- Research Article
- Open Access
A Fair Opportunistic Access Scheme for Multiuser OFDM Wireless Networks
© C. Gueguen and S. Baey. 2009
- Received: 2 July 2008
- Accepted: 1 February 2009
- Published: 15 March 2009
We propose a new access scheme for efficient support of multimedia services in OFDM wireless networks, both in the uplink and in the downlink. This scheme further increases the benefits of opportunistic scheduling by extending this cross-layer technique to higher layers. Access to the medium is granted based on a system of weights that dynamically accounts for both the experienced QoS and the transmission conditions. This new approach enables the full support of multimedia services with the adequate traffic and QoS differentiation while maximizing the system capacity and keeping a special attention on fairness. Performance evaluation shows that the proposed access technique outperforms existing wireless access schemes and demonstrates that choosing between high fairness and high system throughput is no more required.
- Traffic Load
- Proportional Fair
- Delay Constraint
- Buffer Occupancy
- Multiuser Diversity
Providing mobile multimedia transmission services with an adequate QoS is very challenging. In contrast with wired communications, wireless transmissions are subject to many channel impairments such as path loss, shadowing, and multipath fading [1–4]. These phenomena severely affect the transmission capabilities and in turn the QoS experienced by applications, in terms of data integrity but also in terms of the supplementary delays or packet losses which appear when the effective bit rate at the physical layer is low. The past decades have witnessed intense research efforts on wireless digital communications. Among all the studied transmission techniques, IOrthogonal Frequency Division Multiplexing (OFDM) has clearly emerged for future broadband wireless multimedia networks (4G systems) and is already widely implemented in most recent wireless systems like 802.11a/g or 802.16. The basic principle of OFDM for fighting the effects of multipath propagation is to subdivide the available channel bandwidth in subfrequency bands of width inferior to the coherence bandwidth of the channel (inverse of the delay spread). The transmission of a high-speed signal on a broadband frequency selective channel is then substituted with the transmission on multiple subcarriers of slow speed signals which are very resistant to intersymbol interference and subject to flat fading. This subdivision of the overall bandwidth in multiple channels provides frequency diversity which added to time, and multiuser diversity may result in a very spectrally efficient system subject to an adequate scheduling.
MAC protocols currently used in wireless local area networks were originally and primarily designed in the wired local area network context. However, conventional access methods like Round Robin (RR) and Random Access (RA) are not well adapted to the wireless environment and provide poor throughput. Much interest has recently been given to the design of scheduling algorithms that maximize the performance of multiuser OFDM systems. Opportunistic scheduling techniques take advantage of multiuser diversity by preferably allocating the resources to the active mobile(s) with the most favourable channel conditions at a given time. This technique was explored first in single carrier communications . More recently, opportunistic scheduling has been exploited in multicarrier systems [6, 7]. These schemes are derived from the maximum signal-to-noise ratio (MaxSNR), also known as maximum carrier to interference ratio (MaxC/I), technique which allocates the resource at a given time to the active mobile with the greatest SNR. Dynamically adapting the modulation and coding allows then to always make the most efficient use of the radio resource and come closer to the Shannon limit. This maximizes the system capacity of an information theory point of view. However, it assumes that the user with the most favourable transmission conditions has information to transmit at the considered time instant. It does not take into account the variability of the traffic and the queuing aspects.
Pure opportunistic scheduling does not take into account the delay constraints of the flows to convey and suffer of a lack of fairness. References [8, 9] introduce opportunistic schemes coupled with a system of quota. This improves fairness but reduces the efficiency of utilization of the multiuser diversity with prejudice on system throughput. Proportional fair (PF) algorithms have recently been proposed to incorporate a certain level of fairness while keeping the benefits of multiuser diversity [10–14]. The basic principle is to allocate resources to a user, when its channel conditions are the most favourable with respect to its time average. In these schemes, fairness consists in guaranteeing an equal share of the total available bandwidth to each mobile, whatever its position or channel conditions.
However, performance analysis of PF-based protocols has shown that fairness issues persist since these algorithms do not ensure an equal throughput [15, 16]. The main issues are fairness considering mobiles with unequal spatial positioning, different traffic types, or different QoS targets. PF scheduling does not take into account the delay constraints and is not well adapted to multimedia services which introduce heterogeneous users, new traffic patterns with highly variable bit rates and stringent QoS requirements in terms of delay, and packet loss. Recently,  proposed the multimedia adaptive OFDM proportional fair (MAOPF) algorithm, an evolution of the classical PF that considers multimedia services. The principle of the MAOPF is to share the total available bandwidth among users proportionally to their bit rate requirement. Although this enables the coexistence of applications with unequal bit rates, heterogeneous QoS requirements are still not well supported. Moreover, the MAOPF allocates all OFDM subcarriers to the same mobile. This does not fully take advantage of the multiuser diversity and has a negative impact on the system capacity.
The paper is organized as follows. Section 2 provides a detailed description of the system under study. Section 3 introduces the QoS management principle embodied in the proposed protocol. Section 4 describes the integrated scheduling algorithm. In Section 5, we present a detailed performance evaluation through a simulation study. Section 6 concludes the paper.
The elementary resource unit (RU) is defined as any (subcarrier, time slot) pair. Each of these RUs may be allocated to any mobile with a specific modulation order. Transmissions performed on different RUs by different mobiles have independent channel state variations . On each RU, the modulation scheme is QAM with a modulation order adapted to the channel state between the access point and the mobile to which it is allocated. This provides the flexible resource allocation framework required for opportunistic scheduling.
The system is operated using time division duplexing with four subframes: the downlink feedback subframe, the downlink data subframe, the uplink contention subframe, and the uplink data subframe. The uplink and downlink data subframes are used for transmission of user data. In the downlink feedback subframe, the access point sends control information towards its mobiles. This control information is used for signalling to each mobile the RU(s) which have been allocated in the next uplink and downlink data subframes, the modulation order selected for each of these RUs and the recommended emission power in the uplink. In the uplink contention subframe, the active mobiles send their current traffic backlog and information elements such as QoS measures and transmit power. The uplink contention subframe is also used by the mobiles for establishing their connections. This frame structure supposes a perfect time and frequency synchronization between the mobiles and the access point as described in . Therefore, each frame starts with a preamble used for synchronization purposes. Additional preambles may also be used in the frame.
The crucial objective of the WFO protocol is to fully support multimedia transmission services, including the widest range of services: VoIP, videoconference, email, and file transfer. This requires the coexistence of delay sensitive flows as well as non-real-time traffic with looser delay constraints but with tight data integrity targets. In order to deal with the various and heterogeneous QoS requirements of multimedia services, the WFO protocol relies on a generic approach of QoS management.
We define a service flow as a traffic stream and its QoS profile, in a given transmission direction. A mobile may have multiple service flows both in the uplink and the downlink. An application may also use several service flows enabling for instance the implementation of Unequal Error Protection schemes in the physical layer. Each service flow possesses its own transmission buffer. In the following, index k is used to designate a given service flow among the set of service flows to be scheduled in a given transmission direction.
The QoS profile is defined as the set of parameters that characterizes the QoS requirements of a service flow mainly in terms of data integrity and delay. In the following, data integrity requirements are specified by a bit error rate (BER) target, which we denote by for service flow . Delay requirements are specified at the packet level. We assume traffic streams are organized at the MAC level in blocks of bits of constant size that we call packets. The packet delay is defined as the time between the arrival of the packet in the transmission buffer and the time of its reception by the mobile or the access point. This delay is roughly equal to the packet waiting time in the service flow transmission buffer neglecting the transmission and propagation delays.
In the WFO protocol, QoS management is organized in two parts: data integrity management and delay management. Data integrity is guaranteed by the physical layer mainly by adapting the modulation scheme and the transmit power to the mobile specific channel state. This is achieved considering each service flow independently. Delay management is performed considering all service flows jointly and scheduling the packets according to their distance to the PDOR target. Fairness is provided by guaranteeing the same level of satisfaction of delay constraints to all service flows, that is, guaranteeing the same PDOR to all service flows. The joint satisfaction of the delay constraints relies on the dynamics of the traffic streams that are multiplexed. Data integrity and delay management are integrated using the WFO scheduling algorithm.
its QoS profile (BER target, delay threshold, and PDOR target),
its currently experienced QoS (BER and PDOR),
its traffic backlog,
its channel state.
The QoS profile is signaled in the connection establishment phase. In the uplink, the currently experienced PDOR and the traffic backlog (buffer occupancy) are signaled by the mobile in the contention subframe. The experienced BER is tracked directly by the access node. Reciprocally, in the downlink, the currently experienced PDOR and the traffic backlog are calculated by the access node, and the experienced BER is signaled.
Additionally, knowledge of the channel state is supposed to be available at the receiver . The current channel attenuation on each subcarrier and for each mobile is estimated by the access node based on the SNR of the signal sent by each mobile during the uplink contention subframe. Assuming that the channel state is stable on a scale of 50 milliseconds , and using a frame duration of 2 milliseconds, the mobiles will transmit their control information alternatively on each subcarrier so that the access node may refresh the channel state information once every 25 frames.
The WFO scheduling algorithm relies on weights that set the dynamic priorities for allocating the resource. These weights are built in order to satisfy two major objectives: system throughput maximization and fairness as explained below.
4.1. System Throughput Maximization
The WFO maximizes the system throughput in a MAC/PHY opportunistic approach. Data integrity requirements of the service flows are enforced considering each service flow independently adapting the modulation scheme and the transmit power to the mobile specific channel state. At each scheduling epoch, the scheduler computes the maximum number of bits that can be transmitted in a time slot of subcarrier if assigned to service flow , for all and all . This number of bits is limited by two main factors: the data integrity requirement and the supported modulation orders.
where , and is the complementary error function. may also be determined in practice based on BER history and updated according to information collected on experienced BER.
MaxSNR-based schemes allocate the resources to the flows which have the greatest values. This bandwidth allocation strategy maximizes the bandwidth usage efficiency but suffers of a significant lack of fairness. In order to provide fairness while preserving the system throughput maximization, a new parameter is introduced which modulates this pure opportunistic resource allocation.
4.2. Fairness Support
Based on the PDOR, the WF parameters directly account for the level of satisfaction of the delay constraints for an efficient QoS management. The PDOR is more relevant and simpler to use than the service flow throughput, the buffer occupancy, or the waiting time of each packet to schedule which would introduce a great complexity in the scheduling algorithm. The WFO parameters introduce dynamic priorities that delay the flows which currently easily respect their delay threshold to the benefit of others which go through a critical period.
The exponent parameter allows being more sensitive and reactive to PDOR fluctuations which guarantees fairness at a short-time scale. is a normalization parameter that ensures that and are in the same order of magnitude. Given that has an order of magnitude , should be set to . With this choice, is always in the same order of magnitude as and allows to manage both fairness and system throughput maximization.
Additionally, Figures 5(a) and 5(b) show the potential of the WFO. Indeed, when or equals zero, the function is constant and only has influence in the scheduling. With this setting, the WFO behaves as the MaxSNR yielding unfair performances. In contrast, the adequate tuning of and brings the wanted fairness.
The dynamic priorities introduced by the WFO algorithm evolve as a function of the specific channel conditions and currently experienced QoS of each service flow in a cross-layer higher layers/MAC/PHY approach. This result in a well-balanced resource allocation which keeps a maximum number of service flows active across time but with continuously low traffic backlogs. Preserving this multiuser diversity allows to continuously take a maximal benefit of opportunistic scheduling and thus maximize the bandwidth usage efficiency. Additionally, this also achieves a time uniform fair allocation of the RUs to the service flows ensuring the required short term fairness [24, 25].
4.3. Global WFO Scheduling Algorithm Description
The scheduler refreshes the current and buffer occupancy values of each service flow and computes the , , and parameters for each service flow and each subcarrier. Then, and are initialized to 1.
For subcarrier , the scheduler selects the service flow with the greatest value.
If the virtual buffer occupancy (we define the virtual buffer occupancy as the current buffer occupancy of service flow minus the number of bits already allocated to this service flow) of service flow is positive, the schedulers go to Substep 2.2. Else, if all virtual buffers are null or negative, the scheduler goes to Step 3. Otherwise, the scheduler selects the next service flow with the greatest value and restarts Substep 2.1.
The scheduler allocates time slot of subcarrier to service flow with a capacity bits, removes bits of its virtual buffer, and increments the value of . If is smaller than the maximum number of time slots by subcarrier, go to Substep 2.1 for allocating the next time slot. Else, go to next substep.
Increment the value of . If is smaller than the maximum number of subcarriers, go to Step 2 for allocating the time slots of the next subcarrier. Otherwise, go to Step 3.
All virtual buffers are empty; or all time slots of all subcarriers are allocated and the scheduling ends.
In this section, we compare the proposed weighted fair opportunistic scheduling with the Round Robin (RR), MaxSNR, PF, and MAOPF schemes implemented with subcarrier by subcarrier allocation. Performance evaluation results are obtained using OPNET discrete event simulations.
where is the distance to the access point of the mobile owning the service flow , and represents the flat fading experienced by this service flow if transmitted on subcarrier . In the following, is Rayleigh distributed with an expectancy equal to unity.
The BER target is taken equal to . With this setting, the value of for the mobiles situated at the reference distance is 6 bits when equals unity.
We assume all mobiles run the same videoconference application. This demanding type of application generates a high volume of data with high sporadicity and requires tight delay constraints which substantially complicates the task of the scheduler. Each mobile has only one service flow with a traffic composed of an MPEG-4 video stream  and an AMR voice stream .
The problem we are studying is quite different with the sum-rate maximization with water filling for instance. The purpose of the scheduler proposed in this paper is to maximize the traffic load that can be admitted in the wireless access network while fulfilling delay constraints. This is achieved by both taking into account the radio conditions but also the variations in the incoming traffic. In this context, we cannot for instance assume that each mobile has some traffic to send at each scheduling epoch. Traffic overload is not realistic in a wireless access network because it corresponds to situations where the excess traffic experiences an unbounded delay. This is why, in all our simulations, the traffic load (offered traffic) does not exceed the system capacity. In these conditions, the offered traffic is strictly equal to the traffic carried over the wireless interface and all mobiles get served sooner or later. The bit rate sent by each mobile is equal to its incoming traffic. Fairness in terms of bit rate sent by each mobile is rigorously achieved. The purpose of the scheduler is to dynamically assign the resource units to the mobiles at the best time in order to meet the traffic delay constraints. This is why we adopted the PDOR as a measure of the fairness in terms of QoS level obtained by each mobile.
Four simulation scenarii were used in the performance evaluation. In the first scenario, we analyzed the behavior of the schedulers when mobiles occupy different geographical positions. The second scenario examines the performance of the schedulers when mobiles have heterogeneous bit rate requirements. QoS differentiation is evaluated in the third scenario. The fourth simulation scenario considers mobiles with both heterogeneous geographical positions, bit rate, and QoS requirements.
5.1. First Scenario: Influence of the Distance on the Schedulers Performances
First scenario setup.
Highly unfair, MaxSNR fully satisfies the required QoS of close mobiles at the expense of the satisfaction of far mobiles. Indeed, only 54.5 percents of these latter experience a final PDOR inferior to a PDOR target of 5% (cf. Figure 9(a)). Unnecessary priorities are given to close mobiles which easily respect their QoS constraints while more attention should be given to the farther. These inadequate priority management dramatically increases the global mobile dissatisfaction which reaches 23% as shown in Figures 9(a) and 10(a).
PF brings more fairness and allocates more priority to far mobiles. Compared to MaxSNR, PF offers a QoS support improvement with only 12.8% of dissatisfied mobiles (cf. Figures 9(b) and 10(a)). Fairness is still not total since the farther mobiles have a lower spectral efficiency than the closer ones due to path loss. All mobiles do not all benefit of an equal average throughput despite they all obtain an equal share of bandwidth. This induces heterogeneous delays and unequal QoS. This fairness improvement compared to MaxSNR indicates however that some flows can be slightly delayed to the benefit of others without significantly affecting their QoS.
The WFO was built on this idea. The easy satisfaction of close mobiles (with better spectral efficiency) offers a degree of freedom which ideally should be exploited in order to help the farther ones. WFO allocates to each mobile the accurate share of bandwidth required for the satisfaction of its QoS constraints, whatever its position is. With WFO, only 0.8 percents of the mobiles are dissatisfied (cf. Figures 9(c) and 10(a)). Additionally, compared to Figures 9(a), 9(b), and 9(c) exhibits superimposed curves which proves the WFO high fairness, included at short term.
Figure 10 shows that the WFO brings the largest level of satisfaction. Indeed, for a tight PDOR target of 5% (see Figure 10(a)), the dissatisfaction ratio with a high traffic load of 1120 Kbps is equal to 18% with the WFO versus 29.7% with the best of the other scheduling schemes. If we set the PDOR target to 10%, the dissatisfaction ratio with a high traffic load of 1120 Kbps is 0% with the WFO versus 13.8% with the best of the other scheduling schemes (PF).
The performance of the four schedulers can be further qualified by computing the theoretical maximal system throughput. Considering the Rayleigh distribution, it can be noticed that is greater or equal to 8 with a probability of only 0.002. In these ideal situations, close mobiles can transmit/receive 6 bits per RU while far mobiles may transmit/receive 4 bits per RU. If the scheduler always allocated the RUs to the mobiles in these ideal situations, an overall efficiency of 5 bits per RU would be obtained which yields a theoretical maximal system throughput of 1600 Kbps. Comparing this value to the highest traffic load in Figure 11(a) (1280 Kbps) further demonstrates the good efficiency obtained with the opportunistic schedulers that nearly always serve the mobiles when their channel conditions are very good. This result also shows that the WFO scheduling has slightly better performances than the two other opportunistic schedulers. Keeping more mobiles active (cf. Figure 11(b)) but with a relatively lower traffic backlog (cf. Figure 8(a)), the WFO scheme preserves multiuser diversity and takes more advantage of it obtaining a slightly higher bit rate per subcarrier (cf. Figure 11(a)).
5.2. Second Scenario: Performance with Heterogeneous Bit Rate Sources
Second scenario setup.
Number of mobiles
5.3. Third Scenario: Performance with Heterogeneous Delay Constraints
Third scenario setup.
Number of mobiles
As explained above, the sum of incoming traffics of the mobiles is inferior to the system throughput. In this context, the traffic of each mobile is served sooner or later, and the bit rate sent by each mobile is equal to its incoming traffic. Fairness is absolute in terms of bit rate sent by each mobile. High-delay-sensitive mobiles are not served more often than other mobiles but earlier. It is only the time instant at which each high-delay-sensitive mobile and background mobile is served that differs. The purpose of the tested schedulers is to set dynamic priorities between the different types of traffics.
5.4. Fourth Scenario: Global Scheduling Performances Analysis
Fourth scenario setup.
Number of mobiles
In this paper, we propose a new MAC protocol for wireless multimedia networks, called "weighted fair opportunistic (WFO)" protocol. This access scheme operates on top of an OFDM-based physical layer and shows a good compatibility with the existing 802.16 standard. Full support of evolved multimedia services and QoS differentiation is enabled with the introduction of generic QoS attributes. Based on a system of weights, the WFO scheduling introduces dynamic priorities between the mobiles according to their transmission conditions and the delay they currently experience in a higher layers/MAC/PHY cross-layer approach. With its well-balanced resource allocation, the WFO scheme keeps a maximum number of service flows active across time but with relatively low traffic backlogs. Preserving the multiuser diversity, it takes a maximal benefit of the opportunistic scheduling technique for maximizing the system capacity. Simulation results show that the WFO outperforms other wireless OFDM-based scheduling schemes providing efficient QoS management. Fairness is ensured whatever the mobile position, the bit rate, or the delay constraints and without never sacrificing system capacity.
This work has been partially supported by the IST European Project WIP under Contract no. 27402. The authors also thanks the reviewers for their constructive comments.
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