2,485 research outputs found
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
To Stay Or To Switch: Multiuser Dynamic Channel Access
In this paper we study opportunistic spectrum access (OSA) policies in a
multiuser multichannel random access cognitive radio network, where users
perform channel probing and switching in order to obtain better channel
condition or higher instantaneous transmission quality. However, unlikely many
prior works in this area, including those on channel probing and switching
policies for a single user to exploit spectral diversity, and on probing and
access policies for multiple users over a single channel to exploit temporal
and multiuser diversity, in this study we consider the collective switching of
multiple users over multiple channels. In addition, we consider finite
arrivals, i.e., users are not assumed to always have data to send and demand
for channel follow a certain arrival process. Under such a scenario, the users'
ability to opportunistically exploit temporal diversity (the temporal variation
in channel quality over a single channel) and spectral diversity (quality
variation across multiple channels at a given time) is greatly affected by the
level of congestion in the system. We investigate the optimal decision process
in this case, and evaluate the extent to which congestion affects potential
gains from opportunistic dynamic channel switching
Optimal resource allocation in femtocell networks based on Markov modeling of interferers' activity
Femtocell networks offer a series of advantages with respect to conventional cellular networks. However, a potential massive deployment of femto-access points (FAPs) poses a big challenge in terms of interference management, which requires proper radio resource allocation techniques. In this article, we propose alternative optimal power/bit allocation strategies over a time-frequency frame based on a statistical modeling of the interference activity. Given the lack of knowledge of the interference activity, we assume a Bayesian approach that provides the optimal allocation, conditioned to periodic spectrum sensing, and estimation of the interference activity statistical parameters. We consider first a single FAP accessing the radio channel in the presence of a dynamical interference environment. Then, we extend the formulation to a multi-FAP scenario, where nearby FAP's react to the strategies of the other FAP's, still within a dynamical interference scenario. The multi-user case is first approached using a strategic non-cooperative game formulation. Then, we propose a coordination game based on the introduction of a pricing mechanism that exploits the backhaul link to enable the exchange of parameters (prices) among FAP's
Learning in A Changing World: Restless Multi-Armed Bandit with Unknown Dynamics
We consider the restless multi-armed bandit (RMAB) problem with unknown
dynamics in which a player chooses M out of N arms to play at each time. The
reward state of each arm transits according to an unknown Markovian rule when
it is played and evolves according to an arbitrary unknown random process when
it is passive. The performance of an arm selection policy is measured by
regret, defined as the reward loss with respect to the case where the player
knows which M arms are the most rewarding and always plays the M best arms. We
construct a policy with an interleaving exploration and exploitation epoch
structure that achieves a regret with logarithmic order when arbitrary (but
nontrivial) bounds on certain system parameters are known. When no knowledge
about the system is available, we show that the proposed policy achieves a
regret arbitrarily close to the logarithmic order. We further extend the
problem to a decentralized setting where multiple distributed players share the
arms without information exchange. Under both an exogenous restless model and
an endogenous restless model, we show that a decentralized extension of the
proposed policy preserves the logarithmic regret order as in the centralized
setting. The results apply to adaptive learning in various dynamic systems and
communication networks, as well as financial investment.Comment: 33 pages, 5 figures, submitted to IEEE Transactions on Information
Theory, 201
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
Introducing Hierarchy in Energy Games
In this work we introduce hierarchy in wireless networks that can be modeled
by a decentralized multiple access channel and for which energy-efficiency is
the main performance index. In these networks users are free to choose their
power control strategy to selfishly maximize their energy-efficiency.
Specifically, we introduce hierarchy in two different ways: 1. Assuming
single-user decoding at the receiver, we investigate a Stackelberg formulation
of the game where one user is the leader whereas the other users are assumed to
be able to react to the leader's decisions; 2. Assuming neither leader nor
followers among the users, we introduce hierarchy by assuming successive
interference cancellation at the receiver. It is shown that introducing a
certain degree of hierarchy in non-cooperative power control games not only
improves the individual energy efficiency of all the users but can also be a
way of insuring the existence of a non-saturated equilibrium and reaching a
desired trade-off between the global network performance at the equilibrium and
the requested amount of signaling. In this respect, the way of measuring the
global performance of an energy-efficient network is shown to be a critical
issue.Comment: Accepted for publication in IEEE Trans. on Wireless Communication
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