in Cellular Networks Based on MPT
In recent years, there has been a rapid increase in wireless network deployment
and mobile device market penetration. With vigorous research that promises higher
data rates, future wireless networks will likely become an integral part of the
global communication infrastructure. Ultimately, wireless users will demand the
same reliable service as today's wire-line telecommunications and data networks.
However, there are some unique problems in cellular networks that challenge their
In addition to problems introduced by fading, user mobility places stringent requirements
on network resources. Whenever an active mobile terminal (MT) moves from one cell
to another, the call needs to be handed off to the new base station (US), and
network resources must be reallocated. Resource demands could fluctuate abruptly
due to the movement of high data rate users. Quality of service (QoS) degradation
or even forced termination may occur when there are insufficient resources to
accommodate these handoffs.
If the system has prior knowledge of the
exact trajectory of every MT, it could take appropriate steps to reserve resources
so that QoS may be guaranteed during the MT's connection lifetime. However, such
an ideal scenario is very unlikely to occur in real life. Instead, much of the
work on resource reservation has adopted a predictive approach.
approach uses pattern matching techniques and a self-adaptive extended Kalman
filter for next-cell prediction based on cell sequence observations, signal strength
measurements, and cell geometry assumptions. Another approach proposes the concept
of a shadow cluster: a set of BSs to which an MT is likely to attach in the near
future. The scheme estimates the probability of each MT being in any cell within
the shadow cluster for future time intervals, based on knowledge about individual
MTs' dynamics and call holding patterns.
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