Wireless Network Design: Optimization Models and Solution ...

312 Hang Jin and Li Guo
where ρ min
k
constraint
K
N
∑ ∑
k=1 n=1
ck,n ≤ N
N
∑ ck,nρ (pk,n,γk,n) ≥ ρ
n=1
min
k
∀k1 ∈ {1,K}
pk,n ≥ 0, ck,n = 0 or 1 ∀k ∀n
is the minimum required data rate of user k or one service flow. With the
N
∑ pk,n =
n=1
On the other hand, with the constraint
N
∑ pk2,n, BS allocates same share of BS power to the users.
n=1
N
∑
n=1
ck,nρ (pk,n,γk,n) ≥ ρ min
k , BS meets the
minimum data rate requirement for each user. Proportional fair scheduler takes the
channel condition into consideration, and generates a sense of fairness in terms of
resource allocation. However, it may starve the user on the cell edge and cause poor
user experience to some users.
13.4.3 Max System Throughput
As implied by its name, the max system throughput is to schedule the resources to
maximize the system throughput. To certain degree, max system throughput scheduler
is the opposite of the round robin scheduler. Given the path loss distribution of
all users, max system throughput scheduler allocates more resources to users closest
to BS than those on cell edge as the power and spectrum efficiencies of the close-in
users are much higher than users on the cell edge. From the overall systems point of
view, max systems throughput scheduler makes best uses of the systems resources.
However, it has the same issue as proportional fair scheduler, i.e., some users may
be starved if they are on the cell edge and have poor channel conditions.
The max system throughput optimization problem can be formulated as maximizing
the sum data rate:
subject to
arg
c k,n
max
p k,n
K N
∑ ∑ ck,nρ(pk,n,γk,n)
k=1 n=1
K N
∑ ∑ pk,n ≤ PT x
k=1 n=1
K
N
∑ ∑
k=1 n=1
ck,n ≤ N