31,629 research outputs found
Monitoring Control Updating Period In Fast Gradient Based NMPC
In this paper, a method is proposed for on-line monitoring of the control
updating period in fast-gradient-based Model Predictive Control (MPC) schemes.
Such schemes are currently under intense investigation as a way to accommodate
for real-time requirements when dealing with systems showing fast dynamics. The
method needs cheap computations that use the algorithm on-line behavior in
order to recover the optimal updating period in terms of cost function
decrease. A simple example of constrained triple integrator is used to
illustrate the proposed method and to assess its efficiency.Comment: 6 pages, 8 Figure
Joint Spectrum Sensing and Resource Allocation for OFDM-based Transmission with a Cognitive Relay
In this paper, we investigate the joint spectrum sensing and resource
allocation problem to maximize throughput capacity of an OFDM-based cognitive
radio link with a cognitive relay. By applying a cognitive relay that uses
decode and forward (D&F), we achieve more reliable communications, generating
less interference (by needing less transmit power) and more diversity gain. In
order to account for imperfections in spectrum sensing, the proposed schemes
jointly modify energy detector thresholds and allocates transmit powers to all
cognitive radio (CR) subcarriers, while simultaneously assigning subcarrier
pairs for secondary users (SU) and the cognitive relay. This problem is cast as
a constrained optimization problem with constraints on (1) interference
introduced by the SU and the cognitive relay to the PUs; (2) miss-detection and
false alarm probabilities and (3) subcarrier pairing for transmission on the SU
transmitter and the cognitive relay and (4) minimum Quality of Service (QoS)
for each CR subcarrier. We propose one optimal and two sub-optimal schemes all
of which are compared to other schemes in the literature. Simulation results
show that the proposed schemes achieve significantly higher throughput than
other schemes in the literature for different relay situations.Comment: EAI Endorsed Transactions on Wireless Spectrum 14(1): e4 Published
13th Apr 201
Joint Channel Probing and Proportional Fair Scheduling in Wireless Networks
The design of a scheduling scheme is crucial for the efficiency and
user-fairness of wireless networks. Assuming that the quality of all user
channels is available to a central controller, a simple scheme which maximizes
the utility function defined as the sum logarithm throughput of all users has
been shown to guarantee proportional fairness. However, to acquire the channel
quality information may consume substantial amount of resources. In this work,
it is assumed that probing the quality of each user's channel takes a fraction
of the coherence time, so that the amount of time for data transmission is
reduced. The multiuser diversity gain does not always increase as the number of
users increases. In case the statistics of the channel quality is available to
the controller, the problem of sequential channel probing for user scheduling
is formulated as an optimal stopping time problem. A joint channel probing and
proportional fair scheduling scheme is developed. This scheme is extended to
the case where the channel statistics are not available to the controller, in
which case a joint learning, probing and scheduling scheme is designed by
studying a generalized bandit problem. Numerical results demonstrate that the
proposed scheduling schemes can provide significant gain over existing schemes.Comment: 26 pages, 8 figure
Inter-tier Interference Suppression in Heterogeneous Cloud Radio Access Networks
Incorporating cloud computing into heterogeneous networks, the heterogeneous
cloud radio access network (H-CRAN) has been proposed as a promising paradigm
to enhance both spectral and energy efficiencies. Developing interference
suppression strategies is critical for suppressing the inter-tier interference
between remote radio heads (RRHs) and a macro base station (MBS) in H-CRANs. In
this paper, inter-tier interference suppression techniques are considered in
the contexts of collaborative processing and cooperative radio resource
allocation (CRRA). In particular, interference collaboration (IC) and
beamforming (BF) are proposed to suppress the inter-tier interference, and
their corresponding performance is evaluated. Closed-form expressions for the
overall outage probabilities, system capacities, and average bit error rates
under these two schemes are derived. Furthermore, IC and BF based CRRA
optimization models are presented to maximize the RRH-accessed users' sum rates
via power allocation, which is solved with convex optimization. Simulation
results demonstrate that the derived expressions for these performance metrics
for IC and BF are accurate; and the relative performance between IC and BF
schemes depends on system parameters, such as the number of antennas at the
MBS, the number of RRHs, and the target signal-to-interference-plus-noise ratio
threshold. Furthermore, it is seen that the sum rates of IC and BF schemes
increase almost linearly with the transmit power threshold under the proposed
CRRA optimization solution
Adaptive Threshold Sampling and Estimation
Sampling is a fundamental problem in both computer science and statistics. A
number of issues arise when designing a method based on sampling. These include
statistical considerations such as constructing a good sampling design and
ensuring there are good, tractable estimators for the quantities of interest as
well as computational considerations such as designing fast algorithms for
streaming data and ensuring the sample fits within memory constraints.
Unfortunately, existing sampling methods are only able to address all of these
issues in limited scenarios.
We develop a framework that can be used to address these issues in a broad
range of scenarios. In particular, it addresses the problem of drawing and
using samples under some memory budget constraint. This problem can be
challenging since the memory budget forces samples to be drawn
non-independently and consequently, makes computation of resulting estimators
difficult.
At the core of the framework is the notion of a data adaptive thresholding
scheme where the threshold effectively allows one to treat the non-independent
sample as if it were drawn independently. We provide sufficient conditions for
a thresholding scheme to allow this and provide ways to build and compose such
schemes.
Furthermore, we provide fast algorithms to efficiently sample under these
thresholding schemes
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