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Found 12 papers, 2 papers with code
Low Complexity Deep Learning Augmented Wireless Channel Estimation for Pilot-Based OFDM on Zynq System on Chip
The first contribution of this paper is to efficiently implement the existing state-of-the-art CE algorithms on Zynq SoC (ZSoC), comprising of ARM processor and field programmable gate array (FPGA), via hardware-software co-design and fixed point analysis.
Hardware Software Co-design of Statistical and Deep Learning Frameworks for Wideband Sensing on Zynq System on Chip
The first contribution of this work is efficiently mapping the OMP algorithm on the Zynq system-on-chip (ZSoC) consisting of an ARM processor and FPGA.
Exploiting Side Information for Improved Online Learning Algorithms in Wireless Networks
In this work, we treat any such measurable quality that has a non-zero correlation with the rate achieved as side-information and study how it can be exploited to quickly learn the channel that offers higher throughput (reward).
Multi-armed Bandit Algorithms on System-on-Chip: Go Frequentist or Bayesian?
is unknown and hence, a single algorithm may not be optimal.
Towards Intelligent Reconfigurable Wireless Physical Layer (PHY)
Next-generation wireless networks are getting significant attention because they promise 10-factor enhancement in mobile broadband along with the potential to enable new heterogeneous services.
Reconfigurable and Intelligent Ultra-Wideband Angular Sensing: Prototype Design and Validation
The emergence of beyond-licensed spectrum sharing in FR1 (0. 45-6 GHz) and FR2 (24 - 52 GHz) along with the multi-antenna narrow-beam based directional transmissions demand a wideband spectrum sensing in temporal as well as spatial domains.
Distributed Learning in Ad-Hoc Networks: A Multi-player Multi-armed Bandit Framework
In this paper, we discuss state-of-the-art multi-armed multi-player bandit based distributed learning algorithms that allow users to adapt to the environment and coordinate with other players/users.
Intelligent and Reconfigurable Architecture for KL Divergence Based Online Machine Learning Algorithm
In this paper, we efficiently map the KLUCB algorithm on SoC by realizing optimization routine via alternative synthesizable computation without compromising on the performance.
Novel Deep Learning Framework for Wideband Spectrum Characterization at Sub-Nyquist Rate
Spectrum characterization involves the identification of vacant bands along with center frequency and parameters (energy, modulation, etc.)
Multiplayer Multi-armed Bandits for Optimal Assignment in Heterogeneous Networks
However, such transmissions and sensing for information exchange do not add to network throughput.
Multi-player Multi-armed Bandits for Stable Allocation in Heterogeneous Ad-Hoc Networks
Next generation networks are expected to be ultradense and aim to explore spectrum sharing paradigm that allows users to communicate in licensed, shared as well as unlicensed spectrum.
Multi-Player Bandits: A Trekking Approach
We provide algorithms based on a novel `trekking approach' that guarantees constant regret for the static case and sub-linear regret for the dynamic case with high probability.
