Comparative Study of Radio Resource Distribution Algorithms
Received: 23 December 2023 | Revised: 2 January 2024 | Accepted: 10 January 2024 | Online: 20 January 2024
Corresponding author: Atef Gharbi
Abstract
The equitable distribution of radio resources among different users in wireless networks is a difficult problem and has attracted the interest of many studies. This study presents the Proportional Fair Q-Learning Algorithm (PFLA) to enable the equitable distribution of radio resources among diverse users through the integration of Q-learning and proportional fairness principles. The PFLA, Round Robin (RR), and Max Throughput (MaxTP) algorithms were compared to evaluate their effectiveness in resource allocation. Performance was measured in terms of sum-rate throughputs and fairness index. The comparison results showed an improvement in the fairness index metrics for PFLA compared to the other algorithms. PFLA showed gains of 11.62 and 43% in the fairness index compared to RR and MaxTP, respectively. These results show that PFLA is more efficient in utilizing available resources, leading to higher overall system throughput and demonstrating its ability to balance performance metrics between users, especially when the number of users increases.
Keywords:
radio resource distribution, proportional fair Q-learning algorithm, round robin, max throughputDownloads
References
G. I. Tsiropoulos, O. A. Dobre, M. H. Ahmed, and K. E. Baddour, "Radio Resource Allocation Techniques for Efficient Spectrum Access in Cognitive Radio Networks," IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 824–847, 2016.
A. Ahmad, S. Ahmad, M. H. Rehmani, and N. U. Hassan, "A Survey on Radio Resource Allocation in Cognitive Radio Sensor Networks," IEEE Communications Surveys & Tutorials, vol. 17, no. 2, pp. 888–917, 2015.
H. Beshley, M. Beshley, M. Medvetskyi, and J. Pyrih, "QoS-Aware Optimal Radio Resource Allocation Method for Machine-Type Communications in 5G LTE and beyond Cellular Networks," Wireless Communications and Mobile Computing, vol. 2021, May 2021, Art. no. e9966366.
V. F. Monteiro, D. A. Sousa, T. F. Maciel, F. R. M. Lima, E. B. Rodrigues, and F. R. P. Cavalcanti, "Radio resource allocation framework for quality of experience optimization in wireless networks," IEEE Network, vol. 29, no. 6, pp. 33–39, Aug. 2015.
Z. Wang, H. Hu, B. Jia, and T. Xu, "A windowing-based joint user pairing and resource allocation algorithm for V-MIMO systems," Computer Communications, vol. 144, pp. 1–7, Aug. 2019.
M. A. Ruder, A. Moldovan, and W. H. Gerstacker, "SC-FDMA user pairing and frequency allocation with imperfect channel state information," Physical Communication, vol. 13, pp. 88–98, Dec. 2014.
Y. Gao and S. Roy, "Achieving Proportional Fairness for LTE-LAA and Wi-Fi Coexistence in Unlicensed Spectrum," IEEE Transactions on Wireless Communications, vol. 19, no. 5, pp. 3390–3404, Feb. 2020.
I. Budhiraja, N. Kumar, and S. Tyagi, "Deep-Reinforcement-Learning-Based Proportional Fair Scheduling Control Scheme for Underlay D2D Communication," IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3143–3156, Mar. 2021.
A. Celik, M.-C. Tsai, R. M. Radaydeh, F. S. Al-Qahtani, and M.-S. Alouini, "Distributed User Clustering and Resource Allocation for Imperfect NOMA in Heterogeneous Networks," IEEE Transactions on Communications, vol. 67, no. 10, pp. 7211–7227, Jul. 2019.
K. Long, P. Wang, W. Li, and D. Chen, "Spectrum Resource and Power Allocation With Adaptive Proportional Fair User Pairing for NOMA Systems," IEEE Access, vol. 7, pp. 80043–80057, 2019.
Y. S. Nasir and D. Guo, "Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks," IEEE Journal on Selected Areas in Communications, vol. 37, no. 10, pp. 2239–2250, Jul. 2019.
M. M. Wadu, S. Samarakoon, and M. Bennis, "Joint Client Scheduling and Resource Allocation Under Channel Uncertainty in Federated Learning," IEEE Transactions on Communications, vol. 69, no. 9, pp. 5962–5974, Sep. 2021.
X. Wang, T. Q. S. Quek, M. Sheng, and J. Li, "Throughput and Fairness Analysis of Wi-Fi and LTE-U in Unlicensed Band," IEEE Journal on Selected Areas in Communications, vol. 35, no. 1, pp. 63–78, Jan. 2017.
U. Challita, L. Dong, and W. Saad, "Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective," IEEE Transactions on Wireless Communications, vol. 17, no. 7, pp. 4674–4689, Jul. 2018.
B. Damera, P. C. Babu, and J. S. Mohamed, "Optimized MCE scheduling algorithm to allocate radio resources using evolved Round Robin scheduling," in 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Bangalore, India, Jul. 2016, pp. 770–775.
S. Alotaibi, "A Fairness-based Cell Selection Mechanism for Ultra-Dense Networks (UDNs)," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11524–11532, Oct. 2023.
F. O. Ombongi, H. O. Absaloms, and P. L. Kibet, "Energy Efficient Resource Allocation in Millimeter-Wave D2D Enabled 5G Cellular Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 6152–6160, Aug. 2020.
S. Panbude, B. Iyer, A. B. Nandgaonkar, and P. S. Deshpande, "DFPC: Dynamic Fuzzy-based Primary User Aware clustering for Cognitive Radio Wireless Sensor Networks," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12058–12067, Dec. 2023.
T. Hori and T. Ohtsuki, "QoE and throughput aware radio resource allocation algorithm in LTE network with users using different applications," in 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Valencia, Spain, Sep. 2016, pp. 1–6.
Downloads
How to Cite
License
Copyright (c) 2024 Atef Gharbi, Abdulsamad Ebrahim Yahya, Mohamed Ayari
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
