National Institute of Technology Hamirpur (HP)
Cross-Layer Optimization Framework for QoS Aware WiMAX Systems Arijit Ukil#, Jaydip Sen* #*
Wireless and Multimedia Innovation Lab, Tata Consultancy Services Kolkata, India 1
[email protected] 3
[email protected]
Abstract— Cross-layer design approaches are critical for efficient utilization of the scarce radio resources with QoS provisioning in the 3G and 4G wireless networks and beyond. Better system performance can be obtained from information exchanges across protocol layers, which may not be available in the traditional layered architecture. Cross-layer design approaches are critical for efficient utilization of the scarce radio resources with QoS provisioning in the WiMAX based 4G networks. Here we have proposed a cross-layer framework for WiMAX networks to optimize the system performance as well as maintaining the endto-end QoS of individual users. As proper utilization of limited network resources is very much essential to achieve the objective of high degree of optimization, resource allocation and scheduling are important issues. We also discuss in detail the cross-layer resource allocation and scheduling scheme in the WiMAX system and propose an algorithm to cater the need of better resource management particularly for heterogeneous traffic consisting of soft and hard QoS constraints applications in 4G networks. Simulation results depict the improved performance of our proposed algorithm. Keywords— Cross-layer optimization, WiMAX, OFDMA; resource allocation; QoS; proportional fair; scheduling;
I. INTRODUCTION Next-generation wireless communication systems are expected to provide a wide range of services with high as well as time-varying bandwidth requirements, with various and variable quality of service (QoS) constraints. Rapid growth of wireless technology, coupled with the explosive growth of the Internet, has increased the demand for wireless data services. Traffic on 4G networks like WiMAX is heterogeneous with random mix of real and non-real time traffic with applications requiring widely varying and diverse QoS guarantee. This enforces a robust and application specific optimization of limited system resources. The requirement of providing endto-end QoS with scarce resources calls for high spectral efficiency. To fulfill these two requirements of high spectral efficiency and QoS provision in the highly dynamic environment of mobile radio requires the collaboration of several layers in the system and effectively demands for an optimization scheme which is cross-layer adaptive. In short, to achieve the prerequisite service guarantees like high minimum data rate, low latency, user fairness of next generation wireless networks, proper designing of cross-layer optimized system is very important. In a packet network, one important component to achieve the aforementioned efficiency goals is a
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properly designed scheduling and resource allocation algorithm. Cross-layer design and optimization to improve the overall system gain as well as achieving the essential requirement of 4G networks like high throughput, low latency is a research topic for quite some time. As the problem is very broad in nature, we will concentrate on cross-layer optimization framework for resource allocation and scheduling. In [1], cross-layer scheduling algorithm with QoS support in wireless networks is proposed. Here a prioritybased scheduler at the medium access control (MAC) layer for multiple connections with diverse QoS requirements, where each connection employs adaptive modulation and coding (AMC) scheme at the physical (PHY) layer is introduced. Each admitted user is assigned a dynamic priority based on its channel quality, QoS satisfaction, and service priority and the user with the highest priority is scheduled first each time. Guan-Ming Su, Zhu Han, Min Wu, and K. J. Ray Liu, [2] discussed how to dynamically allocate the system resources according to the varying channel condition, so as to improve the overall system performance and ensure individual QoS. Specifically, they concentrated on two important aspects with regard to design issues: cross-layer design, which jointly optimizes resource utilization from the physical layer to the application layer, and multiuser diversity, which explores source and channel heterogeneity for different users. Orthogonal Frequency Division Multiple Access (OFDMA) is widely considered one of the most promising multiple access solutions for today's high speed wireless networks [3]. OFDMA is based on Orthogonal Frequency Division Multiplexing (OFDM), with its immunity to inter symbol interference (ISI) and frequency-selective fading. In OFDMA, total system bandwidth is divided into a number of orthogonal subcarriers, which can be allocated to different users providing a flexible multiple access scheme with the opportunity of efficiently exploit multiuser diversity. The subcarriers which are in deep fade for one user can be good for other users, thus the spectral efficiency of the system can be greatly improved by an adaptive resource allocation algorithm which takes channel state information into account [4]. Resource allocation in an OFDMA system basically consists of dynamically assigning the subcarriers to the users with respect to the system objective and the decision is based on the channel quality indicator (CQI), packet-delay and other QoS related parameters like minimum maintainable data rate of the user. Resource allocation policy has a great impact on
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overall system performance. It should be remembered that only aiming at maximizing the efficiency will usually result in an unfair resource assignment, and will not be able to satisfy QoS requirements of each individual user and increases the network outage probability. To maximize the system’s overall performance, most of the radio resources have to be allocated to the users with good channel condition, which in turn arises fairness issue. So, a trade-off is required between system performance and fairness among the users. In literature, this trade-off is tackled by the concept of proportional fair [5]. Resource allocation algorithms in OFDMA dynamically assign radio resources to the users from apriori knowledge of the channel condition according to the system objective function and implemented in media access control (MAC) layer. Dynamic resource allocation in general is a kind of cross layer optimization mainly involving PHY and MAC layers to manage the system resources, like bandwidth, transmit power by exploiting the frequency and temporal dimensions of the resource space in channel-adaptive manner. In this paper, we will discuss cross-layer optimization scheme for OFDMA based WiMAX networks and introduce a robust resource allocation scheme, which balances efficiency, fairness and optimizes the system performance to guarantee user’s QoS requirement. We also present an associated resource allocation algorithm to optimally utilize the scarce OFDMA resources in order to improve the overall system performance and to provide individual QoS requirement. We observe that our proposed algorithm optimizes the system better and produces better performance gain and converges to the user’s QoS profile in a heterogeneous traffic condition with diverse QoS requirement. The paper is organized as follows. The next section describes the system model. In section III our proposed crosslayer optimization and the corresponding resource allocation scheme and algorithm are presented in detail. In section IV simulation results and analysis of the proposed algorithm are presented. Section V provides summary and conclusion.
transmitted to kth user, where Pkn = PT/N. Let th
ω kt be
the
th
achievable rate for k user at t instant: N
ω kt = ∑ s n × ρ knt n =1
⎛ ⎛ hknt 2 × Pkn × ∆ gap ⎜ × log 2 ⎜1 + ⎜ ⎜ NT × sn ⎝ ⎝
⎞⎞ ⎟ ⎟ (1) ⎟⎟ ⎠⎠
Fig.1 Typical downlink multiuser wireless system
where
ρ knt
is the sub-carrier assignment matrix, which is
equal to 1 if nth subcarrier assigned to kth user at tth time instant, else equal to 0. ∆ gap is the imperfection of theoretical value of achievable data rate to the actual data rate, called as ∆ gap , can be approximated as:
− ln(5BER) [6]. 1.6
The QoS parameter for the traffic of kth user is defined as γ k , ∂ kMAX , where γ k , δ kMAX are the minimum
[
]
throughput requirement and maximum packet delay for kth user. MAC utilizes these to parameters as the system constraint for the optimization.
II. SYSTEM MODEL The considered downlink OFDMA system consists of single cell with one base station (BS) communicating simultaneously with K user terminals using N number of OFDMA subcarriers (fig.1&2). Each user has different channel condition which is time-varying. Perfect channel characteristic is assumed in the form of channel quality indicator (CQI). Channel gain for subcarrier n for user k at tth allocation instant is taken as hknt , which is estimated from CQI information. The interference from adjacent cells is treated as background noise. Total noise power density including background noise and AWGN noise is taken as N T . The mutually disjoint OFDMA subcarriers are denoted as: s1, s2, ….sN , where sn = B/N and sn≤ Bc , where Bc is the coherence bandwidth of the channel and B is the total available bandwidth. PT is the total available transmit power and Pkn is the transmit power for nth subcarrier when
Fig. 2 Multiuser OFDMA System with Resource Allocation Module
III. CROSS-LAYER OPTIMIZATION FRAMEWORK AND RESOURCE ALLOCATION From a layered architecture point of view, cross-layer means enabling new interactions between non-adjacent layers and exchanging information and control between layers that
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was not possible in the original architecture. This makes it possible to optimize the operation of the protocol stack in a joint manner taking aspects of multiple layers into account. In general, the cross-layer optimization problem is a complex problem involving both information theory (to model the physical layer) and queuing theory (to model the application level delay). Here, we shall tackle the cross-layer optimization problem from the information-theoretic angle only. In [9], resource allocation that optimizes total packet throughput subject to the user’s outage probability constraint was proposed. Their algorithm assumes a finite queue size for arrival packets, and dynamically allocates the resources every time-slot based on the users’ average SNR, traffic patterns, and QoS requirements. The objective of our cross-layer optimization problem is to improve the spectral efficiency of the system and fairness of the total network in oreder to guarantee individual QoS requirements. As OFDMA is the de facto multiple access standard for next generation wireless systems, our will be on OFDMA based cross-layer resource allocation. In multiuser scenario, channel gains of a specific subcarrier vary from wireless terminal to wireless terminal. At any given time in a large network for each sub-channel there is a high probability that there is a user whose OFDMA subcarrier assigned to the users who see good channel gains on them. Maximization of system performance can be achieved by allocating the OFDMA subcarriers to the users with good channel condition, which leads to fairness issue [7]. While maximal throughput schedulers can result in optimal network resource utilization, it does not take into account each user’s QoS perspective. For example, users with poor channel conditions will experience discrimination in the maximal throughput scheduling policy and will suffer from starvation. This is obviously undesirable from the end-user perspective. Hence, another popular utility function used to strike a balance between system capacity and fairness among users is called proportional fairness (PF). A scheduler is called proportional fair [5, 8] if it optimizes the utility function given by:
(
)
K
( )
UFPF ϖ 1 ,ϖ 2 ,....,ϖ K = ∑ ln ϖ k k =1
(2)
where UFPF proportional fair utility function to reflect the method of resource utilization and throughput of user k and
ωk
ϖk
is the mean
is the instantaneous throughput
of user k at the resource allocation instant, i.e, ϖ k = E{ ω k }. It can be seen from the utility function in Equation (2) that there is a heavy penalty for terms with small ϖ k due to the concavity of the natural logarithmic function. Hence, to maximize the utility, the scheduler has to avoid the situation where some users obtain very small throughput ϖ k . PF optimization is pure outcome fairness metric. From convex optimization theory, the utility function (2) can be
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equivalently represented as the function that maximizes
ωk
∑ϖ
(3)
k
k
Equation (3) is called the PF metric (PFm). So, the objective of the PF optimization is: max(PFm) But, equation (3) represents non-causal system because of the non-causality of ϖ k . So, ϖ k is approximated as movingaverage (5), which in turn gives low pas filter effect to smoothen out the greedy users (equivalent to high frequency components). The objective of PF optimization is thus to allocate subcarrier n to k* user at t-th allocation instant when: k* =
arg max k
ϖ kt
=
(1 −
ω kt ϖ kt
1 1 )ϖ k ( t −1) + ϖ k (t −1) ∆τ ∆τ
(4)
QoS parameter minimum data rate is the constraint of the optimization (2/3), i.e., ω kt ≥ γ k , ∀k (5) To fully optimize wireless broadband networks, both the challenges from the physical medium (channel variation: hknt ) and the QoS-demands ( γ k ) from the application layer have to be taken into account in a joint way. These two parameters from two different layers are required to get associated through the above-discussed co-operative management. PF has the property that it cannot be replaced by any other arbitrary allocation that does not lead to a reduction in the aggregate fractional rate change, which is basically the notion of Nash Bargaining Solution (NBS) from game theory. Resource allocation is MAC phenomenon. It is a crosslayer design issue, which is implemented in MAC from the information exchange with other layers. So, our goal is to map this optimization of OFDMA resource allocation (2-5) method in a cross-layer framework. Fig. 3 depicts the cross-layer optimization reference model. It can be noted that in fig. 3 all the parameters including external and internal, are highly dynamic in nature. The wireless channel conditions and user requirements of QoS may change continuously, requiring constant updating of parameters, which makes the problem of cross-layer resource allocation highly complex with significant degrees of freedom. Cross-layer design can be classified in four categories based on the order in which optimization is performed: 1) Top-down approach: Higherlayer protocols optimize their parameters and the strategies at the next lower layer. This cross-layer solution has been deployed in most existing systems, wherein the application dictates the MAC parameters and strategies, while the MAC selects the optimal PHY layer modulation and coding scheme. 2) Bottom-up approach: The lower layers try to insulate the
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higher layers from losses and bandwidth variations. This cross-layer solution is not optimal for multimedia transmission, due to the incurred delays and unnecessary throughput reductions. 3) MAC-centric approach: In this approach the application layer passes its traffic information and requirements to the MAC, which decides which application layer packets should be transmitted and at what QoS level. The MAC also decides the PHY layer parameters based on the available channel information. 4) Integrated approach: In this approach, strategies are determined jointly. Unfortunately, exhaustively trying all the possible strategies and their parameters in order to choose the composite strategy leading to the best quality performance is impractical due to the associated complexity. A possible solution to solve this complex cross-layer optimization problem in an integrated manner is to use learning and classification techniques. This approach leads to the most optimum but complicated design. Here, we shall consider MAC-centric optimization, which is relatively simpler, easy to implement and most suitable for our problem. Conceptual framework of MACcentric cross-layer optimized resource allocation is shown in fig.4.
Fig. 3 Cross-layer optimization reference model
From our optimization scheme, the PF algorithm will be placed as the central engine, which receives system constraint, particularly packet delay, CQI from network and PHY layers respectively and QoS parameters from application layer. Accordingly it performs the optimization function to map the resources, e.g. OFDMA subcarriers to the users; mostly frame by frame basis. Cross-layer design approaches for efficient utilization of the scarce radio resources with QoS provisioning in the 3G wireless networks and beyond is discussed in [10] mainly in CDMA system for multimedia applications. PF optimization described above (2-5), distributes radio resources to guarantee instantaneous QoS without any differentiation based on user’s traffic-class. PF optimization is a NP hard problem with non-linear constraints. Hence, it is highly improbable that polynomial time algorithms can be used to solve it optimally. Therefore certain simplifications are needed in order to make the problem tractable and can well be implemented in real-time applications.
Fig. 4 Conceptual framework of cross-layer optimized resource allocation
With large number of degrees of freedom like throughput maximization, fairness, latency, channel variation, we proposed long-term proportional fair algorithm (LTPF) [11], which instead of taking delay as a constraint, use the delay parameter to exploit the time-diversity gain. The proposed algorithm is a modification on the traditional PF algorithm, suitable for heterogeneous traffic scenario with diverse QoS based applications. LTPF algorithm is less complicated and with less overhead. LTPF optimization optimizes the non realtime traffic, where as guaranteeing the real-time applications. Time-diversity is a less implemented diversity technique as it needs delay-tolerant applications and highly dynamic mobile wireless environment for some significant performance gain. Time diversity technique fundamentally consists of retransmitting the corrupted information at times when the channel is expected to be more favourable, that is at time spacing exceeding the channel coherence time of the channel. Basically by principal, a well designed communication system should take all the available degrees of freedom of the channel as much as possible. Wireless channel is normally very much dynamic in nature and over long duration, time diversity gain becomes high as the mean channel condition ( hknt
δkMAX t =0
)
follows similar distribution according to the Bernoulli’s Law of Large Numbers. Fundamentally, we have introduced granularity in the system optimization from the obvious fact that different QoS class, cross-layer parameters are different for different users and single or universal optimization is not the best suitable. From system perspective also, optimization parameters should be user dependent. So, we modified the traditional PF scheme to make it more relevant in WiMAX systems. The system constraint (5) is modified for non realtime traffic as:
ϖk
∂kMAX t =0
≥ γ k , ∀k nonreal −time
For real-time applications, equation (5) is applicable. So our modified optimization scheme is: k*= arg max k
ω kt ϖ kt
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ϖ kt
=
(1 −
1 1 )ϖ k ( t −1) + ϖ k (t −1) ∆τ k ∆τ k
(6)
≥ γ k , ∀k nonreal −time
(7)
subject to:
ϖk
∂kMAX t =0
ϖ kt ≥ γ k
, ∀k
realtime
IV. SIMULATION AND ANALYSIS Here, we present simulation results of the proposed crosslayer optimized resource allocation algorithm under the system parameters given in Table 2. The system parameters are roughly based on Mobile WiMAX Scalable OFDMAPHY. Heterogeneous traffic with diverse QoS class based users is taken. For the sake of generality, random mix of UGS, ertps, rtps, nrtps, BE service class of users with diverse QoS parameter is considered.
V. SUMMARY AND CONCLUSIONS Here an efficient and optimized resource allocation algorithm in a cross-layer framework is presented, which has shown the characteristics of better performance for non realtime traffic in QoS diversified heterogeneous traffic condition. We mainly concentrate on OFDMA systems. Only limitation of the proposed algorithm is the maximum bound of the packet-delay constraint. More relaxed the constraint is, better is the performance of the proposed resource allocation method. Future scope of work lies in implementing this algorithm to WiMAX or other 4G wireless systems and evaluating the performance through real-life field trials.
TABLE I Simulation and System Parameters
Available Bandwidth Total Transmitted Power Number of users Number of subcarriers BER Frame duration Channel model Modulation Channel sampling frequency Maximum Doppler
1.25 MHz 20 dBm 20 72 10-3 5 msec Rayleigh 16QAM 1.5 MHz 100Hz
Fig. 5 depicts the plot comparing QoS profile of users’ achieved mean data-rate at overall packet-delay is very tight. Here it is clear that the achieved data-rate profile deviates from the QoS profile considerably but attempts to follow it, as the proposed algorithm becomes purely proportional fair in this kind of scenario. Fig. 6 shows the result when overall packet-delay parameter is considerably relaxed. Fig 6 depicts much better performance than in fig. 5, which is the consequence of achieving more time-diversity gain. In other words, with more number of delay-tolerant applications present in the WiMAX system, our proposed scheme performs better, which proves our intuition of improved performance gain in soft-delay constraint scenario.
Fig 5. Performance of proposed cross-layer optimized resource allocation algorithm at tight delay-constraint scenario
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Fig 6. Performance of proposed cross-layer optimized resource allocation algorithm at low delay-constraint scenario
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Qingwen Liu, Xin Wang, and Georgios B. Giannakis, “A Cross-Layer Scheduling Algorithm With QoS Support in Wireless Networks”, IEEE Trans. in Vehicular Technology, vol. 55, no. 3, pp. 839-847, May 2005. [2] Guan-Ming Su, Zhu Han, Min Wu, and K. J. Ray Liu, “Multiuser CrossLayer Resource Allocation for Video Transmission over Wireless Networks”, IEEE Network, pp: 21-27, March/April 2006. [3] Ahmad R. S. Bahai, Burton R. Saltzberg, Mustafa Ergen. “Multi-Carrier Digital Communications Theory and Applications of OFDM”. 2nd ed. Springer. [4] Guocong Song, Ye (Geoffrey) Li, “Utility-Based Resource Allocation and Scheduling in OFDM-Based Wireless Broadband Networks”, IEEE Communications Magazine, pp: 127-134, December 2005. [5] Tien-Dzung Nguyen and Youngnam Han., “A Proportional Fairness Algorithm with QoS Provision in Downlink OFDMA Systems”. IEEE Communication Letters. Vol-2, No.-11, Nov 2006. [6] Guocong Song,Ye (Geoffrey) Li, “Cross-Layer Optimization for OFDM Wireless Networks—Part II: Algorithm Development”, IEEE Trans. on Wireless Comm., vol. 4, no. 2, pp. 625-634, March 2005. [7] W. Rhee and J. M. Cioffi. “Increase in capacity of multiuser OFDM system using dynamic subchannel allocation”. Proc., IEEE VTC 2000, Page: 1085–89. [8] Christian Wengerter, Jan Ohlhorst, Alexander Golitschek Edler von Elbwart., “Fairness and Throughput Analysis for Generalized Proportional Fair Frequency Scheduling in OFDMA.”, IEEE VTC, 2005. [9] G. Li and H. Liu, “Dynamic resource allocation with finite buffer constraint in broadband OFDMA networks,” in Proc. IEEE Wireless Communications and Networking Conf., vol. 2, Mar. 2003, pp. 1037– 1042. [10] Hai Jiang, Weihua Zhuang, and Xuemin (Sherman) Shen, “Cross-Layer Design for Resource Allocation in 3G Wireless Networks and Beyond”, IEEE Communications Magazine, pp: 120-126, Dec 2005. [11] Arijit Ukil, Jaydip Sen, Debasish Bera, “Long-Term Proportional Fair QoS Profile Follower Sub-carrier Allocation Algorithm in Dynamic OFDMA Systems”, 13th International OFDM Workshop, pp: 1-5, 2008.
