58,513 research outputs found
Joint Access Point Selection and Power Allocation for Uplink Wireless Networks
We consider the distributed uplink resource allocation problem in a
multi-carrier wireless network with multiple access points (APs). Each mobile
user can optimize its own transmission rate by selecting a suitable AP and by
controlling its transmit power. Our objective is to devise suitable algorithms
by which mobile users can jointly perform these tasks in a distributed manner.
Our approach relies on a game theoretic formulation of the joint power control
and AP selection problem. In the proposed game, each user is a player with an
associated strategy containing a discrete variable (the AP selection decision)
and a continuous vector (the power allocation among multiple channels). We
provide characterizations of the Nash Equilibrium of the proposed game, and
present a set of novel algorithms that allow the users to efficiently optimize
their rates. Finally, we study the properties of the proposed algorithms as
well as their performance via extensive simulations.Comment: Revised and Resubmitted to IEEE Transactions on Signal Processin
Why It Takes So Long to Connect to a WiFi Access Point
Today's WiFi networks deliver a large fraction of traffic. However, the
performance and quality of WiFi networks are still far from satisfactory. Among
many popular quality metrics (throughput, latency), the probability of
successfully connecting to WiFi APs and the time cost of the WiFi connection
set-up process are the two of the most critical metrics that affect WiFi users'
experience. To understand the WiFi connection set-up process in real-world
settings, we carry out measurement studies on million mobile users from
representative cities associating with million APs in billion WiFi
sessions, collected from a mobile "WiFi Manager" App that tops the Android/iOS
App market. To the best of our knowledge, we are the first to do such large
scale study on: how large the WiFi connection set-up time cost is, what factors
affect the WiFi connection set-up process, and what can be done to reduce the
WiFi connection set-up time cost. Based on the measurement analysis, we develop
a machine learning based AP selection strategy that can significantly improve
WiFi connection set-up performance, against the conventional strategy purely
based on signal strength, by reducing the connection set-up failures from
to and reducing time costs of the connection set-up
processes by more than times.Comment: 11pages, conferenc
Joint Distributed Access Point Selection and Power Allocation in Cognitive Radio Networks
Spectrum management has been identified as a crucial step towards enabling
the technology of the cognitive radio network (CRN). Most of the current works
dealing with spectrum management in the CRN focus on a single task of the
problem, e.g., spectrum sensing, spectrum decision, spectrum sharing or
spectrum mobility. In this work, we argue that for certain network
configurations, jointly performing several tasks of the spectrum management
improves the spectrum efficiency. Specifically, we study the uplink resource
management problem in a CRN where there exist multiple cognitive users (CUs)
and access points (APs), with each AP operates on a set of non-overlapping
channels. The CUs, in order to maximize their uplink transmission rates, have
to associate to a suitable AP (spectrum decision), and to share the channels
belong to this AP with other CUs (spectrum sharing). These tasks are clearly
interdependent, and the problem of how they should be carried out efficiently
and distributedly is still open in the literature.
In this work we formulate this joint spectrum decision and spectrum sharing
problem into a non-cooperative game, in which the feasible strategy of a player
contains a discrete variable and a continuous vector. The structure of the game
is hence very different from most non-cooperative spectrum management game
proposed in the literature. We provide characterization of the Nash Equilibrium
(NE) of this game, and present a set of novel algorithms that allow the CUs to
distributively and efficiently select the suitable AP and share the channels
with other CUs. Finally, we study the properties of the proposed algorithms as
well as their performance via extensive simulations.Comment: Accepted by Infocom 2011; Infocom 2011, The 30th IEEE International
Conference on Computer Communication
Autonomous flying WiFi access point
Unmanned aerial vehicles (UAVs), aka drones, are
widely used civil and commercial applications. A promising one is
to use the drones as relying nodes to extend the wireless coverage.
However, existing solutions only focus on deploying them to
predefined locations. After that, they either remain stationary
or only move in predefined trajectories throughout the whole
deployment. In the open outdoor scenarios such as search and
rescue or large music events, etc., users can move and cluster
dynamically. As a result, network demand will change constantly
over time and hence will require the drones to adapt dynamically.
In this paper, we present a proof of concept implementation
of an UAV access point (AP) which can dynamically reposition
itself depends on the users movement on the ground. Our solution
is to continuously keeping track of the received signal strength
from the user devices for estimating the distance between users
devices and the drone, followed by trilateration to localise them.
This process is challenging because our on-site measurements
show that the heterogeneity of user devices means that change
of their signal strengths reacts very differently to the change of
distance to the drone AP. Our initial results demonstrate that
our drone is able to effectively localise users and autonomously
moving to a position closer to them
- …