213,640 research outputs found
General Model for Infrastructure Multi-channel Wireless LANs
In this paper we develop an integrated model for request mechanism and data
transmission in multi-channel wireless local area networks. We calculated the
performance parameters for single and multi-channel wireless networks when the
channel is noisy. The proposed model is general it can be applied to different
wireless networks such as IEEE802.11x, IEEE802.16, CDMA operated networks and
Hiperlan\2.Comment: 11 Pages, IJCN
Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret
The problem of distributed learning and channel access is considered in a
cognitive network with multiple secondary users. The availability statistics of
the channels are initially unknown to the secondary users and are estimated
using sensing decisions. There is no explicit information exchange or prior
agreement among the secondary users. We propose policies for distributed
learning and access which achieve order-optimal cognitive system throughput
(number of successful secondary transmissions) under self play, i.e., when
implemented at all the secondary users. Equivalently, our policies minimize the
regret in distributed learning and access. We first consider the scenario when
the number of secondary users is known to the policy, and prove that the total
regret is logarithmic in the number of transmission slots. Our distributed
learning and access policy achieves order-optimal regret by comparing to an
asymptotic lower bound for regret under any uniformly-good learning and access
policy. We then consider the case when the number of secondary users is fixed
but unknown, and is estimated through feedback. We propose a policy in this
scenario whose asymptotic sum regret which grows slightly faster than
logarithmic in the number of transmission slots.Comment: Submitted to IEEE JSAC on Advances in Cognitive Radio Networking and
Communications, Dec. 2009, Revised May 201
Adaptive Power Allocation and Control in Time-Varying Multi-Carrier MIMO Networks
In this paper, we examine the fundamental trade-off between radiated power
and achieved throughput in wireless multi-carrier, multiple-input and
multiple-output (MIMO) systems that vary with time in an unpredictable fashion
(e.g. due to changes in the wireless medium or the users' QoS requirements).
Contrary to the static/stationary channel regime, there is no optimal power
allocation profile to target (either static or in the mean), so the system's
users must adapt to changes in the environment "on the fly", without being able
to predict the system's evolution ahead of time. In this dynamic context, we
formulate the users' power/throughput trade-off as an online optimization
problem and we provide a matrix exponential learning algorithm that leads to no
regret - i.e. the proposed transmit policy is asymptotically optimal in
hindsight, irrespective of how the system evolves over time. Furthermore, we
also examine the robustness of the proposed algorithm under imperfect channel
state information (CSI) and we show that it retains its regret minimization
properties under very mild conditions on the measurement noise statistics. As a
result, users are able to track the evolution of their individually optimum
transmit profiles remarkably well, even under rapidly changing network
conditions and high uncertainty. Our theoretical analysis is validated by
extensive numerical simulations corresponding to a realistic network deployment
and providing further insights in the practical implementation aspects of the
proposed algorithm.Comment: 25 pages, 4 figure
Cognitive Medium Access: Exploration, Exploitation and Competition
This paper establishes the equivalence between cognitive medium access and
the competitive multi-armed bandit problem. First, the scenario in which a
single cognitive user wishes to opportunistically exploit the availability of
empty frequency bands in the spectrum with multiple bands is considered. In
this scenario, the availability probability of each channel is unknown to the
cognitive user a priori. Hence efficient medium access strategies must strike a
balance between exploring the availability of other free channels and
exploiting the opportunities identified thus far. By adopting a Bayesian
approach for this classical bandit problem, the optimal medium access strategy
is derived and its underlying recursive structure is illustrated via examples.
To avoid the prohibitive computational complexity of the optimal strategy, a
low complexity asymptotically optimal strategy is developed. The proposed
strategy does not require any prior statistical knowledge about the traffic
pattern on the different channels. Next, the multi-cognitive user scenario is
considered and low complexity medium access protocols, which strike the optimal
balance between exploration and exploitation in such competitive environments,
are developed. Finally, this formalism is extended to the case in which each
cognitive user is capable of sensing and using multiple channels
simultaneously.Comment: Submitted to IEEE/ACM Trans. on Networking, 14 pages, 2 figure
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