![2 Computer Science & Information Technology ( CS & IT )
condition is affected by many factors such as fading, shadowing and interference which degrade
the quality of the signal and cause delay, which affect the total capacity of the system. In
addition, two forms of interferences, inter-cell interference that occurs between cells and
intra-cell interference caused by own users can be distinguished. In order to achieve efficient
resource utilization in all sorts of deployment scenarios and QoS requirements in the future
wireless cellular systems, new resource allocation methods must be developed. Importance of
resource scheduling was appreciated with the support of high data rate services in the evolution of
universal Mobile Telecommunication System (UMTS), Standard to High Speed Downlink Packet
Access (HSDPA) and enhanced uplink.
The main difference between uplink and downlink transmissions is the fact that in the first case
each user is affected by its individual channel, whereas the signals arriving at a certain mobile are
passed through the same channel in the downlink, but need a perfect power control that ensures
the same power level for all users at the receiver. If multiple access or co-channel interference in
cellular networks disturbs the transmission, interferers that are separable in space can be
suppressed with multiple antennas, resulting in an improved Signal to Interference plus Noise
Ratio (SINR). Assessing the quality of resource allocation, one can distinguish cell capacity
achieved in the system with fairness amongst users, in terms of offered transmission
opportunities. The trade-off between cell capacity and fairness is very important in a scheduling
algorithm while allocating resources. The objective of the allocation scheme is to maximize total
network utility can be utilized for optimal resource allocation. [1]
In this paper, we discuss intercell interference problem of scheduling process by introducing load
matrix concept and using HSUPA system to prove it. Section II describes effect of interference on
HSUPA in terms of user terminals, is limited by total received power at the base station limits the
uplink capacity. In section III gives the uplink resource allocation in both single cell and multicell
cases to achieve maximum capacity. The load matrix concept is detailed in Section IV. The
performance of the LM scheduling can be observed in the simulation results provided in section
V and finally section VI provides conclusion for this paper.[4][6].
2. Inter Cell Interference
A cellular system consists of many cells with channels (timeslots, bandwidth, or codes) reused at
spatially separate locations. Due to the fundamental nature of wireless propagation, transmissions
in a cell are not limited to within that cell, and thus there is intercell interference between users
and base stations, that use the same channels. The majority of current systems are interference
limited rather than noise limited. Interference is part of every mobile cellular communications
system, and it constitutes a limitation to both radio network capacity and quality of service
provided to users [3]. Intercell interference is managed via averaging of the effects of multiple
interferers. It is more effective in the uplink than in the downlink. Interference averaging also
allows statistical multiplexing of bursty users, thus increasing system capacity.
Resource allocation schemes in the uplink are of two categories, distributed and centralized. The
objective of distributed allocation is to reduce the complexity to the Radio Network Controller
(RNC). This scheme does not know the channel conditions of adjacent cells. Where as in case of
centralized schemes, the network controller is responsible for allocating the resources in every
cell.On the forward link, the data is split by the RNC to a number of base stations and the
received data is combined by the mobile terminal. On the reverse link, the participating base
stations forward the received data to the serving RNC to combine. [12]
In interference limited systems, the uplink capacity is limited by the total transmitted power at the
base station and this power was limited by uplink capacity. Intercell interference calculation is](https://image.slidesharecdn.com/csit2201-180202084607/85/RESOURCE-ALLOCATION-TECHNIQUE-USING-LOAD-MATRIX-METHOD-IN-WIRELESS-CELLULAR-SYSTEMS-2-320.jpg)
![Computer Science & Information Technology ( CS & IT ) 3
done by multiplying the number of users in a cell by the average interference offered in this cell,
this kind of calculation, being suitable for real-time interference simulations based on the number
of users, their path loss, slow fading, and the cell area. But in uplink, intercell interference density
analysis is performed by assuming perfect power control. The number of users is taken into
account, as well as the received signal power and the activity factor according to the user’s
service calculates the average intercell interference per cell. [7]
Usually, in radio network planning, a fixed value is taken for the inter-to-intra cell interferences
ratio (ifactor) 0.65, as well as for the interference margin in the power budget is 3 dB. The
intercell interference will be particularly significant when intercell users are near the cell
boundary and when the frequency reuse factor is equal to one, hence it is a crucial factor and
cannot be ignored in the scheduling process for future wireless cellular systems.
2.1 Interference Model
The performance of a high SINR user is dominated by intracell (or inter-user) interference,
increasing number of users also results in increased intracell interference and an orthogonal
access benefits by eliminating the intracell interference. We know that as the other-cell to own-
cell signal ratio increases, the performance is dominated by other-cell interference rather than
only by intracell interference. [8] Therefore, in case of heavy inter-cell interference, the gains of
orthogonal access over non-orthogonal access go down.
2.2 Intra - Cell Model
The intracell interference limits the maximum achievable data rates and limits the capacity of the
uplink. This model calculates the average inter-cell interference per cell, being necessary to use a
user distribution in the cell area. The calculation of the intra-cell interference in Down link, on
MT i is given by equation 1
I୍୬୲୰ୟ,୨
ୈ
= (P୭୲ୟ୪,ୗ-Pୗ→
)×∝× Lୗ→
[W] (1)
Where PTotal,BS is the total power transmitted by the Base Station (BS), P BS→MTi is the power
transmitted by the BS to the (Mobile Terminal) MT in which interference is being calculated,
and L BS→MTi is the propagation loss between BS and MT. The orthogonality factor α can take
values between 0-1. In Uplink Interference is given by equation 2
I୍୬୲୰ୟ,୨
= ∑ Pୗ୨←
ୋ
ୀଵ ×ηg × N୨, [W] (2)
Where PBSj←MT is the power received at BS j from an MT, ηg is the activity factor of service g, N
j,g is the number of MTs using service g on the cell of BS j, and G is the total number of services
used.
2.3 Inter-Cell Model
Power control on the down link has less impact on intercell interference than on the uplink
because the downlink transmissions all originate from the cell center. Whereas Uplink
transmissions can come from the cell boundaries. Hence need to focus more on the effect of
power control on the uplink. In DL, the model used for inter-cell interference, in an MT using a
service g is given by,
I୍୬୲ୣ୰,୨
ୈ
=∑ P୭୲ୟ୪,ୗ୨
ా
୨ୀଶ ×∝ r୨
ିୟ
×10∆୨/ଵ
[W] (3)](https://image.slidesharecdn.com/csit2201-180202084607/85/RESOURCE-ALLOCATION-TECHNIQUE-USING-LOAD-MATRIX-METHOD-IN-WIRELESS-CELLULAR-SYSTEMS-3-320.jpg)
![4 Computer Science & Information Technology ( CS & IT )
where Ptotal,BSj is, BS j’s total transmitted
the path loss, a representing path loss exponent,
statistical distribution with zero mean and a certain standard deviation, and
the distance between the interfering BS
I୍୬୲ୣ୰,୨
=∑ ∑ ܲௌ
ீ
ୀଵ
ேಳೄ
ୀଵ,ୀ ܶெ×ηg
PBSj←TMk is BSj power received from the M
the number of users using service
hence increases user BER. Intercell interference can be reduced by separating
the same channel by a large distance.
Figure 1. RoT fluctuation in multicell.
Good cellular system designs are
than the noise power. Figure.1 shows the RoT fluctuation due to intercell interference in a typical
cell. The RoT of a cell dramatically increases to above the threshold and rapidly decrea
below the threshold while allocating resources
interference management this RoT should be close to threshold (RoT
increases user terminals have to
interference level at the base station.
user terminals is limited by total received power at the base station actually limits the uplink
capacity. [5]
III. Resourse Allocation
In next generation networks a
communications, broadband Internet access, email services are expected. Consequently, packet
scheduling mechanisms and resource
role. The Radio resource allocation is a challenging problem in wireless networks due to diffe
channel conditions of user and the main aim of resource allocation is to assign radio resources
individual users in order to achieve maximum capacity while meeting the required quality of
service. A contiguous resource allocation scheme is defined for both the uplink and the downlink.
In uplink the distributed and centralised allocatio
The resource allocation problem in
utilize multiple and maximize the system capacity
access control in conjunction with reso
Computer Science & Information Technology ( CS & IT )
total transmitted power, NBS is the number of interfering BS
representing path loss exponent, ∆Lj is associated to slow fading, following a
statistical distribution with zero mean and a certain standard deviation, and r j represents
the distance between the interfering BS j and the MT.
g ൈ N୩,×A[W] (4)
power received from the MT that is being covered by an adjacent cell
the number of users using service g in interfering cell k. A larger interference reduces SINR
hence increases user BER. Intercell interference can be reduced by separating cells operating on
the same channel by a large distance.
Figure 1. RoT fluctuation in multicell.
designs are interference limited that is the interference power is much larger
Figure.1 shows the RoT fluctuation due to intercell interference in a typical
cell. The RoT of a cell dramatically increases to above the threshold and rapidly decrea
below the threshold while allocating resources to users. But for ideal performance in terms of
interference management this RoT should be close to threshold (RoTtarget). As the uplink load
increases user terminals have to their transmit power substantially to overcome the increased
interference level at the base station. Due to the fact that interference cell, the transmit power of
total received power at the base station actually limits the uplink
variety of services with different requirements, like real time
, broadband Internet access, email services are expected. Consequently, packet
resource allocation techniques for QoS guarantees will play a key
The Radio resource allocation is a challenging problem in wireless networks due to diffe
the main aim of resource allocation is to assign radio resources
users in order to achieve maximum capacity while meeting the required quality of
A contiguous resource allocation scheme is defined for both the uplink and the downlink.
he distributed and centralised allocation schemes reduce the complexity of network.
roblem in these systems causes inefficient use of radio spectrums and
and maximize the system capacity, but they have to consider admission and
access control in conjunction with resource allocation mechanism, subcarriers in wireless
is the number of interfering BS’s, rj
-a
is
is associated to slow fading, following a
represents
T that is being covered by an adjacent cell k, N k,g is
A larger interference reduces SINR and
cells operating on
is the interference power is much larger
Figure.1 shows the RoT fluctuation due to intercell interference in a typical
cell. The RoT of a cell dramatically increases to above the threshold and rapidly decreases to
. But for ideal performance in terms of
). As the uplink load
tantially to overcome the increased
transmit power of
total received power at the base station actually limits the uplink
variety of services with different requirements, like real time
, broadband Internet access, email services are expected. Consequently, packet
guarantees will play a key
The Radio resource allocation is a challenging problem in wireless networks due to different
the main aim of resource allocation is to assign radio resources to
users in order to achieve maximum capacity while meeting the required quality of
A contiguous resource allocation scheme is defined for both the uplink and the downlink.
the complexity of network.
spectrums and to
they have to consider admission and
subcarriers in wireless systems](https://image.slidesharecdn.com/csit2201-180202084607/85/RESOURCE-ALLOCATION-TECHNIQUE-USING-LOAD-MATRIX-METHOD-IN-WIRELESS-CELLULAR-SYSTEMS-4-320.jpg)
![Computer Science & Information Technology ( CS & IT ) 5
such as OFDM (orthogonal frequency division multiplexing)[9][14]. Allocating different number
of subcarriers intelligently, the inefficiency issue can be handled. In order to provide various
choices of scheduling performance and signalling overhead, multiple resources allocation types
are defined.
In multiuser OFDMA systems, multiuser diversity can be easily achieved by the allocation of sub
channels to users, and these channels are independent for each user, with this the resource
allocation problem for multiuser OFDMA systems has been extensively investigated. The quality
of resource allocation can be assessed by overall throughput and fairness. In a wireless network
environment the trade-off between throughput and fairness is important for scheduling. Due to
the nature of resource allocation (time and frequency), transmissions suffer no interference from
within the cell and further see minimal interference from neighbouring cells.[11]
IV. Load Matrix Concept
The Load Matrix (LM) concept has the facility to joint management of interference within and
between cells while allocating radio resources to users and this concept proposed intakes the
intercell interference information into account in order to avoid RoT outage. In a multicell system
one of the main challenge in resource allocation is the control of intercell interference. LM is a
centralized scheduler, uses a database containing the load contribution of all active users in the
network and it assigns radio resources to all active users in the network.The basic problem in the
uplink scheduler is to assign appropriate transmission rate and time to all active users, result
maximum radio resource utilization across the network while satisfying the QoS requirements of
all the users. [13]
The important factor in the resource allocation is the users transmit power. The constraints to be
satisfied for a network of M users and N cells are
Constraint1: This constraint states that the maximum user power Pi,max . For each active user i in
the network, its transmit power Pi must be maintained in an acceptable region defined as
0 ≤ Pi ≤ Pmax i Є {1,….., M} (5)
Constrain2: The total received power at base station should be kept below a certain threshold for
all N base stations in the network it uses Rise over Thermal noise (RoT) to represent the
interference constraints.
RoTj ≤ RoT target j Є {1,………, N} (6)
RoTj is the total in band received power fixed target value to maintain uplink interference level at
the base station j (BSj ) over thermal noise. The RoTj for M active users in the network given
below is used to estimate RoT of cells, can be written as
ROTj= (ܰ′
+ P୧ G୧୨
ெ
ୀ
)/ ܰ′
(7)
Constraint3 : The signal to noise plus interference ratio required at the serving base station j if
rate k is being assign to the user to achieve a given frame error rate is SINRtarget,k . For each user,
depending on its channel type and speed, each rate k has a minimum required SINR called
SINRtarget,k . This constraint satisfies only by considering SINRtarget,k as SINR.
SINRi,j ≥ SINRtarget,k i Є {1,., M}, k Є{1,… ,K} (8)](https://image.slidesharecdn.com/csit2201-180202084607/85/RESOURCE-ALLOCATION-TECHNIQUE-USING-LOAD-MATRIX-METHOD-IN-WIRELESS-CELLULAR-SYSTEMS-5-320.jpg)
![6 Computer Science & Information Technology ( CS & IT )
Figure 2. Centralize LM Scheduling in a 3G LTE System.
LM scheduling can be implemented in both centralized and decentralized strategies. In a central
scheduler entity assigns radio resources to all the users in the network where as in decentralized
scheduling each base station implemented with identical LM scheduling, Figure. 2 is
implementation of LM scheduling for the 3rd
Generation Long-Term Evolution (3G LTE).
A centralized scheduler assigns radio resources to all the M users and N cells in the network, LM
i,j is the load factor contribution by user i at BS j defined as
LM i,j =
Pi Gij
ே′ା∑ ீೕ
ಾ
సభ
(9)
Where Gij is the channel gain from user i to BSj averaged over scheduling period, N' is the
thermal noise and Pi is the transmitted power. The LM i,j values stored in column j of LM
database, RoT of cell j can be written ROTj =
1
1−∑ LMij
ܯ
݅=1
(10)
SINR i,j can be written as
SINRi,j=
ୋౠ
ܰ′ܴ݆ܶ− ୋౠ
(11)
The required transmitted power for user i at rate k is,
Pi,k=
ே′ோ்ೌೝ
Gij
SINR target,k
1+SINR target,k
(12)
If above all constraints are satisfied then only power P i,k is acceptable and user i will be
scheduled for transmision. After that LM elements are updated and RoT is calculated for each cell
using [10]. The performance of the LM scheduling has the best RoT over other algorithms
because this scheduler significantly reduce the probability of the RoT exceeding its target.
Priority functions are used to rank the uers in the scheduling process and make a balance between
cell throughput and fairness. Commonly used priority functions shown in table 1.](https://image.slidesharecdn.com/csit2201-180202084607/85/RESOURCE-ALLOCATION-TECHNIQUE-USING-LOAD-MATRIX-METHOD-IN-WIRELESS-CELLULAR-SYSTEMS-6-320.jpg)
![Computer Science & Information Technology ( CS & IT ) 7
Table 1. Comparison between different type schedulers.
A priority function in LM scheduling is introduced based on users load vector that includes intra
and inter cell impact on the network and it tries to maximise the network capacity through these
interference managements. In each base station the LM allocation process simultaneously
increases allocated resources to avoid interference imbalance among the cells. A user with higher
channel gain has highest priority it is evident that giving priority to a user with better channel
condition increases the cell throughput but in a multicell network could have severe impact on the
throughput of other cells. Hence a new priority approach to load matrix is the Global
Proportional Priority (GPP) considers interference contribution of the user to other cells is
defined as
Priority i=
ୋౠ
∑ ୋ,
ಾ
సభ,ಯೕ
∀݅ ⋲ {1, … }ܯ (13)
Where Gi,j is the total channel gain from under i to BSj averaged over the scheduling period. LM
process uses Capacity Check (CC) process to assign rates to the highest priority user in each cell
to update LM elements and while performing capacity checking it maintains overall performnace
of the network. The CC operates on small margin around RoT target instead of a fixed RoT target
threshold result much improved interference outage performance and higher resource
utilization.[15]
V. Simulation Results
To evaluate the performance of the LM concept a HSUPA system simulator is used in a
simulation scenario consisting of 16 cells with 8 users in each cell randomly and uniformly
distributed. The RoT is set at 5.23dB with carrier frequency of 2GHz and simulation time 20 sec.
LM scheduler has the capability to maintain RoT not to exceed its target. LM scheduling is
compared with Benchmark algorithm [2] to show effictiveness of Load Matrix concept and to
carry the performance of resource allocation in terms of interference outage probability and
average cell throughput. The user data, BPSK modulated signal and RoT response have been
shown in figures 3,4 and 5. The figures 6,7 and 8 are for Cumulative Density Function (CDF) of
RoT of inter cell, CDF of intra cell and Probability density function (PDF) of RoT. These
diagrams demonstrate considerable performance in interference outage compared to benchmark
algorithm in terms of cell th
Parameter
Traditional Schedulers
Load Matrix
Round
Rabin
Max C/I Proportional fair Score- based
Throughput Low High Medium Moderate High
Fairness High Low Medium Moderate High
Performance
Less due
to Low TP
Less due to
low fairness
Trade-off between
TP&F
Moderate
compare to PF
Efficient than
traditional
schedulers](https://image.slidesharecdn.com/csit2201-180202084607/85/RESOURCE-ALLOCATION-TECHNIQUE-USING-LOAD-MATRIX-METHOD-IN-WIRELESS-CELLULAR-SYSTEMS-7-320.jpg)
![Computer Science & Information Technology ( CS & IT ) 9
Figure 7. CDF of RoT for Intra cell Interference
Figure 8. PDF of ROT
VI. Conclusion
In this paper we have evaluated system capacity and fairness performance of several transmission
schemes with LM scheduling. LM concept is presented specifically to provide an efficient
resource allocation by jointly considering inter cell and intracellular interferences before
allocating radio resources. A novel approach towards efficient uplink scheduling is presented.
The importance of resource allocation mechanisms is discussed. We have developed a system
level simulator for HSUPA system based on the proposed simulation conditions. The effect on
the scheduling performance can be observed in the simulation results provided and these results
indicate that selection of RoT as well as transmit power significantly affect the performance. By
incorporating a new concept of separate margins for inter cell and intra cell interferences in the
LM has better control over interferences results high overall network performance.
REFERENCES
[1] L. Wu and Z. Tian, “Capacity-Maximizing Resource Allocation for Data-Aided Timing and Channel
Estimation”IEEE International Conference on Acoustics, Speech and Signal Proceeding, Montreal,
Canada, vol. 4, pp. 525–528, May 2004.
[2] 3GPP TR 25.896, “Feasibility Study of Enhanced Uplink for UTRA FDD V6.0.0,” www.3gpp.org,
Mar. 2004.
[3] 3GPP, TS 36.211, “Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Channels and
Modulation (Release 8)”.
[4] H. Holma and A. Toskala, WCDMA for UMTS: Radio Access for Third Generation Mobile
Communications, second edition. John Wiley & Sons, 2002.](https://image.slidesharecdn.com/csit2201-180202084607/85/RESOURCE-ALLOCATION-TECHNIQUE-USING-LOAD-MATRIX-METHOD-IN-WIRELESS-CELLULAR-SYSTEMS-9-320.jpg)
![10 Computer Science & Information Technology ( CS & IT )
[5] Hassibi B. and Hochwald B. M., “High-rate codes that are linear in space and time,” IEEE
Transactions on b Information Theory, vol. 48, no. 7, pp. 1804–1824, 2002.
[6] Khan, F., “A time-orthogonal CDMA high speed uplink data transmission scheme for 3G and
beyond,” IEEE Communication Magazine, pp. 88–94, Feb. 2005
[7] D. Tse and P. Viswanath. Fundamentals of wireless communication. Cambridge University Press,
Cambridge, UK, 2005.
[8] Andrea Goldsmith, wireless communications, Cambridge, 2005.
[9] Farooq Khan, “LTE for 4G mobile broadband”, Cambridge: Cambridge University Press, 2009.
[10] S. Moshavi, “Multi-user detection for DS-CDMA communications,” IEEE Communication
Magazine, vol. 34, pp. 124–136, Oct. 1993.
[11] M. K. Varanasi and B. Aazhang, “Multistage Detection in Asynchronous Code Division Multiple
access communications,” IEEE Trans. Commun., vol. 38, pp. 509–519, Apr. 1990.
[12] Joon Bae Kim and Micheal L.Honig “Resource allocation for Multiple classes of DS-CDMA
Traffic,”IEEE Transactions On Vehicular Technology, vol.49, no.2, pp. 506-518,March 2000.
[13] Saied Abedi, “Efficient Radio Resource Management for Wireless Multimedia Communications,”
IEEE Transactions Wireless Communications, vol.4, no.6, pp. 2811-2822, March 2005.
[14] U. Saraydar, Narayan B.Mandayam, “Pricing and Power Control in a Multicell Wireless Data
Network,”IEEE Journal on Communications,” vol.19, no.10, pp. 1883-1892,
[15] Hannes Ekstrom, Anders Furuskar, Stefan Parkvall, “Technical Solutions for the 3G Long-Term
Evolution,”IEEE Communications Magazine, pp 38-45, March 2006.](https://image.slidesharecdn.com/csit2201-180202084607/85/RESOURCE-ALLOCATION-TECHNIQUE-USING-LOAD-MATRIX-METHOD-IN-WIRELESS-CELLULAR-SYSTEMS-10-320.jpg)
RESOURCE ALLOCATION TECHNIQUE USING LOAD MATRIX METHOD IN WIRELESS CELLULAR SYSTEMS
- 1. David C. Wyld, et al. (Eds): CCSEA, SEA, CLOUD, DKMP, CS & IT 05, pp. 01–11, 2012. © CS & IT-CSCP 2012 DOI : 10.5121/csit.2012.2201 RESOURCE ALLOCATION TECHNIQUE USING LOAD MATRIX METHOD IN WIRELESS CELLULAR SYSTEMS S.Prabhakar1 , Banda Sreenivas2 , D.Karunakar Reddy3 , and S. Ramesh Babu4 1 Department of Electronic and communication Engineering, RRSCET,Hyderabad,India [email protected] 2 Department of Electronic and communication Engineering, Jyothishmathi Institute of Technology & Science,Thimmapur, Karimnagar,India [email protected] 3 Department of Electronic and communication Engineering, JNTU- Hyderabad,India [email protected] 4Department of Electronic and communication Engineering, Nigama Engineering College Sambhayapally, Karimnagar,India [email protected] ABSTRACT An efficient resource allocation is one of the greatest challenges in wireless cellular communication. The resource allocation schemes avoid wastage of resources by allocating resources to a mobile terminal over a short period of time, providing quality of service over wireless networks is the most stressing point for service providers. In general a high degree of sharing is efficient, but requires service protection mechanisms to guarantee the QoS for all services. In this paper we address the multi cell interference on overall radio resource utilization and propose a new strategy for resource allocation in multi cell systems. we also propose a joint management of interference within and between cells for allocation of radio resources , Simulation results are showing that there is a significant improvement in the resource utilization so that overall network performance. KEYWORDS Resource Allocation, Load Matrix, HSUPA, Interference Management. 1. Introduction Wireless communication is one of the most vibrant areas in the communication field today. With growing demand for wireless communications, advanced mobile cellular systems have evolved in many countries. The major challenge in supporting multimedia content and real-time services over wireless network is the QoS. Future wireless communications will be a major move toward ubiquitous wireless communication system and seamless high quality services. Wireless channel
- 2. 2 Computer Science & Information Technology ( CS & IT ) condition is affected by many factors such as fading, shadowing and interference which degrade the quality of the signal and cause delay, which affect the total capacity of the system. In addition, two forms of interferences, inter-cell interference that occurs between cells and intra-cell interference caused by own users can be distinguished. In order to achieve efficient resource utilization in all sorts of deployment scenarios and QoS requirements in the future wireless cellular systems, new resource allocation methods must be developed. Importance of resource scheduling was appreciated with the support of high data rate services in the evolution of universal Mobile Telecommunication System (UMTS), Standard to High Speed Downlink Packet Access (HSDPA) and enhanced uplink. The main difference between uplink and downlink transmissions is the fact that in the first case each user is affected by its individual channel, whereas the signals arriving at a certain mobile are passed through the same channel in the downlink, but need a perfect power control that ensures the same power level for all users at the receiver. If multiple access or co-channel interference in cellular networks disturbs the transmission, interferers that are separable in space can be suppressed with multiple antennas, resulting in an improved Signal to Interference plus Noise Ratio (SINR). Assessing the quality of resource allocation, one can distinguish cell capacity achieved in the system with fairness amongst users, in terms of offered transmission opportunities. The trade-off between cell capacity and fairness is very important in a scheduling algorithm while allocating resources. The objective of the allocation scheme is to maximize total network utility can be utilized for optimal resource allocation. [1] In this paper, we discuss intercell interference problem of scheduling process by introducing load matrix concept and using HSUPA system to prove it. Section II describes effect of interference on HSUPA in terms of user terminals, is limited by total received power at the base station limits the uplink capacity. In section III gives the uplink resource allocation in both single cell and multicell cases to achieve maximum capacity. The load matrix concept is detailed in Section IV. The performance of the LM scheduling can be observed in the simulation results provided in section V and finally section VI provides conclusion for this paper.[4][6]. 2. Inter Cell Interference A cellular system consists of many cells with channels (timeslots, bandwidth, or codes) reused at spatially separate locations. Due to the fundamental nature of wireless propagation, transmissions in a cell are not limited to within that cell, and thus there is intercell interference between users and base stations, that use the same channels. The majority of current systems are interference limited rather than noise limited. Interference is part of every mobile cellular communications system, and it constitutes a limitation to both radio network capacity and quality of service provided to users [3]. Intercell interference is managed via averaging of the effects of multiple interferers. It is more effective in the uplink than in the downlink. Interference averaging also allows statistical multiplexing of bursty users, thus increasing system capacity. Resource allocation schemes in the uplink are of two categories, distributed and centralized. The objective of distributed allocation is to reduce the complexity to the Radio Network Controller (RNC). This scheme does not know the channel conditions of adjacent cells. Where as in case of centralized schemes, the network controller is responsible for allocating the resources in every cell.On the forward link, the data is split by the RNC to a number of base stations and the received data is combined by the mobile terminal. On the reverse link, the participating base stations forward the received data to the serving RNC to combine. [12] In interference limited systems, the uplink capacity is limited by the total transmitted power at the base station and this power was limited by uplink capacity. Intercell interference calculation is
- 3. Computer Science & Information Technology ( CS & IT ) 3 done by multiplying the number of users in a cell by the average interference offered in this cell, this kind of calculation, being suitable for real-time interference simulations based on the number of users, their path loss, slow fading, and the cell area. But in uplink, intercell interference density analysis is performed by assuming perfect power control. The number of users is taken into account, as well as the received signal power and the activity factor according to the user’s service calculates the average intercell interference per cell. [7] Usually, in radio network planning, a fixed value is taken for the inter-to-intra cell interferences ratio (ifactor) 0.65, as well as for the interference margin in the power budget is 3 dB. The intercell interference will be particularly significant when intercell users are near the cell boundary and when the frequency reuse factor is equal to one, hence it is a crucial factor and cannot be ignored in the scheduling process for future wireless cellular systems. 2.1 Interference Model The performance of a high SINR user is dominated by intracell (or inter-user) interference, increasing number of users also results in increased intracell interference and an orthogonal access benefits by eliminating the intracell interference. We know that as the other-cell to own- cell signal ratio increases, the performance is dominated by other-cell interference rather than only by intracell interference. [8] Therefore, in case of heavy inter-cell interference, the gains of orthogonal access over non-orthogonal access go down. 2.2 Intra - Cell Model The intracell interference limits the maximum achievable data rates and limits the capacity of the uplink. This model calculates the average inter-cell interference per cell, being necessary to use a user distribution in the cell area. The calculation of the intra-cell interference in Down link, on MT i is given by equation 1 I୍୬୲୰ୟ,୨ ୈ = (P୭୲ୟ୪,ୗ-Pୗ→ )×∝× Lୗ→ [W] (1) Where PTotal,BS is the total power transmitted by the Base Station (BS), P BS→MTi is the power transmitted by the BS to the (Mobile Terminal) MT in which interference is being calculated, and L BS→MTi is the propagation loss between BS and MT. The orthogonality factor α can take values between 0-1. In Uplink Interference is given by equation 2 I୍୬୲୰ୟ,୨ = ∑ Pୗ୨← ୋ ୀଵ ×ηg × N୨, [W] (2) Where PBSj←MT is the power received at BS j from an MT, ηg is the activity factor of service g, N j,g is the number of MTs using service g on the cell of BS j, and G is the total number of services used. 2.3 Inter-Cell Model Power control on the down link has less impact on intercell interference than on the uplink because the downlink transmissions all originate from the cell center. Whereas Uplink transmissions can come from the cell boundaries. Hence need to focus more on the effect of power control on the uplink. In DL, the model used for inter-cell interference, in an MT using a service g is given by, I୍୬୲ୣ୰,୨ ୈ =∑ P୭୲ୟ୪,ୗ୨ ా ୨ୀଶ ×∝ r୨ ିୟ ×10∆୨/ଵ [W] (3)
- 4. 4 Computer Science & Information Technology ( CS & IT ) where Ptotal,BSj is, BS j’s total transmitted the path loss, a representing path loss exponent, statistical distribution with zero mean and a certain standard deviation, and the distance between the interfering BS I୍୬୲ୣ୰,୨ =∑ ∑ ܲௌ ீ ୀଵ ேಳೄ ୀଵ,ୀ ܶெ×ηg PBSj←TMk is BSj power received from the M the number of users using service hence increases user BER. Intercell interference can be reduced by separating the same channel by a large distance. Figure 1. RoT fluctuation in multicell. Good cellular system designs are than the noise power. Figure.1 shows the RoT fluctuation due to intercell interference in a typical cell. The RoT of a cell dramatically increases to above the threshold and rapidly decrea below the threshold while allocating resources interference management this RoT should be close to threshold (RoT increases user terminals have to interference level at the base station. user terminals is limited by total received power at the base station actually limits the uplink capacity. [5] III. Resourse Allocation In next generation networks a communications, broadband Internet access, email services are expected. Consequently, packet scheduling mechanisms and resource role. The Radio resource allocation is a challenging problem in wireless networks due to diffe channel conditions of user and the main aim of resource allocation is to assign radio resources individual users in order to achieve maximum capacity while meeting the required quality of service. A contiguous resource allocation scheme is defined for both the uplink and the downlink. In uplink the distributed and centralised allocatio The resource allocation problem in utilize multiple and maximize the system capacity access control in conjunction with reso Computer Science & Information Technology ( CS & IT ) total transmitted power, NBS is the number of interfering BS representing path loss exponent, ∆Lj is associated to slow fading, following a statistical distribution with zero mean and a certain standard deviation, and r j represents the distance between the interfering BS j and the MT. g ൈ N୩,×A[W] (4) power received from the MT that is being covered by an adjacent cell the number of users using service g in interfering cell k. A larger interference reduces SINR hence increases user BER. Intercell interference can be reduced by separating cells operating on the same channel by a large distance. Figure 1. RoT fluctuation in multicell. designs are interference limited that is the interference power is much larger Figure.1 shows the RoT fluctuation due to intercell interference in a typical cell. The RoT of a cell dramatically increases to above the threshold and rapidly decrea below the threshold while allocating resources to users. But for ideal performance in terms of interference management this RoT should be close to threshold (RoTtarget). As the uplink load increases user terminals have to their transmit power substantially to overcome the increased interference level at the base station. Due to the fact that interference cell, the transmit power of total received power at the base station actually limits the uplink variety of services with different requirements, like real time , broadband Internet access, email services are expected. Consequently, packet resource allocation techniques for QoS guarantees will play a key The Radio resource allocation is a challenging problem in wireless networks due to diffe the main aim of resource allocation is to assign radio resources users in order to achieve maximum capacity while meeting the required quality of A contiguous resource allocation scheme is defined for both the uplink and the downlink. he distributed and centralised allocation schemes reduce the complexity of network. roblem in these systems causes inefficient use of radio spectrums and and maximize the system capacity, but they have to consider admission and access control in conjunction with resource allocation mechanism, subcarriers in wireless is the number of interfering BS’s, rj -a is is associated to slow fading, following a represents T that is being covered by an adjacent cell k, N k,g is A larger interference reduces SINR and cells operating on is the interference power is much larger Figure.1 shows the RoT fluctuation due to intercell interference in a typical cell. The RoT of a cell dramatically increases to above the threshold and rapidly decreases to . But for ideal performance in terms of ). As the uplink load tantially to overcome the increased transmit power of total received power at the base station actually limits the uplink variety of services with different requirements, like real time , broadband Internet access, email services are expected. Consequently, packet guarantees will play a key The Radio resource allocation is a challenging problem in wireless networks due to different the main aim of resource allocation is to assign radio resources to users in order to achieve maximum capacity while meeting the required quality of A contiguous resource allocation scheme is defined for both the uplink and the downlink. the complexity of network. spectrums and to they have to consider admission and subcarriers in wireless systems
- 5. Computer Science & Information Technology ( CS & IT ) 5 such as OFDM (orthogonal frequency division multiplexing)[9][14]. Allocating different number of subcarriers intelligently, the inefficiency issue can be handled. In order to provide various choices of scheduling performance and signalling overhead, multiple resources allocation types are defined. In multiuser OFDMA systems, multiuser diversity can be easily achieved by the allocation of sub channels to users, and these channels are independent for each user, with this the resource allocation problem for multiuser OFDMA systems has been extensively investigated. The quality of resource allocation can be assessed by overall throughput and fairness. In a wireless network environment the trade-off between throughput and fairness is important for scheduling. Due to the nature of resource allocation (time and frequency), transmissions suffer no interference from within the cell and further see minimal interference from neighbouring cells.[11] IV. Load Matrix Concept The Load Matrix (LM) concept has the facility to joint management of interference within and between cells while allocating radio resources to users and this concept proposed intakes the intercell interference information into account in order to avoid RoT outage. In a multicell system one of the main challenge in resource allocation is the control of intercell interference. LM is a centralized scheduler, uses a database containing the load contribution of all active users in the network and it assigns radio resources to all active users in the network.The basic problem in the uplink scheduler is to assign appropriate transmission rate and time to all active users, result maximum radio resource utilization across the network while satisfying the QoS requirements of all the users. [13] The important factor in the resource allocation is the users transmit power. The constraints to be satisfied for a network of M users and N cells are Constraint1: This constraint states that the maximum user power Pi,max . For each active user i in the network, its transmit power Pi must be maintained in an acceptable region defined as 0 ≤ Pi ≤ Pmax i Є {1,….., M} (5) Constrain2: The total received power at base station should be kept below a certain threshold for all N base stations in the network it uses Rise over Thermal noise (RoT) to represent the interference constraints. RoTj ≤ RoT target j Є {1,………, N} (6) RoTj is the total in band received power fixed target value to maintain uplink interference level at the base station j (BSj ) over thermal noise. The RoTj for M active users in the network given below is used to estimate RoT of cells, can be written as ROTj= (ܰ′ + P୧ G୧୨ ெ ୀ )/ ܰ′ (7) Constraint3 : The signal to noise plus interference ratio required at the serving base station j if rate k is being assign to the user to achieve a given frame error rate is SINRtarget,k . For each user, depending on its channel type and speed, each rate k has a minimum required SINR called SINRtarget,k . This constraint satisfies only by considering SINRtarget,k as SINR. SINRi,j ≥ SINRtarget,k i Є {1,., M}, k Є{1,… ,K} (8)
- 6. 6 Computer Science & Information Technology ( CS & IT ) Figure 2. Centralize LM Scheduling in a 3G LTE System. LM scheduling can be implemented in both centralized and decentralized strategies. In a central scheduler entity assigns radio resources to all the users in the network where as in decentralized scheduling each base station implemented with identical LM scheduling, Figure. 2 is implementation of LM scheduling for the 3rd Generation Long-Term Evolution (3G LTE). A centralized scheduler assigns radio resources to all the M users and N cells in the network, LM i,j is the load factor contribution by user i at BS j defined as LM i,j = Pi Gij ே′ା∑ ீೕ ಾ సభ (9) Where Gij is the channel gain from user i to BSj averaged over scheduling period, N' is the thermal noise and Pi is the transmitted power. The LM i,j values stored in column j of LM database, RoT of cell j can be written ROTj = 1 1−∑ LMij ܯ ݅=1 (10) SINR i,j can be written as SINRi,j= ୋౠ ܰ′ܴ݆ܶ− ୋౠ (11) The required transmitted power for user i at rate k is, Pi,k= ே′ோ்ೌೝ Gij SINR target,k 1+SINR target,k (12) If above all constraints are satisfied then only power P i,k is acceptable and user i will be scheduled for transmision. After that LM elements are updated and RoT is calculated for each cell using [10]. The performance of the LM scheduling has the best RoT over other algorithms because this scheduler significantly reduce the probability of the RoT exceeding its target. Priority functions are used to rank the uers in the scheduling process and make a balance between cell throughput and fairness. Commonly used priority functions shown in table 1.
- 7. Computer Science & Information Technology ( CS & IT ) 7 Table 1. Comparison between different type schedulers. A priority function in LM scheduling is introduced based on users load vector that includes intra and inter cell impact on the network and it tries to maximise the network capacity through these interference managements. In each base station the LM allocation process simultaneously increases allocated resources to avoid interference imbalance among the cells. A user with higher channel gain has highest priority it is evident that giving priority to a user with better channel condition increases the cell throughput but in a multicell network could have severe impact on the throughput of other cells. Hence a new priority approach to load matrix is the Global Proportional Priority (GPP) considers interference contribution of the user to other cells is defined as Priority i= ୋౠ ∑ ୋ, ಾ సభ,ಯೕ ∀݅ ⋲ {1, … }ܯ (13) Where Gi,j is the total channel gain from under i to BSj averaged over the scheduling period. LM process uses Capacity Check (CC) process to assign rates to the highest priority user in each cell to update LM elements and while performing capacity checking it maintains overall performnace of the network. The CC operates on small margin around RoT target instead of a fixed RoT target threshold result much improved interference outage performance and higher resource utilization.[15] V. Simulation Results To evaluate the performance of the LM concept a HSUPA system simulator is used in a simulation scenario consisting of 16 cells with 8 users in each cell randomly and uniformly distributed. The RoT is set at 5.23dB with carrier frequency of 2GHz and simulation time 20 sec. LM scheduler has the capability to maintain RoT not to exceed its target. LM scheduling is compared with Benchmark algorithm [2] to show effictiveness of Load Matrix concept and to carry the performance of resource allocation in terms of interference outage probability and average cell throughput. The user data, BPSK modulated signal and RoT response have been shown in figures 3,4 and 5. The figures 6,7 and 8 are for Cumulative Density Function (CDF) of RoT of inter cell, CDF of intra cell and Probability density function (PDF) of RoT. These diagrams demonstrate considerable performance in interference outage compared to benchmark algorithm in terms of cell th Parameter Traditional Schedulers Load Matrix Round Rabin Max C/I Proportional fair Score- based Throughput Low High Medium Moderate High Fairness High Low Medium Moderate High Performance Less due to Low TP Less due to low fairness Trade-off between TP&F Moderate compare to PF Efficient than traditional schedulers
- 8. 8 Computer Science & Information Technology ( CS & IT ) Figure. 3 User data Figure 4. BPSK Modulated Signal Figure 5. ROT Response Figure 6. CDF of RoT for Inter cell Interferen
- 9. Computer Science & Information Technology ( CS & IT ) 9 Figure 7. CDF of RoT for Intra cell Interference Figure 8. PDF of ROT VI. Conclusion In this paper we have evaluated system capacity and fairness performance of several transmission schemes with LM scheduling. LM concept is presented specifically to provide an efficient resource allocation by jointly considering inter cell and intracellular interferences before allocating radio resources. A novel approach towards efficient uplink scheduling is presented. The importance of resource allocation mechanisms is discussed. We have developed a system level simulator for HSUPA system based on the proposed simulation conditions. The effect on the scheduling performance can be observed in the simulation results provided and these results indicate that selection of RoT as well as transmit power significantly affect the performance. By incorporating a new concept of separate margins for inter cell and intra cell interferences in the LM has better control over interferences results high overall network performance. REFERENCES [1] L. Wu and Z. Tian, “Capacity-Maximizing Resource Allocation for Data-Aided Timing and Channel Estimation”IEEE International Conference on Acoustics, Speech and Signal Proceeding, Montreal, Canada, vol. 4, pp. 525–528, May 2004. [2] 3GPP TR 25.896, “Feasibility Study of Enhanced Uplink for UTRA FDD V6.0.0,” www.3gpp.org, Mar. 2004. [3] 3GPP, TS 36.211, “Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Channels and Modulation (Release 8)”. [4] H. Holma and A. Toskala, WCDMA for UMTS: Radio Access for Third Generation Mobile Communications, second edition. John Wiley & Sons, 2002.
- 10. 10 Computer Science & Information Technology ( CS & IT ) [5] Hassibi B. and Hochwald B. M., “High-rate codes that are linear in space and time,” IEEE Transactions on b Information Theory, vol. 48, no. 7, pp. 1804–1824, 2002. [6] Khan, F., “A time-orthogonal CDMA high speed uplink data transmission scheme for 3G and beyond,” IEEE Communication Magazine, pp. 88–94, Feb. 2005 [7] D. Tse and P. Viswanath. Fundamentals of wireless communication. Cambridge University Press, Cambridge, UK, 2005. [8] Andrea Goldsmith, wireless communications, Cambridge, 2005. [9] Farooq Khan, “LTE for 4G mobile broadband”, Cambridge: Cambridge University Press, 2009. [10] S. Moshavi, “Multi-user detection for DS-CDMA communications,” IEEE Communication Magazine, vol. 34, pp. 124–136, Oct. 1993. [11] M. K. Varanasi and B. Aazhang, “Multistage Detection in Asynchronous Code Division Multiple access communications,” IEEE Trans. Commun., vol. 38, pp. 509–519, Apr. 1990. [12] Joon Bae Kim and Micheal L.Honig “Resource allocation for Multiple classes of DS-CDMA Traffic,”IEEE Transactions On Vehicular Technology, vol.49, no.2, pp. 506-518,March 2000. [13] Saied Abedi, “Efficient Radio Resource Management for Wireless Multimedia Communications,” IEEE Transactions Wireless Communications, vol.4, no.6, pp. 2811-2822, March 2005. [14] U. Saraydar, Narayan B.Mandayam, “Pricing and Power Control in a Multicell Wireless Data Network,”IEEE Journal on Communications,” vol.19, no.10, pp. 1883-1892, [15] Hannes Ekstrom, Anders Furuskar, Stefan Parkvall, “Technical Solutions for the 3G Long-Term Evolution,”IEEE Communications Magazine, pp 38-45, March 2006.
- 11. Computer Science & Information Technology ( CS & IT ) 11 Authors short Biography BANDA SREENIVAS working as an Associate professor in Department of Electronics and Communication Engineering in JYOTHISHMATHI INSTITUTE OF TECHNOLOGY AND SCIENCE,Karimnagar.,Graduated from KITS- Warangal , compleated M.Tech from JNTU-H.published and presented TWO papers in the International conferences and TWO papers in National conferences. Has 13 years of experience in teaching and industry together.Reviwer for two international journals and life member of ISTE SAGGURTHY RAMESH BABU working as a professor and HOD ,in Department of Electronics and Communication Engineering in NIGAMA ENGINEERING COLLEGE ,Karimnagar., Graduated from pullareddy engg.college karnool, completed M.Tech from JNTU-A.published papers in an International conferences/National conferences.have around 13 years of experience in teaching life member of ISTE Mr.Saggurthi.Prabhakara Rao received his Bachelor of Engineering (B.E.in E.C.E) in electronics and communications engineering from Vasavi College of Engineering Hyderabad in 1991 and Master of Engineering in Communications from C.B.I.T Hyderabad in 2005.From 2005 to 2008 he was faculty of Engineering as Associate Professor in ECE department in Sri Devi Women’s Engineering College, Hyderabad.joined in RRSCET as Professor in 2008 after that he taken many responsibilities as Head of the Department, Vice-Principal, In-Charge for inspections of AICTE, SBTET, JNTUHAs Prof. & HOD he conducted and attended many seminars, workshops, conferences.He was enrolled his PhD Course and doing his research work with JNTUH in 2007 onwards in wireless communications and completed his Pre-PhD course.He is currently working as Dean Research & Development center and project /technical manager for several projects for Diploma, UG and PG Courses. He completed his Pre-PhD course and published 3 internationals journals and 3 national conferences and participated in one international conference.Being IEEE senior member He was actively involved in IEEE workshops and conferences.His research interests include communications, wireless networks, security and quality of service in wireless communications.He is currently working for wireless security and quality of service in wireless communications. D.Karunakar reddy working as a lecturer in JNTU Hyderabad Reserch scholar in Department of Electronics and Communication Engineering in JNTU hyderabad completed graduation in 2002 completed his M.Tech in 2005.having 5 years experience in IT industry