10 research outputs found
Optimal Energy Allocation For Delay-Constrained Traffic Over Fading Multiple Access Channels
In this paper, we consider a multiple-access fading channel where users
transmit to a single base station (BS) within a limited number of time slots.
We assume that each user has a fixed amount of energy available to be consumed
over the transmission window. We derive the optimal energy allocation policy
for each user that maximizes the total system throughput under two different
assumptions on the channel state information. First, we consider the offline
allocation problem where the channel states are known a priori before
transmission. We solve a convex optimization problem to maximize the
sum-throughput under energy and delay constraints. Next, we consider the online
allocation problem, where the channels are causally known to the BS and obtain
the optimal energy allocation via dynamic programming when the number of users
is small. We also develop a suboptimal resource allocation algorithm whose
performance is close to the optimal one. Numerical results are presented
showing the superiority of the proposed algorithms over baseline algorithms in
various scenarios.Comment: IEEE Global Communications Conference: Wireless Communications
(Globecom2016 WC
Proactive Location-Based Scheduling of Delay-Constrained Traffic Over Fading Channels
In this paper, proactive resource allocation based on user location for
point-to-point communication over fading channels is introduced, whereby the
source must transmit a packet when the user requests it within a deadline of a
single time slot. We introduce a prediction model in which the source predicts
the request arrival slots ahead, where denotes the prediction
window (PW) size. The source allocates energy to transmit some bits proactively
for each time slot of the PW with the objective of reducing the transmission
energy over the non-predictive case. The requests are predicted based on the
user location utilizing the prior statistics about the user requests at each
location. We also assume that the prediction is not perfect. We propose
proactive scheduling policies to minimize the expected energy consumption
required to transmit the requested packets under two different assumptions on
the channel state information at the source. In the first scenario, offline
scheduling, we assume the channel states are known a-priori at the source at
the beginning of the PW. In the second scenario, online scheduling, it is
assumed that the source has causal knowledge of the channel state. Numerical
results are presented showing the gains achieved by using proactive scheduling
policies compared with classical (reactive) networks. Simulation results also
show that increasing the PW size leads to a significant reduction in the
consumed transmission energy even with imperfect prediction.Comment: Conference: VTC2016-Fall, At Montreal-Canad
A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy
A distinguishing characteristic of federated learning is that the (local)
client data could have statistical heterogeneity. This heterogeneity has
motivated the design of personalized learning, where individual (personalized)
models are trained, through collaboration. There have been various
personalization methods proposed in literature, with seemingly very different
forms and methods ranging from use of a single global model for local
regularization and model interpolation, to use of multiple global models for
personalized clustering, etc. In this work, we begin with a generative
framework that could potentially unify several different algorithms as well as
suggest new algorithms. We apply our generative framework to personalized
estimation, and connect it to the classical empirical Bayes' methodology. We
develop private personalized estimation under this framework. We then use our
generative framework for learning, which unifies several known personalized FL
algorithms and also suggests new ones; we propose and study a new algorithm
AdaPeD based on a Knowledge Distillation, which numerically outperforms several
known algorithms. We also develop privacy for personalized learning methods
with guarantees for user-level privacy and composition. We numerically evaluate
the performance as well as the privacy for both the estimation and learning
problems, demonstrating the advantages of our proposed methods
Fundamental limits of cache-aided MIMO wireless networks
This paper studies the Multi-Input-Multi-Output (MIMO) interference networks with arbitrary number of transmitters and receivers, where both the transmitters and receivers are equipped with caches. The main goal is to design content placement and delivery schemes that minimize the worst case normalized delivery time (NDT). First, we propose a delivery scheme for the cache-aided Single-Input-Multiple-Output (SIMO) interference networks. Then, we obtain the achievable NDT of the cache-aided MIMO interference networks using the decomposition property of splitting each multi-antenna transmitter into multiple single antenna transmitters. Furthermore, we derive an information-theoretic bound on the optimal NDT of the cache-aided MIMO interference network. Analytical results show that the proposed scheme is within a multiplicative gap of 2 from the derived lower bound independent of all system parameters for any uncoded cache placement scheme. We also derive a novel delivery scheme for the cache-aided Multi-Input-Single-Output (MISO) interference network outperforming our proposed scheme for the cache-aided MIMO interference network. The numerical results show the superiority of our proposed scheme over the state-of-the-art schemes in the literature. Our results show that the coded caching gain has a more significant contribution in reducing the transmission latency than the spatial multiplexing gain. Our results indicate that the receive-antennas become more effective in reducing the NDT than the transmit-antennas in the presence of caches at the receiver-side. In addition, we show that increasing the number of transmit-antennas has a higher gain in reducing the NDT than adding more transmitters in the cache-aided MISO interference network
Differentially Private Stochastic Linear Bandits: (Almost) for Free
In this paper, we propose differentially private algorithms for the problem
of stochastic linear bandits in the central, local and shuffled models. In the
central model, we achieve almost the same regret as the optimal non-private
algorithms, which means we get privacy for free. In particular, we achieve a
regret of matching the known lower
bound for private linear bandits, while the best previously known algorithm
achieves . In the local case, we achieve
a regret of which matches the
non-private regret for constant , but suffers a regret penalty when
is small. In the shuffled model, we also achieve regret of
%for small as in the
central case, while the best previously known algorithm suffers a regret of
. Our numerical evaluation validates
our theoretical results
Proactive location-based scheduling of delay-constrained traffic over fading channels
In this paper, proactive resource allocation based on user location for point-to-point communication over fading channels is introduced, whereby the source must transmit a packet when the user requests it within a deadline of a single time slot. We introduce a prediction model in which the source predicts the request arrival Tp slots ahead, where Tp denotes the prediction window (PW) size. The source allocates energy to transmit some bits proactively for each time slot of the PW with the objective of reducing the transmission energy over the non-predictive case. The requests are predicted based on the user location utilizing the prior statistics about the user requests at each location. We also assume that the prediction is not perfect. We propose proactive scheduling policies to minimize the expected energy consumption required to transmit the requested packets under two different assumptions on the channel state information at the source. In the first scenario, offline scheduling, we assume the channel states are known a-priori at the source at the beginning of the PW. In the second scenario, online scheduling, it is assumed that the source has causal knowledge of the channel state. Numerical results are presented showing the gains achieved by using proactive scheduling policies compared with classical (reactive) netwo