387 research outputs found
Nearly Optimal Bounds for Distributed Wireless Scheduling in the SINR Model
We study the wireless scheduling problem in the SINR model. More
specifically, given a set of links, each a sender-receiver pair, we wish to
partition (or \emph{schedule}) the links into the minimum number of slots, each
satisfying interference constraints allowing simultaneous transmission. In the
basic problem, all senders transmit with the same uniform power.
We give a distributed -approximation algorithm for the scheduling
problem, matching the best ratio known for centralized algorithms. It holds in
arbitrary metric space and for every length-monotone and sublinear power
assignment. It is based on an algorithm of Kesselheim and V\"ocking, whose
analysis we improve by a logarithmic factor. We show that every distributed
algorithm uses slots to schedule certain instances that
require only two slots, which implies that the best possible absolute
performance guarantee is logarithmic.Comment: Expanded and improved version of ICALP 2011 conference pape
On Wireless Scheduling Using the Mean Power Assignment
In this paper the problem of scheduling with power control in wireless
networks is studied: given a set of communication requests, one needs to assign
the powers of the network nodes, and schedule the transmissions so that they
can be done in a minimum time, taking into account the signal interference of
concurrently transmitting nodes. The signal interference is modeled by SINR
constraints. Approximation algorithms are given for this problem, which use the
mean power assignment. The problem of schduling with fixed mean power
assignment is also considered, and approximation guarantees are proven
Fractional Power Control for Decentralized Wireless Networks
We consider a new approach to power control in decentralized wireless
networks, termed fractional power control (FPC). Transmission power is chosen
as the current channel quality raised to an exponent -s, where s is a constant
between 0 and 1. The choices s = 1 and s = 0 correspond to the familiar cases
of channel inversion and constant power transmission, respectively. Choosing s
in (0,1) allows all intermediate policies between these two extremes to be
evaluated, and we see that usually neither extreme is ideal. We derive
closed-form approximations for the outage probability relative to a target SINR
in a decentralized (ad hoc or unlicensed) network as well as for the resulting
transmission capacity, which is the number of users/m^2 that can achieve this
SINR on average. Using these approximations, which are quite accurate over
typical system parameter values, we prove that using an exponent of 1/2
minimizes the outage probability, meaning that the inverse square root of the
channel strength is a sensible transmit power scaling for networks with a
relatively low density of interferers. We also show numerically that this
choice of s is robust to a wide range of variations in the network parameters.
Intuitively, s=1/2 balances between helping disadvantaged users while making
sure they do not flood the network with interference.Comment: 16 pages, in revision for IEEE Trans. on Wireless Communicatio
The Price of Local Power Control in Wireless Scheduling
We consider the problem of scheduling wireless links in the physical model,
where we seek an assignment of power levels and a partition of the given set of
links into the minimum number of subsets satisfying the
signal-to-interference-and-noise-ratio (SINR) constraints. Specifically, we are
interested in the efficiency of local power assignment schemes, or oblivious
power schemes, in approximating wireless scheduling. Oblivious power schemes
are motivated by networking scenarios when power levels must be decided in
advance, and not as part of the scheduling computation.
We first show that the known algorithms fail to obtain sub-logarithmic
bounds; that is, their approximation ratio are , where is the number of links, is the ratio of
the maximum and minimum link lengths, and hides
doubly-logarithmic factors. We then present the first
-approximation algorithm, which is known to be best
possible (in terms of ) for oblivious power schemes. We achieve this by
representing interference by a conflict graph, which allows the application of
graph-theoretic results for a variety of related problems, including the
weighted capacity problem. We explore further the contours of approximability,
and find the choice of power assignment matters; that not all metric spaces are
equal; and that the presence of weak links makes the problem harder. Combined,
our result resolve the price of oblivious power for wireless scheduling, or the
value of allowing unfettered power control.Comment: 23 pages, 2 figure
On a game theoretic approach to capacity maximization in wireless networks
We consider the capacity problem (or, the single slot scheduling problem) in
wireless networks. Our goal is to maximize the number of successful connections
in arbitrary wireless networks where a transmission is successful only if the
signal-to-interference-plus-noise ratio at the receiver is greater than some
threshold. We study a game theoretic approach towards capacity maximization
introduced by Andrews and Dinitz (INFOCOM 2009) and Dinitz (INFOCOM 2010). We
prove vastly improved bounds for the game theoretic algorithm. In doing so, we
achieve the first distributed constant factor approximation algorithm for
capacity maximization for the uniform power assignment. When compared to the
optimum where links may use an arbitrary power assignment, we prove a approximation, where is the ratio between the largest and the
smallest link in the network. This is an exponential improvement of the
approximation factor compared to existing results for distributed algorithms.
All our results work for links located in any metric space. In addition, we
provide simulation studies clarifying the picture on distributed algorithms for
capacity maximization.Comment: 16 pages, 5 figures (to appear in INFOCOM 2011
Randomized Distributed Configuration Management of Wireless Networks: Multi-layer Markov Random Fields and Near-Optimality
Distributed configuration management is imperative for wireless
infrastructureless networks where each node adjusts locally its physical and
logical configuration through information exchange with neighbors. Two issues
remain open. The first is the optimality. The second is the complexity. We
study these issues through modeling, analysis, and randomized distributed
algorithms. Modeling defines the optimality. We first derive a global
probabilistic model for a network configuration which characterizes jointly the
statistical spatial dependence of a physical- and a logical-configuration. We
then show that a local model which approximates the global model is a two-layer
Markov Random Field or a random bond model. The complexity of the local model
is the communication range among nodes. The local model is near-optimal when
the approximation error to the global model is within a given error bound. We
analyze the trade-off between an approximation error and complexity, and derive
sufficient conditions on the near-optimality of the local model. We validate
the model, the analysis and the randomized distributed algorithms also through
simulation.Comment: 15 pages, revised and submitted to IEEE Trans. on Networkin
Resource Allocation via Sum-Rate Maximization in the Uplink of Multi-Cell OFDMA Networks
In this paper, we consider maximizing the sum-rate in the uplink of a
multi-cell OFDMA network. The problem has a non-convex combinatorial structure
and is known to be NP hard. Due to the inherent complexity of implementing the
optimal solution, firstly, we derive an upper and lower bound to the optimal
average network throughput. Moreover, we investigate the performance of a near
optimal single cell resource allocation scheme in the presence of ICI which
leads to another easily computable lower bound. We then develop a centralized
sub-optimal scheme that is composed of a geometric programming based power
control phase in conjunction with an iterative subcarrier allocation phase.
Although, the scheme is computationally complex, it provides an effective
benchmark for low complexity schemes even without the power control phase.
Finally, we propose less complex centralized and distributed schemes that are
well-suited for practical scenarios. The computational complexity of all
schemes is analyzed and performance is compared through simulations. Simulation
results demonstrate that the proposed low complexity schemes can achieve
comparable performance to the centralized sub-optimal scheme in various
scenarios. Moreover, comparisons with the upper and lower bounds provide
insight on the performance gap between the proposed schemes and the optimal
solution
Resource Optimization in Device-to-Device Cellular Systems Using Time-Frequency Hopping
We develop a flexible and accurate framework for device-to-device (D2D)
communication in the context of a conventional cellular network, which allows
for time-frequency resources to be either shared or orthogonally partitioned
between the two networks. Using stochastic geometry, we provide accurate
expressions for SINR distributions and average rates, under an assumption of
interference randomization via time and/or frequency hopping, for both
dedicated and shared spectrum approaches. We obtain analytical results in
closed or semi-closed form in high SNR regime, that allow us to easily explore
the impact of key parameters (e.g., the load and hopping probabilities) on the
network performance. In particular, unlike other models, the expressions we
obtain are tractable, i.e., they can be efficiently optimized without extensive
simulation. Using these, we optimize the hopping probabilities for the D2D
links, i.e., how often they should request a time or frequency slot. This can
be viewed as an optimized lower bound to other more sophisticated scheduling
schemes. We also investigate the optimal resource partitions between D2D and
cellular networks when they use orthogonal resources
A Fast Distributed Approximation Algorithm for Minimum Spanning Trees in the SINR Model
A fundamental problem in wireless networks is the \emph{minimum spanning
tree} (MST) problem: given a set of wireless nodes, compute a spanning tree
, so that the total cost of is minimized. In recent years, there has
been a lot of interest in the physical interference model based on SINR
constraints. Distributed algorithms are especially challenging in the SINR
model, because of the non-locality of the model.
In this paper, we develop a fast distributed approximation algorithm for MST
construction in an SINR based distributed computing model. For an -node
network, our algorithm's running time is and produces
a spanning tree whose cost is within times the optimal (MST cost),
where denotes the diameter of the disk graph obtained by using the maximum
possible transmission range, and denotes
the "distance diversity" w.r.t. the largest and smallest distances between two
nodes. (When is -polynomial, .)
Our algorithm's running time is essentially optimal (upto a logarithmic
factor), since computing {\em any} spanning tree takes time; thus
our algorithm produces a low cost spanning tree in time only a logarithmic
factor more than the time to compute a spanning tree. The distributed
scheduling complexity of the spanning tree resulted from our algorithm is
. Our algorithmic design techniques can be useful in designing
efficient distributed algorithms for related "global" problems in wireless
networks in the SINR model
Leveraging Multiple Channels in Ad Hoc Networks
We examine the utility of multiple channels of communication in wireless
networks under the SINR model of interference. The central question is whether
the use of multiple channels can result in linear speedup, up to some
fundamental limit. We answer this question affirmatively for the data
aggregation problem, perhaps the most fundamental problem in sensor networks.
To achieve this, we form a hierarchical structure of independent interest, and
illustrate its versatility by obtaining a new algorithm with linear speedup for
the node coloring problem.Comment: 20 pages, appeared in PODC'1
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