On the Design of Interference Alignment Scheme for Two-Cell MIMO Interfering Broadcast Channels

Shin, Wonjae; Lee, Namyoon; Lim, Jong Bong; Shin, Changyong; Jang, Kyunghun

doi:10.1109/twc.2011.120810.101097

Cited by 166 publications

(140 citation statements)

References 12 publications

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“…As shown in Fig.2, the base station , 1, 2,..., 1 l l L   and the users in the cell 1 l  are grouped into group l according to the paper [5] and [6]. The base station L and the users in the cell 1 are divided into the group L .…”

Section: Figure 2 Extended User Grouping Diagrammentioning

confidence: 99%

“…This algorithm has achieved better results under the two communities, but it will cause greater interference leakage for multi-cell systems, due to the need to select the main interference and residual interference, and the remaining interference is treated as noise. In [5], a two-cell interference alignment algorithm is proposed. By aligning the inter-cell interference of each user in the cell into the same subspace, the transmitter uses the zero forcing algorithm to eliminate inter-cell interference and inter-user interference.…”

Section: Introductionmentioning

confidence: 99%

“…By aligning the inter-cell interference of each user in the cell into the same subspace, the transmitter uses the zero forcing algorithm to eliminate inter-cell interference and inter-user interference. On the basis of [5], in [6], an interference alignment algorithm of Extension User Group (EUG) is proposed and is extended to any cell. In [7], a regularized zero-interference alignment algorithm is proposed for the two-cell system, which effectively reduces the complexity of the algorithm, but it is difficult to extend to any number of cells.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Interference Alignment Algorithm Based on SLNR for MIMO Interfering Broadcast Channel

Wang¹,

Zhu²,

Ba³

2017

2017 6th International Conference on Advanced Materials and Computer Science (ICAMCS 2017)

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Abstract. Aiming at the problem that the existing EUG IA algorithm does not have antinoise performance, this paper proposes an interference alignment algorithm based on SLNR. The algorithm uses a hierarchical design idea. The outer layer first uses the extended grouping strategy to group the cells and users, and solves the outer precoding matrix and the interference suppression matrix to eliminate the inter-cell interference, so as to transform the multi-cell multi-user interference cancellation into single-cell multi-user interference cancellation problem. The inner layer uses the Max-SLNR criterion to iterate to solve the single-cell precoding matrix and the interference suppression matrix to eliminate inter-user interference. The simulation results show that the proposed algorithm has better performance and anti-noise performance than the original algorithm.

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Section: Figure 2 Extended User Grouping Diagrammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Interference Alignment Algorithm Based on SLNR for MIMO Interfering Broadcast Channel

Wang¹,

Zhu²,

Ba³

2017

2017 6th International Conference on Advanced Materials and Computer Science (ICAMCS 2017)

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“…The DoF regions are characterized for several wireless channels such as MIMO BC, interference channels (ICs), including X and multihop ICs, and the CR channels [68]. The DoF metric has been extensively used for interference mitigation and alignment objectives in various wireless networks such as interference mitigation in multicell networks [10,31], interference mitigation in two-cell MIMO interfering BCs [63], IA in CR networks [14,34,57]. The main limitation of the DoF metric is that it does not provide much insight to optimally manage interference when all signals are not comparable, since it forces all channels to be equally strong.…”

Section: Degrees Of Freedom (Dof)mentioning

confidence: 99%

Cognitive Interference Alignment for Spectral Coexistence

Sharma

Chatzinotas

Ottersten

2014

Cognitive Radio and Networking for Heterogeneous Wireless Networks

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Interference Alignment (IA) has been widely recognized as a promising interference mitigation technique since it can achieve the optimal degrees of freedom in certain interference limited channels. In the context of Cognitive Radio (CR) networks, this technique allows the coexistence of two heterogeneous wireless systems in an underlay cognitive mode. The main concept behind this technique is the alignment of the interference on a signal subspace in such a way that it can be filtered out at the non-intended receiver by sacrificing some signal dimensions. This chapter starts with an overview of IA principle, Degree of Freedom (DoF) concept, and the classification of existing IA techniques. Furthermore, this chapter includes a discussion about IA applications in CR networks. Moreover, a generic system model is presented for allowing the coexistence of two heterogeneous networks using IA approach while relevant precoding and filtering processes are described. In addition, two important practical applications of the IA technique are presented along with the numerical results for underlay spectral coexistence of (i) femtocell-macrocell systems, and (ii) monobeam-multibeam satellite systems. More specifically, an uplink IA scheme is investigated in order to mitigate the interference of femtocell User Terminals (UTs) towards the macrocell Base Station (BS) in the spatial domain and the interference of multibeam satellite terminals towards the monobeam satellite in the frequency domain.

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“…The key idea of interference alignment is to consolidate the interference signals from the undesired sources as much as possible while separate the desired signal from interference at the desired receiver. Interference alignment approach has also been applied to MIMO cellular networks in [8][9][10][11]. However, these works have focused on two-cell configuration with one data stream for each user [8], [9], and their extension to general MIMO cellular networks (with arbitrarily number of cells) and to the two-tier heterogeneous network is highly non-trivial.…”

Section: Introductionmentioning

confidence: 99%

Interference Alignment for the Uplink Heterogeneous Cellular Networks

Gao

Wang

Cui

2016

MATEC Web of Conferences

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Abstract. Heterogeneous cellular networks, due to its multi-tier topological structure, provide significant improvements in terms of increased data rates and cell coverage. However, there are important intra-tier and inter-tier interference management problem to be solved. In this paper, we model a two-tier uplink heterogeneous network, which include a macrocell and K overlaid femtocells. Meanwhile we constitute a heterogeneous configuration that each femto BS is equipped with two antennas and the macro BS is equipped with 2 2antennas. We proved that the sum-DoF outer bound of the network is 2 4 4 K K A , and the DoF outer bound for the femtocell and macrocell is 2 4 K A K A and 4 A K A respectively. Moreover, we present the achievable scheme, which is based on interference alignment. The simulation results show that the sum rate of the whole HetNet increases as the number of macro BS's antennas increases.

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On the Design of Interference Alignment Scheme for Two-Cell MIMO Interfering Broadcast Channels

Cited by 166 publications

References 12 publications

An Interference Alignment Algorithm Based on SLNR for MIMO Interfering Broadcast Channel

An Interference Alignment Algorithm Based on SLNR for MIMO Interfering Broadcast Channel

Cognitive Interference Alignment for Spectral Coexistence

Interference Alignment for the Uplink Heterogeneous Cellular Networks

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