Equalization in chaos-based communication systems using kernel adaptive filtering
Chaos-based communication systems have attracted attention of researchers in academy and industry in the last decades. A particular family of such systems has as basic idea to use the transmitted message to modify a known nonlinear chaotic signal generator (CSG). In the receiver, the knowledge of the employed nonlinear CSG in conjunction with chaotic synchronization permits to recover the original message. These systems are an alternative for spread spectrum communication with a possible increase in the security in the physical layer, since it is necessary to perfectly know the CSG in the receiver to decode the message. However, the lack of robustness of chaotic synchronization in relation to channel noise and intersymbol interference still poses a barrier for their practical use. The problem of equalization for such systems have been tackled for a while, and algorithms based on the normalized least-mean squares have presented auspicious results for linear channels. For nonlinear channels, Kernel Adaptive Filters (KAFs) have been used since they are able to solve nonlinear problems implicitly projecting the input vector into a larger dimension space, where they can be linearly solved. Therefore, in this paper, we propose the use of KAFs with two purposes: to equalize linear and nonlinear channels and, at the same time, decode the message without knowledge of the CSG in the receiver. Simulation results show that the proposed solution is able to perform these tasks.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).