Performance of closed access and open access femtocells in a heterogeneous LTE microcellular environment

Arunachalam, Shivram S.; Sekhar, Kishore Kumar

doi:10.1109/iccsp.2013.6577164

Cited by 1 publication

(3 citation statements)

References 9 publications

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“…(1 − β i )P S i ) + P 0 BS is the whole network power consumption, and k∈K r k is the sum of rate demands of all users in the network. Constraints in (15) and (16) represent that the traffic loads of the eNB and all RSs could not exceed their resource limit. In constraint (15),…”

Section: Optimization Problemmentioning

confidence: 99%

“…k∈K x BS,k ρ BS,k is the traffic load from the users served by the eNB. The traffic load constraint of each RS cannot be more than the limit in (16). Eq.…”

Section: Optimization Problemmentioning

confidence: 99%

“…User cell association schemes have significant impact on energy saving. In [15,16], maximum SNR-based (SNRB) user association scheme is employed in energy saving algorithms, while impact of user association is not considered. In [17], an energy minimization algorithm in case of dynamic user association in macro relay network is proposed, and results show that the system EE is improved compared with SNRB usercell association algorithm.…”

Section: Introductionmentioning

confidence: 99%

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An energy minimization algorithm based on distributed dynamic clustering for long term evolution (LTE) heterogeneous networks

Tong

Ding

Pan

et al. 2015

Sci. China Inf. Sci.

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Energy saving has become an important issue in wireless communications from both environment and economic considerations. In this paper, an energy minimization algorithm based on distributed dynamic clustering (DDCEM) with lower complexity, higher performance, and better adaptability for heterogeneous network (HetNet) is proposed. A HetNet could be divided into several clusters, which is defined as one group of a network node and users served by the node. A energy efficient user association, which dynamically changes according to real-time energy efficiency (EE) evaluation with traffic load and location distribution of each cluster, can be employed to save consumed energy. Then, the optimal sleeping relay is found as follows. First, the sleeping probability cost of each relay station (RS) is computed and ranked based on the user traffic and the position distribution of each cluster, and the relay with minimum sleeping probability is selected to be switched off. Hence, the sleep node is selected taking into account the traffic load and location of the Evolved Node B (eNB) and all the RSs. The complexity of the algorithm is greatly reduced because the user association operation and network load evaluation are fulfilled cluster by cluster. Simulation results show that the proposed DDCEM strategy offers EE gain with low system complexity.

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Section: Optimization Problemmentioning

confidence: 99%

“…k∈K x BS,k ρ BS,k is the traffic load from the users served by the eNB. The traffic load constraint of each RS cannot be more than the limit in (16). Eq.…”

Section: Optimization Problemmentioning

confidence: 99%