9,333 research outputs found
Channel Characterization for Chip-scale Wireless Communications within Computing Packages
Wireless Network-on-Chip (WNoC) appears as a promising alternative to
conventional interconnect fabrics for chip-scale communications. WNoC takes
advantage of an overlaid network composed by a set of millimeter-wave antennas
to reduce latency and increase throughput in the communication between cores.
Similarly, wireless inter-chip communication has been also proposed to improve
the information transfer between processors, memory, and accelerators in
multi-chip settings. However, the wireless channel remains largely unknown in
both scenarios, especially in the presence of realistic chip packages. This
work addresses the issue by accurately modeling flip-chip packages and
investigating the propagation both its interior and its surroundings. Through
parametric studies, package configurations that minimize path loss are obtained
and the trade-offs observed when applying such optimizations are discussed.
Single-chip and multi-chip architectures are compared in terms of the path loss
exponent, confirming that the amount of bulk silicon found in the pathway
between transmitter and receiver is the main determinant of losses.Comment: To be presented 12th IEEE/ACM International Symposium on
Networks-on-Chip (NOCS 2018); Torino, Italy; October 201
An Analytical Model of Packet Collisions in IEEE 802.15.4 Wireless Networks
Numerous studies showed that concurrent transmissions can boost wireless
network performance despite collisions. While these works provide empirical
evidence that concurrent transmissions may be received reliably, existing
signal capture models only partially explain the root causes of this
phenomenon. We present a comprehensive mathematical model that reveals the
reasons and provides insights on the key parameters affecting the performance
of MSK-modulated transmissions. A major contribution is a closed-form
derivation of the receiver bit decision variable for arbitrary numbers of
colliding signals and constellations of power ratios, timing offsets, and
carrier phase offsets. We systematically explore the root causes for successful
packet delivery under concurrent transmissions across the whole parameter space
of the model. We confirm the capture threshold behavior observed in previous
studies but also reveal new insights relevant for the design of optimal
protocols: We identify capture zones depending not only on the signal power
ratio but also on time and phase offsets.Comment: Accepted for publication in the IEEE Transactions on Wireless
Communications under the title "On the Reception of Concurrent Transmissions
in Wireless Sensor Networks.
A Communication Monitor for Wireless Sensor Networks Based on Software Defined Radio
Link quality estimation of reliability-crucial wireless sensor networks (WSNs) is often limited by the observability and testability of single-chip radio transceivers. The estimation is often based on collection of packer-level statistics, including packet reception rate, or vendor-specific registers, such as CC2420's Received Signal Strength Indicator (RSSI) and Link Quality Indicator (LQI). The speed or accuracy of such metrics limits the performance of reliability mechanisms built in wireless sensor networks. To improve link quality estimation in WSNs, we designed a powerful wireless communication monitor based on Software Defined Radio (SDR). We studied the relations between three implemented link quality metrics and packet reception rate under different channel conditions. Based on a comparison of the metrics' relative advantages, we proposed using a combination of them for fast and accurate estimation of a sensor network link
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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