4 research outputs found
Coalitional games for downlink multicell beamforming
A coalitional game is proposed for multi-cell multiuser downlink beamforming. Each base station intends to minimize its transmission power while aiming to attain a set of target signal-to-interference-plus-noise-ratio (SINRs) for its users. In order to reduce power consumption, base stations have incentive to cooperate with other base stations to mitigate intercell interference. The coalitional game is introduced where base stations are allowed to forge partial cooperation rather than full cooperation. The partition form coalitional game is formulated with the consideration that beamformer
design of a coalition depends on the coalition structure outside the considered coalition. We first formulate the beamformer
design for a given coalition structure, in which base stations in a coalition greedily minimize the total weighted transmit
power without considering interference leakage to users in other coalitions. This can be considered as a non-cooperative game
with each player as a distinct coalition. By introducing cost for cooperation, the coalition formation game is considered for the power minimization based beamforming. A merge-regret based sequential coalition formation algorithm has been developed that
is capable of reaching a unique stable coalition structure. Finally, an α-Modification algorithm has been proposed to improve the performance of the coalition formation algorithm
Game theoretic analysis for MIMO radars with multiple targets
This paper considers a distributed beamforming
and resource allocation technique for a radar system in the
presence of multiple targets. The primary objective of each
radar is to minimize its transmission power while attaining an
optimal beamforming strategy and satisfying a certain detection
criterion for each of the targets. Therefore, we use convex
optimization methods together with noncooperative and partially
cooperative game theoretic approaches. Initially, we consider
a strategic noncooperative game (SNG), where there is no
communication between the various radars of the system. Hence
each radar selfishly determines its optimal beamforming and
power allocation. Subsequently, we assume a more coordinated
game theoretic approach incorporating a pricing mechanism.
Introducing a price in the utility function of each radar/player,
enforces beamformers to minimize the interference induced to
other radars and to increase the social fairness of the system.
Furthermore, we formulate a Stackelberg game by adding a
surveillance radar to the system model, which will play the role of
the leader, and hence the remaining radars will be the followers.
The leader applies a pricing policy of interference charged to the followers aiming at maximizing his profit while keeping the
incoming interference under a certain threshold. We also present
a proof of the existence and uniqueness of the Nash Equilibrium
(NE) in both the partially cooperative and noncooperative games.
Finally, the simulation results confirm the convergence of the
algorithm in all three cases
Secrecy rate optimizations for MIMO communication radar
In this paper, we investigate transmit beampattern optimization techniques for a multiple-input multiple-output (MIMO) radar in the presence of a legitimate communications receiver and an eavesdropping target. The primary objectives of the radar are to satisfy a certain target detection criterion
and to simultaneously communicate safely with a legitimate receiver by maximizing the secrecy rate against the eavesdropping target. Therefore, we consider three optimization problems,
namely, target return signal to interference plus noise ratio (SINR) maximization, secrecy rate maximization and transmit power minimization. However, these problems are non-convex
due to the non-concavity of the secrecy rate function, which appears in all three optimizations either as the objective function or as a constraint. To solve this issue, we use Taylor series approximation of the non-convex elements through an iterative
algorithm, which recasts the problem as a convex problem. Two transmit covariance matrices are designed to detect the target and convey the information safely to the communication receiver. Simulation results are presented to validate the efficiency of the aforementioned optimizations
Bayesian multiple extended target tracking using labelled random finite sets and splines
In this paper, we propose a technique for the joint tracking and labelling of multiple extended targets. To achieve multiple extended target tracking using this technique, models for the target measurement rate, kinematic component and target extension are defined and jointly propagated in time under the generalised labelled multi-Bernoulli (GLMB) filter framework. In particular, we developed a Poisson mixture variational Bayesian (PMVB) model to simultaneously estimate the measurement rate of multiple extended targets and extended target extension was modelled using B-splines. We evaluated our proposed method with various performance metrics. Results demonstrate the effectiveness of our approach