Fourth IEEE International Workshop on Selected Topics in Mobile and Wireless Computing
Adaptive Time Domain Scheduling Algorithm for OFDMA Based LTEAdvanced Networks Rehana Kausar, Y. Chen and K. K. Chai School of Electronic Engineering and Computer Science Queen Mary University of London London, UK rehana.kausar,yue.chen,
[email protected] To meet these requirements radio resource management (RRM) is the key area to enhance the utility of the scarce radio resources and packet scheduling being the core of RRM, is very crucial. An efficient PS algorithm is vital for the next generation of mobile communications, where the competition to get radio resources is very high. This is because of multi-service mixed traffic environments with different QoS requirements. Various data services such as internet browsing, SMS, emails, etc and voice require different levels of service; a few seconds delay in delivering an email is negligible but makes web browsing annoying and voice conversation unsustainable [4]. In addition, the achieved QoS of real time (RT) and non real time (NRT) traffic changes rapidly because wireless channel conditions are highly dynamic. To cope with unstable achieved QoS, channel capacity allocation decisions to different traffic types need to be adaptive to the achieved QoS and fast enough to follow these changes. For example if PDR of RT traffic is increasing by time then RT capacity must be increased to reduce the RT packet delays so that PDR can be reduced. To enhance the reliability, Artificial Intelligence (AI) has been used in communication networks. The AI technologies offer many new and exciting possibilities for the next generation of communication networks [5]. Learning rules in AI, for a connectionist system, are algorithms or equations which govern changes in the weights of the connections in a network. One of the learning procedures for two- layer networks is the Hebbian Learning Rule, which is based on a rule initially proposed by Hebb in 1949. The Hebbian Learning Rule states how much the weight of the connection between two units should be increased or decreased in proportion to the product of their activation [6-8]. It is used in [9] for dynamic spectrum management in Cognitive Radio (CR) to estimate the presence of primary users (PUs) in the environment; PUs are the licensed users and allowed to operate in the spectrum band bought by the wireless service provider. It helps in preventing collisions of CR units with PUs. Hebbian rule can be integrated to PS algorithm to take decisions on capacity allocation to RT traffic based on achieved QoS parameters such as PDR. This paper proposes an adaptive time domain scheduling algorithm (ATDSA) to improve the QoS of different traffic types while maintaining system overall
Abstract: In this paper an Adaptive Time Domain Scheduling Algorithm (ATDSA) is proposed for Long Term EvolutionAdvanced (LTE-A) downlink (DL) transmission. This algorithm uses the Hebbian learning process to allocate radio resource adaptively to different types of traffic. The aim is to improve QoS provision for different traffic types while maintaining a reasonable tradeoff between system throughput and user fairness. The proposed ATDSA is implemented and validated in a dynamic packet scheduling framework via a system level simulation. Results show that ATDSA reduces the average delay, delay viability and packet drop rate (PDR) of real time traffic; guarantees the minimum throughput of non real time traffic while balancing the tradeoff between system throughput and user fairness. Keywords-Long Term Evolution-Advanced (LTE-A); Packet Scheduling (PS); Hebbian learning; Qaulity of Service (QoS);Orthogonal Frequency Division Multiple Access (OFDMA);
I.
INTRODUCTION
The wireless industry has rapidly grown through the development of multiple standards and technologies. The world of telecommunications is now aiming towards 4th generation of mobile communication systems with superior performance as compared to the existing standards. LTE-A is future wireless communication network which is an evolution of LTE networks and aims to meet or exceed IMT-Advanced requirements and its own requirements for advancing LTE for long term competitiveness [1-2]. The following performance targets are set for LTE-A to meet the IMT-A requirements, while maintaining the backward compatibility with LTE release 8 [1-3]. • • • • •
Average spectral efficiencies of up to 3.7b/s/Hz/cell in the DL (4 4 antenna configuration) and 2.0 b/s/Hz/cell in uplink (UL) (2 4). Cell edge spectral efficiencies of 0.12 b/s/Hz in the DL (4 4) and 0.07 b/s/Hz in the UL (2 4). Peak data rates in order of 1 G bps in the DL and 500 Mbps in the UL transmission. Peak spectrum efficiencies of 30 b/s/Hz in the DL and 15 b/s/Hz in the UL with antenna configuration of 8 8 and 4 4 respectively. Low cast of infrastructure deployments and terminals and power efficiency in the network and terminals.
978-1-4577-2014-7/11/$26.00 ©2011 IEEE
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Adaptive time domain (TD) Scheduler, and frequency domain (FD) Scheduler, QoS Measures and Learning process.
performance in terms of system throughput and fairness among all users at good level. The ATDSA uses Hebbian learning process to learn the environment in terms of QoS parameter, PDR of RT traffic, and allocate the available bandwidth proportion to RT traffic accordingly. The rest of the paper is organized as follows. In section II, we provide the system model and the proposed packet scheduling framework and its detailed description is presented in section III. Section IV describes performance measures used in this paper. Simulation model and discussion on results is presented in section V. Finally conclusions and future work are presented in section VI. II.
SYSTEM MODEL
An OFDMA system is considered in which minimum allocation unit is one Physical Resource Block (PRB) containing 12 sub-carriers in each Transmission Time Interval (TTI) of 1ms duration. There are K mobile users and M PRBs. The downlink channel is a fading channel within each scheduling drop. The received symbol , at the mobile user on PRB is the sum of the additive white Gaussian noise (AWGN) and the product of actual data and channel gain, as given in (5) [10] [11]. ,
,
,
Fig. 1 The cross layer packet scheduling architecture
(1)
,
Where, Y k , m (t ) is data symbol from eNodeB to user
at
is the input, is the complex PRB , , , is the channel gain of PRB for user , and , complex White Gaussian Noise [11]. It is assumed, as in [11] ⁄ [13-16], that the power allocation is uniform, on all PRBs. Where, is the total transmit power of is the power allocated at channel and M is eNodeB, total number of PRBs. At the start of each scheduling drop, is known by the the channel state information (CSI) , eNodeB. The achievable throughput of a user on PRB can be calculated by (6) as used in [11] and [12]. ,
1
,
(2)
Where, is the bandwidth of each PRB, σ is the variance of additive white Gaussian noise and Γ is a constant signal-to-noise ratio (SNR) gap and has a simple relationship with the required Bit Error Rate (BER) as given in (3). 2
.
III.
The input is a mixed traffic which is differentiated in queues of four traffic types: Control queue, RT queue (voice), NRT queue (streaming video) and Best Effort (BE) queue (email, SMS). Service specific queue sorting algorithms are used to sort users in these queues. To exploit multiuser diversity in the TD and FD respectively the Channel Quality Information (CQI) reports are fed back to queue management, and FD scheduler. The QoS measures unit performs analysis on QoS parameters such as PDR of RT traffic and minimum throughput of NRT users. In addition, feedback of achieved throughput and user delay is given to the queue management which is used in the priority metrics of queues. The learning process takes information on PDR of RT traffic and decides the proportion of available radio resources to RT and NRT traffic types. A. Queue Management Users in the control queue are sorted by Round Robin (RR) as control information is equally important for all users. Control queue is at the top and is allocated resources before all other queues because control information is the most important. The QoS requirement for RT traffic is defined as where is delay of user k , is the delay budget which is the upper bound of delay for RT traffic. To support QoS guarantee to RT traffic, RT queue is sorted by (4). (4)
(3)
PACKET SCHEDULING ARCHITECTURE
Where is the priority, is waiting RT time, H k ∈K is the average channel gain of PRBs for RT user and is queue length of RT user at time . The priority metric takes into account waiting time, channel
The PS framework is shown in Fig 1. It consists of different functionalities such as, queue management,
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conditions and queue length of RT users to improve fairness, multiuser diversity in the TD and delay viability respectively. By prioritizing users having longer waiting time helps allocating fair share of bandwidth among users. And when waiting time is normalized by the DB, the metric tries to keep each user’s waiting time lower or equal to the DB. To reduce PDR, users with longer queues are prioritized so that packets may not drop due to time out. The QoS requirement for NRT is defined as , where is the instantaneous throughput of user at time and is throughput requirement of NRT user . Minimum throughput requirements of NRT traffic are fulfilled by sorting NRT queue with (5). ,
weight of RT traffic accordingly. If the current value of PDR is higher than the previous value, Hebbian learning process increments the weight of RT traffic. The weight given to RT traffic is continuously updated using the following Hebbian learning rule: 1
1 1
(5)
Where is the priority metric, is the is the average average achieved throughput and channel condition of PRBs for NRT user at time . In (5) is the delay upper bound for NRT video packets which is taken equal to RT packet in this paper. Equation (5) takes into account waiting time, minimum throughput requirement and channel conditions to support fairness, throughput guarantee and exploit multiuser diversity in the TD, respectively. Normalised waiting time helps improving fairness and keeping user’s delay lower or equal to DB. By prioritising users with lower average achieved throughput, minimum throughput guarantee is satisfied. In (4) and (5) the channel state information helps exploiting multiuser diversity thus improving overall system throughput. Users in BE queue does not have any QoS requirement, however to maintain fairness level among users, proportional Fairness (PF) [13] algorithm is used to sort users in this queue as given in (6).
(7)
Where is the weight given to the RT traffic at time , 0 1 is learning rate. is maintained between 0 and 1 and increases each time by η, if the PDR of RT traffic increases. It becomes 1 when PDR of RT traffic exceeds the PDR threshold , at which resource allocation to RT traffic is increased. If PDR is lower than the threshold then there is a decrease in RT weight equal to η. When the weight of RT traffic is decreased, the allocation of radio resources to RT traffic is also decreased so that resources can be allocated to other traffic types. Let C be the total available radio resources and is the proportion of C allocated to RT traffic then 1 is the proportion of available radio resources allocated to NRT traffic types. The adaptive change in λ based on Hebbian learning process is defined by (8). 1
(8)
Where λ (t) is resources allocated to RT traffic at time and min allocation unit is 1PRB. The adaptive change in RT capacity based on Hebbian learning process is shown in Fig. 2.
(6) Where is instantaneous and achieved throughput of BE user .
WRT (t ) = WRT (t −1) −η
is average
W RT (t ) = W RT (t − 1) + η
B. Adaptive TD Scheduling Algorithm (ATDSA) The ATDSA uses Hebbian learning process to allocate the radio resources dynamically based on the QoS feedback. The Hebbian learning process learns the environment in terms of the activity rate of a parameter and takes decisions based on comparison of current and previous occurrence of that activity. In ATDSA, QoS measure unit (Fig. 1) continuously calculates PDR of RT traffic during each TTI and saves this information in a vector named PRB Allocation Priority (PAP) vector. The Hebbian learning process compares the current PDR with the previous values of PDR in PAP vector and changes the
W RT = 1 Fig.2 Hebbian learning process The proportion of available resources allocated to the NRT traffic is further divided in different types of the NRT traffic (NRT streaming video and BE) by prioritizing the streaming queue to guarantee its throughput requirements.
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As BE queue does not have any QoS requirements and rest of the resources are allocated to the BE queue.
(10)
max
The performance of long-term throughput for NRT user is evaluated by the minimal throughput amongst all NRT users and is given by (11) as used in [11].
C. FD Scheduler In FD scheduler, radio resources are allocated to the prioritized users. At a given time t, PRBs are allocated to users by the following algorithm shown in Fig 3.
(11)
min
The system throughput is the sum of throughput achieved by all users and the fairness among users is calculated by the Raj Jain index given by (12) [12] [16]. ∑
(12)
∑
The value of fairness index is 1 for the highest fairness when all users have same throughput. In (12) K is the total number of users, is the index of users and is the time average throughput of user . V.
SIMULATION MODEL AND RESULTS
A. Simulation Model A single cell with one eNodeB, total system bandwidth of 10 MHz and PRB size of 180 kHz is considered. Total system bandwidth is divided into 55 PRBs. The wireless environment is typical Urban Non Line of Sight (NLOS) and the LTE system works with a carrier frequency of 2GHz. The most suitable path loss model in this case is the COST 231Walfisch-Ikegami (WI) [16] as used in many other papers on LTE. The simulation parameters used for system level simulation are based on [17] and these are typical values used in many papers. These parameters are listed in Table 1. TABLE 1 SIMULATION PARAMETERS Parameter
Fig. 3 FD scheduling algorithm
Cell topology Cell Radius
Resource allocation is completed when all PRBs are allocated. IV.
UE distribution Smallest distance from UE to eNodeB/m
PERFORMANCE METRICS
In this paper, the performance of ATDSA is evaluated in terms of QoS parameters of different traffic types, system throughput and user fairness. The QoS measurements of RT traffic includes, average delay, delay viability and PDR. PDR is the ratio of dropped packets to the total packets of a user and is given by (9) below,
Path Loss model
(9)
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Single cell 1 km Random 35 m COST 231 WalfischIkegami (WI) model
Shadow fading standard deviation/dB
8 dB
System bandwidth/MHz
10 MHz
PRB bandwidth/kHz
180 kHz
Carrier frequency/GHz
2 GHz
BS transmission power
46dBm(40w)
Traffic model
And the delay violation probability is given by (10), as used in [11].
Value/comment
Full buffer
and given priority to users with longer delays. In addition, it is further reduced by ATDSA when RT capacity is increased on higher PDR. The users waiting for longer time get opportunity to be allocated reducing average delay of RT traffic.
Users are assumed to have a random distribution and the total number of RT users is assumed to be equal to total number of NRT users as in [11]. The delay budget for RT traffic is 40ms in OFDMA-based networks [11] [18] and the required throughput by NRT traffic is taken 240kbps as in [11]. Total eNodeB transmission power is 46dBm (40w) and maximum BER requirement is 10 − 4 for all users.
1
B. Simulation Results The performance of ATDSA is compared against QoS aware mixed traffic packet scheduling algorithm (abbreviated as MIX in all figures) [19] and SWBS [11]. MIX [19] classifies mixed traffic in different queues and sort users in these queues with queue specific algorithms at classifier, picks users from queues by fair scheduling in the TD and allocates resources to these users in the FD. In Fair TD scheduling, users are picked from the queues one-byone thus allocating a fair share of radio resources to all queues. SWBS [11] uses a sum waiting time based scheduling algorithm in which sum waiting time of packets is taken into account while prioritizing RT and NRT traffic types. For RT traffic it takes real arrival time and for NRT traffic, it takes virtual sum arrival time of packets which is related to the minimum throughput requirement of NRT traffic. In these simulations, the packet arrival process for RT, NRT streaming video and BE traffic is Poisson distribution with 0.35 ON time. The total number of active users is varied from 50 to 100.
0.8
Delay viability
0.6
0.4
0.2
0
Total number of active users
Fig. 5 Delay viability of RT traffic Fig. 5 shows delay viability of RT users verses total number of active users. Delay viability is significantly decreased with the proposed ATDSA as compared to the reference algorithms. This is because queue length is considered while prioritizing users in RT queue at queue management and in the TD scheduler; ATDSA takes into account Hebbian learning process to track the PDR performance of users by PAP vector. ATDSA takes adaptive decisions on resource allocation to RT traffic based on this information and these decisions are fast enough to keep delay viability under control. This results in the significant decrease in delay viability and thus reducing the average PDR of RT traffic as well.
MIX ATDSA SWBS
0.6
0.4
0.5
0.2
Average PDR of RT traffic
Ave delay of RT traffic (ms)
1
0.8
MIX ATDSA SWBS
0
Total number of active users
Fig. 4 Average delay of RT traffic Fig. 4 shows average delay of RT traffic verses total number of active users. Average delay increases with the number of users for all algorithms as shown. However ATDSA shows the least delay as compared to QoS aware SWBS and MIX. At a system load of 100 active users its value is 0.43 for ATDSA, 0.61 for MIX and 0.8 for SWBS. This is because the waiting time of users is taken into account while prioritizing users in the queue management
0.4
MIX ATDSA SWBS
0.3
0.2
0.1
0
Total number of active users
Fig. 6 Average PDR of RT traffic
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Average PDR of RT traffic is shown in Fig 6 which increases with number of users for all algorithms. ATDSA keeps the average PDR lower than MIX and SWBS because of adaptive capacity allocation to the RT traffic based on Hebbian leaning process. It adaptively changes RT capacity based on PDR thresholds to tackle unstable PDR values due to highly dynamic wireless channel conditions. In this way users with longer packet delays can transmit their packets reducing average PDR of RT traffic. The SWBS and MIX do not have adaptive allocation of capacity and cannot cope with rapidly changing achieved PDR values.
because it is not designed to improve fairness along with QoS of RT and NRT traffic types.
1.2
1
Fairness
0.8
0.6
Minimum throughput (kb/s)
0.4
0.2
MIX ATDSA SWBS
0
Total number of active users
Fig. 8 Fairness among users System overall throughput achieved by all algorithms versus total number of active users is shown in Fig. 9.
MIX ATDSA SWBS
Total number of active users System throughput (Mb/s)
Fig. 7 Minimum throughput of NRT streaming video traffic Minimum throughput of NRT streaming video traffic is calculated by (11) and is shown in Fig. 7. All algorithms fulfill the minimum throughput requirements of NRT queue and achieve throughput higher than minimum throughput requirement (240kb/s). The achieved throughput of MIX drops significantly at a system load equal to100 number of users and becomes the lowest of all. This is because MIX algorithm uses fair scheduling in the TD and gives equal priority to all traffic queues. Fair scheduling keeps the minimal throughput stable at lower system loads but ca not maintain it at higher system load. ATDSA however maintains a good level of minimal achieved throughput at all system loads and guarantees throughput requirements of NRT traffic. BE queue is not the focus of this work and the results on BE traffic are not included.
MIX ATDSA SWBS
Total number of active users
Fig. 9 System throughput System overall throughput is shown in Fig. 9. ATDSA achieves similar system throughput as MIX. SWBS achieves the lowest system throughput which is decreased by a significant value of 10 Mb/s at lower system load and 5Mb/s at the higher system load as shown. This is because SWBS only concentrates on supporting QoS to RT and NRT traffic and is not designed to improve overall system throughput at the same time.
Fig. 8 shows fairness among users achieved by all algorithms verses total number of active users. Fairness achieved by MIX and proposed ATDSA is almost same as shown. This is because ATDSA is designed to maintain fairness among users at a good level by equalizing users’ waiting time during prioritizing users at the queue management stage. And MIX uses fair scheduling in the TD to improve fairness among users. The SWBS algorithm shows lower fairness as compared to MIX and ATDSA
V.
CONCLUSIONS
In this paper, an adaptive TD scheduling algorithm (ATDSA) is proposed which uses Hebbian learning process
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[8]
to adaptively allocate resources to different types of traffic. In each TTI, the proportion of resource reserved for RT traffic is adaptively adjusted based on the Hebbian learning process. Simulation results show that the proposed ATDSA reduces average delay, delay viability and PDR of RT traffic and supports minimum throughput guarantee to NRT streaming video traffic. In addition it maintains a good trade-off between system overall throughput and fairness among users.
[9]
[10] [11]
ACKNOWLEDGMENT We thank to AMJ student association created by the founder of AMJ community section for their valuable sponsorship to carry out this work.
[12] [13]
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