7,578 research outputs found
Approximation Algorithms for Wireless Link Scheduling with Flexible Data Rates
We consider scheduling problems in wireless networks with respect to flexible
data rates. That is, more or less data can be transmitted per time depending on
the signal quality, which is determined by the
signal-to-interference-plus-noise ratio (SINR). Each wireless link has a
utility function mapping SINR values to the respective data rates. We have to
decide which transmissions are performed simultaneously and (depending on the
problem variant) also which transmission powers are used.
In the capacity-maximization problem, one strives to maximize the overall
network throughput, i.e., the summed utility of all links. For arbitrary
utility functions (not necessarily continuous ones), we present an O(log
n)-approximation when having n communication requests. This algorithm is built
on a constant-factor approximation for the special case of the respective
problem where utility functions only consist of a single step. In other words,
each link has an individual threshold and we aim at maximizing the number of
links whose threshold is satisfied. On the way, this improves the result in
[Kesselheim, SODA 2011] by not only extending it to individual thresholds but
also showing a constant approximation factor independent of assumptions on the
underlying metric space or the network parameters.
In addition, we consider the latency-minimization problem. Here, each link
has a demand, e.g., representing an amount of data. We have to compute a
schedule of shortest possible length such that for each link the demand is
fulfilled, that is the overall summed utility (or data transferred) is at least
as large as its demand. Based on the capacity-maximization algorithm, we show
an O(log^2 n)-approximation for this problem
Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks
Emerging 5G systems will need to efficiently support both enhanced mobile
broadband traffic (eMBB) and ultra-low-latency communications (URLLC) traffic.
In these systems, time is divided into slots which are further sub-divided into
minislots. From a scheduling perspective, eMBB resource allocations occur at
slot boundaries, whereas to reduce latency URLLC traffic is pre-emptively
overlapped at the minislot timescale, resulting in selective
superposition/puncturing of eMBB allocations. This approach enables minimal
URLLC latency at a potential rate loss to eMBB traffic.
We study joint eMBB and URLLC schedulers for such systems, with the dual
objectives of maximizing utility for eMBB traffic while immediately satisfying
URLLC demands. For a linear rate loss model (loss to eMBB is linear in the
amount of URLLC superposition/puncturing), we derive an optimal joint
scheduler. Somewhat counter-intuitively, our results show that our dual
objectives can be met by an iterative gradient scheduler for eMBB traffic that
anticipates the expected loss from URLLC traffic, along with an URLLC demand
scheduler that is oblivious to eMBB channel states, utility functions and
allocation decisions of the eMBB scheduler. Next we consider a more general
class of (convex/threshold) loss models and study optimal online joint
eMBB/URLLC schedulers within the broad class of channel state dependent but
minislot-homogeneous policies. A key observation is that unlike the linear rate
loss model, for the convex and threshold rate loss models, optimal eMBB and
URLLC scheduling decisions do not de-couple and joint optimization is necessary
to satisfy the dual objectives. We validate the characteristics and benefits of
our schedulers via simulation
Beyond Geometry : Towards Fully Realistic Wireless Models
Signal-strength models of wireless communications capture the gradual fading
of signals and the additivity of interference. As such, they are closer to
reality than other models. However, nearly all theoretic work in the SINR model
depends on the assumption of smooth geometric decay, one that is true in free
space but is far off in actual environments. The challenge is to model
realistic environments, including walls, obstacles, reflections and anisotropic
antennas, without making the models algorithmically impractical or analytically
intractable.
We present a simple solution that allows the modeling of arbitrary static
situations by moving from geometry to arbitrary decay spaces. The complexity of
a setting is captured by a metricity parameter Z that indicates how far the
decay space is from satisfying the triangular inequality. All results that hold
in the SINR model in general metrics carry over to decay spaces, with the
resulting time complexity and approximation depending on Z in the same way that
the original results depends on the path loss term alpha. For distributed
algorithms, that to date have appeared to necessarily depend on the planarity,
we indicate how they can be adapted to arbitrary decay spaces.
Finally, we explore the dependence on Z in the approximability of core
problems. In particular, we observe that the capacity maximization problem has
exponential upper and lower bounds in terms of Z in general decay spaces. In
Euclidean metrics and related growth-bounded decay spaces, the performance
depends on the exact metricity definition, with a polynomial upper bound in
terms of Z, but an exponential lower bound in terms of a variant parameter phi.
On the plane, the upper bound result actually yields the first approximation of
a capacity-type SINR problem that is subexponential in alpha
Content Distribution by Multiple Multicast Trees and Intersession Cooperation: Optimal Algorithms and Approximations
In traditional massive content distribution with multiple sessions, the
sessions form separate overlay networks and operate independently, where some
sessions may suffer from insufficient resources even though other sessions have
excessive resources. To cope with this problem, we consider the universal
swarming approach, which allows multiple sessions to cooperate with each other.
We formulate the problem of finding the optimal resource allocation to maximize
the sum of the session utilities and present a subgradient algorithm which
converges to the optimal solution in the time-average sense. The solution
involves an NP-hard subproblem of finding a minimum-cost Steiner tree. We cope
with this difficulty by using a column generation method, which reduces the
number of Steiner-tree computations. Furthermore, we allow the use of
approximate solutions to the Steiner-tree subproblem. We show that the
approximation ratio to the overall problem turns out to be no less than the
reciprocal of the approximation ratio to the Steiner-tree subproblem.
Simulation results demonstrate that universal swarming improves the performance
of resource-poor sessions with negligible impact to resource-rich sessions. The
proposed approach and algorithm are expected to be useful for
infrastructure-based content distribution networks with long-lasting sessions
and relatively stable network environment
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