1,294 research outputs found
MIMO-OFDM Based Energy Harvesting Cooperative Communications Using Coalitional Game Algorithm
This document is the Accepted Manuscript version. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, we consider the problem of cooperative communication between relays and base station in an advanced MIMO-OFDM framework, under the assumption that the relays are supplied by electric power drawn from energy harvesting (EH) sources. In particular, we focus on the relay selection, with the goal to guarantee the required performance in terms of capacity. In order to maximize the data throughput under the EH constraint, we model the transmission scheme as a non-transferable coalition formation game, with characteristic function based on an approximated capacity expression. Then, we introduce a powerful mathematical tool inherent to coalitional game theory, namely: the Shapley value (Sv) to provide a reliable solution concept to the game. The selected relays will form a virtual dynamically-configuredMIMO network that is able to transmit data to destination using efficient space-time coding techniques. Numerical results, obtained by simulating the EH-powered cooperativeMIMO-OFDMtransmission with Algebraic Space-Time Coding (ASTC), prove that the proposed coalitional game-based relay selection allows to achieve performance very close to that obtained by the same system operated by guaranteed power supply. The proposed methodology is finally compared with some recent related state-of-the-art techniques showing clear advantages in terms of link performance and goodput.Peer reviewe
Distributed stochastic optimization via matrix exponential learning
In this paper, we investigate a distributed learning scheme for a broad class
of stochastic optimization problems and games that arise in signal processing
and wireless communications. The proposed algorithm relies on the method of
matrix exponential learning (MXL) and only requires locally computable gradient
observations that are possibly imperfect and/or obsolete. To analyze it, we
introduce the notion of a stable Nash equilibrium and we show that the
algorithm is globally convergent to such equilibria - or locally convergent
when an equilibrium is only locally stable. We also derive an explicit linear
bound for the algorithm's convergence speed, which remains valid under
measurement errors and uncertainty of arbitrarily high variance. To validate
our theoretical analysis, we test the algorithm in realistic
multi-carrier/multiple-antenna wireless scenarios where several users seek to
maximize their energy efficiency. Our results show that learning allows users
to attain a net increase between 100% and 500% in energy efficiency, even under
very high uncertainty.Comment: 31 pages, 3 figure
Jamming Games in the MIMO Wiretap Channel With an Active Eavesdropper
This paper investigates reliable and covert transmission strategies in a
multiple-input multiple-output (MIMO) wiretap channel with a transmitter,
receiver and an adversarial wiretapper, each equipped with multiple antennas.
In a departure from existing work, the wiretapper possesses a novel capability
to act either as a passive eavesdropper or as an active jammer, under a
half-duplex constraint. The transmitter therefore faces a choice between
allocating all of its power for data, or broadcasting artificial interference
along with the information signal in an attempt to jam the eavesdropper
(assuming its instantaneous channel state is unknown). To examine the resulting
trade-offs for the legitimate transmitter and the adversary, we model their
interactions as a two-person zero-sum game with the ergodic MIMO secrecy rate
as the payoff function. We first examine conditions for the existence of
pure-strategy Nash equilibria (NE) and the structure of mixed-strategy NE for
the strategic form of the game.We then derive equilibrium strategies for the
extensive form of the game where players move sequentially under scenarios of
perfect and imperfect information. Finally, numerical simulations are presented
to examine the equilibrium outcomes of the various scenarios considered.Comment: 27 pages, 8 figures. To appear, IEEE Transactions on Signal
Processin
- …