Downlink Radio Resource Management Through CoMP and Carrier Aggregation for LTE-Advanced Network


Long Term Evolution-Advanced (LTE-A) offers several new technologies to improve the performance of the user. However, poor received signal and interference from adjacent cells in the cell-edge area can reduce the efficiency of using individual technology. Therefore, the cell-edge users have lower throughput compared to the other users in the cell and LTE-A standard. An efficient downlink radio resource management scheme is proposed in this paper by combining the coordinated multipoint transmission and reception technique along with carrier aggregation technique to achieve higher throughput for the cell-edge user and better overall performance. The proposed method jointly transmits multiple component carriers to the cell-edge user from different cells to increase the bandwidth, strengthen the received signal, and reduce the interference while it satisfies several constraints. Modified largest weighted delay first packet scheduling algorithm is deployed for resource allocation, which takes into account the delay parameters, the probability of packet loss, and data rates of the user. The obtained system-level simulation results show that the proposed method significantly enhances the throughput performances, spectral efficiency, and fairness index, compared with the existing conventional methods.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14


  1. 1.

    Requirements for Further Advancements for Evolved Universal Terrestrial Radio Access (E-UTRA) (LTE-Advanced), Technical Report 36.913 V11.0.0, 3rd Generation Partnership Project (3GPP): Valbonne, France, September 2012.

  2. 2.

    Akyildiz, I. F., Gutierrez-Estevez, D. M., Balakrishnan, R., & Chavarria-Reyes, E. (2014). LTE-Advanced, and the evolution to Beyond 4G (B4G) systems. Physical Communication,10, 31–60.

    Google Scholar 

  3. 3.

    Lu, Z., Pan, Q., & Wang, L. (2016). Wen X Overload Control for Signaling Congestion of Machine-Type Communications in 3GPP Networks. PLoS ONE,11(12), e0167380.

    Google Scholar 

  4. 4.

    Lee, D., Seo, H., Clerckx, B., Hardouin, E., Mazzarese, D., Nagata, S., et al. (2012). Coordinated multipoint transmission and reception in LTE-advanced: deployment scenarios and operational challenges. IEEE Communications Magazine,50(2), 148–155.

    Google Scholar 

  5. 5.

    Irmer, R., Droste, H., Marsch, P., Grieger, M., Fettweis, G., Brueck, S., et al. (2011). Coordinated multipoint: concepts, performance, and field trial results. IEEE Communications Magazine,49(2), 102–111.

    Google Scholar 

  6. 6.

    Sawahashi, M., Kishiyama, Y., Morimoto, A., Nishikawa, D., & Tanno, M. (2010). Coordinated multipoint transmission/reception techniques for LTE-advanced [Coordinated and Distributed MIMO]. IEEE Wireless Communications,17(3), 26–34.

    Google Scholar 

  7. 7.

    Yuan, G., Zhang, X., Wang, W., & Yang, Y. (2010). Carrier aggregation for LTE-advanced mobile communication systems. Communications Magazine, IEEE.,48(2), 88–93.

    Google Scholar 

  8. 8.

    Ghosh, A., Ratasuk, R., Mondal, B., Mangalvedhe, N., & Thomas, T. (2010). LTE-advanced: next-generation wireless broadband technology [Invited Paper]. IEEE Wireless Communications,17(3), 10–22.

    Google Scholar 

  9. 9.

    Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN), Overall Description, Stage 2, Technical Specification 36.300 V11.3.0, 3rd Generation Partnership Project (3GPP): Valbonne, France, September 2012.

  10. 10.

    Andrews, M., Kumaran, K., Ramanan, K., Stolyar, A., Whiting, P., & Vijayakumar, R. (2001). Providing quality of service over a shared wireless link. IEEE Communications Magazine,39(2), 150–154.

    Google Scholar 

  11. 11.

    Li, Z., Liu, Y., Zhang, Y., & Wu, W. (2016). Downlink CoMP resource allocation based on limited backhaul capacity. China Communications.,13(4), 38–48.

    Google Scholar 

  12. 12.

    Chung, W.-C., Chang, C.-J., & Teng, H.-Y. (2014). A green radio resource allocation scheme for LTE-A downlink systems with CoMP transmission. Wireless Networks,20(6), 1409–1420.

    Google Scholar 

  13. 13.

    Ginting, D., Fahmi, A., & Kurniawan, A., eds. (2015). Performance evaluation of inter-cell interference of LTE-A system using carrier aggregation and CoMP techniques. In 2015 9th international conference on telecommunication systems services and applications (TSSA). IEEE.

  14. 14.

    Xiaoyong, W., Dengkun, X., & Xiaojun, J., eds. (2009). A norvel power allocation algorithm under CoMP with CA. In 2nd IEEE international conference on broadband network and multimedia technology, 2009 IC-BNMT’09 (pp. 66–70).

  15. 15.

    Jia, J., Deng, Y., Chen, J., Aghvami, A. H., & Nallanathan, A. (2017). Availability analysis and optimization in CoMP and CA-enabled HetNets. IEEE Transactions on Communications, 65(6), 2438–2450.

    Google Scholar 

  16. 16.

    Yu, J., & Yin, C. (2016). Block-level resource allocation with limited feedback in multicell cellular networks. Journal of Communications and Networks.,18(3), 420–428.

    Google Scholar 

  17. 17.

    Zhao, F., Miao, Y., & Chen, H. (2017). Joint beamforming and power control for auction-based spectrum allocation in CoMP systems. Ad Hoc Networks, 58, 191–197.

    Google Scholar 

  18. 18.

    Yu, W., Kwon, T., & Shin, C. (2013). Multicell coordination via joint scheduling, beamforming, and power spectrum adaptation. IEEE Transactions on Wireless Communications,12(7), 1–14.

    Google Scholar 

  19. 19.

    Mosleh, S., Liu, L., & Zhang, C. (2016). Proportional-fair resource allocation for coordinated multi-point (CoMP) transmission in LTE-advanced. IEEE Transactions on Wireless Communications,15(8), 5355–5367.

    Google Scholar 

  20. 20.

    Checko, A., Christiansen, H. L., Yan, Y., Scolari, L., Kardaras, G., Berger, M. S., et al. (2015). Cloud RAN for mobile networks—A technology overview. IEEE Communications Surveys and Tutorials.,17(1), 405–426.

    Google Scholar 

  21. 21.

    Peng, M., Li, Y., Jiang, J., Li, J., & Wang, C. (2014). Heterogeneous cloud radio access networks: A new perspective for enhancing spectral and energy efficiencies. IEEE Wireless Communications,21(6), 126–135.

    Google Scholar 

  22. 22.

    Zhang, H., Liu, H., Jiang, C., Chu, X., Nallanathan, A., & Wen, X. (2015). A practical semidynamic clustering scheme using affinity propagation in cooperative picocells. IEEE Transactions on Vehicular Technology,64(9), 4372–4377.

    Google Scholar 

  23. 23.

    Li, J., Peng, M., Cheng, A., Yu, Y., & Wang, C. (2014). Resource allocation optimization for delay-sensitive traffic in Fronthaul constrained cloud radio access networks. IEEE Systems Journal,99, 1–12.

    Google Scholar 

  24. 24.

    Fu, S., Wen, H., Wu, J., & Wu, B. (2015). Energy-efficient precoded coordinated multi-point transmission with pricing power game mechanism. IEEE Systems Journal,99, 1–10.

    Google Scholar 

  25. 25.

    Huq, K. M. S., Mumtaz, S., Rodriguez, J., & Aguiar, R. L. (2014). A novel energy efficient packet-scheduling algorithm for CoMP. Computer Communications,50, 53–63.

    Google Scholar 

  26. 26.

    Chand, P., Mahapatra, R., & Prakash, R. (2016). Energy efficient radio resource management for heterogeneous wireless network using CoMP. Wireless Networks,22(4), 1093–1106.

    Google Scholar 

  27. 27.

    Huq, K. M. S., Mumtaz, S., Bachmatiuk, J., Rodriguez, J., Wang, X., & Aguiar, R. L. (2015). Green HetNet CoMP: Energy efficiency analysis and optimization. IEEE Transactions on Vehicular Technology,64(10), 4670–4683.

    Google Scholar 

  28. 28.

    Määttänen, H.-L., Hämäläinen, K., Venäläinen, J., Schober, K., Enescu, M., & Valkama, M. (2012). System-level performance of LTE-Advanced with joint transmission and dynamic point selection schemes. EURASIP Journal on Advances in Signal Processing,2012(1), 1–18.

    Google Scholar 

  29. 29.

    Zhao, J., Quek, T. Q., & Lei, Z. (2013). Coordinated multipoint transmission with limited backhaul data transfer. IEEE Transactions on Wireless Communications,12(6), 2762–2775.

    Google Scholar 

  30. 30.

    Okamawari, T., Zhang, L., Nagate, A., Hayashi, H., & Fujii, T., eds. (2011). Design of control architecture for downlink CoMP joint transmission with inter-eNB coordination in next generation cellular systems. In IEEE vehicular technology conference (VTC Fall) (pp. 1–5).

  31. 31.

    Park, S. Y., Choi, J., & Love, D. J. (2014). Multicell cooperative scheduling for two-tier cellular networks. IEEE Transactions on Communications,62(2), 536–551.

    Google Scholar 

  32. 32.

    Abdelaal, R. A., Elsayed, K. M., & Ismail, M. H. (2015). Optimized joint power and resource allocation for coordinated multi-point transmission for multi-user LTE-advanced systems. Wireless Personal Communications,83(4), 2497–2518.

    Google Scholar 

  33. 33.

    Fu, S., Wu, B., Wen, H., Ho, P.-H., & Feng, G. (2014). Transmission scheduling and game theoretical power allocation for interference coordination in CoMP. IEEE Transactions on Wireless Communications,13(1), 112–123.

    Google Scholar 

  34. 34.

    Zhou, W., Chen, W., Tan, Z., Chen, S., & Zhang, Y. (2011). A modified RR scheduling scheme based CoMP in LTE-A system. In IET international conference on communication technology and application (ICCTA 2011) (pp. 176–180).

  35. 35.

    Wireless technology evolution towards 5G: 3gpp release 13 to release 15 and beyond, White paper 5G America, February, 2017, Available Online: (Accessed on 26 May 2020).

  36. 36.

    Kumar, S. (2018). Energy detection in hoyt/gamma fading channel with micro-diversity reception. Wireless Personal Communications,101(2), 723–734.

    Google Scholar 

  37. 37.

    Kumar, S. (2018). Performance of ED based spectrum sensing over α–η–μ fading channel. Wireless Personal Communications,100(4), 1845–1857.

    Google Scholar 

  38. 38.

    Rasethuntsa, T. R., & Kumar, S. (2019). An integrated performance evaluation of ED-based spectrum sensing over α − κ − μ and α − κ − μ-Extreme fading channels. Transactions on Emerging Telecommunications Technologies.,30(5), e3569.

    Google Scholar 

  39. 39.

    Kumar, S., Chauhan, P. S., Raghuwanshi, P., & Kaur, M. (2018). ED performance over α-η-μ/IG and α-κ-μ/IG generalized fading channels with diversity reception and cooperative sensing: A unified approach. AEU-International Journal of Electronics and Communications.,97, 273–279.

    Google Scholar 

  40. 40.

    Kumar, S., Kaur, M., Singh, N. K., Singh, K., & Chauhan, P. S. (2018). Energy detection based spectrum sensing for gamma shadowed α–η–μ and α–κ–μ fading channels. AEU-International Journal of Electronics and Communications.,93, 26–31.

    Google Scholar 

  41. 41.

    Stolyar, A. L., & Ramanan, K. (2001). Largest weighted delay first scheduling: Large deviations and optimality. Annals of Applied Probability,11(1), 1–48.

    MathSciNet  MATH  Google Scholar 

  42. 42.

    General Packet Radio Service (GPRS) enhancements for Evolved Universal Terrestrial Radio Access Network (E-UTRAN) access, TS 23.401 V10.6.0, 3rd Generation Partnership Project (3GPP): Valbonne, France, December 2011.

  43. 43.

    Lee, K. K., & Chanson, S. T. (2002). Packet loss probability for real-time wireless communications. IEEE Transactions on Vehicular Technology,51(6), 1569–1575.

    Google Scholar 

  44. 44.

    Rupp, M., Schwarz, S., & Taranetz, M. (2016). The Vienna LTE-advanced simulators. Berlin: Springer.

    Google Scholar 

  45. 45.

    Vienna LTE-A simulators, Institute of Telecommunications, Vienna University of Technology, Austria. Available Online: (Accessed on 26 May 2020)

  46. 46.

    Evolved Universal Terrestrial Radio Access (E-UTRA), Physical Layer Procedures, Technical Specification 36.213 V11.0.0, 3rd Generation Partnership Project (3GPP): Valbonne, France, September 2012.

  47. 47.

    Jain, R., Chiu, D.-M., & Hawe, W. R. (1984). A quantitative measure of fairness and discrimination for resource allocation in shared computer system: Eastern Research Laboratory. MA: Digital Equipment Corporation Hudson.

    Google Scholar 

  48. 48.

    Chayon, H., Dimyati, K., Ramiah, H., & Reza, A. (2017). An improved radio resource management with carrier aggregation in LTE advanced. Applied Sciences,7(4), 394.

    Google Scholar 

  49. 49.

    Nasralla, M. M., Hewage, C. T., & Martini, M. G. (2014). Subjective and objective evaluation and packet loss modeling for 3d video transmission over LTE networks. In International conference on telecommunications and multimedia (TEMU) (pp. 254–259).

  50. 50.

    Anchora, L., Canzian, L., Badia, L., & Zorzi, M. (2010). A characterization of resource allocation in LTE systems aimed at game theoretical approaches. In 15th IEEE international workshop on computer aided modeling, analysis and design of communication links and networks (CAMAD) (pp. 47–51).

Download references

Author information



Corresponding author

Correspondence to Hasibur Rashid Chayon.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.



The Markov chain model has been used by previous researchers [49, 50] in the LTE system to model the correlated errors in different ways. The Markov model has two states- success and failure, as in Fig. 15. In success state, the transmitter can send a packet successfully, whereas packet transmission can be failed in a failure state. The probability of packet loss δ can be calculated by considering maximum delay, probability of transforming from one state to another state, packet arrival rate, and error probability [43].

Fig. 15

Two-state Markov chain model

For simplicity, notation of user i will not include in the following derivation. The δ can be written as

$$ \delta_{{}} = \frac{{\pi_{\tau ,1} }}{\beta } $$

Here πτ,1 is the steady-state probability of (τ, 1) state where packets can be dropped for the extensive delay and β is the arrival rate. πτ,1 can be expressed as follows.

$$ \pi_{\tau ,1} = \frac{1}{{C_{sum} }} $$


$$ C_{sum} \approx \frac{{Wx^{\tau } }}{\lambda \beta } + \frac{{W(1 - x^{\tau } )}}{\psi (1 - x)} + \frac{1}{\beta } $$
$$ C_{sum} \approx \frac{{x^{\tau } W^{2} (\beta - 1) + \lambda (\lambda \beta - W + 2\beta W)}}{\lambda \beta (\lambda \beta - W + \beta W)} $$

Here Csum is the sum of coefficient, λ is the probability from success state to the failure state, \( \Psi \approx 1- {{\upbeta }} \) when λ and β are small. So, from Eqs. (19), (20) and (22) we can write

$$ \delta = \frac{{\pi_{\tau ,1} }}{\beta } \approx \frac{\lambda (\lambda \beta - W + \beta W)}{{x^{\tau } W^{2} (\beta - 1) + \lambda (\lambda \beta - W + 2\beta W)}} $$

Success state: The error probability ε in success state with small error probability scenario is small to make the system usable. Since \( \varepsilon = \lambda /\left( {\lambda + W} \right) \), W is relatively large compare to λ. Thus \( \lambda /W \) is small and \( x \approx 1/\left( {1 - W} \right) \). Therefore, δ can be written as follows.

$$ \delta \approx \frac{\lambda (\lambda /W\;\beta - 1 + \beta )}{{x^{\tau } W(\beta - 1) + \lambda /W(\lambda \beta - W + 2\beta W)}} $$
$$ \delta \approx \frac{\lambda (\beta - 1)}{{x^{\tau } W(\beta - 1)}} $$
$$ \delta \approx \frac{{\lambda (1 - W)^{\tau } }}{W} $$

Since \( \varepsilon \approx \lambda /W \), finally, we, have

$$ \delta = \varepsilon \left( {1 - W} \right)^{\tau } $$

Failure state: In the failure state, ε is relatively large, which can make the system unusable for undue error. Since \( {{\upvarepsilon = \uplambda }}/ ( {{\uplambda + W)}} \) is large, W is relatively small, thus \( \lambda \gg W \). Therefore, we can write

$$ \varepsilon = {\raise0.7ex\hbox{$\lambda $} \!\mathord{\left/ {\vphantom {\lambda {(\lambda + W)}}}\right.\kern-0pt} \!\lower0.7ex\hbox{${(\lambda + W)}$}} \approx {\raise0.7ex\hbox{$\lambda $} \!\mathord{\left/ {\vphantom {\lambda \lambda }}\right.\kern-0pt} \!\lower0.7ex\hbox{$\lambda $}} = 1 $$

The terms βW, xτW2(β − 1) and 2βW from the above Eq. (23) can be neglected as W is relatively small. We can calculate the δ for the failure state with the probability of large error as follows.

$$ \delta \approx \frac{\lambda (\lambda \beta - W + \beta W)}{{x^{\tau } W^{2} (\beta - 1) + \lambda (\lambda \beta - W + 2\beta W)}} \approx 1 \approx \varepsilon $$

Markov model is used to calculate the probability of packet loss in this paper to estimate the variable αi and eventually gets the weighted transmission rate of the user from Eq. (5). Higher packet loss probability with shorter maximum delay makes higher αi value and higher weighted transmission rate value. Therefore, there will be a higher chance for that user to get the RB from the scheduler. Moreover, the Markov model is comparatively less complicated and the parameters of this method match with the parameter of the MLWDF scheduling algorithm, which is used in this paper. Thus, the Markov model is a suitable approach to implement the proposed method.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chayon, H.R., Ramiah, H. Downlink Radio Resource Management Through CoMP and Carrier Aggregation for LTE-Advanced Network. Wireless Pers Commun (2020).

Download citation


  • LTE-Advanced
  • Radio resource management
  • CoMP
  • Carrier Aggregation
  • MLWDF algorithm