Clustering for Interference Alignment in Multiuser Interference Network

Chen, Sujie; Cheng, R.S.

doi:10.1109/tvt.2013.2292897

Cited by 69 publications

(82 citation statements)

References 20 publications

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“…A CSI acquisition overhead model which is similar, but less refined, than our proposed model was used and several heuristics were provided for the clustering. In [10], approximated rate losses were used as fixed edge weights in an interference graph together with intercluster spectrum sharing. By applying graph partitioning algorithms, two heuristics were provided for the clustering.…”

Section: A Related Workmentioning

confidence: 99%

“…II and III, we propose a model for the long-term throughputs under intracoalition IA. We further propose a generalized-compared to [9] and [10]-frame structure which allows for temporal resource allocation into two phases: intercoalition time sharing and intercoalition spectrum sharing.…”

Section: B Contributions and Outlinementioning

confidence: 99%

“…[10]), such that only distant BSs contribute unmitigated intercoalition interference. With this approach, the spectral efficiency is lower than in phase 1 due to the unmitigated intercoalition interference, but the temporal resources are maximally used.…”

Section: B Management Of Intercoalition Interferencementioning

confidence: 99%

“…S = {I}. From the literature, we also compare to the k-means clustering of [10], whose weight matrix has been extended in the obvious way for the cellular case. This method may give coalitions that are larger than what is strictly IA feasible, and we thus only evaluate its performance together with WMMSE precoding.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…In absolute terms, both algorithms have very low complexities however. Robust WMMSE Naive WMMSE [16] Robust MaxSINR [10] Naive MaxSINR [40] (b) Comparing precoding methods. The higher complexity of the attach-or-supplant version does pay off in terms of higher achieved IIA sum throughputs.…”

Section: Coalition Formation Complexity For Large Networkmentioning

confidence: 99%

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Distributed Long-Term Base Station Clustering in Cellular Networks Using Coalition Formation

Brandt

Mochaourab

Bengtsson

2016

IEEE Trans. on Signal and Inf. Process. over Networks

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Abstract-Interference alignment (IA) is a promising technique for interference mitigation in multicell networks due to its ability to completely cancel the intercell interference through linear precoding and receive filtering. In small networks, the amount of required channel state information (CSI) is modest and IA is therefore typically applied jointly over all base stations. In large networks, where the channel coherence time is short in comparison to the time needed to obtain the required CSI, base station clustering must be applied however. We model such clustered multicell networks as a set of coalitions, where CSI acquisition and IA precoding is performed independently within each coalition. We develop a long-term throughput model which includes both CSI acquisition overhead and the level of interference mitigation ability as a function of the coalition structure. Given the throughput model, we formulate a coalitional game where the involved base stations are the rational players. Allowing for individual deviations by the players, we formulate a distributed coalition formation algorithm with low complexity and low communication overhead that leads to an individually stable coalition structure. The dynamic clustering is performed using only long-term CSI, but we also provide a robust shortterm precoding algorithm which accounts for the intercoalition interference when spectrum sharing is applied between coalitions. Numerical simulations show that the distributed coalition formation is generally able to reach long-term sum throughputs within 10 % of the global optimum.

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Section: A Related Workmentioning

confidence: 99%

Section: B Contributions and Outlinementioning

confidence: 99%

Section: B Management Of Intercoalition Interferencementioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Coalition Formation Complexity For Large Networkmentioning

confidence: 99%

See 3 more Smart Citations

Distributed Long-Term Base Station Clustering in Cellular Networks Using Coalition Formation

Brandt

Mochaourab

Bengtsson

2016

IEEE Trans. on Signal and Inf. Process. over Networks

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Interference Management inD2D

Denis¹,

Assaad²

2020

Wiley 5G Ref

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Device‐to‐device (D2D) technology is a promising communication paradigm that enables direct communication between spatially close devices. The D2D technology is very appealing from the standpoint of efficient spatial reuse of resources since it permits to establish communications in networks with higher density of users. Consequently, the introduction of D2D will lead to network performance improvement in terms of spectral and energy efficiency, power consumption, delay reduction, as well as coverage extension. However, implementing D2D technology in 5G networks comes with a set of technical challenges, mainly due to interferences incurred between D2D links and between D2D and cellular users. This article provides a survey of the main interference management frameworks in D2D network in general and underlay D2D communication, in particular. Interference management‐based frameworks can be divided into two main categories: (i) power control and resource management schemes and (ii) advanced physical layer schemes, such as interference cancellation, interference alignment, and multiantenna systems. We provide a brief description of the main studies for the aforementioned categories and discuss the main results.

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Interference alignment and scheduling in clustered partially connected small cell networks

Ghorbani

Falahati

2020

Int J Communication

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Interference alignment (IA) is a beneficial interference management technique in heterogeneous networks (HetNets). Bearing this in mind, we target partially connected networks of small cells that are allocated randomly in a macrocell, whereas IA feasibility conditions may not be satisfied. To address this issue, we propose a new scheme with novel clustering and scheduling algorithms that is accomplished in three stages. The proposed scheme is accomplished by knowing the connection matrix of the established links that is obtained by different threshold values in the first stage. In the second stage, a new clustering algorithm is proposed to separate the cells to groups in which IA is feasible, and the feedback overhead is reduced. In the last stage, cluster scheduling method is proposed to efficiently schedule the clusters. The cluster scheduling allows the selected clusters with trivial intercluster interferences to transmit at the same time slot. The simulation results indicate the improvement of achievable averaged sum rate by the proposed clustering and cluster scheduling.

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Clustering for Interference Alignment in Multiuser Interference Network

Cited by 69 publications

References 20 publications

Distributed Long-Term Base Station Clustering in Cellular Networks Using Coalition Formation

Distributed Long-Term Base Station Clustering in Cellular Networks Using Coalition Formation

Interference Management inD2D

Interference alignment and scheduling in clustered partially connected small cell networks

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