and security to wireless communications at the physical layer. However, since the signal of interest (SI) of a wireless communication system acts as estimation noise, i.e., interference, to KIC, the SI limits the extent to which the known interference (KI) can be canceled and that in turn limits the throughput of the wireless communication system that is being hidden or secured. In this letter, we analyze a decision feedback-aided known-interference cancellation (DF-KIC) structure in which both the KI and SI are canceled iteratively and successively. Measurement results demonstrate that introducing decision feedback to KIC improves its KI cancellation capability and hence increases the wireless communication system's useful throughput, albeit at the expense of a higher computational load.