Resource Block Allocation Algorithm in CP-OFDM Networks with 5G Characteristics Based on Delay Estimation
Abstract
In this paper, a resource allocation algorithm for wireless communication systems is proposed, which considers the channel transmission quality and data delay. In the proposed approach, the delay value is estimated to decide on the allocation of available radio resources, aiming to reduce the system’s average delay. For the delay estimation, an approach involving concepts of Network Calculus such as service curve and envelope process MFBAP (Multifractal Bounded Arrival Process) is proposed. In view of the recent techniques for 5G communication systems, mmWa- ves propagation, 256-QAM (Quadrature Amplitude Modulation) and carrier aggregation technologies are considered. Simulations are carried out to compare the performance of the proposed resources allocation algorithm with other algorithms in the literature in terms of QoS (Quality of Service) parameters such as delay, throughput, processing time, loss rate and fairness index, verifying the efficiency of the proposed algorithm.
References
3GPP (2011). 3gpp tr 36.931 version 9.0.0 release 9. lte; evolved universal terrestrial radio access (e-utra); radio frequency (rf) requirements for lte pico node b.
3GPP (2016). 3gpp ts 36.213 version 13.0.0 release 13. lte; evolved universal terrestrial radio access (e-utra); physical layer procedures.
3GPP (2017). 3gpp tr 38.901 version 14.0.0 release 14. 5g; study on channel model for frequencies from 0.5 to 100 ghz.
3GPP (2018a). 3gpp ts 38.104 version 15.2.0 release 15. 5g; nr; base station (bs) radio transmission and reception.
3GPP (2018b). 3gpp ts 38.211 version 15.2.0 release 15. 5g; nr; physical channels and modulation.
3GPP (2019). 3gpp ts 38.306 version 15.7.0 release 15. 5g; nr; user equipment (ue) radio access capabilities.
Delgado, O. and Jaumard, B. (2010). Scheduling and resource allocation in lte uplink with a delay requirement. In 2010 8th Annual Communication Networks and Services Research Conference, pages 268–275.
Ferreira, M. V. G., Vieira, F. H. T., and Abrahão, D. C. (2015). Minimizing delay in resource block allocation algorithm of lte downlink. In 2015 International Workshop on Telecommunications (IWT), pages 1–7.
Guan, N., Zhou, Y., Tian, L., Sun, G., and Shi, J. (2011). Qos guaranteed resource block allocation algorithm for lte systems. In 2011 IEEE 7th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pages 307–312.
Jain, R., Chiu, D.-M., and Hawe, W. (1998). A quantitative measure of fairness and discrimination for resource allocation in shared computer systems. CoRR, cs.NI/9809099.
Le Boudec, J.-Y. and Thiran, P. (2004). Network calculus: A theory of deterministic queuing systems for the internet. 2050.
Ni, M., Xu, X., and Mathar, R. (2013). A channel feedback model with robust sinr prediction for lte systems. In 2013 7th European Conference on Antennas and Propagation (EuCAP), pages 1866–1870.
P, M., Gadre, V., and Desai, U. (2003). Multifractal Based Network Traffic Modeling. Springer US.
Santos Jr, J. and Vieira, F. H. (2015). Estimation of backlog and delay in ofdm/tdma systems with traffic policing through network calculus. Latin America Transactions, IEEE (Revista IEEE America Latina), 13:796–803.
Su, L., Wang, P., and Liu, F. (2012). Particle swarm optimization based resource block allocation algorithm for downlink lte systems. In 2012 18th Asia-Pacific Conference on Communications (APCC), pages 970–974.
Vieira, F., Costa, V., Ling, L., and Rocha, F. (2016). Estimação de Probabilidade de Transbordo do Buffer em Redes OFDM-TDMA utilizando Cadeias de Markov e Curva de Serviço. TEMA (São Carlos), 17:3 – 20.
