106 research outputs found
A Software Radio Challenge Accelerating Education and Innovation in Wireless Communications
This Innovative Practice Full Paper presents our methodology and tools for
introducing competition in the electrical engineering curriculum to accelerate
education and innovation in wireless communications. Software radio or
software-defined radio (SDR) enables wireless technology, systems and standards
education where the student acts as the radio developer or engineer. This is
still a huge endeavor because of the complexity of current wireless systems and
the diverse student backgrounds. We suggest creating a competition among
student teams to potentiate creativity while leveraging the SDR development
methodology and open-source tools to facilitate cooperation. The proposed
student challenge follows the European UEFA Champions League format, which
includes a qualification phase followed by the elimination round or playoffs.
The students are tasked to build an SDR transmitter and receiver following the
guidelines of the long-term evolution standard. The metric is system
performance. After completing this course, the students will be able to (1)
analyze alternative radio design options and argue about their benefits and
drawbacks and (2) contribute to the evolution of wireless standards. We discuss
our experiences and lessons learned with particular focus on the suitability of
the proposed teaching and evaluation methodology and conclude that competition
in the electrical engineering classroom can spur innovation.Comment: Frontiers in Education 2018 (FIE 2018
LTE Spectrum Sharing Research Testbed: Integrated Hardware, Software, Network and Data
This paper presents Virginia Tech's wireless testbed supporting research on
long-term evolution (LTE) signaling and radio frequency (RF) spectrum
coexistence. LTE is continuously refined and new features released. As the
communications contexts for LTE expand, new research problems arise and include
operation in harsh RF signaling environments and coexistence with other radios.
Our testbed provides an integrated research tool for investigating these and
other research problems; it allows analyzing the severity of the problem,
designing and rapidly prototyping solutions, and assessing them with
standard-compliant equipment and test procedures. The modular testbed
integrates general-purpose software-defined radio hardware, LTE-specific test
equipment, RF components, free open-source and commercial LTE software, a
configurable RF network and recorded radar waveform samples. It supports RF
channel emulated and over-the-air radiated modes. The testbed can be remotely
accessed and configured. An RF switching network allows for designing many
different experiments that can involve a variety of real and virtual radios
with support for multiple-input multiple-output (MIMO) antenna operation. We
present the testbed, the research it has enabled and some valuable lessons that
we learned and that may help designing, developing, and operating future
wireless testbeds.Comment: In Proceeding of the 10th ACM International Workshop on Wireless
Network Testbeds, Experimental Evaluation & Characterization (WiNTECH),
Snowbird, Utah, October 201
Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis
Open Radio Access Network (O-RAN) has introduced an emerging RAN architecture
that enables openness, intelligence, and automated control. The RAN Intelligent
Controller (RIC) provides the platform to design and deploy RAN controllers.
xApps are the applications which will take this responsibility by leveraging
machine learning (ML) algorithms and acting in near-real time. Despite the
opportunities provided by this new architecture, the progress of practical
artificial intelligence (AI)-based solutions for network control and automation
has been slow. This is mostly because of the lack of an endto-end solution for
designing, deploying, and testing AI-based xApps fully executable in real O-RAN
network. In this paper we introduce an end-to-end O-RAN design and evaluation
procedure and provide a detailed discussion of developing a Reinforcement
Learning (RL) based xApp by using two different RL approaches and considering
the latest released O-RAN architecture and interfaces.Comment: This article has been accepted for publication in IEEE GLOBECOM 202
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