39,528 research outputs found
Fair Coexistence of Scheduled and Random Access Wireless Networks: Unlicensed LTE/WiFi
We study the fair coexistence of scheduled and random access transmitters
sharing the same frequency channel. Interest in coexistence is topical due to
the need for emerging unlicensed LTE technologies to coexist fairly with WiFi.
However, this interest is not confined to LTE/WiFi as coexistence is likely to
become increasingly commonplace in IoT networks and beyond 5G. In this article
we show that mixing scheduled and random access incurs and inherent
throughput/delay cost, the cost of heterogeneity. We derive the joint
proportional fair rate allocation, which casts useful light on current LTE/WiFi
discussions. We present experimental results on inter-technology detection and
consider the impact of imperfect carrier sensing.Comment: 14 pages, 8 figures, journa
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning
Automatic decision-making approaches, such as reinforcement learning (RL),
have been applied to (partially) solve the resource allocation problem
adaptively in the cloud computing system. However, a complete cloud resource
allocation framework exhibits high dimensions in state and action spaces, which
prohibit the usefulness of traditional RL techniques. In addition, high power
consumption has become one of the critical concerns in design and control of
cloud computing systems, which degrades system reliability and increases
cooling cost. An effective dynamic power management (DPM) policy should
minimize power consumption while maintaining performance degradation within an
acceptable level. Thus, a joint virtual machine (VM) resource allocation and
power management framework is critical to the overall cloud computing system.
Moreover, novel solution framework is necessary to address the even higher
dimensions in state and action spaces. In this paper, we propose a novel
hierarchical framework for solving the overall resource allocation and power
management problem in cloud computing systems. The proposed hierarchical
framework comprises a global tier for VM resource allocation to the servers and
a local tier for distributed power management of local servers. The emerging
deep reinforcement learning (DRL) technique, which can deal with complicated
control problems with large state space, is adopted to solve the global tier
problem. Furthermore, an autoencoder and a novel weight sharing structure are
adopted to handle the high-dimensional state space and accelerate the
convergence speed. On the other hand, the local tier of distributed server
power managements comprises an LSTM based workload predictor and a model-free
RL based power manager, operating in a distributed manner.Comment: accepted by 37th IEEE International Conference on Distributed
Computing (ICDCS 2017
Adaptive multi-channel MAC protocol for dense VANET with directional antennas
Directional antennas in Ad hoc networks offer more benefits than the traditional antennas with omni-directional mode. With directional antennas, it can increase the spatial reuse of the wireless channel. A higher gain of directional antennas makes terminals a further transmission range and fewer hops to the destination. This paper presents the design, implementation and simulation results of a multi-channel Medium Access Control (MAC) protocols for dense Vehicular Ad hoc Networks using directional antennas with local beam tables. Numeric results show that our protocol performs better than the existing multichannel protocols in vehicular environment
MorphIC: A 65-nm 738k-Synapse/mm Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning
Recent trends in the field of neural network accelerators investigate weight
quantization as a means to increase the resource- and power-efficiency of
hardware devices. As full on-chip weight storage is necessary to avoid the high
energy cost of off-chip memory accesses, memory reduction requirements for
weight storage pushed toward the use of binary weights, which were demonstrated
to have a limited accuracy reduction on many applications when
quantization-aware training techniques are used. In parallel, spiking neural
network (SNN) architectures are explored to further reduce power when
processing sparse event-based data streams, while on-chip spike-based online
learning appears as a key feature for applications constrained in power and
resources during the training phase. However, designing power- and
area-efficient spiking neural networks still requires the development of
specific techniques in order to leverage on-chip online learning on binary
weights without compromising the synapse density. In this work, we demonstrate
MorphIC, a quad-core binary-weight digital neuromorphic processor embedding a
stochastic version of the spike-driven synaptic plasticity (S-SDSP) learning
rule and a hierarchical routing fabric for large-scale chip interconnection.
The MorphIC SNN processor embeds a total of 2k leaky integrate-and-fire (LIF)
neurons and more than two million plastic synapses for an active silicon area
of 2.86mm in 65nm CMOS, achieving a high density of 738k synapses/mm.
MorphIC demonstrates an order-of-magnitude improvement in the area-accuracy
tradeoff on the MNIST classification task compared to previously-proposed SNNs,
while having no penalty in the energy-accuracy tradeoff.Comment: This document is the paper as accepted for publication in the IEEE
Transactions on Biomedical Circuits and Systems journal (2019), the
fully-edited paper is available at
https://ieeexplore.ieee.org/document/876400
Life-Add: Lifetime Adjustable Design for WiFi Networks with Heterogeneous Energy Supplies
WiFi usage significantly reduces the battery lifetime of handheld devices
such as smartphones and tablets, due to its high energy consumption. In this
paper, we propose "Life-Add": a Lifetime Adjustable design for WiFi networks,
where the devices are powered by battery, electric power, and/or renewable
energy. In Life-Add, a device turns off its radio to save energy when the
channel is sensed to be busy, and sleeps for a random time period before
sensing the channel again. Life-Add carefully controls the devices' average
sleep periods to improve their throughput while satisfying their operation time
requirement. It is proven that Life-Add achieves near-optimal proportional-fair
utility performance for single access point (AP) scenarios. Moreover, Life-Add
alleviates the near-far effect and hidden terminal problem in general multiple
AP scenarios. Our ns-3 simulations show that Life-Add simultaneously improves
the lifetime, throughput, and fairness performance of WiFi networks, and
coexists harmoniously with IEEE 802.11.Comment: This is the technical report of our WiOpt paper. The paper received
the best student paper award at IEEE WiOpt 2013. The first three authors are
co-primary author
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