5,497 research outputs found
Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures
Reinforcement learning (RL) constitutes a promising solution for alleviating
the problem of traffic congestion. In particular, deep RL algorithms have been
shown to produce adaptive traffic signal controllers that outperform
conventional systems. However, in order to be reliable in highly dynamic urban
areas, such controllers need to be robust with the respect to a series of
exogenous sources of uncertainty. In this paper, we develop an open-source
callback-based framework for promoting the flexible evaluation of different
deep RL configurations under a traffic simulation environment. With this
framework, we investigate how deep RL-based adaptive traffic controllers
perform under different scenarios, namely under demand surges caused by special
events, capacity reductions from incidents and sensor failures. We extract
several key insights for the development of robust deep RL algorithms for
traffic control and propose concrete designs to mitigate the impact of the
considered exogenous uncertainties.Comment: 8 page
Performance Analysis of Micro Unmanned Airborne Communication Relays for Cellular Networks
This paper analyses the potential of utilising small unmanned-aerial-vehicles
(SUAV) as wireless relays for assisting cellular network performance. Whilst
high altitude wireless relays have been investigated over the past 2 decades,
the new class of low cost SUAVs offers new possibilities for addressing local
traffic imbalances and providing emergency coverage.We present field-test
results from an SUAV test-bed in both urban and rural environments. The results
show that trough-to-peak throughput improvements can be achieved for users in
poor coverage zones. Furthermore, the paper reinforces the experimental study
with large-scale network analysis using both stochastic geometry and multi-cell
simulation results.Comment: conferenc
Elastic Multi-resource Network Slicing: Can Protection Lead to Improved Performance?
In order to meet the performance/privacy requirements of future
data-intensive mobile applications, e.g., self-driving cars, mobile data
analytics, and AR/VR, service providers are expected to draw on shared
storage/computation/connectivity resources at the network "edge". To be
cost-effective, a key functional requirement for such infrastructure is
enabling the sharing of heterogeneous resources amongst tenants/service
providers supporting spatially varying and dynamic user demands. This paper
proposes a resource allocation criterion, namely, Share Constrained Slicing
(SCS), for slices allocated predefined shares of the network's resources, which
extends the traditional alpha-fairness criterion, by striking a balance among
inter- and intra-slice fairness vs. overall efficiency. We show that SCS has
several desirable properties including slice-level protection, envyfreeness,
and load driven elasticity. In practice, mobile users' dynamics could make the
cost of implementing SCS high, so we discuss the feasibility of using a simpler
(dynamically) weighted max-min as a surrogate resource allocation scheme. For a
setting with stochastic loads and elastic user requirements, we establish a
sufficient condition for the stability of the associated coupled network
system. Finally, and perhaps surprisingly, we show via extensive simulations
that while SCS (and/or the surrogate weighted max-min allocation) provides
inter-slice protection, they can achieve improved job delay and/or perceived
throughput, as compared to other weighted max-min based allocation schemes
whose intra-slice weight allocation is not share-constrained, e.g., traditional
max-min or discriminatory processor sharing
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