29-06-2012, 06:17 PM
Co-Channel Interference
[attachment=26217]
This paper studies the problem of centralized dynamic channel assignment (DCA) in wireless
cellular systems under space and time-varying channel demand. The objective is
to minimize the number of channels required to satisfy demand while also satisfying cochannel
interference constraints. Cumulative co-channel interference constraints govern
channel reuse, via a threshold decision criterion based on the carrier-to-interference ratio.
The paper makes two contributions. First, it provides an empirical bound on the difference
between the minimal number of channels required based only on geographic reuse distance
versus the cumulative interference case in the context of linearly increasing demand. The
bound is characterized using only the reuse distance.
Introduction
Emerging wireless communication systems will increasingly
rely on smart systems and intelligent networks
to optimize resources and maximize performance.
Internet data services with highly variable application
specific bandwidth requirements will represent
a major traffic component on wireless networks.
Protocols and algorithms that support bandwidth efficient
distribution of resources for such applications
are critical to the new generation of wireless systems.
The adaptive allocation of wireless spectrum based on
traffic characteristics and their performance requirements
may be examined in the context of a Dynamic
Channel Assignment (DCA) model.
Traffic and Channel Demand Model
The influence of traffic inhomogeneity on the channel
assignment problem is studied taking into consideration
both spatial and temporal variations in the
demand. Spatial variations in channel demand are
addressed by classifying each cell in the network as
a variable (Typev) or constant (Typec) demand rate
space, driven by a two state on-off Markov arrival
process and occupy the channels with uniformly distributed
holding times. The demand in Typec cells is
assumed to be deterministic in time and satisfied by
Dc channels in each time unit.
IP-Based Algorithm
This section presents an IP-based algorithm that simulates
dynamic channel assignment (DCA) under space
and time-varying channel demand. Unlike most channel
assignment work we model geographic distance
and the cumulative effect of interference across the
cellular system. A cellular environment is modeled
by associating with each transmitter a unique cell that
represents its geographic transmission region of responsibility.
Let C = {c1, c2, . . . ,ccmax
} denote a
sequence of cmax cells, one for each transmitter. Demand
at time t is denoted by Dt(ci). Dt(ci) = t+ 1
for each Typev cell. The Typec cell demands are
fixed at one channel in each time unit.
Core IP Model
The CIPt model’s goal is to minimize the number
of channels used while satisfying demand and
co-channel interference constraints. There are fmax
available channels. CIPt consists of integer, binary
variables, a minimization objective function, and a
collection of linear constraints, as described below.
In the square cellular system, cells are ordered by increasing
row, followed by increasing column order.
Results
This section discusses the effectiveness of the new
DCA strategy of Section III.D with respect to two
goals. The first is to assess in Section III.F.1 the
impact of cumulative interference constraints versus
those based solely on reuse distance by empirically
bounding.
Conclusions
This paper presents schemes for centralized DCA
under spatio-temporal demand variation. Cumulative
co-channel interference constrains reuses of the
same channel. In this context the paper makes two
main contributions. First, it establishes that obtaining
the optimal number of channels using cumulative cochannel
interference constraints is at least as hard as
finding the optimal number of channels using interference
constraints based only on reuse distance. Then,
to quantify this, an empirical bound is obtained on
the difference between the optimal number of channels
required using cumulative co-channel interference
constraints versus interference constraints based
only on reuse distance.
[attachment=26217]
This paper studies the problem of centralized dynamic channel assignment (DCA) in wireless
cellular systems under space and time-varying channel demand. The objective is
to minimize the number of channels required to satisfy demand while also satisfying cochannel
interference constraints. Cumulative co-channel interference constraints govern
channel reuse, via a threshold decision criterion based on the carrier-to-interference ratio.
The paper makes two contributions. First, it provides an empirical bound on the difference
between the minimal number of channels required based only on geographic reuse distance
versus the cumulative interference case in the context of linearly increasing demand. The
bound is characterized using only the reuse distance.
Introduction
Emerging wireless communication systems will increasingly
rely on smart systems and intelligent networks
to optimize resources and maximize performance.
Internet data services with highly variable application
specific bandwidth requirements will represent
a major traffic component on wireless networks.
Protocols and algorithms that support bandwidth efficient
distribution of resources for such applications
are critical to the new generation of wireless systems.
The adaptive allocation of wireless spectrum based on
traffic characteristics and their performance requirements
may be examined in the context of a Dynamic
Channel Assignment (DCA) model.
Traffic and Channel Demand Model
The influence of traffic inhomogeneity on the channel
assignment problem is studied taking into consideration
both spatial and temporal variations in the
demand. Spatial variations in channel demand are
addressed by classifying each cell in the network as
a variable (Typev) or constant (Typec) demand rate
space, driven by a two state on-off Markov arrival
process and occupy the channels with uniformly distributed
holding times. The demand in Typec cells is
assumed to be deterministic in time and satisfied by
Dc channels in each time unit.
IP-Based Algorithm
This section presents an IP-based algorithm that simulates
dynamic channel assignment (DCA) under space
and time-varying channel demand. Unlike most channel
assignment work we model geographic distance
and the cumulative effect of interference across the
cellular system. A cellular environment is modeled
by associating with each transmitter a unique cell that
represents its geographic transmission region of responsibility.
Let C = {c1, c2, . . . ,ccmax
} denote a
sequence of cmax cells, one for each transmitter. Demand
at time t is denoted by Dt(ci). Dt(ci) = t+ 1
for each Typev cell. The Typec cell demands are
fixed at one channel in each time unit.
Core IP Model
The CIPt model’s goal is to minimize the number
of channels used while satisfying demand and
co-channel interference constraints. There are fmax
available channels. CIPt consists of integer, binary
variables, a minimization objective function, and a
collection of linear constraints, as described below.
In the square cellular system, cells are ordered by increasing
row, followed by increasing column order.
Results
This section discusses the effectiveness of the new
DCA strategy of Section III.D with respect to two
goals. The first is to assess in Section III.F.1 the
impact of cumulative interference constraints versus
those based solely on reuse distance by empirically
bounding.
Conclusions
This paper presents schemes for centralized DCA
under spatio-temporal demand variation. Cumulative
co-channel interference constrains reuses of the
same channel. In this context the paper makes two
main contributions. First, it establishes that obtaining
the optimal number of channels using cumulative cochannel
interference constraints is at least as hard as
finding the optimal number of channels using interference
constraints based only on reuse distance. Then,
to quantify this, an empirical bound is obtained on
the difference between the optimal number of channels
required using cumulative co-channel interference
constraints versus interference constraints based
only on reuse distance.