74 research outputs found
Energy-Efficient Resource Management in Ultra Dense Small Cell Networks: A Mean-Field Approach
In this paper, a novel approach for joint power control and user scheduling
is proposed for optimizing energy efficiency (EE), in terms of bits per unit
power, in ultra dense small cell networks (UDNs). To address this problem, a
dynamic stochastic game (DSG) is formulated between small cell base stations
(SBSs). This game enables to capture the dynamics of both queues and channel
states of the system. To solve this game, assuming a large homogeneous UDN
deployment, the problem is cast as a mean field game (MFG) in which the MFG
equilibrium is analyzed with the aid of two low-complexity tractable partial
differential equations. User scheduling is formulated as a stochastic
optimization problem and solved using the drift plus penalty (DPP) approach in
the framework of Lyapunov optimization. Remarkably, it is shown that by weaving
notions from Lyapunov optimization and mean field theory, the proposed solution
yields an equilibrium control policy per SBS which maximizes the network
utility while ensuring users' quality-of-service. Simulation results show that
the proposed approach achieves up to 18:1% gains in EE and 98.2% reductions in
the network's outage probability compared to a baseline model.Comment: 6 pages, 7 figures, GLOBECOM 2015 (published
Ultra Dense Small Cell Networks: Turning Density into Energy Efficiency
In this paper, a novel approach for joint power control and user scheduling
is proposed for optimizing energy efficiency (EE), in terms of bits per unit
energy, in ultra dense small cell networks (UDNs). Due to severe coupling in
interference, this problem is formulated as a dynamic stochastic game (DSG)
between small cell base stations (SBSs). This game enables to capture the
dynamics of both the queues and channel states of the system. To solve this
game, assuming a large homogeneous UDN deployment, the problem is cast as a
mean-field game (MFG) in which the MFG equilibrium is analyzed with the aid of
low-complexity tractable partial differential equations. Exploiting the
stochastic nature of the problem, user scheduling is formulated as a stochastic
optimization problem and solved using the drift plus penalty (DPP) approach in
the framework of Lyapunov optimization. Remarkably, it is shown that by weaving
notions from Lyapunov optimization and mean-field theory, the proposed solution
yields an equilibrium control policy per SBS which maximizes the network
utility while ensuring users' quality-of-service. Simulation results show that
the proposed approach achieves up to 70.7% gains in EE and 99.5% reductions in
the network's outage probabilities compared to a baseline model which focuses
on improving EE while attempting to satisfy the users' instantaneous
quality-of-service requirements.Comment: 15 pages, 21 figures (sub-figures are counted separately), IEEE
Journal on Selected Areas in Communications - Series on Green Communications
and Networking (Issue 2
Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations
In this paper, we investigate the problem of robust Reconfigurable
Intelligent Surface (RIS) phase-shifts configuration over heterogeneous
communication environments. The problem is formulated as a distributed learning
problem over different environments in a Federated Learning (FL) setting.
Equivalently, this corresponds to a game played between multiple RISs, as
learning agents, in heterogeneous environments. Using Invariant Risk
Minimization (IRM) and its FL equivalent, dubbed FL Games, we solve the RIS
configuration problem by learning invariant causal representations across
multiple environments and then predicting the phases. The solution corresponds
to playing according to Best Response Dynamics (BRD) which yields the Nash
Equilibrium of the FL game. The representation learner and the phase predictor
are modeled by two neural networks, and their performance is validated via
simulations against other benchmarks from the literature. Our results show that
causality-based learning yields a predictor that is 15% more accurate in unseen
Out-of-Distribution (OoD) environments.Comment: 6 pages, 4 figure
Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail
While the notion of age of information (AoI) has recently emerged as an
important concept for analyzing ultra-reliable low-latency communications
(URLLC), the majority of the existing works have focused on the average AoI
measure. However, an average AoI based design falls short in properly
characterizing the performance of URLLC systems as it cannot account for
extreme events that occur with very low probabilities. In contrast, in this
paper, the main objective is to go beyond the traditional notion of average AoI
by characterizing and optimizing a URLLC system while capturing the AoI tail
distribution. In particular, the problem of vehicles' power minimization while
ensuring stringent latency and reliability constraints in terms of
probabilistic AoI is studied. To this end, a novel and efficient mapping
between both AoI and queue length distributions is proposed. Subsequently,
extreme value theory (EVT) and Lyapunov optimization techniques are adopted to
formulate and solve the problem. Simulation results shows a nearly two-fold
improvement in terms of shortening the tail of the AoI distribution compared to
a baseline whose design is based on the maximum queue length among vehicles,
when the number of vehicular user equipment (VUE) pairs is 80. The results also
show that this performance gain increases significantly as the number of VUE
pairs increases.Comment: Accepted in IEEE GLOBECOM 2018 with 7 pages, 6 figure
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