A Distributed Numerical Approach to Interference Alignment and Applications to Wireless Interference Networks

Gomadam, K.S.; Cadambe, Viveck R.; Jafar, Syed A.

doi:10.1109/tit.2011.2142270

Cited by 821 publications

(1,094 citation statements)

References 27 publications

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“…Then, we show that the proposed method indeed achieves the optimal multiuser diversity gain KM log(SNR log N) provided that the two thresholds are properly determined and the number of per-cell MSs, N, is greater than a certain level SNR KM−L 1− for a small constant > 0, where SNR denotes the signal-to-noise ratio (SNR). Note that the the multiuser diversity can be achieved in the presence of intra-cell and inter-cell interference in a distributed manner, operating based on local CSI at each MS as in [10]. Simulation results show that the proposed method outperforms two distributed baseline schemes in terms of sum-rates under practical network environments.…”

Section: Contributionsmentioning

confidence: 99%

“…This is because network coordination is difficult in practical systems assuming not only no information exchange among BSs but also local channel state information (CSI) at the transmitters. In [10], a distributed interference alignment (IA) technique was proposed for the K-user MIMO interference channel, where each transmitter adopts the beamforming vector such that the generating interference to other receivers is minimized except for its own receiver and each receiver adopts the beamforming vector such that the received interference from other transmitters is minimized except for its own transmitter. However, the distributed IA technique requires an iterative beamformer optimization for data transmission.…”

Section: Previous Workmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Multiuser Diversity in Multi-Cell MIMO Uplink Networks: User Scaling Law and Beamforming Design

Jung

Kim

Shin

et al. 2017

Entropy

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Abstract:We introduce a distributed protocol to achieve multiuser diversity in a multicell multiple-input multiple-output (MIMO) uplink network, referred to as a MIMO interfering multiple-access channel (IMAC). Assuming both no information exchange among base stations (BS) and local channel state information at the transmitters for the MIMO IMAC, we propose a joint beamforming and user scheduling protocol, and then show that the proposed protocol can achieve the optimal multiuser diversity gain, i.e., KM log(SNR log N), as long as the number of mobile stations (MSs) in a cell, N, scales faster than SNR KM−L 1− for a small constant > 0, where M, L, K, and SNR denote the number of receive antennas at each BS, the number of transmit antennas at each MS, the number of cells, and the signal-to-noise ratio, respectively. Our result indicates that multiuser diversity can be achieved in the presence of intra-cell and inter-cell interference even in a distributed fashion. As a result, vital information on how to design distributed algorithms in interference-limited cellular environments is provided.

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Section: Contributionsmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Optimal Multiuser Diversity in Multi-Cell MIMO Uplink Networks: User Scaling Law and Beamforming Design

Jung

Kim

Shin

et al. 2017

Entropy

View full text Add to dashboard Cite

show abstract

“…IA algorithms used for estimating the precoding/decoding vectors (e.g., [8], [9]) are compute intensive as they involve multiple eigen-vector calculations and matrix multiplications. These calculations need to be performed in a fraction of the channel coherence time T c (defined as the duration over which the impulse response is considered to be time invariant) so that the estimated precoding/decoding vectors are reusable for the remaining time of the coherence time.…”

Section: Computational Complexity and Communication Delaymentioning

confidence: 99%

“…Distributed IA Algorithm: We now present an iterative IA algorithm [8] for which our framework provides computing infrastructure to support its distributed capabilities. This algorithm is computationally intensive as it involves multiple matrix and eigen-vector calculations.…”

Section: Distributed Computing Frameworkmentioning

confidence: 99%

Region of Feasibility of Interference Alignment in Underwater Sensor Networks

Pandey

Hajimirsadeghi

Pompili

2014

IEEE J. Oceanic Eng.

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Abstract-To enable underwater applications such as coastal and tactical surveillance, undersea explorations, and picture/video acquisition, there is a need to achieve high datarate underwater acoustic communications, which translates into attaining high acoustic channel spectral efficiencies. Interference Alignment (IA) technique, which has recently been proposed for radio-frequency MIMO terrestrial communication systems, aims at improving the spectral efficiency by enabling nodes to transmit data simultaneously at a rate equal to half of the interference-free channel capacity. While promising, there are challenges to be solved for the use of IA underwater, i.e., imperfect acoustic channel knowledge, high computational complexity, and high communication delay. In this paper, a feasibility study on the practical employment of IA underwater is presented, and a novel distributed computing framework for sharing processing resources in the network so to parallelize an iterative IA algorithm is proposed.

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“…Several iterative algorithms in the literature have focused on finding the alignment solutions numerically. The motivation for an iterative approach in [22] is to achieve IA with only local channel knowledge, by exploiting the two way nature of communication and the reciprocal nature of the physical propagation medium. The alternating minimization approach proposed in [49] uses similar distributed IA but does not explicitly assume channel reciprocity.…”

Section: Classification Of Ia Techniquesmentioning

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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A Distributed Numerical Approach to Interference Alignment and Applications to Wireless Interference Networks

Cited by 821 publications

References 27 publications

Optimal Multiuser Diversity in Multi-Cell MIMO Uplink Networks: User Scaling Law and Beamforming Design

Optimal Multiuser Diversity in Multi-Cell MIMO Uplink Networks: User Scaling Law and Beamforming Design

Region of Feasibility of Interference Alignment in Underwater Sensor Networks

Cognitive Interference Alignment for Spectral Coexistence

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