Journal article
Learning Sequential Channel Selection for Interference Alignment using Reconfigurable Antennas
17 Dec 2017
Abstract
In recent years, machine learning techniques have been explored to support,
enhance or augment wireless systems especially at the physical layer of the
protocol stack. Traditional ML based approach or optimization is often not
suitable due to algorithmic complexity, reliance on existing training data
and/or due to distributed setting. In this paper, we formulate a reconfigurable
antenna based channel selection problem for interference alignment in a
multi-user wireless network as a learning problem. More specifically, we
propose that by using sequential learning, an effective channel or combination
of channels can be selected in order to enhance interference alignment using
reconfigurable antennas. We first formulate the channel selection as a
multi-armed problem that aims to optimize the sum rate of the network. We show
that by using an adaptive sequential learning policy, each node in the network
can learn to select optimal channels without requiring full and instantaneous
CSI for all the available antenna states. We conduct performance analysis of
our technique for a MIMO interference channel using a conventional IA scheme
and quantify the benefits of pattern diversity and learning channel selection.
Metrics
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Details
- Title
- Learning Sequential Channel Selection for Interference Alignment using Reconfigurable Antennas
- Creators
- Nikhil GulatiRohit BahlKapil R Dandekar
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Identifiers
- 991019186648404721