FBMC Channel Equalization Techniques

Baltar, Leonardo Gomes; Chevalier, Pascal; Renfors, Markku; Yli‐Kaakinen, Juha; Louveaux, Jérôme; Mestre, Xavier; Bader, Faouzi; Savaux, Vincent

doi:10.1016/b978-0-12-810384-5.00012-8

Cited by 2 publications

(3 citation statements)

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“…from ( 28) that the channel and (virtual) symbol matrices can be determined (up to scaling ambiguity) through a Khatri-Rao factorization (KRF): compute the rank-1 LS approximation (with the aid of a singular value decomposition (SVD) 8 ) of the matricized version of each column of Y T (1) [78] to come up with estimates (up to scaling ambiguity) of the corresponding columns of H and C T . Some (limited) training information can be exploited to resolve the scaling ambiguity.…”

Section: Tensor-based Formulationmentioning

confidence: 99%

“…Remarks. 8 An alternative method, of linear instead of cubic complexity, is proposed in [36], [44] for MIMO-OFDM, however with rather poor results for the channel matrix estimation. 9 The fact that one of the three factor matrices is known could justify a characterization of this problem as a bilinear instead of a trilinear one.…”

Section: Tensor-based Formulationmentioning

confidence: 99%

“…Although FBMC research has been rapidly advancing in the last decade or so, resulting in a number of well performing techniques for receiver design, blind (i.e., using no pilots) or semi-blind (i.e., with limited pilot information) FBMC methods have been very little studied so far, and mainly for the single-antenna case (see [7] and [8] for a recent review in (semi-) blind FBMC/OQAM channel estimation and equalization, respectively). With the advent of massive MIMO systems and the difficulties they imply in trainingbased estimation, semi-blind or joint channel estimation/data detection (JCD) techniques 1 are being considered as an alternative approach [10]- [12], re-surging the interest in blind source separation (BSS) for wireless communications [13].…”

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confidence: 99%

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A Tensor-based Approach to Joint Channel Estimation / Data Detection in Flexible Multicarrier MIMO Systems

Kofidis

2019

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Filter bank-based multicarrier (FBMC) systems have attracted increasing attention recently in view of their many advantages over the classical cyclic prefix (CP)-based orthogonal frequency division multiplexing (CP-OFDM) modulation. However, their more advanced structure (resulting in, for example, self interference) complicates signal processing tasks at the receiver, including synchronization, channel estimation and equalization. In a multiple-input multiple-output (MIMO) configuration, the multi-antenna interference has also to be taken into account. (Semi-) blind receivers, of increasing interest in (massive) MIMO systems, have been little studied so far for FBMC and mainly for the single-antenna case only. The design of such receivers for flexible MIMO FBMC systems, unifying a number of existing FBMC schemes, is considered in this paper through a tensorbased approach, which is shown to encompass existing joint channel estimation and data detection approaches as special cases, adding to their understanding and paving the way to further developments. Simulation-based results are included, for realistic transmission models, demonstrating the estimation and detection performance gains from the adoption of these receivers over their training only-based counterparts. I. INTRODUCTIONF ILTER bank-based multicarrier (FBMC) systems [1] have recently attracted increasing attention as a competent alternative to the classical cyclic prefix (CP)-based orthogonal frequency division multiplexing (CP-OFDM) modulation, in a wide range of applications, including both wireless (e.g., ad-hoc public safety) and wired (e.g., fiber optics) communications [2]. The potential of FBMC transmission stems from its increased ability to carrying a flexible spectrum shaping, along with a major increase in spectral efficiency and robustness to synchronization requirements, features of fundamental importance in modern and future networks. However, its more advanced structure (resulting in, for example, self interference) complicates signal processing tasks at the receiver, including synchronization, channel estimation and equalization [2]. In a multiple-input multiple-output (MIMO) configuration, the multi-antenna interference has also to be taken into account, which can be a challenge in, for example, offset quadrature amplitude modulation (OQAM)-based FBMC [3]. Nevertheless FBMC(/OQAM) is being also considered for massive MIMO systems [4], [5] and has also shown its potential in the envisaged millimeter wave (mmWave) communications [6].

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Section: Tensor-based Formulationmentioning

confidence: 99%

Section: Tensor-based Formulationmentioning

confidence: 99%