Traffic models impact on OFDMA scheduling design
© Ameigeiras et al; licensee Springer. 2012
Received: 24 October 2011
Accepted: 24 February 2012
Published: 24 February 2012
This article studies the impact on the design of scheduling algorithms for Orthogonal Frequency Division Multiple Access (OFDMA) systems of two traffic models described in the evaluation methodology proposals from standardization bodies: the full buffer and the finite buffer traffic models. The analysis concentrates on utility-based scheduling with an α-fair utility function for Non-Real Time (NRT) services. The results show that a gradient scheduling algorithm is able to maximize the aggregate utility over all the users when the less realistic full buffer model is adopted; but not when the finite buffer model is applied. The results also show that with the full buffer model a gradient scheduler exhibits a trade-off between average user throughput and the user throughput at 5% outage, but it does not when the more realistic finite buffer is used. Therefore, it is concluded that designs of scheduling algorithms for NRT services for OFDMA systems carried out under the full buffer model assumption may fail to provide the desired performance benefits in realistic scenarios. Based on the results presented, a recommendation on scheduling design is provided.
Keywordsfinite buffer traffic model full buffer traffic model OFDMA packet scheduling α-fair function fairness.
Orthogonal Frequency Division Multiple Access (OFDMA) has become an essential technology in broadband wireless communications. Systems such as the 3GPP Long-Term Evolution (LTE) , IEEE 802.16d (i.e. fixed WiMAX) , IEEE 802.16e (i.e. mobile WiMAX) , LTE-Advanced  and IEEE 802.16m  have adopted OFDMA as their multiple access technique for either downlink or both uplink and downlink air interfaces.
Full buffer model [8–10]: it is a simplified version of the traffic received/transmitted by a user in a data session. It is characterized by two facts: the number of users in the cell is constant and the buffers of the users' data flows always have unlimited amount of data to transmit. The full buffer model has widely been adopted in OFDMA in simulation-based [11–14] and theoretical [15–17] investigations due to its simplicity.
Finite buffer model [8, 10] (in  the so-called FTP traffic model 1): it is a simplified version of the interactive or background traffic type. It includes both a user arrival (birth) and a user departure (death) process. With this traffic model, a user is assigned a finite payload to transmit or receive when it arrives, and it leaves the system after the payload transmission or reception is completed. The user arrival process of this model captures the fact that the users in the network are not simultaneously active at the same time, but they rather become active when they start a data session that requires the download/upload of data [18–21]. This model has been less extensively adopted [22, 23].
The evaluation methodology documents [8, 9] have also proposed more complex models based on statistical distributions where users are assumed to execute applications (e.g. FTP, Web browsing, etc.). However, these models do not include a user arrival or departure process and have been much less extensively adopted due to their increased complexity.
Many investigations about scheduling in OFDMA systems have been carried out only under the full buffer model [14, 16, 17, 24, 25] and therefore their results and conclusions are influenced by the full queue effect. This article studies the influence of the traffic model on the performance of packet scheduling algorithms in OFDMA systems. In particular, we concentrate on utility-based scheduling [17, 26] for two reasons: first, it has been reported that it provides an effective trade-off among spectral efficiency and fairness in wireless networks [14, 27]; and second, proportional fairness scheduling , which is a particular case of utility-based scheduling , has widely been adopted in OFDMA systems [24, 29], and it is the basis for some QoS scheduling algorithms [16, 30].
The evaluation of the traffic model impact will be carried out by means of quasi-dynamic system-level simulations. For the evaluation, two OFDMA network models are considered: (i) initially, a simple OFDMA network model. This simple network model assumes an ideal achievable transmission rate per hertz through the Shannon's law, and that the base station has ideal knowledge of the user's Signal-to-Interference Ratio (SIR). (ii) Later, a realistic 3G LTE network model will be considered. It will include relevant features in an LTE system such as the Modulation and Coding Schemes (MCS), Hybrid-ARQ and the Channel Quality Information (CQI) feedback. The evaluation with these two network models will allow us to determine if the effect of the traffic models on the performance of a realistic OFDMA network model, such as 3G LTE, significantly differs from the case of a basic OFDMA network model.
The obtained results will show that under the OFDMA network model:
A gradient scheduling algorithm is able to maximize the aggregate utility over all the users (for a given utility function) when the less realistic full buffer model is adopted; but not when the finite buffer model is applied.
With the full buffer model, a gradient scheduling algorithm exhibits a trade-off between average user throughput and the user throughput at 5% outage. However, when the more realistic finite buffer is used, this trade-off does not exist.
The relative comparison of key performance indicators (KPIs), such as the average user throughput or the user throughput at 5% outage, between versions of a gradient scheduler with different fairness degrees is insensitive to the network load under the full buffer traffic model, but sensitive under the finite buffer traffic model.
Moreover, with the realistic 3G LTE network model, the same trends are found as with the simple OFDMA network model.
Based on these results, it is concluded that designing scheduling algorithms for NRT services for OFDMA systems under the full buffer model assumption may not yield the desired performance benefits in realistic scenarios where the finite buffer model is more appropriate due to its birth and death process.
As a consequence of these findings, we propose a scheduling algorithm based on the gradient scheduler combined with a simple utility function. This proposal is able to achieve a similar user throughput at 5% outage compared to the Proportional Fair scheduler but an average user throughput gain that reaches up to 60-70% for heavy offered load conditions.
The rest of this article is organized as follows. Section 2 describes the simple OFDMA system model and Section 3 presents the adopted scheduling algorithm based on Utility Theory. Section 4 presents the full buffer and the finite buffer traffic models as well as KPIs. Section 5 evaluates the influence of the full and finite buffer traffic models on an OFDMA network. Additionally, Section 6 includes a similar analysis in a realistic 3G LTE network. Finally, Section 7 draws the main conclusions.
2. Simple OFDMA system model
is computed as the sum of the interference power I j, k [n, s] received by user k from every j th interfering cell based on the deterministic path loss, shadow fading and interfering base station transmission power.
where R k [n,s] denotes the achievable transmission rate, W represents the subcarrier bandwidth and ρ is the CP efficiency factor.
There is no user multiplexing per subcarrier, and hence at maximum one user is assigned on each subcarrier. In equation , note that there can be unassigned subcarriers only when the finite buffer model is used. With the full buffer model, there is always data to transmit, and .
where T is the TTI duration.
3. Packet scheduling based on utility theory
where U(·) is a utility function that quantifies the degree to which a network satisfies the user demands of NRT applications  based on the user throughput r k .
where ρw = T/Tw, Tw is the length of the averaging window, and r k [n] is the actual transmitted data rate for user k at time n across its assigned subcarriers.
where U α (·) is the α-fair function.
As described in [14, 27, 34, 35], the application of the α-fair function as a utility function in the objective (11) leads to a resource allocation where the parameter α controls the degree of fairness of the allocation. When α = 0, the network throughput is maximized at the expenses of the throughput starvation of some users, when α = 1, the throughput allocation is proportionally fair, and when α → ∞ the allocation corresponds to max-min fairness.
for α ≥ 0. This scheduling discipline will be hereafter referred to as α-fair gradient scheduler.
It is relevant to mention that the optimal solution of objective (11) changes over time because both the channel capacity and the set of active users M vary with time . In fact, the scheduling decisions determine the user departure process, which ultimately influences the set of active users M. Therefore, it is questionable if the optimal solution to the objective (11) over a long observation period is to continuously target at the "present" optimal solution by moving in the direction of the gradient as in (12).
4. Description of the full buffer traffic model, finite buffer traffic model, and KPIs
4.1. Full buffer traffic model
The so-called full buffer traffic model is widely used in the performance evaluation of wireless networks [11–13]. It is characterized by the fact that the buffers of the users' data flows always have unlimited amount of data to transmit  and, therefore, transmission of their data payloads never finishes. This implies that q k [n] = ∞ ∀k and ∀n in Equation (10). In addition, there is no user arrival process in the full buffer model and, therefore, the number of users does not vary.
In simulation-based evaluations, typically, during a simulation run the locations of the users are kept invariable and therefore multiple independent runs (snapshots) are required with the objective of sampling the geographical distribution of the users in the footprint area. At the end of the process, the statistics are collected from all the simulated snapshots. In this study, it will be assumed that the number of users in the cell for each snapshot follows a Poisson distribution.
In theoretical investigations, this model allows a simplified analysis.
4.2. Finite buffer traffic model
The so-called finite buffer traffic model is characterized by the fact that the users have a finite amount of data to transmit or receive. Once the data transmission is finished, the session is also finished and the user is removed from the system. For simplicity purposes, the session is typically composed of a single data payload of a given size , and for the same reason, the effects of higher layer protocols such as TCP, HTTP, are not considered. The model includes a process of user arrivals of a given rate λ that provides the instants when the users arrive in the system and start their data transmission or reception. This process models the fact that all users in the OFDMA network are not simultaneously active, but they rather become active when they demand the download/upload of data [18–21]. This study models the user arrivals with a Poisson process (i.e. exponentially distributed inter-arrival times). Note that the sum of a large number of independent stationary renewal processes tends to a Poisson process .
With the finite buffer traffic model, a user stays in the system from the instant of the arrival until the data transmission is finished. This period depends on the throughput achieved by the user, and therefore the instantaneous number of users in the system depends on how the data rates are assigned by the base station. With this model, as the number of users in the cell instantaneously varies with the time, there exist temporary periods when the number of users reach crests caused by peaks of user arrivals or accumulation of very poor channel quality users.
In simulation-based evaluations, with the finite buffer traffic model typically a single simulation run is executed , but it should be long enough to collect sufficient statistics for accurate sampling of relevant performance metrics. Sampling the load peaks requires a large amount of statistics and therefore increases the complexity compared to the full buffer model.
We consider that the results obtained under the finite buffer model are more generalizable than those obtained with the full buffer because the latter disregards the fact that in a realistic OFDMA network all users do not simultaneously request the download/upload of an infinite amount of data.
4.3. Overview of KPIs
The selection of the KPIs is a decisive issue because the final scheduling design will depend on them. This study makes use of KPIs proposed in [8–10] that are widely applied in performance evaluations of OFDMA systems because they provide information about the average spectral efficiency and throughput outage levels. For convenience, the KPIs will be defined as relative gains over the Proportional Fair case:
In addition, a utility metric, that assesses the degree of user satisfaction, will be considered for the evaluation:
5. Performance evaluation under a simple OFDMA network model
5.1. Experimental set-up
The OFDMA system model presented in Section 2 has precisely been reproduced in a quasi-dynamic network simulator. Users are uniformly distributed in area, they do not to vary their geographical location with time, and therefore, their deterministic path loss and shadow fading remains invariable during the lifetime of the user. The small scale fading of the channel taps is modeled according to the ITU Typical Urban (TU) power delay profile  assuming a sampling frequency fs = 15.36 MHz.
For the full buffer model, a total of 1,000 snapshots are executed to have an accurate sampling of the geographical distribution of the users in the cell as well as the distribution of the number of users (i.e., Poisson distribution). Different average cell loads are achieved by varying the average of the Poisson distribution. Every snapshot is set to last 10 s, which is found to be sufficient for the relevant statistics to converge.
OFDMA network model
OFDMA subcarrier spacing
Number of useful subcarriers
Number of points of FFT
Cyclic prefix length
TTI duration (simulation step)
Number of OFDM symbols per TTI
Path loss exponent
Std of shadow fading
ITU typical urban 20 paths
Base station transmission power
Number of interfering cells
Antenna rx scheme
Bandwidth granularity of scheduling assignments
5.2. Performance results assuming a fixed network load
This section presents the performance of the α-fair gradient scheduling algorithm under the finite and full buffer traffic models for different α values (see (16)) assuming a fixed offered load. For the comparison, the Poisson distribution of the full buffer traffic model is set to have an average number of users in the cell equal to 15. For the finite buffer, the input of the model is the average user arrival rate which decides the offered cell load of the Poisson arrival process. Here, the offered cell load is selected to be equal to 23 Mbps because it yields a load level in the system (in terms of average number of users) in the same order of magnitude as the one used in the case of the full buffer model. This criterion will also be used in the following sections to decide the offered cell load for the finite buffer model.
Figure 2b depicts the user throughput at 5% outage gain over Proportional Fair G O . For the full buffer traffic model, the user throughput at 5% outage increases for higher values of α, thereby, as expected, improving the degree of fairness provided by the α-fair gradient scheduling algorithm (as already shown in ). On the other hand, when the finite buffer model is used, the user throughput at 5% outage exhibits a maximum at α ≈ [0.75-1], and α values outside this range cause a performance loss. The reason is that, with the finite buffer model, giving more resources to users under favorable channel quality conditions (α < 1) reduces their alive time, and therefore users with poor channel quality can get sufficient resources after the departure of the favorable ones, whereas, in the case of full buffer model, users are always competing for resources.
Then, the results show that under the full buffer traffic model there exists a trade-off between the average user throughput and the user throughput at 5% outage, but under the finite buffer model this trade-off does not exist because the user throughput at 5% outage presents an optimum performance in the range α ≈ [0.75-1].
Consequently, the results show that the α-fair gradient scheduling algorithm maximizes the average utility as long as the full buffer traffic model is used. But, when the finite buffer traffic model is used, the gradient scheduling algorithm does not keep this property.
5.3. Performance results under different network load conditions
This section analyzes the sensitivity of the performance of the α-fair gradient scheduler to the network load under the finite and full buffer traffic models. For this purpose, two versions of the α-fair gradient scheduling algorithm will be evaluated under various cell loads: the first is α = 1, which corresponds to the well-known Proportional Fair scheduling; the second is α = 0.6, which provides interesting results from the viewpoint of the combined performance of the average and 5% outage user throughput. Both scheduling algorithms are evaluated under the full buffer traffic model with the following average number of users in the cell N = [8, 11, 14, 17, 20]. For the finite buffer model, the following loads are considered L = [20, 21, 22, 23, 24, 25], where L represents the offered cell load measured in Mbps.
Hence, the results show that the relative comparison of KPIs, such as the average user throughput or the user throughput at 5% outage, between two different α values of the α-fair gradient scheduling algorithm, is insensitive to the network load under the full buffer traffic model. However, the same relative comparison is indeed load sensitive under the finite buffer traffic model.
6. Performance evaluation under a realistic OFDMA network model: 3G LTE
The previous section has shown that the α-fair gradient scheduling algorithm behaves significantly different with full or finite buffer model under a simple OFDMA system model. This section aims at providing the evaluation with realistic system-level assumptions. For this purpose, the downlink of an LTE system is taken as a study case. The performance is also investigated here with various load conditions.
3G LTE network model
Number of sub-carriers per resource block
Pilot and control channel overhead
Modulation and coding rate settings
QPSK: 1/3, 1/2, 2/3
16QAM: 1/2, 2/3, 4/5
64QAM: 2/3, 4/5
Ideal chase combining
Number of SAW processes
CQI measurement sub band
360 kHz (2 RBs)
CQI measurement time window
CQI reporting interval
CQI feedback delay
CQI quantization step
Error in CQI estimation
Gaussian (zero mean, 1 dB std)
BLER target for Outer Loop LA
Admission control constraint
Unlimited number of users per cell
6.1. Performance results assuming a fixed network load
As in previous section, we choose again α = 0.6 for the study with realistic assumptions and Proportional Fair (α = 1) as reference. For the full buffer model, the average number of users is set N = 20, which is selected to be higher than in Section 5.1 (N = 15) due to the better performance of α = 0.6 and α = 1. For finite buffer, the offered load L = 11 Mbps is used.
6.2. Performance results under different network load conditions
The performance with different cell loads is also evaluated under the 3G LTE network model. For the full buffer model, we vary the average number of users in the cell N = [8, 11, 14, 17, 20]; for the finite buffer model, the following offered cell loads are evaluated L = [8, 9, 10, 10.5, 11, 11.5] where L represents the offered cell load measured in Mbps.
This article has studied the impact of the traffic model on the design of the OFDMA scheduling algorithms. Two simple traffic models proposed by the NGMN Alliance, the IEEE, and the 3GPP have been considered: the full buffer and the finite buffer models. With the first model, the number of users in the cell is constant and the buffers of the users' data flows always have unlimited amount of data to transmit. The second model includes a user birth process and every user is assigned a finite payload to transmit or receive.
In particular, this article has evaluated if the properties and trends of utility-based scheduling under the full buffer model also hold when the finite buffer is applied. Initially, a simple OFDMA network model has been used for the evaluation. The results have shown that the optimization objective of maximizing the aggregate utility through a gradient scheduling algorithm can only be achieved when the less realistic full buffer model is used. When the finite buffer model is applied, the gradient scheduling algorithm does not keep this property. In addition, under the full buffer model, the gradient scheduler exhibits a trade-off between average user throughput and the user throughput at 5% outage. However, under the more realistic finite buffer traffic model, the user throughput at 5% outage presents an optimum performance at around α = [0.6-1.0], and hence the gradient scheduler does not exhibit the mentioned trade-off. Moreover, the relative comparison of KPIs, such as the average user throughput or the user throughput at 5% outage, between versions of the α-fair gradient scheduling algorithm (with different fairness levels) is insensitive to the network load under the full buffer traffic model, but sensitive under the finite buffer traffic model. Furthermore, a realistic 3G LTE network model has also been used for the evaluation, where same trends are found as with the simple OFDMA network model.
Based on the presented findings, we propose as scheduling algorithm the gradient scheduler combined with the α-fair utility function and α = 0.6 because it produces a similar user throughput at 5% outage, an average user throughput gain that reaches up to 60-70% for heavy offered load conditions, and an increased aggregate utility (assuming that the utility function is the Natural Logarithm) compared to the Proportional Fair algorithm. This performance gain could be considered in the implementation of QoS scheduling algorithms, which may benefit from incorporating this simple tuning.
These findings may also be an interesting trigger to additional investigations that further analyze if the presented trends also hold in other channel-unaware schedulers (e.g., Round Robin-based schedulers) in OFDMA systems.
This study was supported by the funding of the Ministerio de Ciencia e Innovacion of the Spanish Government under the research project TIN2010-20323.
- 3GPP: Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall description. Tech Spec 36.300 v10.5.0 2011. Available: http://www.3gpp.orgGoogle Scholar
- IEEE std: 802.16TM-2004, IEEE Standard for Local and Metropolitan Area Networks. Part 16: Air Interface for Fixed Broadband Wireless Access Systems 2004.Google Scholar
- IEEE Std: 802.16e™-2005, IEEE Standard for Local and Metropolitan Area Networks. Part 16: Air Interface for Fixed and Mobile Broadband Wireless Access Systems 2006.Google Scholar
- 3GPP: Feasibility study for Further Advancements for E-UTRA (LTE-Advanced). Tech Rep 36.912 v10.0.0 2011. Available: http://www.3gpp.orgGoogle Scholar
- IEEE 802.16m-09/0034r4, IEEE 802.16m System Description Document (SDD) 2010.http://www.ieee802.org Available:
- Strinati EC, Corbellini G, Ktenas D: HYGIENE scheduling for OFDMA wireless cellular networks. In IEEE 69th Vehicular Technology Conference, 2009. VTC Spring 2009. Barcelona, Spain; 2009:1-5.Google Scholar
- Wang F, Ghosh A, Sankaran C, Fleming P, Hsieh F, Benes S: Mobile WiMAX system: performance and evolution. IEEE Commun Mag 2008, 46(10):41-49.View ArticleGoogle Scholar
- NGMN Alliance: Next Generation Mobile Networks Radio Access Performance Evaluation Methodology. White paper 2008. Available: http://www.ngmn.orgGoogle Scholar
- IEEE 802.16 Broadband Wireless Access WG, IEEE 802.16m Evaluation Methodology Document 2008.http://www.ieee802.org Available:
- 3GPP: Further Advancements for E-UTRA Physical Layer Aspects. Tech Rep 36.814 v9.0.0 2010. Available: http://www.3gpp.orgGoogle Scholar
- Long Term Evolution (LTE): Overview of LTE Air-Interface. Technical White Paper 2007. Available: http://www.motorola.comGoogle Scholar
- Wei N, Pokhariyal A, Sorensen TB, Kolding T, Mogensen P: Performance of spatial division multiplexing MIMO with frequency domain packet scheduling: from theory to practice. IEEE Trans JSAC 2008, 26(6):890-900.Google Scholar
- Gao Y, Chen Li, Zhang X, Jiang Y: Performance evaluation of mobile WiMAX with dynamic overhead. Proc Of IEEE Vehicular Technology Conference (VTC), 2008 2008.Google Scholar
- Rodrigues E, Casadevall F: Resource allocation and packet scheduling in OFDMA-based cellular networks. In 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications, 2009. CROWNCOM '09. Hannover, Germany; 2009:1-6.Google Scholar
- Gross J, Bohge M: Dynamic mechanisms in OFDM wireless systems: a survey on mathematical and system engineering contributions. TKN Technical Report 2006.Google Scholar
- Nguyen T-D, Han Y: A proportional fairness algorithm with QoS provision in downlink OFDMA systems. IEEE Commun Lett 2006, 10(11):760-762.View ArticleGoogle Scholar
- Song G, Li Y: Cross-layer optimization for OFDM wireless network--Part I and Part II. IEEE Trans Wirel Commun 2005, 4(2):614-634.MathSciNetView ArticleGoogle Scholar
- Karpinski S, Belding EM, Almeroth KC: Towards realistic models of wireless workload. In Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks and Workshops 2007. WiOpt. Limassol, Cyprus; 2007.Google Scholar
- Divgi G, Chlebus E: User and traffic characteristics of a commercial nationwide Wi-Fi hotspot network. In IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, 2007. PIMRC 2007. Athens, Greece; 2007:1-5.View ArticleGoogle Scholar
- Molina M, Castelli P, Foddis G: Web traffic modeling exploiting TCP connections' temporal clustering through HTML-REDUCE. IEEE Netw 2001, 14: 46-55.View ArticleGoogle Scholar
- Paxson V, Floyd S: Wide area traffic: the failure of Poisson modeling. IEEE/ACM Trans Netw 1995, 3(3):226-244. 10.1109/90.392383View ArticleGoogle Scholar
- Johansson K, Bergman J, Gerstenberger D, Blomgren M, Wallen A: Multi-carrier HSPA evolution. In Proc of IEEE Vehicular Technology Conference (VTC), 2009. Barcelona, Spain; 2009:1-5.View ArticleGoogle Scholar
- Elayoubi SE, Fourestie B: On frequency allocation in 3G LTE systems. In Proc of The 17th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2006. Helsinki, Finland; 2006:1-5.View ArticleGoogle Scholar
- Kela P, Puttonen J, Kolehmainen N, Ristaniemi T, Henttonen T, Moisio M: Dynamic packet scheduling performance in UTRA long term evolution downlink. In 3rd International Symposium on Wireless Pervasive Computing, 2008. ISWPC 2008. Santorini; 2008:308-313.View ArticleGoogle Scholar
- Wengerter C, Ohlhorst J, von Elbwart AGE: Fairness and throughput analysis for generalized proportional fair frequency scheduling in OFDMA. In Proc Of IEEE Vehicular Technology Conference(VTC), 2005. Volume 3. Stockholm, Sweden; 2005:1903-1907.View ArticleGoogle Scholar
- Lee BG, Park D, Seo H: Wireless Communications Resource Management Wiley. Hoboken, NJ, USA; 2009.Google Scholar
- Sadiq B, Madan R, Sampath A: Downlink scheduling for multiclass traffic in LTE. EURASIP J. Wirel Commun Netw 2009, 2009: 1-18.Google Scholar
- Kelly F: Charging and rate control for elastic traffic (corrected version). Eur Trans Telecommun 1997, 8(1):33-37. 10.1002/ett.4460080106View ArticleGoogle Scholar
- Pokhariyal A, Pedersen KI, Monghal G, Kovacs IZ, Rosa C, Kolding TE, Mogensen PE: HARQ aware frequency domain packet scheduler with different degrees of fairness for the UTRAN long term evolution. In IEEE 65th Vehicular Technology Conference, 2007. VTC2007-Spring. Dublin, Ireland; 2007:2761-2765.View ArticleGoogle Scholar
- Holma H, Toskala A: LTE for UMTS--OFDMA and SC-FDMA Based Radio Access Wiley. Hoboken, NJ, USA; 2009.Google Scholar
- Ameigeiras P, Ramos-Munoz JJ, Navarro-Ortiz J, Mogensen P, Lopez-Soler JM: QoE oriented cross-layer design of a resource allocation algorithm in beyond 3G systems. Comput Commun J 2010, 33(5):571-582. 10.1016/j.comcom.2009.10.016View ArticleGoogle Scholar
- Song G, Li Y: Utility-based resource allocation and scheduling in OFDM-based wireless broadband networks. IEEE Commun Mag 2005, 43(12):127-134.MathSciNetView ArticleGoogle Scholar
- Mo J, Walrand J: Fair end-to-end window-based congestion control. IEEE/ACM Trans Netw 2000, 8(5):556-567. 10.1109/90.879343View ArticleGoogle Scholar
- Liu J, Proutiere A, Yi Y, Chiang M: HV Poor, Stability, fairness, and performance: a flow-level study on nonconvex and time-varying rate regions. IEEE Trans Inf Theory 2009, 55(8):3437-3456.MathSciNetView ArticleGoogle Scholar
- Lan T, Kao D, Chiang M, Sabharwal A: An axiomatic theory of fairness in network resource allocation.2009. [http://arxiv.org/abs/0906.0557v4]Google Scholar
- Hosein PA: QoS control for WCDMA high speed packet data. In 4th International Workshop on Mobile and Wireless Communications Network, 2002. Stockholm, Sweden; 2002:169-173.View ArticleGoogle Scholar
- Kleinrock L: Queuing Systems, Volume I: Theory Wiley. Hoboken, NJ, USA; 1975.Google Scholar
- Pokhariyal A: Downlink frequency-domain adaptation and scheduling--a case study based on the UTRA long term evolution. Ph D Thesis Dissertation 2007.Google Scholar
- 3GPP: Deployment aspects. Tech Rep 25.943 v9.0.0 2009. Available: http://www.3gpp.orgGoogle Scholar
- Brueninghaus K, Astely D, Salzer T, Visuri S, Alexiou A, Karger S, Seraji GA: Link performance models for system level simulations of broadband radio access systems. In Proc IEEE PIMRC. Volume 4. Berlin, Germany; 2005:2306-2311.Google Scholar
- Pedersen KI, Monghal G, Kovacs IZ, Kolding TE, Pokhariyal A, Frederiksen F, Mogensen P: Frequency domain scheduling for OFDMA with limited and noisy channel feedback. In Proc of IEEE Vehicular Technology Conference(VTC), 2007. Baltimore, MD; 2007:1792-1796.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.