Sparse Channel Estimation for Massive MIMO-OFDM Systems Over Time-Varying Channels

Qin, Qibo; Gui, Lin; Gong, Bo; Luo, Sheng

doi:10.1109/access.2018.2843783

Cited by 40 publications

(17 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where H n ∈ C M r ×M t is the MIMO channel matrix associated with the n-th OFDM subcarrier. Here, we assume that the channel matrices {H n } are perfectly known at the BS, which can be determined by exploiting the channel reciprocity [14], [15] of time division multiplexing (TDD) systems. It can be easily verified that the ZF has a high computational complexity [16], [17] since it needs to compute large-scale pseudo-inverse matrix.…”

Section: B Multi-user Precoding Schemementioning

confidence: 99%

PAPR-Aware Massive MIMO-OFDM Downlink

2019

View full text Add to dashboard Cite

We investigate the peak-to-average power ratio (PAPR) reduction problem in orthogonal frequency-division multiplexing-based massive multiuser (MU) multiple-input multiple-output (MIMO) downlink systems. In this paper, we develop a downlink transmission scheme that performs jointly MU precoding and PAPR reduction (PP) by exploiting the excess degrees of freedom offered by equipping the BS by a large number of antennas. Specifically, the joint MU precoding and PAPR reduction scheme is formulated as a simple convex optimization problem solved via steepest gradient descent (GD) approach. Then, we develop a novel algorithm, referred to as MU-PP-GDm, to reduce the PAPR of the transmitted signals by exploiting the high-dimensional null space of the MIMO channel matrix while maintaining an excellent transmission quality. The simulation results show that the proposed MU-PP-GDm has low computational complexity and can achieve substantial PAPR performance with a fast convergence rate. INDEX TERMS 5G+, massive multiple-input multiple-output (MIMO), orthogonal frequency-division multiplexing (OFDM), peak-to-average power ratio (PAPR), multiuser (MU) precoding, convex optimization, gradient descent (GD). The associate editor coordinating the review of this manuscript and approving it for publication was Jiayi Zhang. In practice, broadband wireless communications, however, encounter large delay spread, and, therefore, suffer from frequency-selective fading. Orthogonal frequency-division multiplexing (OFDM), a scheme of encoding digital symbols on multiple orthogonal sub-carriers, is an efficient and well-established way to deal with frequency-selective channels. Therefore, massive MIMO-OFDM is a very promising combination to meet the ever growing demands for higher link readability and spectrum efficiency of next-generation wireless communication systems (5G and Beyond). However, massive MIMO precoders exhibit transmit signals with high peak-to-average power ratio (PAPR), regardless of whether single-carrier or OFDM transmission and of whether a low or a high modulation order is used [4]. Consequently, the nonlinearity of the radio frequency (RF) high-power amplifier (HPA), which is necessary in a transmission chain, yields in-band distortion and out-of-band radiation (OBR) which cause signal distortion, phase rotation and adjacent channel interference, respectively. To avoid such heavy distortions, the transmit signals require that the power amplifiers are backed-off and operated in their linear region (i.e., where their transfer characteristics are sufficiently linear). Nevertheless, operating at lower power levels reduces 25474 2169-3536 2019 IEEE. Translations and content mining are permitted for academic research only.

show abstract

Section: B Multi-user Precoding Schemementioning

confidence: 99%

PAPR-Aware Massive MIMO-OFDM Downlink

2019

View full text Add to dashboard Cite

show abstract

“…Paper [5], [27] considered the hidden joint common and individual sparsity in angle domain among the user channel matrices due to the shared local scatterers in the physical propagation environment. A nonorthogonal downlink pilot design was proposed in [8], [12], [14], [28] by exploiting the spatially common sparsity in angle domain and time correlation of massive MIMO channels. The channel sparsity in angle domain with partial support information was utilized in [29], [30] to acquire compressed CSI.…”

Section: A Related Workmentioning

confidence: 99%

Decoupling Channel Estimation for FDD Massive MIMO Systems Utilizing Joint Sparsity

et al. 2020

View full text Add to dashboard Cite

Effective channel estimation for massive multiple-input-multiple-output (MIMO) systems based on frequency division duplex (FDD) protocol is a crucial problem. To reduce pilot overhead, we consider the joint sparsity of the multi-path channel in delay-angle domain. Based on the joint sparsity, we propose a decoupling pilot design scheme. In the proposed scheme, the pilot design is decoupled into two parts. In the first part, a global optimal selection greedy iterative algorithm (GOS-GIA) is proposed to obtain the near-optimal pilot subcarrier pattern. In the second part, a random Rademacher distribution pilot matrix is used as the angle-domain pilot matrix. Accordingly, a two-stage channel estimation strategy is also provided and analyzed. The first stage of the strategy is concerned with retrieving the positions of non-zero dominant taps in the delay domain. The second stage focuses on estimating the channel coefficients at these taps. Algorithm complexity analysis shows that the GOS-GIA can reduce the computational complexity at least one order of magnitude, and the proposed channel estimation strategy reduces the computational complexity by more than two orders of magnitude. Simulation results verify that the proposed pilot design scheme achieves better performance with lower pilot overhead. INDEX TERMS Angle domain, channel estimation, compressive sensing, delay domain, massive multiple-input-multiple-output, pilot design, sparsity.

show abstract

“…which represents the corresponding power matrix of H defined in (11) and (12) and their corresponding power matrices satisfy similar expressions as in (13) and (14).…”

Section: Channel Estimation With Psapsmentioning

confidence: 99%

Phase Shift Adjustable Pilots for Channel Acquisition in Vehicle-to-Vehicle Underlay Wideband Massive MIMO

2020

View full text Add to dashboard Cite

Decrease of the pilot overhead is an important issue in vehicle-to-vehicle (V2V) underlay wideband massive multiple-input multiple-output (mMIMO) communication. To reduce the pilot overhead for channel acquisition, we propose phase shift adjustable pilots (PSAPs) for V2V underlay wideband mMIMO systems employing orthogonal frequency division multiplexing (OFDM). Motivated by the sparse property of the angle-delay (AD) domain channels in mMIMO, we first investigate channel acquisition for V2V underlay mMIMO-OFDM with PSAPs, and derive the optimal conditions to achieve the sum mean square error (sMSE) minimums of channel estimation and channel prediction, respectively. The optimal conditions indicate that the minimum sMSE can be achieved when the AD domain channel power spectra of cellular users (CUs) and transmitters in V2V communication pairs are non-overlapping after appropriate allocation of the available pilot phase shifts. Then an AD domain channel power spectrum based pilot allocation (ADCPS-PA)algorithm is developed according to the optimal conditions. Simulation results indicate that the achievable spectral efficiency for both cellular and V2V links can be significantly improved with PSAPs compared with that obtained by the conventional phase shift orthogonal pilot approach. INDEX TERMS Channel acquisition, phase shift adjustable pilots, pilot phase shift allocation, V2V underlay, wideband massive MIMO.

show abstract

Sparse Channel Estimation for Massive MIMO-OFDM Systems Over Time-Varying Channels

Cited by 40 publications

References 31 publications

PAPR-Aware Massive MIMO-OFDM Downlink

PAPR-Aware Massive MIMO-OFDM Downlink

Decoupling Channel Estimation for FDD Massive MIMO Systems Utilizing Joint Sparsity

Phase Shift Adjustable Pilots for Channel Acquisition in Vehicle-to-Vehicle Underlay Wideband Massive MIMO

Contact Info

Product

Resources

About