Broadband communication networks have experienced rapid growth due to the increasing demand for high-speed wireless connectivity, multimedia services, cloud computing, and Internet-based applications. As network traffic and user density continue to rise, efficient power management has become an essential requirement for maintaining communication quality, improving spectrum utilization, and reducing overall energy consumption. Adaptive power control methods play a critical role in optimizing transmission performance by dynamically adjusting signal power according to network conditions, channel quality, user mobility, and interference levels. This review paper presents a comprehensive study of adaptive power control techniques used in modern broadband networks. The paper examines various power control approaches including centralized control, distributed control, closed-loop power adjustment, open-loop techniques, and artificial intelligence- based optimization methods. The impact of adaptive power control on interference mitigation, energy efficiency, network capacity, and quality of service is critically analyzed. In addition, the review discusses the application of these techniques in heterogeneous cellular networks, multiple-input multiple-output systems, wireless sensor networks, and fifth-generation communication systems. The study further highlights recent advancements involving machine learning, reinforcement learning, and self-organizing network architectures for intelligent power management. Challenges related to scalability, signal fading, mobility management, and real-time optimization are also explored. The review concludes that adaptive power control remains a key enabling technology for improving the efficiency, reliability, and sustainability of future broadband communication networks.

Keywords - Adaptive Power Control, Broadband Communication Networks, Interference Management, Energy-Efficient Wireless Communication, Intelligent Power Optimization

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1. Armanious E, Falconer DD, Yanikomeroglu H. Adaptive modulation, adaptive coding, and power control for fixed cellular broadband wireless systems: some new insights. In2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003. 2003 Mar 16 (Vol. 1, pp. 238-242). IEEE.
2. Lin S, Miao F, Zhang J, Zhou G, Gu L, He T, Stankovic JA, Son S, Pappas GJ. ATPC: Adaptive transmission power control for wireless sensor networks. ACM Transactions on Sensor Networks (TOSN). 2016 Mar 10;12(1):1-31.
3. Leung KK, Wang LC. Controlling QoS by integrated power control and link adaptation in broadband wireless networks. European Transactions on Telecommunications. 2000 Jul;11(4):383-94.
4. Omasheye O. Adaptive Neural-Fuzzy Systems for Adaptive Signal Coverage Power Estimation in Cellular Broadband Networks. Journal of Science Computing and Applied Engineering Research. 2025 Sep 10;1(3):16-26.
5. Rahmani AM, Haider A, Moghaddasi K, Gharehchopogh FS, Aurangzeb K, Liu Z, Hosseinzadeh M. Self-learning adaptive power management scheme for energy-efficient IoT- MEC systems using soft actor-critic algorithm. Internet of Things. 2025 May 1;31:101587.
6. Ghadimi E, Calabrese FD, Peters G, Soldati P. A reinforcement learning approach to power control and rate adaptation in cellular networks. In2017 IEEE International Conference on Communications (ICC) 2017 May 21 (pp. 1-7). IEEE.
7. Koutsopoulos I, Tassiulas L. Channel state-adaptive techniques for throughput enhancement in wireless broadband networks. InProceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No. 01CH37213) 2001 Apr 22 (Vol. 2, pp. 757-766). IEEE.
8. Huang WL, Letaief KB. Cross-layer scheduling and power control combined with adaptive modulation for wireless ad hoc networks. IEEE Transactions on Communications. 2007 Apr 16;55(4):728-39.
9. Zhang R, Xiong K, Guo W, Yang X, Fan P, Letaief KB. Q-learning-based adaptive power control in wireless RF energy harvesting heterogeneous networks. IEEE Systems Journal. 2020 Sep 14;15(2):1861-72.
10. Belete DK, Salau AO. Adaptation and performance evaluation of existing power line for broadband communication. Wireless Personal Communications. 2024 Jul;137(1):237-57.

Broadband communication networks have experienced rapid growth due to the increasing demand for high-speed wireless connectivity, multimedia services, cloud computing, and Internet-based applications. As network traffic and user density continue to rise, efficient power management has become an essential requirement for maintaining communication quality, improving spectrum utilization, and reducing overall energy consumption. Adaptive power control methods play a critical role in optimizing transmission performance by dynamically adjusting signal power according to network conditions, channel quality, user mobility, and interference levels. This review paper presents a comprehensive study of adaptive power control techniques used in modern broadband networks. The paper examines various power control approaches including centralized control, distributed control, closed-loop power adjustment, open-loop techniques, and artificial intelligence- based optimization methods. The impact of adaptive power control on interference mitigation, energy efficiency, network capacity, and quality of service is critically analyzed. In addition, the review discusses the application of these techniques in heterogeneous cellular networks, multiple-input multiple-output systems, wireless sensor networks, and fifth-generation communication systems. The study further highlights recent advancements involving machine learning, reinforcement learning, and self-organizing network architectures for intelligent power management. Challenges related to scalability, signal fading, mobility management, and real-time optimization are also explored. The review concludes that adaptive power control remains a key enabling technology for improving the efficiency, reliability, and sustainability of future broadband communication networks.

Keywords - Adaptive Power Control, Broadband Communication Networks, Interference Management, Energy-Efficient Wireless Communication, Intelligent Power Optimization

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Armanious E, Falconer DD, Yanikomeroglu H. Adaptive modulation, adaptive coding, and power control for fixed cellular broadband wireless systems: some new insights. In2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003. 2003 Mar 16 (Vol. 1, pp. 238-242). IEEE.
2. Lin S, Miao F, Zhang J, Zhou G, Gu L, He T, Stankovic JA, Son S, Pappas GJ. ATPC: Adaptive transmission power control for wireless sensor networks. ACM Transactions on Sensor Networks (TOSN). 2016 Mar 10;12(1):1-31.
3. Leung KK, Wang LC. Controlling QoS by integrated power control and link adaptation in broadband wireless networks. European Transactions on Telecommunications. 2000 Jul;11(4):383-94.
4. Omasheye O. Adaptive Neural-Fuzzy Systems for Adaptive Signal Coverage Power Estimation in Cellular Broadband Networks. Journal of Science Computing and Applied Engineering Research. 2025 Sep 10;1(3):16-26.
5. Rahmani AM, Haider A, Moghaddasi K, Gharehchopogh FS, Aurangzeb K, Liu Z, Hosseinzadeh M. Self-learning adaptive power management scheme for energy-efficient IoT- MEC systems using soft actor-critic algorithm. Internet of Things. 2025 May 1;31:101587.
6. Ghadimi E, Calabrese FD, Peters G, Soldati P. A reinforcement learning approach to power control and rate adaptation in cellular networks. In2017 IEEE International Conference on Communications (ICC) 2017 May 21 (pp. 1-7). IEEE.
7. Koutsopoulos I, Tassiulas L. Channel state-adaptive techniques for throughput enhancement in wireless broadband networks. InProceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No. 01CH37213) 2001 Apr 22 (Vol. 2, pp. 757-766). IEEE.
8. Huang WL, Letaief KB. Cross-layer scheduling and power control combined with adaptive modulation for wireless ad hoc networks. IEEE Transactions on Communications. 2007 Apr 16;55(4):728-39.
9. Zhang R, Xiong K, Guo W, Yang X, Fan P, Letaief KB. Q-learning-based adaptive power control in wireless RF energy harvesting heterogeneous networks. IEEE Systems Journal. 2020 Sep 14;15(2):1861-72.
10. Belete DK, Salau AO. Adaptation and performance evaluation of existing power line for broadband communication. Wireless Personal Communications. 2024 Jul;137(1):237-57.