Interference alignment feasibility in constant coefficient MIMO interference channels

Negro, Francesco; Shenoy, Shakti Prasad; Ghauri, Irfan; Slock, Dirk

doi:10.1109/spawc.2010.5670969

Cited by 19 publications

(19 citation statements)

References 7 publications

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“…Another interesting observation here is that the number of antennas at each transmitter and receiver M, N affects the feasibility only through their sum M + N . Thus, for example, the systems (4 [146] and Negro et al in [104], rigorous connections between proper/improper systems and feasibility/ infeasibility of linear interference alignment were initially only available for a few special cases. For example, Yetis et al used Bernshtein's theorem from algebraic geometry to analytically evaluate the feasibility of linear interference alignment because the number of solutions of the system of polynomials can be evaluated as the mixed volume of the Newton polytopes corresponding to the support (i.e., the monomials with non-zero coefficients) of the polynomials.…”

Section: Feasibility Of Linear Interference Alignmentmentioning

confidence: 99%

Interference Alignment — A New Look at Signal Dimensions in a Communication Network

Jafar¹

2010

FNT in Communications and Information Theory

385

440

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This monograph introduces to the reader the idea of interference alignment, traces its origins, reviews a variety of interference alignment schemes, summarizes the diverse settings where the idea of interference alignment is applicable and highlights the common principles that cut across these diverse applications. The focus is on theoretical aspects.1 To keep the discussion general as well as compact we do not specify if the symbols are from a finite field or if they are real/complex, as the same ideas apply in both cases. In the latter case, however, it is worth mentioning that as usual there are additive noise terms and power constraints present, that are not shown in these equations, but without which the communication problem would be trivial, i.e., the infinite resolution of a real/complex number will allow infinite information transfer in any signal dimension.

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Section: Feasibility Of Linear Interference Alignmentmentioning

confidence: 99%

Interference Alignment — A New Look at Signal Dimensions in a Communication Network

Jafar¹

2010

FNT in Communications and Information Theory

385

440

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“…The next step is to design the receiver equalizers. Assuming the alternating minimization method has converged to an IA solution (see [9] and [28,Section 6] for discussions on its convergence), by knowing a basis for the interference subspace at each receiver i, i.e. columns of C i , (1) can be written as…”

Section: Organization and Notationmentioning

confidence: 99%

MIMO Interference Alignment Over Correlated Channels With Imperfect CSI

Nosrat-Makouei

Andrews

Heath

2011

IEEE Trans. Signal Process.

172

130

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Interference alignment (IA), given uncorrelated channel components and perfect channel state information, obtains the maximum degrees of freedom in an interference channel. Little is known, however, about how the sum rate of IA behaves at finite transmit power, with imperfect channel state information, or antenna correlation. This paper provides an approximate closed-form signalto-interference-plus-noise-ratio (SINR) expression for IA over multiple-input-multiple-output (MIMO) channels with imperfect channel state information and transmit antenna correlation. Assuming linear processing at the transmitters and zero-forcing receivers, random matrix theory tools are utilized to derive an approximation for the post-processing SINR distribution of each stream for each user. Perfect channel knowledge and i.i.d. channel coefficients constitute special cases. This SINR distribution not only allows easy calculation of useful performance metrics like sum rate and symbol error rate, but also permits a realistic comparison of IA with other transmission techniques. More specifically, IA is compared with spatial multiplexing and beamforming and it is shown that IA may not be optimal for some performance criteria.The authors are with the

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“…We initially focus on the duality regime in the MISO-SIMO IFC in order to identify if any structure similar to UL-DL duality of the BC exists in this case. Zero-forcing duality and the more specific interference alignment duality are known to hold in the K-user IFC [5] [6]. To the best of our knowledge, no other results proving any other kind of duality have been established for the IFC.…”

Section: Introductionmentioning

confidence: 88%

Beamforming for the underlay cognitive MISO interference channel via UL-DL duality

Negro¹,

Ghauri

Slock³

2010

Proceedings of the 5th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications

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SINR duality is shown in a multi-input singleoutput (MISO) downlink (DL) interference channel (IFC) and its uplink (UL) single-input multi-output (SIMO) dual with linear transmit (Tx) beamformers (BF). While UL-DL duality under the sum power constraint is well-established between the broadcast channel (BC) for the SINR balancing (max min SINR) beamforming problem and its (easier to solve) UL multipleaccess (MAC) dual channel, such duality does not at first seem relevant for the IFC. We show that SINR duality under the sum power constraint nevertheless holds in the MISO IFC leading to BF design through similar considerations as the BC-MAC case. We next impose further per-Tx power constraints meaningful for the IFC structure and show continued existence of SINR duality in the MISO IFC and the corresponding UL SIMO dual channel, but this time with an uncertain UL noise scaling. The beamformers, Tx powers and noise variances are found through an iterative algorithm. We extend the solution to a cognitive radio network where this MISO IFC is co-deployed in a underlay fashion with a primary network with interference power constraints on primary single antenna receivers.

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Interference alignment feasibility in constant coefficient MIMO interference channels

Cited by 19 publications

References 7 publications

Interference Alignment — A New Look at Signal Dimensions in a Communication Network

Interference Alignment — A New Look at Signal Dimensions in a Communication Network

MIMO Interference Alignment Over Correlated Channels With Imperfect CSI

Beamforming for the underlay cognitive MISO interference channel via UL-DL duality

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