Efficient channel statistics estimation for millimeter-wave MIMO systems

Wang, Yue; Tian, Zhi; Feng, Shulan; Zhang, Philipp

doi:10.1109/icassp.2016.7472310

Cited by 19 publications

(13 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. , aM (f2,K )] are the manifold matrices with their columns aN (f1,i) and aM (f2,i) exhibiting the Vandermonde structures of size N and M respectively [11][12][13],…”

Section: Signal Modelmentioning

confidence: 99%

Efficient Super-Resolution Two-Dimensional Harmonic Retrieval Via Enhanced Low-Rank Structured Covariance Reconstruction

Wang

Tian

Leus

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

This paper develops an enhanced low-rank structured covariance reconstruction (LRSCR) method based on the decoupled atomic norm minimization (D-ANM), for super-resolution two-dimensional (2D) harmonic retrieval with multiple measurement vectors. This LRSCR-D-ANM approach exploits a potential structure hidden in the covariance by transferring the basic LRSCR to an efficient D-ANM formulation, which permits a sparse representation over a matrix-form atom set with decoupled 1D frequency components. The new LRSCR-D-ANM method builds upon the existence of a generalized Vandermonde decomposition of its solution, which otherwise cannot be guaranteed by the basic LRSCR unless a very conservative condition holds. Further, a low-complexity solution of the LRSCR-D-ANM is provided for fast implementation with negligible performance loss. Simulation results verify the advantages of the proposed LRSCR-D-ANM over the basic LRSCR, in terms of the wider applicability and the lower complexity.

show abstract

“…. , aM (f2,K )] are the manifold matrices with their columns aN (f1,i) and aM (f2,i) exhibiting the Vandermonde structures of size N and M respectively [11][12][13],…”

Section: Signal Modelmentioning

confidence: 99%

Efficient Super-Resolution Two-Dimensional Harmonic Retrieval Via Enhanced Low-Rank Structured Covariance Reconstruction

Wang

Tian

Leus

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Works [16], [17] proposed a CSbased narrowband BF training with pseudorandom sounding beamformers in the downlink, and [18] extended this approach for a wideband channel. Other related works include channel covariance estimation [19]- [21] which requires periodic channel observations, and UE centric uplink training [22], [23]. It is worth nothing that all recent works focus on channel estimation alone while assuming perfect cell discovery and synchronization.…”

Section: A Related Workmentioning

confidence: 99%

“…As a consequence, we do not include its complexity here. It is worth noting that the above analysis assumes there is an off-line pre-computation of all required dictionaries for matching pursuit, i.e., p q in (19),ã k in (22). In addition, the directional IA requires the first two steps in Table II.…”

Section: Access Latency Overhead and Dsp Complexitymentioning

confidence: 99%

Compressive Initial Access and Beamforming Training for Millimeter-Wave Cellular Systems

Han

Čabrić

2019

IEEE J. Sel. Top. Signal Process.

View full text Add to dashboard Cite

Initial access (IA) is a fundamental physical layer procedure in cellular systems where user equipment (UE) detects nearby base station (BS) as well as acquire synchronization. Due to the necessity of using antenna array in millimeter-wave (mmW) IA, the channel spatial information can also be inferred. The state-of-the-art directional IA (DIA) uses sector sounding beams with limited angular resolution, and thus requires additional dedicated radio resources, access latency and overhead for refined beam training. To remedy the problem of access latency and overhead in DIA, this work proposes to use a quasi-omni pseudorandom sounding beam for IA, and develops a novel algorithm for joint initial access and fine resolution initial beam training without requiring extra radio resources. We provide the analysis of the proposed algorithm miss detection rate under synchronization error, and further derive Cramér-Rao lower bound of angular estimation under frequency offset. Using QuaDRiGa simulator with mmMAGIC model at 28 GHz, the numerical results show that the proposed approach is advantageous to DIA with hierarchical beam training. The proposed algorithm offers up to two order of magnitude access latency saving compared to DIA, when the same discovery, post training SNR, and overhead performance are targeted. This conclusion holds true in various propagation environments and 3D locations of a mmW pico-cell with up to 140m radius.

show abstract

“…The channel covariance is an important second-order statistic, which remains constant over many channel coherence intervals and therefore can be used for statistics-based design of the precoders, beamformers and linear receivers [21], [22]. To estimate the second-order statistics of the vectorized sparse mmWave MIMO channel, a diagonal-search orthogonal matching pursuit algorithm is developed in [19], which not only utilizes the joint sparsity represented by the available multiple measurement vectors (MMV) but also takes advantage of the Hermitian structure of the channel covariance matrix. In [20], a CS-based channel covariance estimator is proposed by using dynamic sensing schemes and designing dynamic greedy pursuit algorithms for the hybrid architecture.…”

Section: Introductionmentioning

confidence: 99%

“…All the aforementioned techniques aim to estimate the instantaneous channel state information (CSI). Another line of work focuses on estimating the channel statistics, such as the channel covariance [19], [20]. The channel covariance is an important second-order statistic, which remains constant over many channel coherence intervals and therefore can be used for statistics-based design of the precoders, beamformers and linear receivers [21], [22].…”

Section: Introductionmentioning

confidence: 99%

Super-Resolution Channel Estimation for Arbitrary Arrays in Hybrid Millimeter-Wave Massive MIMO Systems

Wang

Zhang

Tian

et al. 2019

IEEE J. Sel. Top. Signal Process.

Self Cite

View full text Add to dashboard Cite

This paper develops efficient channel estimation techniques for millimeter-wave (mmWave) massive multipleinput multiple-output (MIMO) systems under practical hardware limitations, including an arbitrary array geometry and a hybrid hardware structure. Taking on an angle-based approach, this work adopts a generalized array manifold separation approach via Jacobi-Anger approximation, which transforms a non-ideal, non-uniform array manifold into a virtual array domain with a desired uniform geometric structure to facilitate super-resolution angle estimation and channel acquisition. Accordingly, structurebased optimization techniques are developed to effectively estimate both the channel covariance and the instantaneous channel state information (CSI) within a short sensing time. In particular, the difference in time-variation of channel path angles and path gains is capitalized to design a two-step CSI estimation scheme that can quickly sense fading channels. Theoretical results are provided on the fundamental limits of the proposed technique in terms of sample efficiency. For computational efficiency, a fast iterative algorithm is developed via the alternating direction method of multipliers. Other related issues such as spuriouspeak cancellation in nonuniform linear arrays and extensions to higher-dimensional cases are also discussed. Simulations testify the effectiveness of the proposed approaches in hybrid mmWave massive MIMO systems with arbitrary arrays.

show abstract

Efficient channel statistics estimation for millimeter-wave MIMO systems

Cited by 19 publications

References 18 publications

Efficient Super-Resolution Two-Dimensional Harmonic Retrieval Via Enhanced Low-Rank Structured Covariance Reconstruction

Efficient Super-Resolution Two-Dimensional Harmonic Retrieval Via Enhanced Low-Rank Structured Covariance Reconstruction

Compressive Initial Access and Beamforming Training for Millimeter-Wave Cellular Systems

Super-Resolution Channel Estimation for Arbitrary Arrays in Hybrid Millimeter-Wave Massive MIMO Systems

Contact Info

Product

Resources

About