Checks and Balances: A Low-complexity High-gain
Uplink Power Controller for CoMP

arXiv:1610.08491v1 [] 26 Oct 2016

Fangzhou Chen†§ , Yin Sun†§ , Yiping Qin‡ , and C. Emre Koksal†
† Dept. of ECE, The Ohio State University, Columbus, OH
‡ Huawei Technologies Co., Shanghai, China
§ Co-primary authors

Abstract—Coordinated Multipoint (CoMP) promised substantial throughput gain for next-generation cellular systems. However,
realizing this gain is costly in terms of pilots and backhaul
bandwidth, and may require substantial modifications in physicallayer hardware. Targeting efficient throughput gain, we develop
a novel coordinated power control scheme for uplink cellular
networks called Checks and Balances (C&B), which checks the
received signal strength of one user and its generated interference
to neighboring base stations, and balances the two. C&B has some
highly attractive advantages: C&B (i) can be implemented easily
in software, (ii) does not require to upgrade non-CoMP physicallayer hardware, (iii) allows for fully distributed implementation
for each user equipment (UE), and (iv) does not need extra pilots
or backhaul communications. We evaluate the throughput performance of C&B on an uplink LTE system-level simulation platform,
which is carefully calibrated with Huawei. Our simulation results
show that C&B achieves much better throughput performance,
compared to several widely-used power control schemes.
Index Terms—Uplink power control, coordinated multipoint
(CoMP), LTE, system-level simulation, throughput improvement

I. I NTRODUCTION
Next generation of cellular communication, e.g., Long Term
Evolution Advanced (LTE-A) and 5G, is expected to significantly improve the average throughput and cell-edge throughput
for serving user equipments (UEs). One important candidate
technique for achieving such throughput improvement is Coordinated Multipoint (CoMP), which refers to the cooperation
among different Base Stations (BSs).
The promised benefits of CoMP are hard to realize because
of many issues in practical systems [1], [2]. In particular,
the uplink CoMP techniques (such as distributed interference
cancellation/alignment, joint detection, coordinated scheduling)
all require nearby BSs to communicate received signals, control
messages, and channel state information through backhaul
links. In addition, these CoMP techniques are costly in terms
of power and pilot resources, which considerably decreases
the resources allocated for data transmissions. Therefore, the
realized throughput performance is greatly degraded.
Aiming to realize the potential benefits of CoMP in practical
systems, we propose a novel coordinated power control design
for uplink cellular networks. The task of uplink power control
is to make the signal received at the base station sufficiently
This work was supported in part by Huawei, Inc. under Agreement YB
2013110091, National Science Foundation under Grants CNS-1054738 and
CNS-1514260.

TABLE I: Performance of FPC [5], Max Power [6], RLPC [7]
and C&B in Macrocell system-level simulations.
Average Throughput (Mbits/s)
5%-Edge Throughput (Mbits/s)
Power Efficiency (Mbits/J)

FPC
8.05
0.23
751

Max Power
12.01
0.09
6.77

RLPC
9.78
0.22
226

C&B
12.23
0.23
387

strong, and in the meanwhile keep the interference generated
to nearby base stations not severe. In practice, overly high
and low transmission powers are both harmful. Specifically,
increasing the transmission power of one UE can increase its
throughput, but it causes some strong interference to nearby
cells, which will degrade the throughput of other UEs. Hence,
finding the correct balance between a UE’s own performance
and its incurred cost to the other UEs is crucial to achieve a
satisfying performance.
In practical uplink cellular networks, each BS receiver experiences the interference from hundreds of UEs from neighboring cells. Even if perfect CSI knowledge of the signal and
interference channels are available, the optimal power control
problem is strongly NP hard [8], [9]. To make things worse,
the base station in current systems typically estimates the signal
channel of its served UEs, but the channel coefficients of interfering UEs are mostly unavailable. These practical limitations
make the power control problem even more challenging.
In our research, we develop a low-complexity Coordinated
power control scheme that provides significant throughput
gains, with minimum cost and modifications. To that end, the
following are the contributions of this paper:
• We develop a novel coordinated power control scheme,
named Checks and Balances (C&B). C&B requires very
little information, including the large-scale path loss from
one UE to several nearby BSs, coarse power level of cochannel interference, and the throughput vs SINR curve
of Adaptive Modulation and Coding (AMC). Based on
this information, C&B checks the SNR of one UE and its
generated INR to nearby BSs, and balances the two.
• C&B has some highly attractive advantages: C&B (i) can
be implemented easily in software, (ii) does not require to
upgrade non-CoMP physical-layer hardware, (iii) allows
for fully distributed implementation for each UE, and (iv)
does not need extra pilots or backhaul communications.
• We evaluate the throughput performance of C&B on a

system-level simulation platform for LTE uplink, which
is carefully calibrated with Huawei. As shown in Table
I, C&B increases the average throughput by 51.9% over
Fractional Power Control (FPC) [5] and 21.5% over
Reverse Link Power Control (RLPC) [7], and achieves
similar cell-edge throughput with FPC and RLPC. Compared to Max Power Control [6], C&B increases the
average throughput and cell-edge throughput by 1.8% and
156%, respectively, together with greatly improved power
efficiency.
We expect C&B to achieve even better throughput performance when working with physical-layer CoMP techniques,
which will be considered in future work.
Related Studies: Non-coordinated power control is standardized in 3GPP protocols [3] and has attracted vast research interests [5], [6], [10]–[15]. There exist three mainstream schemes:
1) Full Compensation Power Control (FCPC) [10] allocates
transmission power to one UE by making full compensation
of its large-scale path loss such that all UEs have the same
received signal strength, which results in poor performance
in per-cell average throughput and inter-cell interference management. 2) Max Power scheme [6] let all UEs transmit at
their maximum allowable power. It provides high average percell throughput, but performs poorly in power efficiency and
throughput of UEs at the cell edge. 3) Fractional Power Control
(FPC) [5] is currently the most widely adopted scheme [12]–
[15], which allocates transmission power by making fractional
compensation of UEs’ large-scale path losses, such that UEs in
the interior of one cell have stronger received signal strength
than UEs at the cell edge. The key drawback remains in the
unsatisfying average throughput per cell.
Two coordinated power control schemes have been proposed
[7], [16], which make additional compensation for large-scale
path losses from one UE to its neighboring cells. These schemes
are essentially variations of FCPC. Hence, they partially inherit
the drawbacks of FCPC which significantly limits the throughput gain.

II. S YSTEM M ODEL AND P ROBLEM D ESCRIPTION
We consider an LTE uplink multicellular network. In such
network, each UE transmits to its serving cell and meanwhile
generates interference to its neighboring cells. Consider UE u
transmits signal x at power P on a single subcarrier, its received
signal y at the serving cell c can be expressed as:
Xp
√
y = P · hx +
Pj · hj xj + n,
(1)
j

where h and hj denotes the instantaneous complex channel
gain from UE u and uj (served in neighboring cell cj ) to cell
c, respectively, n denotes the experienced noise.P
Hence
p the total
inter-cell interference experienced by UE u is j Pj · hj xj .
Assuming the transmitted signals x and xj have unit variance,
the signal-to-interference-plus-noise ratio (SINR) can thereby

be calculated as:
sinr = P

P · |h|2
,
2
2
j Pj · |hj | + σn

(2)

where σn2 denotes the variance of noise.
The power controller decides the transmission power of UEs
across all cells, which heavily affects their SINR. Unlike Noncoordinated power control that only exploits the CSI of UEs
to their serving cells, coordinated power control additionally
utilizes the CSI of UEs to multiple neighboring cells. The
problem we are tackling is to come up with a coordinated power
control design with low complexity and high throughput gain
over all existing solutions.
Besides the strong impact from power controller, the
throughput performance is also influenced by how each cell
picks the modulation and coding scheme (MCS) for active
UEs. In LTE networks, the existing Turbo-coded modulation
techniques are paired with associated bit rate selections to
form 29 different available MCS options [3]. For each SINR
value, one of these MCS options is chosen by an Adaptive
Modulation and Coding (AMC) module. The selected MCS
should provide a sufficient high throughput and meanwhile
guarantee a low decoding error probability. Usually, the block
decoding error rate is required to be less than 10%, which
will be later compensated by hybrid automatic repeat request
(HARQ). The stairs in the throughput curve are due to the AMC
module. In particular, when the SINR is lower than −6.5 dB,
no MCS can decode successfully and hence the throughput is
zero. When the SINR is higher than 18 dB, the maximum MCS
can decode perfectly, achieving a maximum throughput. Hence,
the SINR region for effective AMC selection is [−6.5 dB, 18
dB].
III. C HECKS

AND

BALANCES : A P OWER C ONTROL D ESIGN

In this section, we present a novel power controller design,
called Checks and balances (C&B), for uplink cellular systems.
This power controller requires very little information, including
the throughput versus SINR curve of the receiver design, the
large-scale path loss from one UE to several nearby BSs,
and some coarse distribution information of the co-channel
interference in the cellular system. The key idea in C&B is
to cooperatively balance the SNR of a UE and its generated
INR to nearby BSs. The complexity of C&B is very low, and
the throughput gain is huge. One can consider C&B as the
simplest implementation of CoMP, which provides significant
throughput improvement without incurring huge cost in pilots
or backhaul. There is no upgrade of the physical layer design,
except for the change of uplink transmission power.
A. Approximations of SINR and Throughput
C&B can operate in the open-loop mode, when only large
scale CSI is utilized, whereas small scale CSI is unavailable
due to the lack of instantaneous channel estimation pilot. The
large-scale path loss between UE uj and cell c is defined as:
−1

,
(3)
PLj , E |hj |2

1

SINR-Throughput Mapping
Piece-wise Approximation f(SINR)

4

0.8

3

0.6

SNR
IoT

cdf

Spectrum Efficiency (bits/s/Hz)

5

2

0.4

1

0.2

0
-20

-10

0
SINR (dB)

10

0
-10

20

0
10
Time-average (dB)

20

Fig. 1: Throughput Curve Approximation.

Fig. 2: Time-average SNR and IoT distributions.

where E[·] denotes the expectation. When we only consider
such large-scale path losses, u’s received signal-to-noise ratio
(SNR) and interference over thermal noise (IoT) at c, and generated interference-to-noise ratio (INR) to cj can be respectively
approximated as:

Hence, we propose a method to estimate IoTS . In the simulation
environment described in Section IV, the distribution of the
IoT in the uplink cellular system is illustrated in Fig. 2. We
select IoTS to be the 95th percentile of IoT that experienced
by an arbitrary UE, which is IoTS = 9 dB. Note that this
statistical distribution can be collected at the BS. In practical
applications, we recommend to initialize the system with this
value and update it according to the measured IoT distribution.
The approximated throughput RS (P ) is plotted in Fig. 3a,
which increases with the transmission power P .

SNR(P )

=

IoT =
INRj (P )

=

PL−1 · P
,
N0
P
N0 + j Pj · PL−1
j
PL−1
u→j
N0

N0
·P
,

(4)
,

(5)
(6)

where N0 denotes the average noise power and is assumed to
be the same and known for all UEs. We also define PLu→j as
the large-scale path loss from UE u to cell cj . Derived from
Eq. (4) and (5), we can approximate the received SINR of UE
u as:
SNR(P )
.
(7)
SINR =
IoT
As C&B only acquires large scale CSI and approximated
SINR information, there is no need to use an accurate throughput curve to determine the transmission power. Therefore, we
introduce a piece-wise function to approximate the foregoing
throughput curve:


f (SINR) = min Tmax , a · log2 (1 + b · SINR) ,
(8)
where Tmax = 4.18, a = 0.7035 and b = 0.7041. These
parameters are achieved by curve fitting as shown in Fig. 1.
Note that for different physical layer designs, we can always
find such an approximated throughput function f (SINR). Next,
we use function f (SINR) to evaluate the throughput of one UE
u and the throughput of the UEs interfered by UE u.
B. Checking the Influence of SNR and INR
Consider an arbitrarily chosen UE u, its uplink throughput
can be approximated as


SNR(P )
RS (P ) = f
,
(9)
IoTS
where IoTS denotes a prior estimation of the interference
experienced by UE u. In fully distributed power control, the
instantaneous interference power that UE u will experience
during its transmissions is not available at the power controller.

Consider the UEs that are notably interfered by UE u, their
sum uplink throughput can be approximated as:


X
SNRI
.
(10)
f
RI (P ) =
IoTI + INRj (P )
PL
<PL
u→j

th

Note that we only consider the non-negligible interference
generated by UE u. To this end, we set the threshold PLth
in (10) as the path loss of a interference link such that the
interference power generated by UE u with the maximum
power Pmax is stronger than noise, i.e.,
PL−1
th · Pmax
> 1,
N0

(11)

where Pmax = 200 mW (23 dBm) [18]. Furthermore, in fully
distributed power control, the received SNR and IoT (excluding
the interference generated by u) of these UEs, denoted by SNRI
and IoTI , are unknown to the power controller of UE u. We
also propose methods to estimate SNRI and IoTI by using
the SINR region [−6.5 dB, 18 dB] in Fig. 1 for effective
AMC selection. Their values are given by SNRI = 24 dB
and IoTI = 5 dB. Due to space limitations, the details for
computing these parameters will be explained in the journal
version. The approximated sum throughput RI (P ) is plotted
in Fig. 3b, which decreases with the transmission power P .
C. Balancing the Two
In order to find a proper balance between SNR and INR, we
consider the following weighted sum throughput maximization
problem:
max

0≤P ≤Pmax

RS (P ) + ζ · RI (P ),

(12)

27

4

26

28
27.5
Weighted Sum Utility

5

3

UI(P)

US(P)

25

2

24

0
-5

22
0

5

10
15
P (dBm)

20

25

(a) Approximated throughput of UE u.

21
-5

26

25.5

23
1

27

26.5

25

24.5
0

5

10
15
P (dBm)

20

25

24
-5

0

5

10
15
P (dBm)

20

25

(b) Approximated total throughput of UEs that (c) Weighted sum throughput approximation.
are interfered by UE u.

Fig. 3: Approximated throughput functions for C&B power controller design.
where ζ is the weight parameter that adjusts the relative
importance of SNR and INR, which is chosen around 1. Large ζ
(e.g. ζ > 1) indicates more focus on mitigating INR rather than
enhancing SNR, which leads to more conservative transmission
power. This benefits the UEs with poor channel states due to
the reduced interference, while constraining the transmission
power of UEs with good channel states that prevents them from
achieving better throughput. It works the other way around
when we choose small ζ (e.g. ζ < 1). Tending to suppress
strong interference, we initially select ζ to be 1.3. As shown in
Fig. 3c, the weighted sum throughput is maximized at a unique
transmission power, i.e., the balance we choose between SNR
and INR.

IV. P ERFORMANCE E VALUATION ON S YSTEM - LEVEL
S IMULATION P LATFORM
A. Simulation Platform Configuration

(13)

We consider an LTE uplink cellular network [19], where the
BSs are located on a typical hexagonal lattice including 19
BS sites, 3 sectors per site. Each sector is regarded as a cell.
The minimal distance between two neighboring sites is 500
meters (Macrocell) [19]. The UEs are uniformly distributed in
the entire network area. The distance from a UE to a nearest
BS is no smaller than 35 meters [18]. Each UE has a single
antenna, and the BSs are equipped with 2 antennas per sector.
The wireless channel coefficients and BS antenna pattern are
generated by following the SCM model for Urban Macro environments in 3GPP TR 25.996 [18], where 3D antenna pattern
and Rayleigh fading [23] are adopted. We set the maximum
doppler shift frequency at 7 Hz according to moving speed 3
km/h and carrier frequency 2.5 GHz. The receivers employ nonCoMP maximum mean square error (MMSE) estimation [25]
with interference rejection combination (IRC) techniques [24].
To obtain the channel coefficients, we estimate the pilots, i.e.,
demodulation reference signals (DMRS) [3], with DFT-based
estimation [22]. Adaptive Transmission Bandwidth (ATB) [21]
non-CoMP packet scheduling scheme is implemented. The
frequency bandwidth is 10MHz, and the noise figure of each BS
receiver is 5 dB [3]. We set a uniform penetration loss of 20 dB
[18] for all users. The delay of control signaling is uniformly
set as 6 ms (i.e., 6 time slots) [17]. The wrap around technique
is employed to avoid the border effect. The parameters of our
system level simulation platform are listed in Table II.
We use the proportional-fair (PF) policy [25] in stochastic
network control in which the weight of UE u used in the ATB
scheduler at time-slot t is
(ru [t])α
,
(14)
(r̄u )β

We use bisection method to find the solution of (13), with
the detailed steps presented in Algorithm 1. This algorithm is
easy to implement in software, and the number of iterations
for convergence is no more than 10. We note that Algorithm 1
is a fully distributed algorithm, where each UE can choose its
transmission power independently.

where ru [t] and r̄u denote the UE u’s potentially achieved data
rate in time slot t and long-term average data rate, respectively.
We set the two associated parameters at: α = 1, β = 1.
We compare C&B with three reference policies: One policy
is the widely used fractional power control (FPC) scheme,
which determines the transmission power P F P C of UE u for

Algorithm 1: Bisection method for solving (13).
1
2
3
4
5
6
7
8
9
10
11
12
13
14

Given l = −10 dBm, r = Pmax , tolerance ǫ = 0.1;
if r − l < ǫ then
m := Pmax ;
else
while r − l ≥ ǫ do
m := (l + r)/2;
if RS′ (m) + ζ · RI′ (m) < 0 then
l := m;
else
r := m;
end
end
end
return P := m.

It is easy to show that Problem (12) is a one-dimensional
quasi-convex optimization problem, and thereby can be obtained by solving the following problem:
RS′ (P ) + ζ · RI′ (P ) = 0.

TABLE II: SYSTEM LEVEL SIMULATION PARAMETERS
Parameter
Deployment Scenario
Inter-site Distance
System Bandwidth
Avg. UEs per Cell
UE/BS Antennas
Distance-dependent Path Loss
Shadowing Standard Deviation
Antenna Pattern
Penetration Loss
Scheduling Decision Delay
Target BLER
Traffic Model
Scheduling Algorithm
Power Control (PC) scheme
Stochastic Network Control Scheme
Link Adaptation
BS Receiver Type
Channel Estimation
Maximum Doppler Shift
α (PF)
β (PF)
Pmax
P0F P C (FPC)
κ (FPC)
P0RL (RLPC)
φ (RLPC)

Setting
19 BS sites, 3 sectors (cells) per site,
wrap-around
500m (Macrocell)
10MHz [50 PRBs, 2 used for control]
10
1/2 per cell
According to 3GPP 36.814 [19]
8 dB
3D
20 dB
6 ms
10%
Fully backlogged queues
ATB
FPC, Max Power, RLPC, C&B
Proportional Fair (PF)
AMC, based on 3GPP TS 36.213 [3]
IRC MMSE
DFT-based Estimation
7Hz
1
1
23 dBm
−87 dBm
0.8
−102 dBm
0.8

each resource block by [3]:
P F P C = min(Pmax , P0F P C + κ · PL),

(15)
P0F P C

where Pmax is the maximum power constraint,
is the
default transmission power, and PL is the large-scale path loss
from UE u to its serving cell. The values of the parameters are
Pmax = 23 dBm, P0F P C = −87 dBm and κ = 0.8. The second
policy is Max Power, which sets all UEs at their maximum
allowable transmission power Pmax . The third reference policy
is the coordinated Reverse Link Power Control (RLPC) scheme,
where the transmission power P RL is decided by [7]:

P RL = min Pmax , P0RL + φ · PL + (1 − φ) · PLmin , (16)

where PLmin denotes the measured minimum path loss from
u to its neighboring cells. The rest parameters are selected as
P0RL = −102 dBm and φ = 0.8. The parameters of C&B are
chosen as we discussed in Section III.
B. Simulation Results

We compare the performance of different power control
schemes in terms of three key metrics: cell average throughput
(i.e. sum average throughput per cell), cell edge throughput and
power efficiency. In particular, cell-edge throughput is defined
as the 5th percentile throughput performance among all UEs,
denoted as 5%-Edge, which is widely used in evaluating the
performance of UE fairness [6], [11], [15].
1) Performance Comparison in Macrocell Scenario: First,
we investigate the performance in enhancing received signal
strength, which is evaluated by time-average SNR as illustrated
in Fig. 4a. It can be seen that C&B, with the weight parameter
set as ζ = 1.3, is able to significantly boost UEs’ timeaverage SNR over FPC. Particularly, more than 40% UEs

have gained at least 3.5 dB and approximately 20% UEs even
achieve more than 10 dB SNR increase. C&B also improves
the SNR of the top 20% UEs compared to RLPC, which
greatly benefit the UEs with good channel conditions. It is
clear that Max Power has the best SNR performance due to the
maximum transmission power. Nevertheless, it simultaneously
incurs severe interference which is quite undesirable.
Next, we switch our attention on the interference mitigation
performance, which is represented by the time-average IoT as
shown in Fig. 4b. Compared with FPC, C&B only slightly
increase the UEs’ average IoT by 1.5 dB. On the other hand,
C&B performs a lot better than Max Power and shows clear
advantage over RLPC in suppressing the inter-cell interference.
Now we concentrate on the throughput performance, which
is the comprehensive result of signal enhancement and interference mitigation. In comparison with FPC and RLPC, C&B
noticeably improves the throughput of UEs with good channel
condition (the top 30%), as illustrated in Fig. 4c. In particular,
the top 20% UEs have even achieved throughput gain of at least
1 Mbits/s. As for UEs with poor channel condition (bottom
10%), C&B shows great advantage over Max Power.
Finally, we present C&B’s advantages over all other candidate power control schemes based on the detailed performance
summary in Table I. C&B shows desirable advantage over FPC
by providing 51.9% improvement on cell average throughput,
while keeping the same 5%-Edge throughput performance and
only dropping the power efficiency by 48%. Further, C&B beats
Max Power in significantly improving 5%-Edge throughput
and power efficiency by 156% and 5, 716% respectively, with
slightly increased cell average throughput. Comparison with a
CoMP power control scheme called RLPC, C&B achieves appreciable gains in cell average throughput and power efficiency
respectively by 25.1% and 71.2%, and simultaneous keeps 5%Edge throughput slightly improved.
Note that the performance improvement of C&B is solely
obtained by power control. One can further incorporate coordinated transceiver and scheduling techniques to achieve even
higher gain. For example, it is known that CoMP receiving techniques can enhance the edge throughput significantly [2]. As
shown in Table I, the throughput gain of C&B is more evident
in the high throughput UEs. Hence, additional improvement is
promising by combining C&B with other CoMP techniques.
2) Tradeoff between cell average throughput and cell edge
throughput: Recall the utility maximization problem (12), we
can vary the weight parameter ζ to adjust the balance between
received signal enhancement and interference mitigation. As
we gradually decrease ζ from 1.3 to 0.7, the cell average
throughput keeps improving in pair with the continuously
degrading 5%-Edge throughput, as summarized in Table III.
This tradeoff between average and edge throughput can be
explained as follows: (a) the UEs with good channel conditions
contribute most part of the average throughput. Increasing
such UEs’ transmission power by decreasing ζ greatly helps
them to achieve stronger received signal, which results in their
throughput gains. Such gains lead the cell average throughput

1

0.8

0.8

0.8

0.6

0.6

0.6

0.4

cdf

cdf

1

cdf

1

0.4
FPC
Max Power
RLPC
C&B

0.2
0
-20

0

20
SNR (dB)

0.4

FPC
Max Power
RLPC
C&B

0.2

40

60

(a) Time-average received SNR.

0

0

5

10
UE IoT (dB)

15

FPC
Max Power
RLPC
C&B

0.2

20

0

0

(b) Time-average experienced IoT.

1
2
3
4
Average Throughput (Mbits/s)

5

(c) Time-average throughput.

Fig. 4: Simulation results in Macrocell scenario.
to improve. (b) In contrast, boosting transmission power incurs
stronger inter-cell interference, which especially jeopardizes
those vulnerable UEs with poor channel states. As a result,
the 5%-Edge throughput is decreased.
TABLE III: Throughput performance comparison between different weight factor ζ selections.
Average Throughput (Mbits/s)
5%-Edge Throughput (Mbits/s)

ζ = 1.3
12.23
0.23

ζ = 1.1
12.41
0.21

ζ = 0.9
12.95
0.18

ζ = 0.7
13.17
0.15

Note that when ζ = 0.7, C&B is remarkably better than
the Max Power policy in terms of both average and edgethroughput. In addition, when ζ = 1.3, C&B is significantly
better than the FPC and RLPC in term of cell average throughput. Therefore, one can adjust the weight parameter in order to
adapt different system requirements.
V. C ONCLUSION

AND

F UTURE W ORK

We investigate how to use limited large scale CSI to achieve
the throughput gain of CoMP through the design of a power
controller named C&B. The optimal power control by itself
is an NP hard problem, which has been open up to date.
Further, as very limited coordination is possible in the openloop mode, the power controllers of different BSs are not
allowed to communicate. C&B satisfies all practical constraints
in cellular systems, and can significantly improve the average
and edge throughput over existing power control schemes with
very low complexity and almost no cost. Further throughput
enhancement is promising by combining C&B with other
coordinated transceiver and scheduling techniques.
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