DTPRA: Dynamic Traffic and Priority-Aware Resource Allocation with Admission Control in Highly Dense Heterogeneous Wireless Networks
Received: 24 April 2025 | Revised: 29 June 2025 | Accepted: 9 July 2025 | Online: 8 December 2025
Corresponding author: Naveen Kalenahalli Bhoganna
Abstract
This study addresses the pressing challenges of next-generation Heterogeneous Wireless Networks (HWNs), specifically the need for higher spectral efficiency, reduced latency, increased throughput, and faster data transmission. With the growth of wireless technology, HWNs are becoming increasingly dense, and achieving seamless connectivity, optimal resource allocation, and ideal admission control mechanisms is crucial for enhancing HWN performance and expanding overall coverage. The complex interference issues associated with urban mobility make resource allocation and admission control extremely challenging, thereby increasing network complexity. Recently, soft-computing techniques such as Machine Learning (ML) and Deep Reinforcement Learning (DRL) have been applied to resource allocation and admission control to improve throughput and enhance spectral efficiency, thereby meeting users' application demands. This paper presents a Dynamic Traffic Priority-aware Resource Allocation (DTPRA) strategy. DTPRA introduces a throughput-gain model that incorporates an interference-optimization model by integrating backoff-time optimization into the resource allocation algorithm. This model then optimizes resource allocation for call admission according to user priority requirements using the Optimized DRL (ODRL) model. The DTPRA-ODRL approach is effective in reducing resource access failure and delay, with higher throughput and delivery ratios, thereby improving resource access efficiency compared to current Efficient Resource Allocation Admission Control based on DRL (ERAAC-DRL) methods.
Keywords:
Dynamic Traffic Priority-aware Resource Allocation (DTPRA), Deep Reinforcement Learning (DRL), Machine Learning (ML), Efficient Resource Allocation Admission Control based on DRL (ERAAC-DRL), Heterogeneous Wireless Networks (HWNs)Downloads
References
S. S. Sefati et al., "A Comprehensive Survey on Resource Management in 6G Network Based on Internet of Things," IEEE Access, vol. 12, pp. 113741–113784, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3444313
A. De La Fuente, G. Femenias, F. Riera-Palou, and G. Interdonato, "Subgroup-Centric Multicast Cell-Free Massive MIMO," IEEE Open Journal of the Communications Society, vol. 5, pp. 6872–6889, 2024. DOI: https://doi.org/10.1109/OJCOMS.2024.3487912
P. K. Gkonis, S. Lavdas, G. Vardoulias, P. Trakadas, L. Sarakis, and K. Papadopoulos, "System Level Performance Assessment of Large-Scale Cell-Free Massive MIMO Orientations With Cooperative Beamforming," IEEE Access, vol. 12, pp. 92073–92086, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3422349
H. Q. Ngo, G. Interdonato, E. G. Larsson, G. Caire, and J. G. Andrews, "Ultradense Cell-Free Massive MIMO for 6G: Technical Overview and Open Questions," Proceedings of the IEEE, vol. 112, no. 7, pp. 805–831, Jul. 2024. DOI: https://doi.org/10.1109/JPROC.2024.3393514
M. H. Lee, C. Yun, G. H. Kim, S. Y. Park, C. W. Yu, and K. W. Choi, "Fully Distributed Cell-Free MIMO Systems: Architecture, Algorithm, and Testbed Experiments," IEEE Internet of Things Journal, vol. 11, no. 5, pp. 7956–7973, Mar. 2024. DOI: https://doi.org/10.1109/JIOT.2023.3317578
J. Chen, X. Liang, J. Xue, Y. Sun, H. Zhou, and X. Shen, "Evolution of RAN Architectures Toward 6G: Motivation, Development, and Enabling Technologies," IEEE Communications Surveys & Tutorials, vol. 26, no. 3, pp. 1950–1988, 2024. DOI: https://doi.org/10.1109/COMST.2024.3388511
B. Abdallah, S. Khriji, R. Chéour, C. Lahoud, K. Moessner, and O. Kanoun, "Improving the Reliability of Long-Range Communication against Interference for Non-Line-of-Sight Conditions in Industrial Internet of Things Applications," Applied Sciences, vol. 14, no. 2, Jan. 2024, Art. no. 868. DOI: https://doi.org/10.3390/app14020868
J. Logeshwaran, R. N. Shanmugasundaram, and J. Lloret, "Load based dynamic channel allocation model to enhance the performance of device-to-device communication in WPAN," Wireless Networks, vol. 30, no. 4, pp. 2477–2509, May 2024. DOI: https://doi.org/10.1007/s11276-024-03680-x
J.-Y. Lin, P.-H. Chou, and R.-H. Hwang, "Dynamic Resource Allocation for Network Slicing with Multi-Tenants in 5G Two-Tier Networks," Sensors, vol. 23, no. 10, May 2023, Art. no. 4698. DOI: https://doi.org/10.3390/s23104698
A. Donatti et al., "Survey on Machine Learning-Enabled Network Slicing: Covering the Entire Life Cycle," IEEE Transactions on Network and Service Management, vol. 21, no. 1, pp. 994–1011, Feb. 2024. DOI: https://doi.org/10.1109/TNSM.2023.3287651
H. Liu, Q. Luo, Z. Liu, S. Luo, P. Xiao, and R. Lin, "BER Analysis of SCMA-OFDM Systems in the Presence of Carrier Frequency Offset," IEEE Communications Letters, vol. 28, no. 1, pp. 213–217, Jan. 2024. DOI: https://doi.org/10.1109/LCOMM.2023.3339469
I. A. Bartsiokas, P. K. Gkonis, D. I. Kaklamani, and I. S. Venieris, "A DL-Enabled Relay Node Placement and Selection Framework in Multicellular Networks," IEEE Access, vol. 11, pp. 65153–65169, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3290482
T. H. Ahmed, J. J. Tiang, A. Mahmud, C. Gwo-Chin, and D.-T. Do, "Proposed CtCNet-HDRNN: A Cornerstone in the Integration of 5G mmWave and DSRC for High-Speed Vehicular Networks," IEEE Access, vol. 11, pp. 126482–126506, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3329872
J. Xie, B. Zhu, and C. Li, "Research of 5G HUDN network selection algorithm based on Dueling-DDQN," EURASIP Journal on Wireless Communications and Networking, vol. 2023, no. 1, Nov. 2023, Art. no. 113. DOI: https://doi.org/10.1186/s13638-023-02323-7
I. Lee and D. K. Kim, "Decentralized Multi-Agent DQN-Based Resource Allocation for Heterogeneous Traffic in V2X Communications," IEEE Access, vol. 12, pp. 3070–3084, 2024. DOI: https://doi.org/10.1109/ACCESS.2023.3349350
M. U. Iqbal, E. A. Ansari, S. Akhtar, M. Farooq-I-Azam, S. R. Hassan, and R. Asif, "Optimal Learning Paradigm and Clustering for Effective Radio Resource Management in 5G HetNets," IEEE Access, vol. 11, pp. 41264–41280, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3268543
L. Wang, J. Guo, J. Zhu, X. Jia, H. Gao, and Y. Tian, "Cross-Layer Wireless Resource Allocation Method Based on Environment-Awareness in High-Speed Mobile Networks," Electronics, vol. 13, no. 3, Feb. 2024, Art. no. 499. DOI: https://doi.org/10.3390/electronics13030499
Z. H. Shaik, R. Sarvendranath, and E. G. Larsson, "Energy-Efficient Resource Allocation for Underlay Spectrum Sharing in Cell-Free Massive MIMO," IEEE Access, vol. 12, pp. 106895–106911, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3432287
T. Wang, L. Shen, Q. Fan, T. Xu, T. Liu, and H. Xiong, "Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning," IEEE Transactions on Services Computing, vol. 17, no. 3, pp. 1001–1015, May 2024. DOI: https://doi.org/10.1109/TSC.2023.3326539
J. Ji, X. Ren, L. Cai, and K. Zhu, "Downlink Scheduler for Delay Guaranteed Services Using Deep Reinforcement Learning," IEEE Transactions on Mobile Computing, vol. 23, no. 4, pp. 3376–3390, Apr. 2024. DOI: https://doi.org/10.1109/TMC.2023.3276697
X. Wang, Y. Wang, J. Zhao, C. Weng, Y. Yan, and Z. Li, "Joint Long-Term User Scheduling and Beamforming Design for Burst IIoT," IEEE Internet of Things Journal, vol. 11, no. 12, pp. 22628–22642, Jun. 2024. DOI: https://doi.org/10.1109/JIOT.2024.3382738
M. Ahmadi, A. Moayyedi, M. Sulaiman, M. A. Salahuddin, R. Boutaba, and A. Saleh, "Generalizable 5G RAN/MEC Slicing and Admission Control for Reliable Network Operation," IEEE Transactions on Network and Service Management, vol. 21, no. 5, pp. 5384–5399, Oct. 2024. DOI: https://doi.org/10.1109/TNSM.2024.3437217
Z. Tao, W. Xu, and X. You, "Digital Twin-Accelerated Online Deep Reinforcement Learning for Admission Control in Sliced Communication Networks," IEEE Transactions on Communications, vol. 73, no. 4, pp. 2490–2504, Apr. 2025. DOI: https://doi.org/10.1109/TCOMM.2024.3476430
A. Shabbir, H. R. Khan, S. A. Ali, and S. Rizvi, "Design and Performance Analysis of Multi-tier Heterogeneous Network through Coverage, Throughput and Energy Efficiency," Engineering, Technology & Applied Science Research, vol. 7, no. 6, pp. 2345–2350, Dec. 2017. DOI: https://doi.org/10.48084/etasr.1256
N. Gadde, R. Shivaswamy, R. B. H. Siddamal, G. Gowrishankar, G. T. Raju, and S. S. P. Vijay, "Hybrid resource optimization strategy in heterogeneous wireless networks," Indonesian Journal of Electrical Engineering and Computer Science, vol. 37, no. 2, pp. 829–838, Feb. 2025. DOI: https://doi.org/10.11591/ijeecs.v37.i2.pp829-838
A. Mahmood, S. Khan, S. Hussain, and M. Zeeshan, "Performance Analysis of Multi-User Downlink PD-NOMA Under SUI Fading Channel Models," IEEE Access, vol. 9, pp. 52851–52859, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3070147
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