148,628 research outputs found
Decentralized Delay Optimal Control for Interference Networks with Limited Renewable Energy Storage
In this paper, we consider delay minimization for interference networks with
renewable energy source, where the transmission power of a node comes from both
the conventional utility power (AC power) and the renewable energy source. We
assume the transmission power of each node is a function of the local channel
state, local data queue state and local energy queue state only. In turn, we
consider two delay optimization formulations, namely the decentralized
partially observable Markov decision process (DEC-POMDP) and Non-cooperative
partially observable stochastic game (POSG). In DEC-POMDP formulation, we
derive a decentralized online learning algorithm to determine the control
actions and Lagrangian multipliers (LMs) simultaneously, based on the policy
gradient approach. Under some mild technical conditions, the proposed
decentralized policy gradient algorithm converges almost surely to a local
optimal solution. On the other hand, in the non-cooperative POSG formulation,
the transmitter nodes are non-cooperative. We extend the decentralized policy
gradient solution and establish the technical proof for almost-sure convergence
of the learning algorithms. In both cases, the solutions are very robust to
model variations. Finally, the delay performance of the proposed solutions are
compared with conventional baseline schemes for interference networks and it is
illustrated that substantial delay performance gain and energy savings can be
achieved
Delay-Optimal User Scheduling and Inter-Cell Interference Management in Cellular Network via Distributive Stochastic Learning
In this paper, we propose a distributive queueaware intra-cell user
scheduling and inter-cell interference (ICI) management control design for a
delay-optimal celluar downlink system with M base stations (BSs), and K users
in each cell. Each BS has K downlink queues for K users respectively with
heterogeneous arrivals and delay requirements. The ICI management control is
adaptive to joint queue state information (QSI) over a slow time scale, while
the user scheduling control is adaptive to both the joint QSI and the joint
channel state information (CSI) over a faster time scale. We show that the
problem can be modeled as an infinite horizon average cost Partially Observed
Markov Decision Problem (POMDP), which is NP-hard in general. By exploiting the
special structure of the problem, we shall derive an equivalent Bellman
equation to solve the POMDP problem. To address the distributive requirement
and the issue of dimensionality and computation complexity, we derive a
distributive online stochastic learning algorithm, which only requires local
QSI and local CSI at each of the M BSs. We show that the proposed learning
algorithm converges almost surely (with probability 1) and has significant gain
compared with various baselines. The proposed solution only has linear
complexity order O(MK)
Non-Gaussian statistical models of surface wave fields for remote sensing applications
Based on the complete Stokes wave model with the bias term and using a simple mapping approach and an iteration solution method, we established a formula for the joint probability density function of the surface slope elevation of a nonlinear random wave field. The formula requires three parameters to define the whole density function: the rms surface elevation and slope values and the significant slope. This model represents the dynamics of the wave in a more direct way than the Gram-Charlier approximation. Based on this new statistical model and laboratory experiments, formula and numerical values of EM bias and dynamics bias are derived. The results indicate that various biases should be considered seriously if accuracy of the altimeter measurement is required in centimeter range
Microscale ocean dynamics
The detailed dynamics of the micro-scale ocean surface phenomena are studied along with the relationships among the surface signatures with the underlying dynamical processes. The approach to advance the understanding in this area is as follows: (1) Conduct rigorous theoretical studies of the ocean surface wave dynamics and statistical properties; (2) Conduct process-oriented laboratory experiments to verify the theoretical results, and to provide guidance for further studies; and (3) Prepare testable hypotheses for field verifications and comparisons during the ONR/NASA sponsored Surface Wave Dynamics Experiment (SWADE). An analytic model was established for wave breaking probability to study the influence of wave breaking on the spectrum shape in both deep and finite-depth waters. The spectrum was processed along with the structures of the water surface under the influence of wind and existing waves
Robust Lattice Alignment for K-user MIMO Interference Channels with Imperfect Channel Knowledge
In this paper, we consider a robust lattice alignment design for K-user
quasi-static MIMO interference channels with imperfect channel knowledge. With
random Gaussian inputs, the conventional interference alignment (IA) method has
the feasibility problem when the channel is quasi-static. On the other hand,
structured lattices can create structured interference as opposed to the random
interference caused by random Gaussian symbols. The structured interference
space can be exploited to transmit the desired signals over the gaps. However,
the existing alignment methods on the lattice codes for quasi-static channels
either require infinite SNR or symmetric interference channel coefficients.
Furthermore, perfect channel state information (CSI) is required for these
alignment methods, which is difficult to achieve in practice. In this paper, we
propose a robust lattice alignment method for quasi-static MIMO interference
channels with imperfect CSI at all SNR regimes, and a two-stage decoding
algorithm to decode the desired signal from the structured interference space.
We derive the achievable data rate based on the proposed robust lattice
alignment method, where the design of the precoders, decorrelators, scaling
coefficients and interference quantization coefficients is jointly formulated
as a mixed integer and continuous optimization problem. The effect of imperfect
CSI is also accommodated in the optimization formulation, and hence the derived
solution is robust to imperfect CSI. We also design a low complex iterative
optimization algorithm for our robust lattice alignment method by using the
existing iterative IA algorithm that was designed for the conventional IA
method. Numerical results verify the advantages of the proposed robust lattice
alignment method
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