An Interference Contribution Rate Based Small Cells On/Off Switching Algorithm for 5G Dense Heterogeneous Networks

Shen, Bin; Lei, Zhenzhu; Huang, Xiaoge; Chen, Qianbin

doi:10.1109/access.2018.2841044

Cited by 26 publications

(39 citation statements)

References 28 publications

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“…The Pmax limits for the considered network deployments are summarized in Table 2. The impact of increased P max and decreased interference was observed by plotting the received SIR in Figure 18 using Equation (26). The trend with an increase in α small can clearly be observed for the UE near the eNodeB (i.e., UE with the index 1); the received SIR decreased from 11.71 dB to 5.4 dB.…”

Section: Hetnet Deployment With Small Cells Near Macro Cell Edgementioning

confidence: 98%

“…To see the impact of the variation in the P max limit on the system performance, the spectral efficiency for the cell average and at the specific position of the UE in the cell was computed from Equations (12) and (13) using the received SIR given in Equation (26).…”

Section: Spectral Efficiencymentioning

confidence: 99%

“…By considering the dormant state of small cells, that is, when no UE is associated to the small cell, a small cell on/off scheme was outlined in Reference [25], where the small cell base stations sense and store the reference signal received power (RSRP) of the neighboring base stations and UEs to determine their activation and deactivation. To reduce the signaling involved in the on/off mechanism of small cells, the authors in Reference [26] presented an interference contribution rate-based algorithm, resulting in a lower inter-cell interference in the downlink. In Reference [27], a dynamic small cell on/off mechanism along with dual connectivity was elaborated, which takes the load balancing and downlink outage probability of the users into account while turning the small cell on or off.…”

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confidence: 99%

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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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Section: Hetnet Deployment With Small Cells Near Macro Cell Edgementioning

confidence: 98%

Section: Spectral Efficiencymentioning

confidence: 99%

mentioning

confidence: 99%

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Maximum Transmit Power for UE in an LTE Small Cell Uplink

Haider

Hwang

2019

Electronics

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“…In [6], the SCs that did not overload the neighboring cells due to handover load were turned off to ensure energy efficiency and QoS. In [7], with an aim to mitigate the co-frequency interference with less signaling overheads and measurement processes, a centralized SC on/off algorithm based on interference contribution ratio was proposed. In [8], since the gain of cell densification is saturated, the appropriate number of active SCs was founded, and excess SCs were turned off.…”

Section: Introductionmentioning

confidence: 99%

UE throughput guaranteed small cell on/off algorithm with machine learning

2020

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Ultra-dense network (UDN) has been considered a promising solution to improve the signal quality of cell edge UEs and enhance the serving coverage. However, a large number of small cells causes severe inter-cell interference and high energy consumption, and efficient small cell management is an important issue. In this paper, a UE throughput guaranteed small cell on/off algorithm in UDN environment is proposed to solve the problem of UE throughput reduction due to small cell off process. The proposed small cell on/off algorithm with machine learning is executed by the following processes: First, the network attributes that affect UE throughput are analyzed. Second, the correlation between UE throughput and network attributes is evaluated through multiple linear regression analysis. Third, through understanding the correlation between UE throughput and network attributes, we determine the proper criteria for small cell on/off process. Simulation results show that the proposed small cell on/off algorithm can improve the total network energy efficiency as well as efficiently ensure sufficient UE throughput. Compare to the conventional algorithm, the proposed algorithm shows more than 75% improvements of average network energy efficiency. whole network.

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“…One of the evolving directions of 5G is to decrease the size of cells, which will significantly improve the cell throughput, energy efficiency and user data rate of cellular system [3]. Dense network deployment exhibits the above advantages, but produces a series of problems such as co-channel interference [4]. Interference becomes a major limiting factor for higher data rates [5].…”

Section: Introductionmentioning

confidence: 99%

Interference Cancellation Scheme for Three-hop Cooperative Relay Networks

2019

KSII TIIS

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In this paper, we focus on interference cancellation for three-hop cognitive radio networks (CRNs) over Rayleigh fading channels. In CRNs, secondary users (SUs) are allowed to opportunistically utilize the licensed spectrum during the idle time of primary users (PUs) to achieve spectrum sharing. However, the SUs maybe power constrained to avoid interference and cover a very short transmission range. We here propose an interference cancellation scheme (ICS) for three-hop CRNs to prolong the transmission range of SUs and improve their transmission efficiency. In the proposed scheme, a flexible transmission protocol is adopted to cancel the interference at both secondary relays and destinations at the same time. And a closed-form expression for the secondary outage probability over Rayleigh fading channels is derived to measure the system performance. Simulation results show that the proposed scheme can significantly reduce the secondary outage probability and increase the secondary diversity in comparison with the traditional cases.

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An Interference Contribution Rate Based Small Cells On/Off Switching Algorithm for 5G Dense Heterogeneous Networks

Cited by 26 publications

References 28 publications

Maximum Transmit Power for UE in an LTE Small Cell Uplink

Maximum Transmit Power for UE in an LTE Small Cell Uplink

UE throughput guaranteed small cell on/off algorithm with machine learning

Interference Cancellation Scheme for Three-hop Cooperative Relay Networks

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