A Fair and Efficient Packet Scheduling Scheme for IEEE 802.16 Broadband Wireless Access Systems

International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.1, No.3, September 2010
DOI : 10.5121/ijasuc.2010.1308 93
A Fair and Efficient Packet
Scheduling Scheme for IEEE 802.16 Broadband
Wireless Access Systems
Prasun Chowdhury1
, Iti Saha Misra2
Electronics and Telecommunication Engineering
Jadavpur University, Kolkata, India
1
prasun.jucal@gmail.com, 2
iti@etce.jdvu.ac.in
Abstract
This paper proposes a fair and efficient QoS scheduling scheme for IEEE 802.16 BWA systems that
satisfies both throughput and delay guarantee to various real and non-real time applications. The proposed
QoS scheduling scheme is compared with an existing QoS scheduling scheme proposed in literature in
recent past. Simulation results show that the proposed scheduling scheme can provide a tight QoS
guarantee in terms of delay, delay violation rate and throughput for all types of traffic as defined in the
WiMAX standard, thereby maintaining the fairness and helps to eliminate starvation of lower priority
class services. Bandwidth utilization of the system and fairness index of the resources are also
encountered to validate the QoS provided by our proposed scheduling scheme.
Keywords
IEEE 802.16, MAC, QoS, Packet Scheduling, Fair and efficient
1. Introduction
Wireless networks are generally less efficient and unpredictable compared to wired networks,
which make Quality of Service (QoS) provisioning a bigger challenge for wireless
communications. The wireless medium has limited bandwidth, higher packet error rate, and
higher packet overheads that altogether limit the capacity of the network to offer guaranteed
QoS. In response to increasing QoS challenge, the IEEE 802.16 standard, also known as
Worldwide Interoperability for Microwave Access (WiMAX), has emerged as the strongest
contender for Broadband wireless technology with the promises to offer guaranteed QoS to
wireless users. WiMAX is a technology aimed at providing last-mile wireless broadband access
at a cheaper cost. The “last mile” is the final leg of delivering connectivity from the service
provider to the customer [1]. This leg is typically seen as an expensive undertaking because of
the considerable costs of wires and cables. The core of WiMAX technology is specified by the
IEEE 802.16 standard that provides specifications for the Medium Access Control (MAC) and
Physical (PHY) layers. The term WiMAX was created by the WiMAX forum that promotes
conformance and interoperability of the standard.
In WiMAX network, traffic from the Base Station (BS) to the Subscriber Station (SS) is
classified as downlink traffic while that from the SS to the BS is classified as uplink traffic. A
scheduling algorithm implemented at the BS has to deal with both uplink and downlink traffic.
In some cases, separate scheduling algorithms are implemented for the uplink and downlink
traffic. Typically, a Call Admission Control (CAC) procedure is also implemented at the BS that
ensures the load supplied by the SSs can be handled by the network [1-5]. A CAC algorithm
ensures admission of a SS into the network if it can satisfy minimum Quality of Service (QoS)

94
requirements and at the same time QoS of existing SSs will not deteriorate. The performance of
the scheduling algorithm for the uplink traffic strongly depends on the CAC algorithm.
Scheduling is a critical component of Worldwide Interoperability of Microwave Access
(WiMAX) impacting significantly on its performance. Scheduling schemes helps in providing
service guarantees to heterogeneous classes of traffic with different QoS requirements. In
addition to scheduling, bandwidth request and bandwidth allocation mechanisms also play
crucial roles in QoS provisioning for WiMAX. In general, a scheduler for WiMAX needs to be
simple, efficient, fair, scalable, and have low computational complexity. It should also be able to
protect against misbehaving flows and provide decoupling and necessary bounds on throughput
and delay performance.
Packet scheduling [5-14] is the process of resolving contention for shared resources in a
network. The process involves allocating bandwidth among the users and determining their
transmission order. Scheduling algorithms for a particular network need to be selected based on
the type of users in the network and their QoS requirements. QoS requirements vary depending
on the type of application/user. For real-time applications such as video conferencing, voice chat
and audio/video streaming, delay and delay jitter are the most important QoS requirements.
Delay jitter is the inter-packet arrival time at the receiver and is required to be reasonably stable
by the real-time applications. On the other hand, for non-real time applications such as file
transfer protocol (FTP), throughput is the most important QoS requirement. Some applications,
such as web-browsing and email do not have any QoS requirements. In a network, different
types of applications, with diverse QoS requirements can co-exist. A task of a scheduling
algorithm in a multi-class network is to categorize the users into one of the pre-defined classes.
Each user is assigned a priority taking into account its QoS requirements. Subsequently,
bandwidth is allocated according to the priority of the users as well as ensuring that fairness
between the users is maintained. Fairness refers to the equal allocation of network resources
among the various users operating in both good and bad channel states. In this paper, fairness is
quantified using Jain’s Fairness Index [15]. In addition to it, Bandwidth Utilization of the system
is also encountered to estimate whether precious bandwidth will get wasted by SS lying in a bad
channel state.
Packet scheduling algorithms are implemented at both the BS and SSs. A scheduling
algorithm at the SS is required to distribute the bandwidth allocation from the BS among its
connections. A scheduling algorithm at the SS is not needed if the BS grants bandwidth to each
connection of the SS separately i.e. the Grant per Connection (GPC) procedure is followed. If
the Grant per Subscriber Station (GPSS) procedure is followed, the scheduling algorithm at the
SS needs to decide on the allocation of bandwidth among its connections. The scheduling
algorithm implemented at the SS can be different than that at the BS [8].
The focus of our work is on scheduling algorithms for the uplink traffic in WiMAX i.e. traffic
from the SSs to the BS. Uplink packet scheduling is a more challenging task than downlink
packet scheduling as all the necessary information of SSs such as queue size for the uplink
scheduling are not available. An uplink algorithm at the BS has to coordinate its decision with
all the SSs where as a downlink algorithm is only concerned in communicating the decision
locally to the BS.
K. Wongthavarawat et al. propose a hybrid scheduling algorithm in [1] that combines Earliest
Deadline First (EDF), Weighted Fair queuing (WFQ) and First in First out (FIFO) scheduling
algorithms. The overall allocation of bandwidth is done in a strict priority manner i.e. all the
higher priority SSs are allocated bandwidth until they do not have any packets to send. The EDF
scheduling algorithm is used for SSs of the rtPS class, WFQ is used for SSs of the Non-Real
Time Polling Service (nrtPS) class and FIFO for SSs of the Best Effort (BE) class. Besides the
scheduling algorithm, an admission control procedure and a traffic policing mechanism are also
proposed. All these components together constitute the proposed QoS architecture. A drawback

95
of this algorithm is that lower priority SSs will essentially starve in the presence of a large
number of higher priority SSs due to the strict priority overall bandwidth allocation.
J. Lin et al. [8] propose architecture called Multi-class Uplink Fair Scheduling Structure
(MUFSS) to satisfy throughput and delay requirements of the multi-class traffic in WiMAX. The
proposed scheduling discipline at the BS is Modified Weighted Round Robin (MWRR),
although details of the modifications to the Weighted Round Robin (WRR) discipline are not
provided by the authors. The model is based on Grant per Subscriber Station (GPSS) bandwidth
grant mode and thus schedulers are implemented at the SSs to distribute the bandwidth granted
among their connections. At the SS, Modified WFQ (MWFQ) is used for Unsolicited Grant
Service (UGS) and Real-Time Polling Service (rtPS) connections, MWRR is used for nrtPS
connections and FIFO is used for BE connections.
K. Vinay et al. [9] propose a hybrid scheme that uses EDF for SSs of the rtPS class and WFQ
for SSs of the nrtPS and BE classes. This algorithm differs from the one in [1] in a couple of
ways. First, the WFQ algorithm is used for SSs of both nrtPS and BE classes. Secondly, the
overall bandwidth allocation is not done in a strict priority manner. Although the details of
overall bandwidth allocation are not specified, it is briefly mentioned that the bandwidth is
allocated among the classes in a fair manner. Since SSs of the BE class do not have any QoS
requirements, using a computationally complex algorithm such as WFQ for them is not needed.
Here author made the comparative study of the scheduling algorithms implemented in GPSS and
GPC and found that GPSS gives better end-to-end delay.
M.Settembre et al. [10] propose a hybrid scheduling algorithm that uses WRR and Round
Robin (RR) algorithms with a strict priority mechanism for overall bandwidth allocation. In the
initial portion of the algorithm, bandwidth is allocated on a strict priority basis to SSs of the rtPS
and nrtPS classes only. After that the WRR algorithm is used to allocate bandwidth among SSs
of rtPS and nrtPS classes until they are satisfied. If any bandwidth remains, it is distributed
among the SSs of the BE class using the RR algorithm. This algorithm starves lower priority SSs
in the presence of a large number of higher priority SSs. The algorithm can also result in low
fairness among SSs as it selects SSs with the most robust burst profiles first.
J. SUN et al. [11] proposed that the scheduler inside the BS may have only limited or even
outdated information about the current state of each uplink connection due to the large Round
Trip Delay (RTD) and possible collision occurred in the uplink channel transmission. So there is
a need of an additional scheduler in each SS to reassign the received transmission opportunities
among different connections. Since the uplink traffic is generated at SS, the distributed scheduler
is able to arrange the transmission based on the up-to-date information and then provide QoS
guarantee for its connections. But here the proposed algorithm is suffered by a problem called as
starvation of lower priority class services.
It is observed that the existing wire line and wireless schedulers do not perform very well
with respect to different traffic classes defined in WiMAX. In addition, each of this traffic
classes has a different scheduling requirement and, consequently, it has become necessary to
design appropriate hybrid scheduling frameworks. So in the proposed method the uplink traffic
is scheduled based on current queue information at SS similarly in the way proposed in [11]. But
a different hybrid algorithm has been implemented at the SS scheduler which helps to eliminate
the starvation of lower priority class services also maintains proper fairness and bandwidth
utilization of the system even at lower traffic intensity. In the proposed method the BS scheduler
can guarantee the minimum bandwidth for each service flow and ensure fairness and QoS in
distributing excess bandwidth among all connections. At the same time, the scheduler in SS can
provide differentiated and flexible QoS support for all of the four scheduling service types. In
this paper EDF algorithm is applied for rtPS class of services and Deficit Fair Priority Queue
(DFPQ) algorithm found in literature [12],[13] is applied for nrtPS and BE class of services. It

96
can both reduce the delay of real-time applications and guarantee the throughput of non-real-
time applications such as nrtPS and BE.
The rest of the paper is organized as follows; The QoS related features of IEEE 802.16
standard are discussed in section 2. Then the proposed scheduling algorithms are introduced in
section 3. Section 4 provides simulation and performance analysis. Finally, this paper is ended
up with the conclusions drawn in section 5.
2. QoS Features of IEEE 802.16
The first version, known as 802.16, was completed in October 2001. It specified a Single
Carrier (SC) air interface for fixed point-to-multipoint (PMP) BWA systems operating between
10-66 GHz. The second amendment, 802.16a, was published in January 2003. It extends the
physical environment towards lower frequency bands below 11 GHz. The next approved version
is 802.16d, which is published in June 2004 and also known as FIXED WiMAX (802.16-2004).
It incorporates all the previous versions to provide fixed BWA. In 2005, IEEE undertakes the
standardization of 802.16e, which is expected to support full mobility up to 70-80miles/sec [6].
Four service types are defined in IEEE 802.16d-2004 (Fixed) standard, which includes UGS
(Unsolicited Grant Service), rtPS (Real-time Polling Service), nrtPS (Non Real-time Polling
Service), and BE (Best Effort). The UGS is designed to support real-time service flow that
generates fixed-size data periodically, such as T1/E1, VoIP without silence suppression [1], [6].
The rtPS is designed to support real-time service flow that generates variable size data, such as
video streaming services while the nrtPS deals with FTP in similar manner. The BE perform
tasks related to e-mail and web browsing. The guaranteed delay aspect is also taken care in video
streaming and VoIP.
Since IEEE 802.16 MAC protocol is connection oriented, the application first establishes the
connection with the BS as well as the associated service flow (UGS, rtPS, nrtPS or BE). BS will
assign the connection with a unique connection ID (CID) [1], [6]. All packets from the
application layer in the SS are classified by the connection classifier based on CID and are
forwarded to the appropriate queue. So, the scheduler inside the BS has outdated information
about the current state of each uplink connection due to the large Round Trip Delay (RTD) and
possible collision occurred in the uplink channel transmission [14].
3. Proposed Scheduling Algorithm
In the proposed method, the uplink bandwidth allocation at BS is done based on the per
connection requests from SSs. Because a SS may have multiple connections at the same time,
the bandwidth request messages should report the bandwidth requirement of each connection in
SS. After that the allocated bandwidth per connection is pooled together and granted to each SS.
Then SS re-distribute the received transmission opportunities among its connections according
to their QoS requirement. Therefore an additional scheduler is needed in each SS to reassign the
received transmission opportunities among different connections. Since the uplink traffic is
generated at SS, the distributed scheduler is able to arrange the transmission based on the up-to-
date information and then provide tight QoS guarantee for its connections.
Since the BS scheduler has limited information on the traffic generated at SS, the computing
of bandwidth allocation should just consider the bandwidth request and reservation for each
connection.
Let BWMINi denote the minimum reserved bandwidth for connection i, and BWREQi
represent the bandwidth currently demanded by the connection i. Since the connection will never
get more resources than it has requested, the bandwidth actually allocated (BWALLOCATEi)
during this phase is

97
BWALLOCATEi = min {BWMINi, BWREQi} (1)
For rtPS and nrtPS, BWMINi is specified by the QoS parameter termed Minimum Reserved
Traffic Rate. Clearly, to guarantee the contracted bandwidth, the sum of minimum reserved
bandwidth for all the connections should not exceed the available bandwidth B. After each
connection gets its guaranteed bandwidth, if there is still excess uplink bandwidth remained, BS
scheduler should distribute the residual bandwidth in proportion to the pre-assigned connection
weight. The algorithm in this phase can be described as:
BWREMAIN = B - ∑ BWALLOCATEi;
i
While BWREMAIN > 0
{
If (BWALLOCATEi < BWREQi)
{
n
BWADDi = BWREMAIN*Wi / ∑ Wk;
k=1
BWALLOCATEi = BWALLOCATEi + BWADDi;
BWREMAIN = BWREMAIN - BWADDi;
}
}
Where BWREMAIN is the remaining bandwidth, BWADDi is the amount of excess
bandwidth allocated to connection queue i and Wi is the weight of connection queue i. Now the
allocated bandwidth per connection is pooled together and granted to each SS.
SS scheduler will select the packet to be transmitted from the highest priority queue. The
priority of the queue is maintained in the following way UGS > rtPS > nrtPS > BE.
3.1 Scheduling algorithm for UGS queues
UGS generate fixed size data packets on a periodic basis. This service has a critical delay and
delay jitter requirement. So, SS scheduler firstly guarantees the bandwidth for UGS queues.
3.2 Scheduling algorithm for rtPS queues
For rtPS service, each packet entering the rtPS queues should be marked with a delivery
deadline equal to t + tolerated delay, where t is the arrival time and tolerated delay is the
Maximum Latency for such a service flow. The packet with smaller deadline will be transmitted
earlier. This greatly reduces the end-to-end delay of rtPS service.
3.3 Scheduling algorithm for nrtPS and BE queues
For nrtPS and BE services Deficit Fair Priority Queue (DFPQ) algorithm found in literature
[12], [13] is employed. DFPQ is almost similar to Deficit Round Robin (DRR) algorithm. This
algorithm has been applied because of the following reasons.

98
i. The algorithm is mostly suited for datagram networks where packet sizes vary.
ii. Since this algorithm requires accurate knowledge of packet size, it is suitable for the
uplink traffic at SS scheduler.
iii. The algorithm is flexible enough as it allows provision of quanta of different sizes
depending on the QoS requirements of the SSs. With this algorithm employed, SS
scheduler can guarantee the minimum bandwidth for every non real time services such
as nrtPS and BE connection and hence maintain an acceptable throughput. Thereby
eliminate starvation of lower priority service classes.
In each service round, the nrtPS queue is served first until its assigned bandwidth finds
deficit, and then BE service flow queue gets a chance to be served. Similar to [12], in DFPQ
algorithm, a Quantum Q is assigned to each queue i. The quantum of a queue i (Q[i]) represent
the maximum number of bits that can be serviced in the first round. The scheduler visits each
nonempty queue and determines the number of bandwidth requests in this queue. If there are
more packets in the queue i after servicing Q[i] bits, the remaining amount of bits is stored in a
queue state variable called Deficit Counter (DC[i]) and the scheduler moves on to serve the next
non-empty queue. In subsequent rounds, the amount of bandwidth usable by this flow is the sum
of DC[i] in the previous round added to Q[i]. The Q[i] is the Maximum Sustained traffic rate
(rmax) of a certain service flow. In case rmax = 0 (BE service flow), rmin is used instead. As a
result of using the quantum variable, connections with larger quantum get more service.
DFPQ algorithm is shown below. Here Ltotal is defined to be the remaining total capacity of
the frame after servicing UGS and rtPS queues and La to be its remaining capacity. p(i,k)
denotes kth
packet of ith
connection, i belongs to nrtPS and BE connections. The algorithm can be
described as follows,
Each service queue i of nrtPS and BE is initialized with
La = Ltotal;
DC[i] = Q[i];
While La > 0
{
While DC[i]>0 and p (i, k) is available
{
DC[i] = DC[i] - p(i,k);
La =La – p(i,k);
k++;
}
DC[i] = DC[i] + Q[i];
i = (i % n) + 1;
}
Go to UGS scheduling for next frame
4. Simulation and Performance Analysis
To evaluate the effectiveness and efficiency of the proposed scheduler, the IEEE 802.16
MAC layer protocol is analyzed using MATLAB under version 7.3. A number of simulations are
conducted in this section. At first, the simulation environment and parameters are described and
simulation results are given along with the discussions.

99
4.1. Simulation Environment and Parameters
A TDD-OFDM system is used in our simulation with the MAC layer application parameters
as shown in Table 1 and the network is configured as consists of one BS and multiple SSs as
shown in Fig 1.
Figure1. Proposed model architecture
The frame duration is taken as 10ms with TDD duplex mode and the bandwidth is 4.3 MHz.
The IEEE 802.16 standards have not specified values for the QoS parameters and we have
assumed these values for the performance analysis.
Table1. MAC layer configuration parameters
Service
Max
Sustained
Rate
(kbps)
Min
Reserved
Rate
(kbps)
Delay
(ms)
UGS 256 - -
rtPS 1024 512 20
nrtPS 1024 512 -
BE - 256 -
QoS parameters such as delay, delay violation rate, throughput, bandwidth utilization and
fairness index are considered to validate our proposed scheduling scheme. Here, the delay
violation rate is defined as the amount of packets whose delay is larger than the Maximum
Latency to the total amount of packets that have been received from network interface. Also the
bandwidth utilization is defined as the average ratio of used bandwidth to the total bandwidth
and Fairness is quantified using Jain’s Fairness Index (JFI) [15] as shown below.
∑
∑
=
=
∗
= n
1i
2
i
2
n
1i
i
rn
)r(
JFI (2)
Where, ri is the data rate of application i.
4.2. Simulation Result and Discussions
In the simulation we consider 802.16 network consisting of one BS and four SSs with
different traffic patterns. The first SS is configured with all types of traffic flows nominated as
UGS_1, rtPS_1, nrtPS_1 and BE_1, the second SS has UGS_2, rtPS_2, nrtPS_2 and BE_2, the
third SS has UGS_3, rtPS_3, nrtPS_3 and BE_3, the fourth SS runs UGS_4, rtPS_4, nrtPS_4 and
BE_4. Two scenarios - with and without SS scheduler are simulated to study the effect of SS
scheduler.

100
Figure 2.Service delay comparison
Our proposed method is compared with the method given in [11]. Here, “without SS
scheduler” and “BS-(service class)” means that BS scheduler designates bandwidth to individual
connection. On the other hand, “with SS-scheduler1” and “SS1-(service class)” means that SS
scheduler designates bandwidth to individual connection in our proposed method. Again “with
SS-scheduler2” and “SS2-(service class)” means that SS scheduler designates bandwidth to
individual connection proposed in [11]. Here, service class refers to UGS, rtPS, nrtPS and BE.
As UGS generates fixed size data packets on a periodic basis so delay is negligible and
throughput is constant and hence it is not shown in our simulation result. Fig. 2 displays the end-
to-end delay of different services with and without SS scheduler. The curves show that after SS
scheduling, low priority service suffered longer delay. From rtPS, nrtPS to BE, the end-to-end
delay increased with the service priority decreased. The fundamental requirement of QoS
scheduling for IEEE 802.16 systems is achieved.
Figure 3.Service delay comparison of rtPS

101
Figure 4.Percentage of packet drop comparison
To further demonstrate this benefit, we simulate the rtPS performance under the same number
of background SS as given in Fig. 3. From Fig.4 we can see that the SS scheduler can effectively
reduce the QoS violation rate of rtPS service flow.
Figure 5.Throughput comparison proposed in [11]
Figure 6.Throughput comparison in our proposed Strategy

102
By introducing DFPQ algorithm for nrtPS and BE services on priority order, SS scheduler can
guarantee the throughput of nrtPS service as well as BE service. Though BE services do not
require any QoS but in our proposed algorithm fairness is maintained for all types of service
classes. But this fairness is not maintained in [11] where we got throughput of BE service is zero
as shown in Fig.5. But our simulation result shows that SS scheduler guarantees the throughput
of nrtPS service as well as BE service as shown in Fig.6 where throughput of BE service is
greater than zero throughout simulation time, hence fairness of all the services is maintained and
the problem of starvation of lower priority class services is eliminated.
Figure 7.Bandwidth Utilization of the system
Figure 8.Jain’s Fairness Index
Fig. 7 and 8 show bandwidth utilization of the system and fairness index for BS-Scheduler,
SS1-Scheduler and SS2-Scheduler with different traffic intensity. Our results show that SS1-
Scheduler performs well even at lower traffic intensity. As bandwidth is considered to be a
limited resource in the network, so SS1-Scheduler will automatically improve the revenues of
the service providers.

103
5. Conclusion
It has been confirmed in many earlier studies that most of the existing wire line and wireless
homogeneous schedulers do not perform very well with respect to different traffic classes
defined in WiMAX. In addition, each of this traffic classes has a different scheduling
requirement and consequently, it has become necessary to design appropriate hybrid scheduling
frameworks. Therefore, we propose an efficient hybrid packet scheduling scheme for IEEE
802.16 WiMAX to satisfy both delay and throughput guarantees for the admitted connections.
An architecture model was developed to demonstrate the performance of the proposed scheme.
Simulation results show that the proposed scheme is the best choice for QoS scheduling in
WiMAX in terms of delay, throughput, bandwidth utilization and fairness of all connections of
the system compared to schemes proposed in [11]. Simulation results also prove that the BS
scheduler can guarantee the minimum bandwidth for each service flow and ensure fairness and
QoS in distributing excess bandwidth among all connections. At the same time, the scheduler in
SS can provide differentiated and flexible QoS support for all of the four scheduling service
types. It can both reduce the delay of real-time applications and guarantee the throughput of non-
real-time applications also enhancing the bandwidth utilization of the system and fairness index
of the resources even at lower traffic intensity. Thereby eliminate starvation problem of lower
priority class services. Therefore, the proposed QoS scheduling architecture can provide tight
QoS guarantees for all types of traffic classes as defined in the scheduler standard.
Acknowledgement
The authors deeply acknowledge the support from DST, Govt. of India for this work in the form
of FIST 2007 Project on “Broadband Wireless Communications” in the Department of ETCE,
Jadavpur University.
REFERENCES
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[2] Haitang Wang, Wei Li and Dharma P. Agrawal, “Dynamic admission control and QoS for IEEE
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60-66, (WTS 2005).
[3] Dusit Niyato and Ekram Hossain, “Connection Admission Control Algorithms for OFDM
Wireless Networks” , Proc. of IEEE Globecom, pp.2455-2459, (2005).
[4] Kalikivayi Suresh, Iti Saha Misra and Kalpana saha (Roy), “Bandwidth and Delay Guaranteed
Call Admission Control Scheme for QOS Provisioning in IEEE 802.16e Mobile WiMAX”
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[5] Chi-Hong Jiang, Tzu-Chieh Tsai, “CAC and Packet Scheduling Using Token bucket for IEEE
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[6] Jeffery G.Andrews, Arunabha Ghosh, Rias Muhamed, “Fundamentals of WiMAX-Understanding
Broadband Wireless Networking”, Pearson Education, March (2007).
[7] Prasun Chowdhury and Iti Saha Misra, “A Comparative Study of Different Packet Scheduling
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[8] J.Lin and H.Sirisena, “Quality of Service Scheduling in IEEE 802.16 Broadband Wireless
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BIOGRAPHIES
1. Mr. Prasun Chowdhury (prasun.jucal@gmail .com) has completed his Masters in Electronics and
Telecommunication Engineering from Jadavpur University, Kolkata, India in 2009. Presently he is
working as Senior Research Fellow (SRF) and also pursuing his PhD in the Department of
Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India. His current
research interests are in the areas of Call Admission Control and Packet Scheduling in IEEE 802.16
BWA Networks.
2. Dr. Iti Saha Misra (itimisra@cal.vsnl.net.in) is presently holding the post of Professor in the
Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata,
India. After the completion of her PhD in Engineering in the field of Microstrip Antennas from
Jadavpur University (1997), she is actively engaged in teaching since 1997. Her current research
interests are in the areas of Mobility Management, Network Architecture and protocols, Integration
Architecture of WLAN and 3G Networks, Call Admission control and packet scheduling in cellular
and WiMAX networks, Location Management for Cellular Wireless Networks. Her other research
activities are related to Design Optimization of Wire Antennas using Numerical Techniques like
GA, PSO and BFA. She has authored more than 100 research papers in refereed Journal and
International Conference and published a Book on Wireless Communications and Networks, by
McGraw Hill, India. She is the recipient of the Career award for Young teachers by All India
Council for Technical Education (AICTE) for the financial year 2003-2004 and obtained the IETE
Gowri memorial award in 2007 for being the best paper in the general topic of “4G networks:
Migration to the Future”. She has developed the OPNET, QualNet and VoIP laboratories in the
Department of Electronics and Telecommunication Engineering of Jadavpur University to carry out
advanced research work in Broadband wireless domain. She is the Senior Member of IEEE, founder
Chair of the Women In Engineering, Affinity Group, IEEE Calcutta Section.

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A Fair and Efficient Packet Scheduling Scheme for IEEE 802.16 Broadband Wireless Access Systems