Worst-Case Additive Noise in Wireless Networks

Worst Case Modelling of Wireless Sensor Networks

At the current state of affairs it is hard to obtain a predictable performance from wireless sensor networks, not to mention performance guarantees. In particular, a widely accepted and established methodology for modelling the performance of wireless sensor networks is missing. In the last two years we have tried to make a step into the direction of an analytical framework for the performance ...

Worst-case-based robust beamforming for wireless cooperative networks

It is known that distributed beamforming techniques can improve the performance of relay networks by using channel state information (CSI). In practical applications, there exist unavoidably estimation errors of the CSI, which results in outage of quality of service (QoS) or overconsumption of transmit power. In this paper, we propose two worst-case-based distributed beamforming techniques that...

Worst Case Dimensioning of Wireless Sensor Networks under Uncertain Topologies

require an absolute predictable network behaviour under all possible traffic and routing conditions. This requires network dimensioning with respect to crucial parameters such as energy usage, message delay and buffer requirements. Network calculus provides a method set that can be used to prove that all possible traffic conditions under a given routing-implied topology can be supported. As it ...

The worst additive noise under a covariance constraint

The Worst Additive Noise Under a Covariance Constraint

The maximum entropy noise under a lag autocorrelation constraint is known by Burg’s theorem to be the th order Gauss–Markov process satisfying these constraints. The question is, what is the worst additive noise for a communication channel given these constraints? Is it the maximum entropy noise? The problem becomes one of extremizing the mutual information over all noise processes with covaria...