Interference Alignment and Degrees of Freedom of the $K$-User Interference Channel

Cadambe, Viveck R.; Jafar, Syed A.

doi:10.1109/tit.2008.926344

Cited by 2,838 publications

(3,705 citation statements)

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“…The relativity of alignment is an important aspect for enabling IA in interference wireless networks [5]. It implies that when there are multiple non-intended receivers, the alignment of signals in these receivers is different i.e., the set of input-output equations observed in each receiver is different from those observed in other receivers.…”

Section: Ia Principlementioning

confidence: 99%

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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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Section: Ia Principlementioning

confidence: 99%

“…For the Gaussian interference channel with K interfering transmitter-receiver pairs with each transmit and receive node having M antennas each, and with random, time-varying channel coefficients drawn from a continuous distribution, the sum-capacity of the network is characterized as [5]:…”

Section: Ia Principlementioning

confidence: 99%

Cognitive Interference Alignment for Spectral Coexistence

Sharma

Chatzinotas

Ottersten

2014

Cognitive Radio and Networking for Heterogeneous Wireless Networks

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“…IA is a promising technique which achieves the maximum degrees of freedom (DoF) for K users in interference channels [4]. This can be achieved by a combination of linear precoding at the transmitters and interference suppression at the receivers.…”

Section: Mimo-uwb Interference Mitigation By Interference Alignmentmentioning

confidence: 99%

“…This can be achieved by a combination of linear precoding at the transmitters and interference suppression at the receivers. IA permits to force interfering signals at each receiver in one subspace and the desired signal in another orthogonal subspace [5].…”

Section: Mimo-uwb Interference Mitigation By Interference Alignmentmentioning

confidence: 99%

“…The proposed 3-user 2x2 MIMO zero-forcing IA antenna selection algorithm computes precoding matrices V, decoding matrices U and a metric η based on [4]. Compared to the experimental testbed for fixed antenna patterns presented in [13], our implementation also chooses a subset of channels (i.e.…”

Section: Case Study: 3-user Antenna Selection Interference Alignmentmentioning

confidence: 99%

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