Reduction of Threshold-Based Feedback in
Multi-Cellular Cluster Networks
Afshin Jahanbin
Faculty of Engineering, Ferdowsi University, Mashhad, Iran [email protected]
Mosarreza Mosavisadr
Lecturer of Technical and Vocational University of Iran Email: [email protected]
Abstract– In this paper, a downward system has cell and dual frequency division characteristics has been considered in which each user send state information of information channel to its home station. Since the cooperation between all stations is not possible in practice, a number of stations are grouped into clusters and users should estimate their own channel by sending pilots to base station and feedback channel state information to the station. Until the station has no receive the channel information of all users, prevents to send it that it cause delay in sending data at downward system. The more the users increase, the greater delay occurs. In this study, two approaches are recommended in order to reduce the feedback of channel state information in a downward system. The basic idea of proposed approaches is that users are divided into two groups based on users’ channel power and their distance from the transmitter. An important issue in cooperative MIMO network is that the channel state information should be collected in a small fraction of channel bonding to have enough time to send the data and observed using a simulation. The effectiveness of the proposed method in order to reduce the feedback of channel state information on system performance is very low compared with other methods.
Keywords – Downward, Multi-Input Multi-Output, Communication Distance, Channel Power, Clustering, Feedback of Channel State Information.
I. I
NTRODUCTIONCoordinated Multi Point Transmission (CoMP) that is also called Coordinated-Multi Input Multi Output (Co-MIMO) or multi-input multi-output network (Cooperative MIMO network) is a developed type of Multi User-Multi Input Multi Output (MU-MIMO) networks and is proposed to increase user performance in cell edge of interference-limited environments to LTE-Advanced and IEEE802.16m [1]-[2]. Old MU-MIMO systems using single-cell processing form a MIMO Interference Channel that its degree of freeness is determined by the number of antenna in each transmitter [3]-[4]. If the neighboring base stations are working together, the entire network can achieve to total degrees of freeness proportional to the number of colleagues stations. Therefore, cooperative MIMO network enables to of provide very high operating efficiency, which is very important issue in wireless telecommunications [5]. Most techniques based on collaborative MIMO network rely on access the full channel state information of terminals of all users on a central processing unit which is a great challenge in the cooperative MIMO network. Besides, in order to provide full channel information in processing unit of each cluster, feedback channel with high capacity of user terminals to related home base station and low capacity backhaul links
between each pair of home station of each central processing unit cluster, a cluster should be considered that is an important issue when implementing cooperative MIMO network. As a result, there is a great interesting in cooperative MIMO network techniques based on reducing need to the channel state information [6]-[7]-[8]. In this paper, since users are single-antenna, we use Zero Forcing Beam Forming (ZFBF) method while for multi-antenna users; Bock Diagnoalization (BD) method is used [9]. In this paper, effective techniques for reducing feedback of channel state information in cooperative MIMO network systems are presented. By dividing users into two groups based on their channel power and their distance from the transmitter station, the feedback of channel state information will be decreased. Simulation results show that with slight reduction in performance, substantially reduce the feedback channel state information is obtained. Simulation results show that by slight reduction in performance, reduce the feedback of channel state information is substantially reduced. Total average rate is a criterion that does not compute the amount of feedback of channel state information while network throughput simultaneously calculates the total rate and feedback of channel state information.
II. S
YSTEMM
ODELConsider a cooperative cell MIMO network that its stations and users are Nt and Nr respectively. In such system, parameters show in Table 1. Consider a large network where the number of cells is very high and stations are connected by high-speed backhaul links. In this network, the cooperation between all stations is impossible. To solve this problem, we divide the network into distinct clusters which each cluster includes a number of neighboring cells.
Table 1: Cooperative MIMO network system parameters in a downward transmission
Description Symbol
Maximum transmission throughput in each station
P
Station number in each cluster B
Considered cluster number C
Users number in cluster K
Antenna number of each transmitter station
t
n
Antenna number of each user
r
n
Cell radius 0
Figure 1 shows an example of structure of cluster cooperative MIMO network. Hence, there are two types of network interference: intra-cluster interference and inter-cluster interference [10]. In this figure an example of a cluster network has shown by B=7 that there is full cooperation between cells of each cluster. Dash-lines indicate the scope of clusters. The cluster is marked with bold lines indicates under analysis central cluster. In this figure, a layer around a central cluster of clusters is considered [11]-[12].
In general, the total number of antennas in all the stations is equal to Nt=Cnt. It is assumed that all receivers are single-antenna and there are K=Nt users for receiving data in each cluster. In case the number of users exceeds the number of antennas, we need to select user which we will discuss it later [13].
Assume that Sc,k is a data vector for user k in group C ,
, ,1
,...,
,T c k c c k
W
w
w
andP
c k,
diag p
c,1,...,
p
c k,
are pre-code vector and throughput assignment matrix respectively. Hence, upstream signal is as follow:
Fig.1.An example of a cluster network
, , , ,
c k c k c k c k
x
p w s
(1)So, downstream signal of k user in group c is as follow:
, , , , , , , , , , , , ,
1, 1
c
c k c k c k c k c k c j c k c j c j c k c k c k c k
j k i j
y p h w s p h w s h w s n
Where
, , ,
c k c k c w
h
L h
(2)[16]-[15]-[14] Downward channel vector 1×Ntis from c group station to k user. Hc,w is 1×Nt vector with complex Gaussian variables CN(0,1) which has independent identical distribution (i.i.d). Lc,kis diagonal matrix with
L
( )b kc, over diameters. In equation 4,h
c kˆ, is representation of channelof surrounding groups which cˆ is in group related to k user in c group.
w
c k, is pre-code matrix of Nt1 for k user in c group.n
c k, is the additive Gaussian white noise2
(0, )
CN
andp
c k, is throughput assigned to k user in ccluster.
Path loss between the transmitter antenna of base stations and receiver antenna of users are calculated according to the following equation:
( ) , ( )
,
0
(
b kc)
c b k
d
L
d
(3)Where d0 is the reference distance and
should beconsidered in 20dB where SNR is without interference in cell border [17].
d
b k( ),c is referred to distance from b base station to k user in c cluster which is considered much more than distance of antennas of stations from each other. Path loss throughput and cell radius are shown by β and d0 respectively [18].In this system, the total rate is obtained from following equation:
max log2
, 1
2 , ,
1 2 2
1 1, , , ˆ 1,ˆ ˆ, ˆ, ˆ,
K RBFw pk k k
p hk c k c kw
K p h w C p h w
j c j
j j k c k c c c c k c k c k
In the condition that 2 1
K
k k
k
w P P
(4)Elimination of interference between users within a group that is conducted by Zero Forcing Beam Forming (ZFBF) method will be explained next. Covariance matrix of intra-cell Interference in the k user of c cluster is given by the following equation:
, , H,
c k c k c k
Q
E I I
Where
ˆ ˆ , ˆ ˆ, , ,
c k c c c c k c k
I
h x
(5)In ZFBF method, wave forming vectors provide conditions to make
h w
k j
0
interference zero for allj k
. This suboptimal pre-coder eliminates intra-cell interference in transmitter. According to be simple and asymptotic optimality, ZFBF method [19] is a suitable method to form wave in cellular networks. Figure 2 shows block diagram of cooperative MIMO network in downward transmission in the case that users are single-antenna using ZFBF pre-coder [20]-[21].Suppose that T
1,...,K T M
, is the output of scheduler that is a subset of users are selected by station to transmit, and H Tc( ) and W Tc( ) are sub-matrix related to1,,..., ,
T
T T
c c k c
H h h and Wc wc,1,...,wc k, respectively.
( ) c
W T (pre-processing matrix of ZF) using calculate
pseudo-inverse as follow:
1( )
( )
( )
H( ) ( )
Hc c c c c
W T H T
H T
H T H T
(6)Pre-code matrix eliminates inter-cluster interference. As a result, we have:
max , log2 1 2
1 1
ˆ 1,ˆ ˆ, ˆ, ˆ,
K pk
RBF w p
k k k C p h w
c c c c k c k c k
(7) Optimal Pks are obtained using Water filling algorithm. Assume that effective channel interest is obtained by the following equation [22]:
2 1
1
1
k
H
k c c
w
H H
In this case, using Water filling algorithm, optimal Pks are obtained as follows:
1
k k
p
(8)Where µ is water level in following described method
1
k T
k
p
(9)Also, using Water filling algorithm, total rate is obtained as follows:
2 ,
( ) max
log 1
,
k k
ZFBF k
p k T
R
T
p
In the condition that 1 1
K
k k
i
p
p
(10)The methods of throughput control can be used to divide throughput among users in cooperative MIMO network systems [23].
III. P
ROPOSEDM
ETHODS3.1. Proposed method based on channel-size:
The feedback of modified home channel state information reducing the feedback of channel state information based on channel power is as following.
Users with weak channel, transmit channel state information to the stations in neighboring cells, in each time slot, and the home station, a time slot in between. Users with strong channels, transmit channel state information to all stations in their own group each time slot.
3.2. Proposed method based on distance:
The feedback of modified home channel state information the position of cell members is available at the base station on emergency or safety reasons. Also, the
position of the cell users can be available in user terminals. Hence, we can look for ways to reduce feedback based on the distance to the channel state information. This method is as follow:
Users that their distance is more than a predetermined distance transmit the channel status information to neighboring station of each time slot and to home station a time slot in between. In contrast, the users that their distance is less than a predetermined distance transmit the channel state information feedback each time slot.
IV. S
IMULATIONR
ESULTSIn this section, simulation results are presented. The cell number in each cluster is considered B=3 and a cluster layer is assumed around considered cluster. Each station has 8 transmitter antennas. To simulation, each user has
1 r
n antenna and there are Nt24 transmitter antennas in each cluster. Normalized Doppler is assumed to 0.05, unless it is stated. Cell radius is assumed to 1km and path loss equals to 3.76. For proposed method based on distance, threshold is 2R/3. In random method, users of cell space for transmitting the channel state information a time slot in between are selected randomly.
Figure 3 indicates net interest of network based on the number of antennas.
Fig.3. Average of net interest of network of multi-cell CoMP system increasing by the increase of SNR in the edge of cell. f Td s is assumed equal to 0.05. Threshold for
Fnorm channel in proposed method based on Fnorm channel is considered equal to 10. Threshold for proposed
method based on distance is equal to R/2.
It shows that the proposed method reduces the amount of feedback of channel state information. Performance results show the various methods of transmitter antennas. Increasing the number of antennas, the total rate increases, as a result, net interest of network is also increased. However, the feedback rate increases due to the increasing number of transmit antennas is more than the rate of performance increase. As a result, for a large number of transmitter antennas, the net interest of network is reduced by increasing the number of antennas.
5 10 15
5 10 15 20 25 30
Antenna
Ne
t-th
ro
ug
hp
ut
Figure 4 shows the system efficiency in terms of throughput. Proposed methods reduce the amount of feedback, thus, the total rate reduces. Reduce amount of CSI feedback (which is obtained numerically) for proposed method based on channel size is approximately 10%.
Fig.4. Average of total rate for two distance values and two value of
f T
d s.V. C
ONCLUSIONIn this study, four techniques for reducing the feedback of channel state information in downlink clustered network MIMO system (CoMP) is proposed. The basic idea of the proposed method is that users are divided into two groups based on the channel power of users and the users' distance from the transmitter station. It can be observed that the overall average rate reductions caused by imperfect feedback of channel state information in cooperative MIMO network system using ZFBF, when users with weak channels transmit data each time slot in between and users with strong channels transmit data each time slot, is small. Using simulation, it can be seen that the impact of proposed methods on the performance of system to reduce the feedback of channel state information is very low compared with other methods. An optimal threshold based on channel power of users obtained in order to separate users using pseudo - analytical method. Also, the net interest of network to prove the power of proposed methods as a compromise between the performances of channel state information has been investigated and compared with other methods.
R
EFERENCES[1] D. Gesbert, H. Huang, S. Shamai Shitz, O. Simeone, and W. Yu, “Multi-cell MIMO cooperative networks: A new look at interference” {EEE J. Select. Areas Commun.}, vol. 28, no.9, pp. 2393-2409, Dec. 2010.
[2] D. J. Love, R. W. H. Jr., and T. Strohmer, ”Grassmannian beamforming for multiple-input multiple-output wireless systems,” {EEE Trans. Inform. Theory}, vol. 49, pp. 2735-2747, Oct. 2003.
[3] Huang, Kaibin, R. W. Heath, and Jeffrey G. Andrews. "Joint beamforming and scheduling for SDMA systems with limited feedback."IEEE Trans. Commun(2006).
[4] D. Kin, Oh. S. Shin, and K. B. Lee, ”Efficient limited feedback schemes for network MIMO systems,” in ي{Proc. 2011, IEEE Globecom}, pp. 1-6.
[5] S. Shamai , O. Simeone, O. Somekh, O. Poor, “ Joint multi-cell processing for downlink channels with limited-capacity backhaul,” in Proc. Information theory and application workshop, pp. 345 – 349, Jun. 2008
[6] Papadogiannis, H. J. Bang, D. Gesbert and E. Hardouin, “Efficient selective feedback design for multicell cooperative network,”IEEE Tarns. Vechular Technology, vol. 60, no. 1, pp 196 – 205,Jan. 2011.
[7] J. Xu, J. G. Andrews, and S. A. Jafar, ”MISO broadcast channels with delayed finite-rate feedback: predict or observe?,” {IEEE Trans. Wireless. Commun.}, vol. 11, no. 4 pp. 1456-1467, Apr. 2012.
[8] S. Akoum, and R. W. Heath Jr, ”Limited feedback for temporally correlated MIMO channels with other cell interference,” {IEEE Trans. Signal Process.}, vol. 58, no. 10, pp. 5219-5232, Oct. 2010.
[9] W. Li and M. Latva-aho, “An efficient channel block diagonalization method for generalized zero forcing assisted MIMO broadcasting system,” {IEEE Trans. On Wireless Commun.}, vol. 10, no. 3, March 2011.
[10] M. Kobayashi, M. Debbah, and J. C. Belfiore, “Outage efficient strategies for network MIMO with partial CSIT,” inProc. IEEE Int. Symp. Infor. Theory, (ISIT’09), Seoul, Korea, June-July 2009
[11] S. Shamai , O. Simeone, O. Somekh, O. Poor, ”Joint multi-cell processing for downlink channels with limited-capacity backhaul,” in {Proc. 2008, Information theory and application workshop}, pp. 345-349.
[12] D. Gesbert, S. Hanly, H. Huang, S. Shamai Shitz, O. Simeone and Wei Yu, ”Multi cell MIMO cooperative networks: a new look at interference,” {IEEE J. Sel. Areas Commun.}, vol. 28, no.9, pp. 1380-1408, Dec. 2010.
[13] M. Karakayali, G. Foschini, and R. Valenzuela, ”Network coordination for spectrally efficient communications in cellular systems,” {IEEE Wireless. Commun. Magazine}, vol. 13, no. 4, pp. 56-61, Aug. 2006.
[14] “Downlink Multiple Antenna Schemes for LTE-Advanced,” Motorola, 3GPP TSG RAN WG1 R1-083229, Aug. 2008. [15] U. Jang, H. Son, J. Park and S. Lee, ”CoMP-CSB for ICI nulling
with user selection,” {IEEE Trans, Wire. Commun}., vol. 10, no. 9, pp. 2982-2993, Sept. 2011.
[16] B. Li, Y. Luo, R. wang, and H. shen, ”A unified interference suppression framework: Joint vector and lattice approach,” in
Proc. 21th IEEE Intl. Symp. On Personal, Indor and mobile Radio Comm. (PIMRC'10),Towards IMT-Advanced and Beyond WorkshopIstanbul, Turkey, Sept. 2010.
[17] M. Sharif and B. Hassibi, ”On the capacity of MIMO broadcast channels with practical side information,” {IEEE Trans. Inf. Theory.}, vol. 51, pp. 506-522, Feb. 2005.
[18] S. G. Kiani and D. Gesbert, “Optimal and distributed scheduling for multicell capacity maximization,” {IEEE Trans. Wireless Commun.}, vol. 7, no. 1, pp. 288-297, Jan. 2008.
[19] S. Kaviani and W. A. Krzymien, “Optimal multiuser zero forcing with per-antenna power constraints for network MIMO coordination,” EURASIP Journal on Wireless Communications and Networking, Volume 2011, Article ID 190461, 12 pages. [20] J. Zhang, ”Adaptive MIMO Network to Manage Interference,”
University of Texas at Austin, 2009.
[21] H. Kim, H. Yu, Y. Sung, and Y. H. Lee, “An efficient algorithm for zero-forcing coordinated beamformin,” {IEEE Commun. Letters}, vol. 16, no. 7, pp. 994-997, July 2012.
[22] E.Bigleiri, R.Calderbank, A. Constantinides, A.Goldsmith, A.Paulraj, and H. Vincent Poor, “ MIMO wireless communication,” Cambridge University Press, 2007
[23] Gjendemsjoe, D. Gesbert, G. Oien, and S. Kiani, “Binary power control for sum rate maximization over multiple interfering links,” {IEEE Trans. Wireless Commun.}, vol. 7, no. 8, pp. 3164-3173, Aug. 2008.
[24] I. S. Gradshtyn, and I. M. Ryzhik, {Table of Integrals, Series, and Products}, Academic Press, 1980.
[25] S. Coles, {An Introduction to Statistical Modeling of Extreme Values}, Springer, 2001.
10 10.5 11 11.5 12 12.5 13 13.5 14
21 22 23 24 25 26 27 28 29 30
Power
Th
ro
ug
hp
ut
Throughput
A
UTHOR’
SP
ROFILEAfshin Jahanbin
Afshin Jahanbin was born in Sabzevar. He studied in Emam Khomeini technical college. Next, he went to Sajjad University to get bachelor degree in 2008. Three years later, in 2011, he went to Ferdowsi University to get Master degree. His master thesis was about clustering in Wireless Sensor Networks (WSN). Furthermore, the main area that he works on is computer forensics and he works on WSN and MIMO telecommunication systems.
![Forecasts of the Scottish economy [November 2004]](data:image/gif;base64,R0lGODlhAQABAIAAAP///wAAACH5BAEAAAAALAAAAAABAAEAAAICRAEAOw==)