Hierarchical Blind Interference Alignment Over Interference Networks With Finite Coherence Time

Yang, Heecheol; Shin, Wonjae; Lee, Jung Woo

doi:10.1109/tsp.2015.2496282

Cited by 14 publications

(13 citation statements)

References 28 publications

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“…Moreover, in high SNR regime, the achievable DoF for h‐BIA is also lower than that of s‐BIA. These results agree with what has been stated in Yang et al and is further confirmed by the less steep slope for the h‐BIA curves.…”

Section: Throughput Evaluationsupporting

confidence: 93%

“…While the preset mode pattern for h‐BIA is constructed, K users are grouped into K G groups ( K E = K / K G ), and M preset modes are also partitioned into M G groups ( M E = M / M G ). Assuming equal power allocation, as in Yang et al, the sum rate for the single cell scenario would be

R^{h‐BIA} = true \sum_{i = 1}^{K_{G}} true \sum_{k = 1}^{K_{E}} \frac{1}{((), M_{E} + K_{E} - 1) ((), M_{G} + K_{G} - 1)} double-struckE ([], \log ((), \det ((), boldI + \frac{P}{M} {\overset{H}{true}}^{false[k, i false]} {\overset{H}{true}}^{{false[k, i false]}^{+}} R_{\overset{z}{true}}^{- 1}))),

where

R_{\overset{z}{true}} = ([], \begin{matrix} array {boldR}_{{\tilde{z}}_{1}} & array & array & array 0 \\ array & array ⋱ & array & array \\ array & array & array {boldR}_{{\tilde{z}}_{1}} & array \\ array 0 & array & array & array {boldR}_{{\tilde{z}}_{2}} \end{matrix})

and

{\overset{H}{true}}^{false[k, i false]} = {([], {h^{false[k, i false]} false(1 false)}^{T}, ⋯, {h^{false[k, i false]} false(M - 1 false)}^{T}, {h^{false[k, i false]} false(M false)}^{T})}^{T}

…”

Section: Blind Interference Alignment Framework and Applicationsmentioning

confidence: 99%

“…Note that the staircase‐like behavior in the DoF versus symbol length curves is due to the optimization introduced in Equation of the hierarchical BIA paper for the two‐layer dynamic supersymbol design (DSD) algorithm.…”

Section: Throughput Evaluationmentioning

confidence: 99%

“…Finally, Yang et al have proposed the use of hierarchical blind interference alignment (h‐BIA) scheme over the interference channel and suggests how to apply it in cellular networks with finite coherence time with no data sharing between BSs. Initially, h‐BIA forms proper groups of users, and then in a hierarchical manner, interference between receivers in the same group (interuser interference, IUI) and subsequently between different groups (intergroup interference, IGI) is handled using s‐BIA.…”

Section: Introductionmentioning

confidence: 99%

“…h‐BIA requires full connectivity among users and all available BSs. In Yang et al, it has been shown that while h‐BIA can significantly reduce the length of the supersymbol length, it would suffer from sum‐DoF loss.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid blind interference alignment in homogeneous cellular networks with limited coherence time

Bakhshi

Ince

2018

Int J Communication

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The work presented in this paper is inspired by the user grouping approach of topological blind interference alignment (top-BIA), which is a semi-blind IA scheme and the fact that the partially connected networks can be advantageous in terms of degree of freedom (DoF) and sum rate. The hybrid-BIA scheme proposed in the paper uses top-BIA to group users that are randomly distributed in a dense small-cell network and aims to reduce the supersymbol length and overcome the DoF loss of the state-of-the-art hierarchical BIA (h-BIA) technique. The proposed scheme is very suitable for channels where coherence time is limited and also could attain a good DoF over a small number of symbol extensions.Both the sum-DoF and the network throughput for hybrid-BIA is greater than that of h-BIA. By varying the number of user groups, the paper shows that h-BIA constitutes a special case of hybrid-BIA. Finally, the paper demonstrates the effect of changing the number of transmit antennas and number of small cells on the sum-DoF gain of hybrid-BIA over that of h-BIA. KEYWORDSblind interference alignment, hierarchical BIA, hybrid-BIA, reconfigurable antenna, topological BIA Int J Commun Syst. 2019;32:e3836.wileyonlinelibrary.com/journal/dac the DoF of the network would collapse, and hence, the transmissions would become unreliable. 10 An interference alignment method that did not require CSIT for the broadcast channel (BC) was first proposed in Tiango et al 2 and then was elaborated upon in Jafar. 3 This new technique referred to as the standard blind interference alignment (s-BIA) assumes a K-user M × 1 MISO BC ([K, M] configuration) in which the BS is equipped with M transmit antennas and each receiver is equipped with a single reconfigurable antenna capable of switching between M preset modes. With no knowledge about CSIT, s-BIA can still align all intracell interference with only a mild assumption on the channel coherence structure and has been shown to achieve KM∕(K + M − 1) DoF. In addition, Lu et al 11,12 have used s-BIA under the K-user M × 1 MISO interference channel (IC) where users were assumed to have different number of preset modes. The s-BIA technique requires that the channel remains unchanged throughout the duration of the transmission. Therefore, coherence time or bandwidth is important when determining whether BIA can be used. 13 This motivates the search for BIA schemes that can provide short supersymbol lengths that will correspond to realistic coherence times. The performance of s-BIA in cellular and clustered systems was first analyzed in Wang et al. 14 It was shown that although s-BIA can totally cancel the intracell interference, the rates of users located at the cell edges will deteriorate due to the intercell interference coming from the neighboring cells. However, in Wang et al, 14,15 it is mentioned that if the BIA codes of the neighboring cells are synchronized (sync-BIA), then the effect of intercell interference can considerably be reduced.Under cellular networks, coordinated BIA scheme (c-BIA) 16 that ...

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Section: Throughput Evaluationsupporting

confidence: 93%

R^{h‐BIA} = true \sum_{i = 1}^{K_{G}} true \sum_{k = 1}^{K_{E}} \frac{1}{((), M_{E} + K_{E} - 1) ((), M_{G} + K_{G} - 1)} double-struckE ([], \log ((), \det ((), boldI + \frac{P}{M} {\overset{H}{true}}^{false[k, i false]} {\overset{H}{true}}^{{false[k, i false]}^{+}} R_{\overset{z}{true}}^{- 1}))),

where

R_{\overset{z}{true}} = ([], \begin{matrix} array {boldR}_{{\tilde{z}}_{1}} & array & array & array 0 \\ array & array ⋱ & array & array \\ array & array & array {boldR}_{{\tilde{z}}_{1}} & array \\ array 0 & array & array & array {boldR}_{{\tilde{z}}_{2}} \end{matrix})

and

{\overset{H}{true}}^{false[k, i false]} = {([], {h^{false[k, i false]} false(1 false)}^{T}, ⋯, {h^{false[k, i false]} false(M - 1 false)}^{T}, {h^{false[k, i false]} false(M false)}^{T})}^{T}

…”

Section: Blind Interference Alignment Framework and Applicationsmentioning

confidence: 99%

Section: Throughput Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid blind interference alignment in homogeneous cellular networks with limited coherence time

Bakhshi

Ince

2018

Int J Communication

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Blind interference alignment: A comprehensive survey

Selvaprabhu

2022

Int J Communication

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SummaryInterference is a crucial impairment for trustworthy wireless communication. To overcome this interference problem, many interference management strategies have been established. Interference alignment (IA) is one such significant interference management approach that handles the positioning of signals so that they form an overlapping shadow at the unintended receivers; the desired interference‐free signals remain separable at the intended receivers. However, the need for global channel state information at the transmitter (CSIT) acts as a practical limitation for IA in the real‐world wireless communication system. IA can mitigate this limitation associated with channel state information with no CSIT, also termed as blind IA (BIA). Therefore, BIA is an interference management strategy that utilizes the concept of IA with no knowledge of CSIT. Therefore, based on the plethora of prevailing significant works, the objective of our paper is to provide a comprehensive survey to draw a picture of BIA in terms of network topologies, applications in different networks, trending research areas, degrees of freedom (DoF), open‐research problems, and future research directions.

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Joint Optimization of User Grouping and Transmitter Connection on Multi-Cell SNR Blind Interference Alignment

Yang

Jiang

et al. 2016

IEEE Access

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Hierarchical Blind Interference Alignment Over Interference Networks With Finite Coherence Time

Cited by 14 publications

References 28 publications

Hybrid blind interference alignment in homogeneous cellular networks with limited coherence time

Hybrid blind interference alignment in homogeneous cellular networks with limited coherence time

Blind interference alignment: A comprehensive survey

Joint Optimization of User Grouping and Transmitter Connection on Multi-Cell SNR Blind Interference Alignment

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