4,741 research outputs found
Distributed Game Theoretic Optimization and Management of Multichannel ALOHA Networks
The problem of distributed rate maximization in multi-channel ALOHA networks
is considered. First, we study the problem of constrained distributed rate
maximization, where user rates are subject to total transmission probability
constraints. We propose a best-response algorithm, where each user updates its
strategy to increase its rate according to the channel state information and
the current channel utilization. We prove the convergence of the algorithm to a
Nash equilibrium in both homogeneous and heterogeneous networks using the
theory of potential games. The performance of the best-response dynamic is
analyzed and compared to a simple transmission scheme, where users transmit
over the channel with the highest collision-free utility. Then, we consider the
case where users are not restricted by transmission probability constraints.
Distributed rate maximization under uncertainty is considered to achieve both
efficiency and fairness among users. We propose a distributed scheme where
users adjust their transmission probability to maximize their rates according
to the current network state, while maintaining the desired load on the
channels. We show that our approach plays an important role in achieving the
Nash bargaining solution among users. Sequential and parallel algorithms are
proposed to achieve the target solution in a distributed manner. The
efficiencies of the algorithms are demonstrated through both theoretical and
simulation results.Comment: 34 pages, 6 figures, accepted for publication in the IEEE/ACM
Transactions on Networking, part of this work was presented at IEEE CAMSAP
201
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
Deep Learning for Frame Error Probability Prediction in BICM-OFDM Systems
In the context of wireless communications, we propose a deep learning
approach to learn the mapping from the instantaneous state of a frequency
selective fading channel to the corresponding frame error probability (FEP) for
an arbitrary set of transmission parameters. We propose an abstract model of a
bit interleaved coded modulation (BICM) orthogonal frequency division
multiplexing (OFDM) link chain and show that the maximum likelihood (ML)
estimator of the model parameters estimates the true FEP distribution. Further,
we exploit deep neural networks as a general purpose tool to implement our
model and propose a training scheme for which, even while training with the
binary frame error events (i.e., ACKs / NACKs), the network outputs converge to
the FEP conditioned on the input channel state. We provide simulation results
that demonstrate gains in the FEP prediction accuracy with our approach as
compared to the traditional effective exponential SIR metric (EESM) approach
for a range of channel code rates, and show that these gains can be exploited
to increase the link throughput.Comment: Submitted to 2018 IEEE International Conference on Acoustics, Speech
and Signal Processin
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