2,516 research outputs found
Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics
Kernel-based methods exhibit well-documented performance in various nonlinear
learning tasks. Most of them rely on a preselected kernel, whose prudent choice
presumes task-specific prior information. Especially when the latter is not
available, multi-kernel learning has gained popularity thanks to its
flexibility in choosing kernels from a prescribed kernel dictionary. Leveraging
the random feature approximation and its recent orthogonality-promoting
variant, the present contribution develops a scalable multi-kernel learning
scheme (termed Raker) to obtain the sought nonlinear learning function `on the
fly,' first for static environments. To further boost performance in dynamic
environments, an adaptive multi-kernel learning scheme (termed AdaRaker) is
developed. AdaRaker accounts not only for data-driven learning of kernel
combination, but also for the unknown dynamics. Performance is analyzed in
terms of both static and dynamic regrets. AdaRaker is uniquely capable of
tracking nonlinear learning functions in environments with unknown dynamics,
and with with analytic performance guarantees. Tests with synthetic and real
datasets are carried out to showcase the effectiveness of the novel algorithms.Comment: 36 page
A Multi-Kernel Multi-Code Polar Decoder Architecture
Polar codes have received increasing attention in the past decade, and have
been selected for the next generation of wireless communication standard. Most
research on polar codes has focused on codes constructed from a
polarization matrix, called binary kernel: codes constructed from binary
kernels have code lengths that are bound to powers of . A few recent works
have proposed construction methods based on multiple kernels of different
dimensions, not only binary ones, allowing code lengths different from powers
of . In this work, we design and implement the first multi-kernel successive
cancellation polar code decoder in literature. It can decode any code
constructed with binary and ternary kernels: the architecture, sized for a
maximum code length , is fully flexible in terms of code length, code
rate and kernel sequence. The decoder can achieve frequency of more than
GHz in nm CMOS technology, and a throughput of Mb/s. The area
occupation ranges between mm for and mm for
. Implementation results show an unprecedented degree of
flexibility: with , up to code lengths can be decoded with
the same hardware, along with any kernel sequence and code rate
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