21,800 research outputs found
An Energy-Efficient Controller for Wirelessly-Powered Communication Networks
In a wirelessly-powered communication network (WPCN), an energy access point
(E-AP) supplies the energy needs of the network nodes through radio frequency
wave transmission, and the nodes store their received energy in their batteries
for possible data transmission. In this paper, we propose an online control
policy for energy transfer from the E-AP to the wireless nodes and for data
transfer among the nodes. With our proposed control policy, all data queues of
the nodes are stable, while the average energy consumption of the network is
shown to be within a bounded gap of the minimum energy required for stabilizing
the network. Our proposed policy is designed using a quadratic Lyapunov
function to capture the limitations on the energy consumption of the nodes
imposed by their battery levels. We show that under the proposed control
policy, the backlog level in the data queues and the stored energy level in the
batteries fluctuate in small intervals around some constant levels.
Consequently, by imposing negligible average data drop rate, the data buffer
size and the battery capacity of the nodes can be significantly reduced
A Design Methodology for Space-Time Adapter
This paper presents a solution to efficiently explore the design space of
communication adapters. In most digital signal processing (DSP) applications,
the overall architecture of the system is significantly affected by
communication architecture, so the designers need specifically optimized
adapters. By explicitly modeling these communications within an effective
graph-theoretic model and analysis framework, we automatically generate an
optimized architecture, named Space-Time AdapteR (STAR). Our design flow inputs
a C description of Input/Output data scheduling, and user requirements
(throughput, latency, parallelism...), and formalizes communication constraints
through a Resource Constraints Graph (RCG). The RCG properties enable an
efficient architecture space exploration in order to synthesize a STAR
component. The proposed approach has been tested to design an industrial data
mixing block example: an Ultra-Wideband interleaver.Comment: ISBN : 978-1-59593-606-
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
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