Interference Alignment for Cooperative MIMO Femtocell Networks

Güler, Başak; Yener, Aylin

doi:10.1109/glocom.2011.6134195

Cited by 23 publications

(23 citation statements)

References 16 publications

(20 reference statements)

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“…The associate editor coordinating the review of this paper and approving it for publication was Y. Zhou. This research was supported in part by the National Natural Science Foundation of China (NSFC) solving the interference problem in multiuser communication systems, IA has been applied to several wireless networks successfully [5], [6], [7], [8], [9]. However, there are still some challenges when IA is leveraged in practical networks that may be summarized as follows [10]:…”

Section: Introductionmentioning

confidence: 99%

Adaptive Energy-Efficient Power Allocation in Green Interference-Alignment-Based Wireless Networks

Zhao

Sun

2015

IEEE Trans. Veh. Technol.

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Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-Interference alignment (IA) is a promising technique for interference management in wireless networks. However, the sum rate may fall short of the theoretical maximum especially at low signal-to-noise ratio (SNR) levels since IA mainly concentrates on mitigating the interference, instead of improving the quality of desired signal. Moreover, most of the previous works focused on improving spectrum efficiency, but the energy efficiency aspect is largely ignored. In this paper, an adaptive energy-efficient IA algorithm is proposed through power allocation and transmission-mode adaptation for green IAbased wireless networks. The power allocation problem for IA is first analyzed, then we propose a power allocation scheme that optimizes the energy efficiency of IA-based wireless networks. When SNR is low, the transmitted power of some users may become zero. Thus the users with low transmitted power are turned into the sleep mode in our scheme to save energy. The transmitted power and transmission mode of the remaining active users are adapted again to further improve the energy efficiency of the network. To guarantee the interests of all the users, fairness among users is also considered in the proposed scheme. Simulation results are presented to show the effectiveness of the proposed algorithm in improving the energy efficiency of IAbased wireless networks.

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Section: Introductionmentioning

confidence: 99%

Adaptive Energy-Efficient Power Allocation in Green Interference-Alignment-Based Wireless Networks

Zhao

Sun

2015

IEEE Trans. Veh. Technol.

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“…In the study of [8], a spectral transmission scheme for femto cell networks, which includes an adaptive subband partition method and an adaptive IA transceiver is introduced. Another IA approach on femtocellular networks is given in [9] where the uplink interference is tried to be aligned caused by the macro cell users to the closest femtocell by satisfying the required quality of service (QoS). In the study of [10] beamforming matrices are sequentially determined for small cells and macro cells in order to mitigate interference in the heterogeneous networks.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical successive stream selection for heterogeneous network interference

Aycan¹,

Özbek²,

Ruyet³

2014

2014 IEEE Wireless Communications and Networking Conference (WCNC)

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Abstract-This paper presents a hierarchical stream selection approach to deal with the interference in a heterogeneous network where different cell types are coexisting with each other to increase the sum capacity. Due to the variety of the transmit powers between the macro and small cells, interference levels are different. The proposed solution hierarchically selects the strongest streams of each cell with a contribution to the sum rate, while constructing the streams via singular value decomposition (SVD). In order to reduce the interference, the channel matrices of the remaining streams are projected orthogonally to the virtual transmit channel and virtual receive channel of the selected stream. The performance evaluations are obtained by considering different locations of small cells with respect to the macro cell. It is shown that the proposed method can dynamically select more streams in heterogeneous networks and achieve higher data rates compared to the existing algorithms.

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“…In other words, the precoders V k , k ∈ S have to verify that the interference brought to the MUEs by the underlay transmissions of the SBSs in S does not excessively deteriorate the MUEs' performance 5 . As an alternative to δ, the impact of the cross-tier interference can also be evaluated by accommodating other metrics, such as the interference constraints [41], [45] or the interference temperature [46]. It can be noted that the case of ∥H kn V k s k ∥ = 0 represents the IA solution, since it provides the interference suppression at MUE n, through the precoding at SBS k. Here, we extend this concept to a coalition S of SBSs, by minimizing the interference leakage caused by the co-channel small cell transmissions, through the precoding matrices V k , k ∈ S. As a result, the problem that we are solving is analogous to construct the precoders so as to solve arg min…”

Section: A Cooperative Spatial Coding Techniques For Small Cell Tranmentioning

confidence: 99%

Improving Macrocell-Small Cell Coexistence Through Adaptive Interference Draining

Pantisano

Bennis²,

Saad³

et al. 2014

IEEE Trans. Wireless Commun.

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Abstract-The deployment of underlay small base stations (SBSs) is expected to significantly boost the spectrum efficiency and the coverage of next-generation cellular networks. However, the coexistence of SBSs underlaid to a macro-cellular network faces important challenges, notably in terms of spectrum sharing and interference management. In this paper, we propose a novel game-theoretic model that enables the SBSs to optimize their transmission rates by making decisions on the resource occupation jointly in the frequency and spatial domains. This procedure, known as interference draining, is performed among cooperative SBSs and allows to drastically reduce the interference experienced by both macro-and small cell users. At the macrocell side, we consider a modified waterfilling policy for the power allocation that allows each macrocell user (MUE) to focus the transmissions on the degrees of freedom over which the MUE experiences the best channel and interference conditions. This approach not only represents an effective way to decrease the received interference at the MUEs but also grants the SBS tier additional transmission opportunities and allows for a more agile interference management. Simulation results show that the proposed approach yields significant gains at both macrocell and small cell tiers, in terms of average achievable rate per user, reaching up to 37%, relative to the non-cooperative case, for a network with 150 MUEs and 200 SBSs.

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Interference Alignment for Cooperative MIMO Femtocell Networks

Cited by 23 publications

References 16 publications

Adaptive Energy-Efficient Power Allocation in Green Interference-Alignment-Based Wireless Networks

Adaptive Energy-Efficient Power Allocation in Green Interference-Alignment-Based Wireless Networks

Hierarchical successive stream selection for heterogeneous network interference

Improving Macrocell-Small Cell Coexistence Through Adaptive Interference Draining

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