15,489 research outputs found
In silico case studies of compliant robots: AMARSI deliverable 3.3
In the deliverable 3.2 we presented how the morphological computing ap-
proach can significantly facilitate the control strategy in several scenarios,
e.g. quadruped locomotion, bipedal locomotion and reaching. In particular,
the Kitty experimental platform is an example of the use of morphological
computation to allow quadruped locomotion. In this deliverable we continue
with the simulation studies on the application of the different morphological
computation strategies to control a robotic system
On the Benefit of Information Centric Networks for Traffic Engineering
Current Internet performs traffic engineering (TE) by estimating traffic
matrices on a regular schedule, and allocating flows based upon weights
computed from these matrices. This means the allocation is based upon a guess
of the traffic in the network based on its history. Information-Centric
Networks on the other hand provide a finer-grained description of the traffic:
a content between a client and a server is uniquely identified by its name, and
the network can therefore learn the size of different content items, and
perform traffic engineering and resource allocation accordingly. We claim that
Information-Centric Networks can therefore provide a better handle to perform
traffic engineering, resulting in significant performance gain.
We present a mechanism to perform such resource allocation. We see that our
traffic engineering method only requires knowledge of the flow size (which, in
ICN, can be learned from previous data transfers) and outperforms a min-MLU
allocation in terms of response time. We also see that our method identifies
the traffic allocation patterns similar to that of min-MLU without having
access to the traffic matrix ahead of time. We show a very significant gain in
response time where min MLU is almost 50% slower than our ICN-based TE method
Dynamic Resource Allocation in Cognitive Radio Networks: A Convex Optimization Perspective
This article provides an overview of the state-of-art results on
communication resource allocation over space, time, and frequency for emerging
cognitive radio (CR) wireless networks. Focusing on the
interference-power/interference-temperature (IT) constraint approach for CRs to
protect primary radio transmissions, many new and challenging problems
regarding the design of CR systems are formulated, and some of the
corresponding solutions are shown to be obtainable by restructuring some
classic results known for traditional (non-CR) wireless networks. It is
demonstrated that convex optimization plays an essential role in solving these
problems, in a both rigorous and efficient way. Promising research directions
on interference management for CR and other related multiuser communication
systems are discussed.Comment: to appear in IEEE Signal Processing Magazine, special issue on convex
optimization for signal processin
Energy-Efficient Power Control in Impulse Radio UWB Wireless Networks
In this paper, a game-theoretic model for studying power control for wireless
data networks in frequency-selective multipath environments is analyzed. The
uplink of an impulse-radio ultrawideband system is considered. The effects of
self-interference and multiple-access interference on the performance of
generic Rake receivers are investigated for synchronous systems. Focusing on
energy efficiency, a noncooperative game is proposed in which users in the
network are allowed to choose their transmit powers to maximize their own
utilities, and the Nash equilibrium for the proposed game is derived. It is
shown that, due to the frequency selective multipath, the noncooperative
solution is achieved at different signal-to-interference-plus-noise ratios,
depending on the channel realization and the type of Rake receiver employed. A
large-system analysis is performed to derive explicit expressions for the
achieved utilities. The Pareto-optimal (cooperative) solution is also discussed
and compared with the noncooperative approach.Comment: Submitted to the IEEE Journal on Selected Topics in Signal Processing
- Special issue on Performance Limits of Ultra-Wideband System
Energy-Aware Competitive Power Allocation for Heterogeneous Networks Under QoS Constraints
This work proposes a distributed power allocation scheme for maximizing
energy efficiency in the uplink of orthogonal frequency-division multiple
access (OFDMA)-based heterogeneous networks (HetNets). The user equipment (UEs)
in the network are modeled as rational agents that engage in a non-cooperative
game where each UE allocates its available transmit power over the set of
assigned subcarriers so as to maximize its individual utility (defined as the
user's throughput per Watt of transmit power) subject to minimum-rate
constraints. In this framework, the relevant solution concept is that of Debreu
equilibrium, a generalization of Nash equilibrium which accounts for the case
where an agent's set of possible actions depends on the actions of its
opponents. Since the problem at hand might not be feasible, Debreu equilibria
do not always exist. However, using techniques from fractional programming, we
provide a characterization of equilibrial power allocation profiles when they
do exist. In particular, Debreu equilibria are found to be the fixed points of
a water-filling best response operator whose water level is a function of
minimum rate constraints and circuit power. Moreover, we also describe a set of
sufficient conditions for the existence and uniqueness of Debreu equilibria
exploiting the contraction properties of the best response operator. This
analysis provides the necessary tools to derive a power allocation scheme that
steers the network to equilibrium in an iterative and distributed manner
without the need for any centralized processing. Numerical simulations are then
used to validate the analysis and assess the performance of the proposed
algorithm as a function of the system parameters.Comment: 37 pages, 12 figures, to appear IEEE Trans. Wireless Commu
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