# Optimal Downlink Scheduling for Heterogeneous Traffic Types in LTE-A Based on MDP and Chance-Constrained Approaches

- 293 Downloads
- 1 Citations

## Abstract

The current mobile broadband market experiences major growth in data demand and average revenue loss. To remain profitable from the perspective of a service provider (SP), one needs to maximize revenue as much as possible by making subscribers satisfied within the limited budget. On the other hand, traffic demands are moving toward supporting the wide range of heterogeneous applications with different quality of service (QoS) requirements. In this paper, we consider two related *packet scheduling* problems, i.e., long-term and short-term approaches in the 4th generation partnership project (3GPP) long term evolution-advanced (LTE-A) system. In the long-term approach, the long-term average revenue of SP subject to the long-term QoS constraints for heterogeneous traffic demands is optimized. The problem is first formulated as a constrained Markov decision process (CMDP) problem, of which the optimal control policy is achieved by utilizing the channel and queue information simultaneously. Subsequently, in the short-term approach, we consider the short-term revenue optimization problem which stochastically guarantees the short-term QoS for heterogeneous traffic demands through a set of chance constraints. To make the proposed chance-constrained programming problem computationally tractable, we use the *Bernstein approximation* technique to analytically approximate the chance constraint as a convex conservative constraint. Finally, the proposed packet scheduling schemes and solution methods are validated via numerical simulations.

## Keywords

Chance-constrained Constrained Markov decision process Bernstein approximation LTE-A Heterogeneous delay requirements## References

- 1.3GPP, TR 25.943, v10.0.0 (2011-04): Technical specification group radio access networks; deployment aspects (release 10) http://www.etsi.org/deliver/etsi_tr/125900_125999/125943/11.00.0060/
- 2.Altman E (1995) Constrained Markov decision processesGoogle Scholar
- 3.Andrews M, Kumaran K, Ramanan K, Stolyar A, Whiting P, Vijayakumar R (2001) Providing quality of service over a shared wireless link. IEEE, Commun Mag 39(2):150–154. doi: 10.1109/35.900644 CrossRefGoogle Scholar
- 4.Balakrishnan R, Canberk B (2014) Traffic-aware QoS provisioning and admission control in ofdma hybrid small cells. IEEE Trans, Veh Technol 63(2):802–810. doi: 10.1109/TVT.2013.2280124 CrossRefGoogle Scholar
- 5.Bertsekas D (1987) Dynamic Programming: Deterministic and Stochastic Models, Pearson Higher Education & Professional Group. http://books.google.com.hk/books?id=-6RiQgAACAAJ
- 6.Cui Y, Lau VKN (2010) Distributive stochastic learning for delay optimal OFDMA power and subband allocation. IEEE Transaction of Signal Procssing 58(9):4848–4858. doi: 10.1109/TSP.2010.2050062 MathSciNetCrossRefGoogle Scholar
- 7.Di Renzo M, Graziosi F, Santucci F (2010) Channel capacity over generalized fading channels: A novel MGF-Based approach for performance analysis and design of wireless communication systems. IEEE Trans Veh Technol 59(1):127–149. doi: 10.1109/TVT.2009.2030894 CrossRefGoogle Scholar
- 8.Kokku R, Mahindra R, Zhang H, Rangarajan S (2013) Cellslice: Cellular wireless resource slicing for active RAN sharing. In: Communication Systems and Networks (COMSNETS), 2013 Fifth International Conference on, pp 1–10. doi: 10.1109/COMSNETS.2013.6465548
- 9.Lee SB, Choudhury S, Khoshnevis A, Xu S, Lu S (2009) Downlink MIMO with frequency-domain packet scheduling for 3GPP LTE. In: IEEE INFOCOM, pp 1269 –1277. doi: 10.1109/INFCOM.2009.5062041
- 10.Nemirovski A, Shapiro A (2006) Convex approximations of chance constrained programs. SIAM J Optim 17:969–996MathSciNetCrossRefMATHGoogle Scholar
- 11.Niafar S, Huang Z, Tsang DHK (2014) An optimal standard-compliant MIMO scheduler for LTE downlink. IEEE Trans Wirel Commun 99:1–10. doi: 10.1109/TWC.2014.012814.131600 Google Scholar
- 12.Powell W (2007) Approximate Dynamic Programming: Solving the Curses of Dimensionality. Wiley Series in Probability and Statistics. Wiley. http://books.google.com.hk/books?id=WWWDkd65TdYC
- 13.Ruby R, Leung V (2011) Towards QoS assurance with revenue maximization of LTE uplink scheduling. In: Communication Networks and Services Research Conference (CNSR), 2011 Ninth Annual, pp 202–209. doi: 10.1109/CNSR.2011.37, pp 202–209
- 14.Sesia S, Toufik I, Baker M (2011) LTE-The UMTS Long Term Evolution: From Theory to Practice. Wiley. http://books.google.com.hk/books?id=beIaPXLzYKcC
- 15.Simon M, Alouini M (2000) Digital communication over fading channels: a unified approach to performance analysis. Wiley series in telecommunications and signal processing. Wiley. http://books.google.com.hk/books?id=CvhSAAAAMAAJ
- 16.Tan C, Beaulieu N (2000) On first-order Markov modeling for the Rayleigh fading channel. IEEE Trans Commun 48(12):2032–2040. doi: 10.1109/26.891214 CrossRefGoogle Scholar
- 17.Xu W, Tajer A, Wang X, Alshomrani S (2014) Power allocation in MISO interference channels with stochastic CSIT. IEEE Trans Wirel Commun 13(3):1716–1727. doi: 10.1109/TWC.2014.012814.131600 CrossRefGoogle Scholar
- 18.Zhang H, Prasad N, Rangarajan S (2012) MIMO downlink scheduling in LTE systems. In: IEEE INFOCOM, pp 2936 – 2940Google Scholar