41 research outputs found
Distribution of Cell Area in Bounded Poisson Voronoi Tessellations with Application to Secure Local Connectivity
Poisson Voronoi tessellations have been used in modeling many types of
systems across different sciences, from geography and astronomy to
telecommunications. The existing literature on the statistical properties of
Poisson Voronoi cells is vast, however, little is known about the properties of
Voronoi cells located close to the boundaries of a compact domain. In a domain
with boundaries, some Voronoi cells would be naturally clipped by the boundary,
and the cell area falling inside the deployment domain would have different
statistical properties as compared to those of non-clipped Voronoi cells
located in the bulk of the domain. In this paper, we consider the planar
Voronoi tessellation induced by a homogeneous Poisson point process of
intensity in a quadrant, where the two half-axes represent
boundaries. We show that the mean cell area is less than when
the seed is located exactly at the boundary, and it can be larger than
when the seed lies close to the boundary. In addition, we
calculate the second moment of cell area at two locations for the seed: (i) at
the corner of a quadrant, and (ii) at the boundary of the half-plane. We
illustrate that the two-parameter Gamma distribution, with location-dependent
parameters calculated using the method of moments, can be of use in
approximating the distribution of cell area. As a potential application, we use
the Gamma approximations to study the degree distribution for secure
connectivity in wireless sensor networks deployed over a domain with
boundaries.Comment: to be publishe
Co-primary inter-operator spectrum sharing over a limited spectrum pool using repeated games
We consider two small cell operators deployed in the same geographical area,
sharing spectrum resources from a common pool. A method is investigated to
coordinate the utilization of the spectrum pool without monetary transactions
and without revealing operator-specific information to other parties. For this,
we construct a protocol based on asking and receiving spectrum usage favors by
the operators, and keeping a book of the favors. A spectrum usage favor is
exchanged between the operators if one is asking for a permission to use some
of the resources from the pool on an exclusive basis, and the other is willing
to accept that. As a result, the proposed method does not force an operator to
take action. An operator with a high load may take spectrum usage favors from
an operator that has few users to serve, and it is likely to return these
favors in the future to show a cooperative spirit and maintain reciprocity. We
formulate the interactions between the operators as a repeated game and
determine rules to decide whether to ask or grant a favor at each stage game.
We illustrate that under frequent network load variations, which are expected
to be prominent in small cell deployments, both operators can attain higher
user rates as compared to the case of no coordination of the resource
utilization.Comment: To be published in proceedings of IEEE International Conference on
Communications (ICC) at London, Jun. 201
Counterfactual Explainer Framework for Deep Reinforcement Learning Models Using Policy Distillation
Deep Reinforcement Learning (DRL) has demonstrated promising capability in
solving complex control problems. However, DRL applications in safety-critical
systems are hindered by the inherent lack of robust verification techniques to
assure their performance in such applications. One of the key requirements of
the verification process is the development of effective techniques to explain
the system functionality, i.e., why the system produces specific results in
given circumstances. Recently, interpretation methods based on the
Counterfactual (CF) explanation approach have been proposed to address the
problem of explanation in DRLs. This paper proposes a novel CF explanation
framework to explain the decisions made by a black-box DRL. To evaluate the
efficacy of the proposed explanation framework, we carried out several
experiments in the domains of automated driving systems and Atari Pong game.
Our analysis demonstrates that the proposed framework generates plausible and
meaningful explanations for various decisions made by deep underlying DRLs.
Source codes are available at:
\url{https://github.com/Amir-Samadi/Counterfactual-Explanation
Multimodal Manoeuvre and Trajectory Prediction for Autonomous Vehicles Using Transformer Networks
Predicting the behaviour (i.e. manoeuvre/trajectory) of other road users,
including vehicles, is critical for the safe and efficient operation of
autonomous vehicles (AVs), a.k.a. automated driving systems (ADSs). Due to the
uncertain future behaviour of vehicles, multiple future behaviour modes are
often plausible for a vehicle in a given driving scene. Therefore, multimodal
prediction can provide richer information than single-mode prediction enabling
AVs to perform a better risk assessment. To this end, we propose a novel
multimodal prediction framework that can predict multiple plausible behaviour
modes and their likelihoods. The proposed framework includes a bespoke problem
formulation for manoeuvre prediction, a novel transformer-based prediction
model, and a tailored training method for multimodal manoeuvre and trajectory
prediction. The performance of the framework is evaluated using two public
benchmark highway driving datasets, namely NGSIM and highD. The results show
that the proposed framework outperforms the state-of-the-art multimodal methods
in the literature in terms of prediction error and is capable of predicting
plausible manoeuvre and trajectory modes.Comment: 8 pages, 3 figures, submitted to IEEE RA
Two-Hop Connectivity to the Roadside in a VANET Under the Random Connection Model
We compute the expected number of cars that have at least one two-hop path to
a fixed roadside unit in a one-dimensional vehicular ad hoc network in which
other cars can be used as relays to reach a roadside unit when they do not have
a reliable direct link. The pairwise channels between cars experience Rayleigh
fading in the random connection model, and so exist, with probability function
of the mutual distance between the cars, or between the cars and the roadside
unit. We derive exact equivalents for this expected number of cars when the car
density tends to zero and to infinity, and determine its behaviour using
an infinite oscillating power series in , which is accurate for all
regimes. We also corroborate those findings to a realistic situation, using
snapshots of actual traffic data. Finally, a normal approximation is discussed
for the probability mass function of the number of cars with a two-hop
connection to the origin. The probability mass function appears to be well
fitted by a Gaussian approximation with mean equal to the expected number of
cars with two hops to the origin.Comment: 21 pages, 7 figure