Back to Search
Start Over
Hyperparameter-Free Transmit-Nonlinearity Mitigation Using a Kernel-Width Sampling Technique
- Source :
- IEEE Transactions on Communications. 69:2613-2627
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- Nonlinear device characteristics present a severe performance-bottleneck for several upcoming next-generation wireless communication systems and prevent them from delivering high data-rates to the end-users. In this context, reproducing kernel Hilbert space (RKHS) based signal processing methods have gained widespread deployment and have been found to outperform classical polynomial-filtering-based solutions significantly. Furthermore, recent RKHS based techniques that rely on explicit feature-maps called random Fourier features (RFF) have emerged. These techniques alleviate the dependence on learning a dictionary and avoid the computations and errors incurred in dictionary-based learning. However, the performance of existing RKHS based solutions depends on choosing a suitable kernel-width. For the widely-used Gaussian kernel, we propose a methodology of assigning kernel-bandwidths that capitalizes on a stochastic sampling of kernel-widths using an ensemble drawn from a pre-designed probability density function. The technique is found to deliver a comparable convergence/error-rate performance to the scenario when the kernel-width is chosen by brute-force trial and error for tuning it for best performance. The desirable properties of the proposed kernel-sampling technique are supported by analytical proofs and are further highlighted by computer-simulations presented in the form of case studies in the context of next-generation communication systems.
- Subjects :
- Hyperparameter
Signal processing
Computer science
Sampling (statistics)
020206 networking & telecommunications
Probability density function
02 engineering and technology
Communications system
symbols.namesake
Kernel (image processing)
0202 electrical engineering, electronic engineering, information engineering
Gaussian function
symbols
020201 artificial intelligence & image processing
Electrical and Electronic Engineering
Algorithm
Reproducing kernel Hilbert space
Subjects
Details
- ISSN :
- 15580857 and 00906778
- Volume :
- 69
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Communications
- Accession number :
- edsair.doi...........835d09d03c2388ff034219e8997691d8
- Full Text :
- https://doi.org/10.1109/tcomm.2020.3048045