A Cross Layer Approach for Dynamic Fair Scheduling of ... - CiteSeerX

A Cross Layer Approach for Dynamic Fair Scheduling of PCF Traffic in OFDM-FDMA based wireless LANs Pradip De, Kalyan Basu and Sajal K. Das Center for Research in Wireless Mobility and Networking(CReWMaN) Department of Computer Science and Engineering University of Texas at Arlington, TX 76019-0015 {pradipde, basu, das}@cse.uta.edu

Abstract In the IEEE 802.11 MAC protocol, the PCF mode of operation is intended to facilitate real time traffic. However, the performance of PCF is highly affected by conditions of the WLAN and it has unnecessary overhead. Moreover, since PCF transmits in a round robin (RR) fashion, a wireless user may not have good channel conditions to receive traffic from the access point when it is actually scheduled in the RR fashion. In this report, we propose a packet scheduling discipline in a Wireless LAN based on an underlying orthogonal frequency division multiple access (OFDMA) system. The basic structure of the framework is for scheduling downlink traffic, where the time axis is divided into slots and the frequency axis is segmented into subchannels. This approach supports the simultaneous transmissions to several wireless terminals, each one getting only some of the available subcarriers. Judiciously assigning subcarriers to terminals can be used to, e.g., increase the wireless throughput. We propose a dynamic scheduling heuristic to allocate subcarriers based on the wireless channel feedback from the user terminals and the queued downlink traffic at the access point such that the wireless channel throughput is optimized.

I. INTRODUCTION Transmission scheduling for wireless LANs has recently attracted a lot of research interests. First, scheduling policies of wireline networks are extended to wireless networks, where the burst of errors in wireless channels is taken into account. To elaborate, a wireless channel is modeled by a two-state Markov chain [1] where a user experiences error-free transmission when it observes a “good” channel, and unsuccessful transmission in a “bad” channel. Using such a channel model, wireless fair scheduling policies have been studied [2], [3], [4], [5]. These works provide various degrees of performance guarantees, including short-term and long-term fairness, and their respective throughput bounds. A survey of these algorithms can be found in [8]. Moreover, future wireless communication systems will have to meet the requirement of providing high data rate transmissions to wireless terminals. However, simply increasing the symbol rate to fulfill this requirement leads to significant performance degradation through Intersymbol Interference (ISI). To overcome this problem, Orthogonal Frequency Division Multiplexing (OFDM) has been suggested as a possible transmission scheme[12], [13]. In OFDM systems, instead of transmitting data on a single carrier, it is divided into multiple sub-carriers. Scheduling algorithms provide mechanisms for bandwidth allocation and multiplexing at the packet level. Many scheduling algorithms, capable of providing a certain guaranteed QoS, have been studied for wireless networks. In the earliest-due-date first (EDD), each packet from a periodic traffic stream, such as real-time

services, is assigned a deadline, and the packets are sent in order of increasing deadlines [6]. The principle of EDD is based on the priority-order-queue mechanism. Real-time traffic, such as voice and video streaming, is very delay sensitive, but can stand a certain level of packet loss. The service-oriented fair packet loss sharing (FPLS) algorithm is introduced in [7], where a time division code division multiple access (TD-CDMA) system is considered. The basic idea of FPLS is that the packet loss of each user is controlled according to the QoS requirements and the traffic characteristics of all the mobile users sharing the same frequency spectrum in the cell. In [6], however, the authors did not consider link adaptation techniques such as adaptive modulation and coding (AMC) in a cellular environment. In order to evaluate the performance of a packet scheduling algorithm deployed in a wireless environment more reliably, the wireless channel environment should be considered. In [14], the authors have talked about using an utility based optimization for adaptive resource allocation in OFDM. The formulation is based on instantaneous rate instead of mean rate which was only mentioned. In [15], the authors propose a computationally efficient bandwidth allocation power control mechanism for OFDMA. In this report we consider a similar scenario for scheduling of downlink traffic at an access point using OFDMA as the underlying physical layer. Using the channel feedback for each receiver we formulate an optimization criterion for maximizing the throughput of the system while trying to exercise fairness among them by providing a fair service to the queues for each mobile user at the access point. We provide two heuristics (both for non-real time and real-time traffic with deadline) to allocate sub-carriers to each mobile station depending on the channel condition feedback. The rest of the report is organized as follows. In section II, we talk about the technology with 802.11a at the MAC and OFDM at the physical layer. In section III we provide the framework for packet scheduling and the formulation of the problem. Subsequently in Section IV, we provide the online heuristics. We finally conclude the report in section V.

II. 802.11a - The Technology Wireless LANs are becoming increasingly popular for providing different kinds of service. The IEEE 802.11 standard MAC protocol supports two kinds of access methods [9]: DCF (Distributed Coordination Function), PCF (Point Coordination Function). The DCF is designed for asynchronous data transmission by using CSMA/CA and must be implemented in all stations. On the another hand, the PCF is optional and based on polling controlled by a PC (Point Coordinator). PCF has been widely implemented. This scheme uses round-robin polling. Thus every terminal is scheduled transmission periodically at fixed intervals. In PCF, time is divided into super-frames. A super-frame includes contention free period (CF) and contention period (CP). PCF works in CF. DCF works in the other period. Contention Period (CP): The basic DCF is CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance). Carrier sensing is performed using physical carrier sensing (by air interface) as well as virtual carrier sensing. Virtual carrier sensing uses the duration of the packet transmission, which is included in the header of RTS, CTS, and DATA frames. The channel is considered to be busy if either physical or virtual carrier sensing indicates that the channel is busy. When a station wants to transmit a packet, it needs to sense the channel. If the channel is idle in DIFS interval, the the station sends a RTS. After it receives a CTS from the receiver, the sender will send a data frame after waiting SIFS. If the sender receives an ACK from the receiver, the transmission is successful. In the meantime, other stations just wait a NAV (Network Allocation Vector) time, which indicates the remaining time of the on-going transmission sessions. Using the duration information in RTS, CTS and Data frames, stations update their NAVs whenever they receive a frame. When the sender finds the medium is busy, the sender waits a back-off window. The length of the back-off window is considered to be a counter. The station will try to retransmit when the counter reaches zero. Contention Free Period (CFP): PCF uses a centralized contention-free polling access method to facilitate real time services. It is done by software called point coordinator (PC) in the Access Point (AP). It performs polling for stations that are capable of being polled. Before a PCF polling cycle, AP contends with other

stations in DCF, but AP only needs to wait a PIFS, which is shorter than DIFS. Because of the priority, no other senders would interrupt AP. To prevent starvation of stations that are not allowed to send during the CF, the CP is at least long enough to transmit one Maximum MAC protocol data Unit (MMPDU). The 802.11 standard does not specify a mechanism for adjusting the relative length of CF and CP. After a station finishes 802.11 association with an AP in CP, the AP gives an association id (AID) to it and puts it in the polling queue based on the stations AID. The AP maintains the polling list that specifies the order in which stations are to be polled. The AP polls the stations in a round-robin fashion. If there is no pending data transmission, the station either responds with a null frame containing no payload or does not respond. If the AP does not get a response from the station, then the AP may re-poll the station after PIFS interval instead of SIFS interval, which is the normal interval between any two pollings and shorter than PIFS. If the CF terminates before all stations have been polled, the polling queue is resumed at the next station in the following CF cycle. However, since the channel condition varies unpredictably in a wireless channel, a wireless user who has been granted a slot in the PCF mode may not find the channel good and thus loose its opportunity to make a successful transmission. It is this issue that we target in this paper. We develop an opportunistic mechanism to grant subchannels which are good to those users who have a larger proportional queue backlog. 802.11a and OFDM: 802.11a is a new specification that represents the next generation of enterprise-class wireless LANs. Among the advantages it has over current technologies are greater scalability, better interference immunity, and significantly higher speed, up to 54 Mbps and beyond, which simultaneously allows for higher bandwidth applications and more users. 802.11a uses Orthogonal Frequency Division Multiplexing (OFDM), a new encoding scheme that offers benefits over spread spectrum in channel availability and data rate. Channel availability is significant because the more independent channels that are available, the more scalable the wireless network becomes. The high data rate is accomplished by combining many lower-speed subcarriers to create one high-speed channel. 802.11a uses OFDM to define a total of 8 nonoverlapping 20 MHz channels across the 2 lower bands;each of these channels is divided into 52 subcarriers,each approximately 300 KHz wide.By comparison,802.11b uses 3 non-overlapping channels. By utilizing these channels with 52 subcarriers contained within data is transmitted in parallel in the subcarriers.

III. THE FRAMEWORK FOR PACKET SCHEDULING We consider scheduling for a wireless system accessed by multiple users in which a centralized scheduler at the access point (AP) controls downlink scheduling. Changing channel conditions are related to three basic phenomena: multipath fading on the order of msec, shadow fading on the order of tens to hundreds of msec, and finally, longtime- scale variations due to user mobility. As our algorithm will exploit the users channel conditions in making the scheduling decision, we consider systems with mechanisms to make predicted channel conditions available to the AP or an out-of-bound reliable continuous feedback channel. Due to inherent limits on the total system resources (e.g., power or time), high resource consumption by one user may prevent other users from being scheduled. Our objective in wireless scheduler design is to ensure fairness while simultaneously employing opportunistic scheduling strategies to increase the total system throughput by selecting users with high-quality channels when possible. To solve the problem, we observe that the two conflicting goals (throughput optimization and fairness guarantees) need to be taken care of. We have a channel estimator which gives its measurements to the scheduler. The scheduler tries to allocate subcarriers to terminals whose channel condition is favorable to a successful transmission. At the same time a system monitor measures the dynamics of the queue buildup and feeds it to the scheduler. The monitor is also capable of sending feedbacks back to the source application to adapt its sending rate whenever the queue buildup is equal to a particular fraction θ of the queue size, to prevent the queue from blocking and packets being dropped. The scheduler not only schedules queues whose channel conditions are good but also gives those terminals priority over others whose queues have been given less service in the past. Considering these factors, the system tries to optimize the overall throughput.

Feedback

Scheduler Monitor

Tx/Rx

.. Channel Estimator

..

Fig. 1. The Scheduling Framework

A. System Model and Problem Formulation Assume N wireless terminals are located in a wireless LAN. We only consider the downlink transmission of data. Time is slotted such that each slot duration T is equivalent to the transmission time a single packet. For data transmission, an OFDM system is employed, providing a total of S subcarriers. Let Qi (t) denote the size of the queue for the ith user at the end of slot t(or the beginning of slot t + 1). During slot t there are Ai (t) external arrivals. Ci (t) represents the number of subcarriers allocated to user i at slot t. The control variable Ui (t) indicates the queue served during slot t. Under this assumption the number of packets in the queue i evolves according to the equation Qi (t) = (Qi (t − 1) − 1{U (t) = i})Ci (t) + Ai (t), t = 1, 2, . . . ,

(1)

where 1{·} is the indicator function of the event enclosed in the brackets. Ci (t) is given by the equation Ci (t) =

S X

Gis (t)

(2)

Ci (t) = S

(3)

Gis (t) = 1

(4)

s=1 N X i=1 N X i=1

Gis (t) is a binary random variable such that (

Gis (t) =

1 0

: :

channel s is assigned to user i in slot t otherwise

Furthermore, let SN Ri (t) specify the vector containing the values of the receiver SNR at each subcarrier s for receiver i at time t. SN Ri (t) = (snri1 (t), snri2 (t), . . . , snriS (t))

(5)

At the beginning of time slot t, the AP knows about the SNRs for all wireless terminals, summarized in matrix SN R(t): SN R(t) = (SN R1 (t), SN R2 (t), . . . , SN RN (t))T

(6)

Thus, snris (t) gives the SNR value of the sth subcarrier for terminal i at time slot t. Let Φ(snris (t)) denote the bit rate achievable from the corresponding SNR value. At the access point of the wireless LAN data streams arrive, one destined for each terminal. The AP then allocates for each wireless terminal the number of subcarriers to be assigned and schedules traffic at the corresponding bit rate using the appropriate modulation and coding scheme. These subcarrier allocations may change over time. Thus we can write the average bit rate of our system to be denoted by X

Υ = E[

Gis (t) · Φ(snris (t))]

(7)

∀s,i

Since our scheduling discipline also needs to take care of fairness we deal with it by considering the queue size of each terminal at the AP. Let the average queue buildup of user i at time slot t be denoted by PN

Qavg (t) =

i=1 Qi (t)

(8) N The scheduling discipline tries to minimize the queue with the maximum buildup. We define a variable λi called the wait value of queue i. It is denoted by λi (t) = Qi (t) − Qavg (t)

(9)

Our fair scheduling discipline actually tries to achieve two optimization objectives. The first is to minimize the maximum wait value among the traffic flows given by Eqn 9 and also maximize the throughput given by Eqn 7. Assigning each wireless terminal i the best possible number of Ci (t) subcarriers in terms of SNR is equivalent to the maximum weighted perfect matching problem in bipartite graphs [10]. An optimal solution can be generated by the Hungarian algorithm [11], which has a complexity of O(S 3 ), S denoting the number of subcarriers. It is obvious that the subcarrier assignment algorithms are computationally intensive and therefore we need to devise efficient heuristics to perform the scheduling.

IV. ONLINE HEURISTICS In this section we present online heuristics to dynamically tackle the channel assignment problem. The algorithm gives higher priority to fairness and assigns those queues with highest load to be assigned to subcarriers that have the best channel conditions. Figure 2 gives a schematic of the algorithm. Furthermore, when we consider real-time traffic, an added constraint of having to schedule the packets within a particular delay is brought in. The scheduling discipline then needs to reconsider the mutual priority of the constraints. Thus for real time traffic, the queues are ordered based on the ascending head-of-line(HOL) packet deadlines. For each queue i, the subcarrier with the best SNR value greater than the required threshold is selected. The HOL packets are then assigned the best subcarrier in ascending order of their deadlines. By assigning a subcarrier to the HOL packet of each queue, we can guarantee fairness among the users. In the next time slot, the queues are again reordered based on the deadlines of the HOL packet and assigned their best

Algorithm: ONLINE SCHEDULE Input: The matrix SNR(t), Qi (t) for each i Output: The transmission schedule for each user i for (each time slot t) { Sort all queues in descending order of current length Qi (t); Get SNR Matrix SN R(t); Sort sub-carrier vector in decreasing order of SNR magnitude in SN Ri (t); for (each user i = 1 → N ) { while (λi (t) ≥ 0) { if (snris (t) ≥ η) { Allocate sub-carrier s to user i; Remove s from sub-carrier list; λi (t) = λi (t) − 1; } } } }

Fig. 2. Algorithm ONLINE SCHEDULE

Algorithm: REAL ONLINE SCHEDULE Input: The matrix SNR(t), the packet deadlines Di (t) for the HOL packet of queue i Output: The transmission schedule for each user i for (each time slot t) { Sort all queues in ascending order of HOL packet deadline Di (t) Get SNR Matrix SN R(t); for (each user i = 1 → N ) { Select the subcarrier s with the best SNR value and snris (t) ≥ η; Allocate sub-carrier s to user i; Remove s from sub-carrier list; } }

Fig. 3. Algorithm REAL ONLINE SCHEDULE

subcarriers based on their deadline priorities. The online algorithm considering real-time packets with deadlines is depicted in Fig 3.

V. CONCLUSION AND FURTHER ISSUES In this work, we have addressed the issue of scheduling downlink traffic in a wireless LAN with OFDM as the underlying layer. OFDM has evolved as a popular mechanism for supporting high data rates by accommodating simultaneous transmissions over orthogonal subcarriers and have been incorporated in the physical layer of the 802.11a standard. The problem was basically to perform fair subcarrier assignment to wireless terminals such that the overall throughput was maximized. Furthermore, for real time packets it was also necessary to consider the individual packet deadlines. The optimal solution is computationally intensive. Subsequently, we have proposed two heuristics to achieve a fair allocation of the subcarriers. For dynamic fair scheduling, most papers have focussed on long term fairness. It would be important to solve the issue of short term fairness in a dynamic fair scheduling scenario. Another assumption made so far has been to allocate a single subchannel to each user. Current 802.11a allocates a portion of a subchannel to each user. My continuation of this work would be to formulate the problem by allocating a part of a subchannel to a user and focus on short term fairness for each user.

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