Link Adaptive Power Control and Allocation for Energy–Efficient Downlink Transmissions in LTE Systems

Madi, Nadim K. M.; Hanapi, Zurina Mohd; Othman, Мohamed; Subramaniam, Shamala

doi:10.1109/access.2018.2821245

Cited by 8 publications

(6 citation statements)

References 42 publications

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“…where th i (n) is the throughput of the i th user (in Megabits/s), and P i (n) the total transmitted power (in W) in the n th TTI [20].…”

Section: A Phase 1: Network Monitoringmentioning

confidence: 99%

“…For example, although many algorithms only consider QoS requirements [10]- [13], applying these policies can cause starvation of low priority classes, especially when real-time priority classes are crowded. Also, other techniques are proposed with the goal to optimize energy consumption [14]- [20]. However, most of the algorithms do not control or maintain the flow of traffic through the network.…”

Section: Introductionmentioning

confidence: 99%

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Dragonfly-Based Joint Delay/Energy LTE Downlink Scheduling Algorithm

et al. 2020

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Managing radio resources in Long Term Evolution (LTE) networks is considered as one of the essential design factors for enhancing the overall system performance. Common approaches are introduced to either achieve fairness between network users or attain maximum spectral efficiency. However, these approaches do not consider optimizing energy consumption. Therefore, in this paper, a novel resource allocation algorithm based on the Dragonfly metaheuristic technique is proposed to allocate bandwidth to users. The new algorithm is called Dragonfly-based Joint Delay/Energy (DJDE) and considers the Quality of Services (QoS) requirements of the users while achieving a high level of energy efficiency. The proposed solution utilizes the Dragonfly algorithm to optimize the integration process of different scheduling policies. To evaluate the proposed algorithm, an extensive set of experiments are conducted to compare the proposed solution to the state-of-the-art techniques. Also, to assess the energy efficiency of the proposed method, another set of experiments are simulated to compare it with various algorithms that optimize energy consumption. The obtained results prove that the DJDE algorithm can satisfy the QoS requirements of the users while improving the overall system performance.

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“…where th i (n) is the throughput of the i th user (in Megabits/s), and P i (n) the total transmitted power (in W) in the n th TTI [20].…”

Section: A Phase 1: Network Monitoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dragonfly-Based Joint Delay/Energy LTE Downlink Scheduling Algorithm

et al. 2020

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“…And, the researcher also proposed an iterative algorithm to solve the proposed optimal problem. In [18], a link adaptive power control and allocation (LaPCA) scheme was designed based on the requirement of LTE service. Instead of optimizing the energy efficiency directly, LaPCA kept the portion of cell transmission power to be proportional to the volume of data flows that were nominated for transmission.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Transmission Power Control Policy of the Delay Tolerable Communication Service

et al. 2020

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In recent years, the development of wireless communication leads to an explosive growth of energy demand, widely application of smart devices and rapid emergence of services. So, the energy efficient communication is expected urgently to save power and prolong the lifetime of the resourceconstrained terminal devices. Especially in 5G age, the excellent spectrum efficiency provides more opportunity to save power by adjusting the transmission power of the delay tolerable (DT) service. Meanwhile, although the tradeoff between energy efficiency and service delay plays a non-negligible role in the energy efficient communication, it is not exploited sufficiently due to the time variation and randomness of wireless communication channel. For this reason, the fundamental tradeoff between energy efficiency and delay of the DT service is investigated and analyzed. And, the optimal problem of energy efficient communication for DT service is formulated as a Markov Decision Process (MDP) which can be solved effectively by statistical dynamic programming (SDP) since the perfect channel state information (CSI) is hard to obtain. To improve the utility of research, the approximate SDP (ASDP) and Q-learning are also investigated to overcome the limitation of the curse of dimensionality and model-based algorithm respectively.

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“…To see the results of the interference offered to the central small cell from the interfering ring as well as the macro cell, Equation (22) was plotted in Figure 14. It is observed that due to the absence of one interfering ring as compared to the previous scenario, the interference offered to the central small cell decreased.…”

Section: Hetnet Deployment With Small Cells Near the Macro Cell Enodebmentioning

confidence: 99%

“…The performance optimization is mainly considered by optimizing the network parameters based on the uplink measurements from the UE. However, with such static deployment, an important problem that arises in energy-efficient cellular networks is that of meeting a set of specified SIR targets using the minimum possible network power [22,23]. A practical policy introduced by the 3GPP specifications for achieving energy efficiency in such networks is to appropriately turn the small cells on/off.…”

mentioning

confidence: 99%

Maximum Transmit Power for UE in an LTE Small Cell Uplink

Haider

Hwang

2019

Electronics

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To furnish the network with small cells, it is vital to consider parameters like cell size, interference in the network, and deployment strategies to maximize the network’s performance gains expected from small cells. With a small cell network, it is critical to analyze the impact of the uplink power control parameters on the network’s performance. In particular, the maximum transmit power (Pmax) for user equipment (UE) needs to be revisited for small cells, since it is a major contributor towards interference. In this work, the network performance was evaluated for different Pmax values for the small cell uplink. Various deployment scenarios for furnishing the existing macro layer in LTE networks with small cells were considered. The Pmax limit for a small cell uplink was evaluated for both homogenous small cell and heterogeneous networks (HetNet). The numerical results showed that it would be appropriate to adopt Pmax = 18 dBm in uniformly distributed small cells rather than Pmax = 23 dBm, as in macro environments. The choice of Pmax = 18 dBm was further validated for three HetNet deployment scenarios. A decrease of 0.52 dBm and an increase of 0.03 dBm and 3.29 dBm in the proposed Pmax = 18 dBm were observed for the three HetNet deployments, respectively. Furthermore, we propose that the fractional power control mode can be employed instead of the full compensation mode in small cell uplinks.

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Link Adaptive Power Control and Allocation for Energy–Efficient Downlink Transmissions in LTE Systems

Cited by 8 publications

References 42 publications

Dragonfly-Based Joint Delay/Energy LTE Downlink Scheduling Algorithm

Dragonfly-Based Joint Delay/Energy LTE Downlink Scheduling Algorithm

Energy Efficient Transmission Power Control Policy of the Delay Tolerable Communication Service

Maximum Transmit Power for UE in an LTE Small Cell Uplink

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