Interference management in macro‐femtocell and micro‐femtocell cluster‐based long‐term evaluation‐advanced green mobile network

Mukherjee, Anwesha; De, Debashis; Deb, Priti

doi:10.1049/iet-com.2015.0982

Cited by 30 publications

(7 citation statements)

References 34 publications

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“…Besides inter-tier load balancing problem in HCNs, the densification of SBSs also leads to an excessive increase in the network interference which significantly impacts the user experience [13], [14]. The interference in HCNs has been extensively studied in literature [15]- [20], and references therein. The conventional cellular systems generally encountered two types of interference, i.e., co-channel and adjacent channel.…”

Section: A Motivation and Related Workmentioning

confidence: 99%

“…From (15), it can be easily inferred that the UE association with any tier is more dominantly influenced by its spatial density when compared with either of the transmit powers or biasing factors. This simplified result depicts a different story to the DS-PLM case which shows a reasonable sensitivity to both critical distance and biasing as they play a vital role in UE offloading to smaller tiers.…”

Section: ) Reduction To Ss-plm and Closed-formmentioning

confidence: 99%

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Impact of Frequency Reuse and Flexible Cell Association on the Performance of Dense Heterogeneous Cellular Networks Using Dual-Slope Path Loss Model

2019

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Densifying low-power/small base stations (SBSs) in support of overlaid macro BSs (MBS) is getting significant consideration as a viable solution for meeting the rapidly increasing capacity demands of emerging wireless networks. Given the disparity in transmit power of BS tiers, load balancing strategies have achieved paramount importance as they ensure balance of user access load amongst BS tiers and assist in availing maximum potential of multi-tier networks. Moreover, it is also essential to utilize efficient interference management schemes to account for the excessively increasing interference in the deployed networks. There is a plethora of literature available on the analysis of dense multi-tier networks that undertake various load balancing and interference management mechanisms but they are strictly limited to single slope (SS) path loss model (PLM). Furthermore, it is also well-known in literature that SS-PLM leads to inaccuracies in estimating the performance of dense networks owing to the simplistic approach of estimating the path loss. To overcome the limitations of SS-PLM, in our work, we have employed DS-PLM to study and analyze the performance of a two-tier dense cellular network that permits flexible user association using SBS biasing in conjunction with a simple frequency reuse mechanism. A comparison of SS-PLM with DS-PLM is performed for tier association probability and coverage probability, which shows that there is significant difference in the estimated overall network performance of our proposed scheme, as compared to SS-PLM. The results clearly signify the benefits of utilizing both load balancing and interference management mechanisms under DS-PLM, and give critical design insights for finding optimal parametric values in cellular planning. INDEX TERMS Coverage probability, dual-slope path loss model, frequency reuse, heterogeneous cellular networks, load balancing, stochastic geometry.

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Section: A Motivation and Related Workmentioning

confidence: 99%

Section: ) Reduction To Ss-plm and Closed-formmentioning

confidence: 99%

Impact of Frequency Reuse and Flexible Cell Association on the Performance of Dense Heterogeneous Cellular Networks Using Dual-Slope Path Loss Model

2019

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“…Therefore, the total interference constraint, h tot th may be used in conjunction with h th to guarantee the MU's data transmission. h tot can be derived from (5) and (6). Due to space limitation, the details are omitted.…”

Section: Fig 5 Flowchart Of Ascentmentioning

confidence: 99%

Adaptive transmission in heterogeneous networks

Dai

et al. 2017

IET Communications

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An Adaptive tranSmission mechanism exploiting both interference loCality and the relationship between dEsired sigNal and inTerference (ASCENT) is proposed for uplink transmission in heterogeneous networks. The authors adopt both X channel and Z channel models according to which spatial signal processing is designed. In the X channel, the picocell base station (PBS) exploits information the macrocell base station (MBS) shares to cancel local interference, and cooperatively decodes the data carried by strong interference from a macro-user (MU), which is then fed to the MBS. As a result, a pico-user (PU) can transmit simultaneously with an MU on the same channel. In addition, adaptive reception is employed to achieve good tradeoff between interference suppression and the desired level of signal distortion. For the Z channel, the PU and the PBS adopt signal processing suitable for their own channel state. At a PBS, interference cancellation is adopted to eliminate disturbance from an MU via inter-base station collaboration. ASCENT is also extended to the case of multiple picocells. The authors' simulation results show that in X channel mode, the achievable uplink rate of an MU can be significantly enhanced. In the case of Z channel, the PU's rate is improved while guaranteeing the MU's data rate.

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“…If the mobile device is present at indoor region, then it accesses the internet through a home node BS (HNB) or a light weight AP (LWAP) to get high speed connectivity. As inside a building the received signal level from the other cellular BSs like macrocell or microcell is very low, HNB is used at indoor region to provide cellular network services at better quality of service [15][16][17]. Sometimes the mobile device is connected to a wireless local area network (WLAN) and accesses the internet at high speed through a LWAP.…”

Section: Layer 2 Componentsmentioning

confidence: 99%

“…The application software associated with sensor data processing run on the cloud macrocell BS or microcell BS is used by the mobile device to access the network. Macrocell BS and microcell BS have coverage area of 1-20 km and 200 m-1 km, respectively [15][16][17]. HNB and LWAP at indoor region: If HNB is used, the mobile device transmits the received sensor data to the HNB which forwards the data to the remote server as shown in Fig.…”

Section: Layer 2 Componentsmentioning

confidence: 99%

Architecture of green sensor mobile cloud computing

Mukherjee

Ray

et al. 2016

IET wirel. sens. syst.

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This study presents an architecture of green sensor mobile cloud computing which integrates sensor network and mobile network with cloud computing. In the proposed scheme, the sensor data are transmitted to the cloud through a mobile device. To develop the proposed architecture both the indoor and outdoor region are considered. For indoor region light weight access point and home node base station are used by the mobile device for sensor data transmission to the cloud, whereas for outdoor region macrocell and microcell base stations are used. Simulation results present that using home node base station the power consumption at indoor region can be reduced by ∼10% than the light weight access point, and using microcell base station at outdoor region the power consumption can be reduced by ∼30% than the macrocell base station. Hence, using home node base station and microcell base station green sensor mobile cloud computing can be obtained at indoor and outdoor regions, respectively. In this study, an experimental analysis of the proposed architecture is also performed.

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Interference management in macro‐femtocell and micro‐femtocell cluster‐based long‐term evaluation‐advanced green mobile network

Cited by 30 publications

References 34 publications

Impact of Frequency Reuse and Flexible Cell Association on the Performance of Dense Heterogeneous Cellular Networks Using Dual-Slope Path Loss Model

Impact of Frequency Reuse and Flexible Cell Association on the Performance of Dense Heterogeneous Cellular Networks Using Dual-Slope Path Loss Model

Adaptive transmission in heterogeneous networks

Architecture of green sensor mobile cloud computing

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