8,498 research outputs found
Makespan Minimization via Posted Prices
We consider job scheduling settings, with multiple machines, where jobs
arrive online and choose a machine selfishly so as to minimize their cost. Our
objective is the classic makespan minimization objective, which corresponds to
the completion time of the last job to complete. The incentives of the selfish
jobs may lead to poor performance. To reconcile the differing objectives, we
introduce posted machine prices. The selfish job seeks to minimize the sum of
its completion time on the machine and the posted price for the machine. Prices
may be static (i.e., set once and for all before any arrival) or dynamic (i.e.,
change over time), but they are determined only by the past, assuming nothing
about upcoming events. Obviously, such schemes are inherently truthful.
We consider the competitive ratio: the ratio between the makespan achievable
by the pricing scheme and that of the optimal algorithm. We give tight bounds
on the competitive ratio for both dynamic and static pricing schemes for
identical, restricted, related, and unrelated machine settings. Our main result
is a dynamic pricing scheme for related machines that gives a constant
competitive ratio, essentially matching the competitive ratio of online
algorithms for this setting. In contrast, dynamic pricing gives poor
performance for unrelated machines. This lower bound also exhibits a gap
between what can be achieved by pricing versus what can be achieved by online
algorithms
Feedback Allocation For OFDMA Systems With Slow Frequency-domain Scheduling
We study the problem of allocating limited feedback resources across multiple
users in an orthogonal-frequency-division-multiple-access downlink system with
slow frequency-domain scheduling. Many flavors of slow frequency-domain
scheduling (e.g., persistent scheduling, semi-persistent scheduling), that
adapt user-sub-band assignments on a slower time-scale, are being considered in
standards such as 3GPP Long-Term Evolution. In this paper, we develop a
feedback allocation algorithm that operates in conjunction with any arbitrary
slow frequency-domain scheduler with the goal of improving the throughput of
the system. Given a user-sub-band assignment chosen by the scheduler, the
feedback allocation algorithm involves solving a weighted sum-rate maximization
at each (slow) scheduling instant. We first develop an optimal
dynamic-programming-based algorithm to solve the feedback allocation problem
with pseudo-polynomial complexity in the number of users and in the total
feedback bit budget. We then propose two approximation algorithms with
complexity further reduced, for scenarios where the problem exhibits additional
structure.Comment: Accepted to IEEE Transactions on Signal Processin
Derandomization of Online Assignment Algorithms for Dynamic Graphs
This paper analyzes different online algorithms for the problem of assigning
weights to edges in a fully-connected bipartite graph that minimizes the
overall cost while satisfying constraints. Edges in this graph may disappear
and reappear over time. Performance of these algorithms is measured using
simulations. This paper also attempts to derandomize the randomized online
algorithm for this problem
Cell-Free Massive MIMO versus Small Cells
A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a
very large number of distributed access points (APs)which simultaneously serve
a much smaller number of users over the same time/frequency resources based on
directly measured channel characteristics. The APs and users have only one
antenna each. The APs acquire channel state information through time-division
duplex operation and the reception of uplink pilot signals transmitted by the
users. The APs perform multiplexing/de-multiplexing through conjugate
beamforming on the downlink and matched filtering on the uplink. Closed-form
expressions for individual user uplink and downlink throughputs lead to max-min
power control algorithms. Max-min power control ensures uniformly good service
throughout the area of coverage. A pilot assignment algorithm helps to mitigate
the effects of pilot contamination, but power control is far more important in
that regard.
Cell-Free Massive MIMO has considerably improved performance with respect to
a conventional small-cell scheme, whereby each user is served by a dedicated
AP, in terms of both 95%-likely per-user throughput and immunity to shadow
fading spatial correlation. Under uncorrelated shadow fading conditions, the
cell-free scheme provides nearly 5-fold improvement in 95%-likely per-user
throughput over the small-cell scheme, and 10-fold improvement when shadow
fading is correlated.Comment: EEE Transactions on Wireless Communications, accepted for publicatio
Competitive-Ratio Approximation Schemes for Minimizing the Makespan in the Online-List Model
We consider online scheduling on multiple machines for jobs arriving
one-by-one with the objective of minimizing the makespan. For any number of
identical parallel or uniformly related machines, we provide a
competitive-ratio approximation scheme that computes an online algorithm whose
competitive ratio is arbitrarily close to the best possible competitive ratio.
We also determine this value up to any desired accuracy. This is the first
application of competitive-ratio approximation schemes in the online-list
model. The result proves the applicability of the concept in different online
models. We expect that it fosters further research on other online problems
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