40 research outputs found
Defeating jamming with the power of silence: a game-theoretic analysis
The timing channel is a logical communication channel in which information is
encoded in the timing between events. Recently, the use of the timing channel
has been proposed as a countermeasure to reactive jamming attacks performed by
an energy-constrained malicious node. In fact, whilst a jammer is able to
disrupt the information contained in the attacked packets, timing information
cannot be jammed and, therefore, timing channels can be exploited to deliver
information to the receiver even on a jammed channel.
Since the nodes under attack and the jammer have conflicting interests, their
interactions can be modeled by means of game theory. Accordingly, in this paper
a game-theoretic model of the interactions between nodes exploiting the timing
channel to achieve resilience to jamming attacks and a jammer is derived and
analyzed. More specifically, the Nash equilibrium is studied in the terms of
existence, uniqueness, and convergence under best response dynamics.
Furthermore, the case in which the communication nodes set their strategy and
the jammer reacts accordingly is modeled and analyzed as a Stackelberg game, by
considering both perfect and imperfect knowledge of the jammer's utility
function. Extensive numerical results are presented, showing the impact of
network parameters on the system performance.Comment: Anti-jamming, Timing Channel, Game-Theoretic Models, Nash Equilibriu
A Learning Approach for Low-Complexity Optimization of Energy Efficiency in Multi-Carrier Wireless Networks
This paper proposes computationally efficient algorithms to maximize the
energy efficiency in multi-carrier wireless interference networks, by a
suitable allocation of the system radio resources, namely the transmit powers
and subcarrier assignment. The problem is formulated as the maximization of the
system Global Energy-Efficiency subject to both maximum power and minimum rate
constraints. This leads to a challenging non-convex fractional problem, which
is tackled through an interplay of fractional programming, learning, and game
theory. The proposed algorithmic framework is provably convergent and has a
complexity linear in both the number of users and subcarriers, whereas other
available solutions can only guarantee a polynomial complexity in the number of
users and subcarriers. Numerical results show that the proposed method performs
similarly as other, more complex, algorithms