Channel Estimation for Massive MIMO: A Semiblind Algorithm Exploiting QAM Structure

IEEE Trans. Wireless Commun.

et al. 2022

We propose a joint channel estimation and data detection (JED) algorithm for densely-populated cell-free massive multiuser (MU) multiple-input multiple-output (MIMO) systems, which reduces the channel training overhead caused by the presence of hundreds of simultaneously transmitting user equipments (UEs). Our algorithm iteratively solves a relaxed version of a maximum a-posteriori JED problem and simultaneously exploits the sparsity of cell-free massive MU-MIMO channels as well as the boundedness of QAM constellations. In order to improve the performance and convergence of the algorithm, we propose methods that permute the access point and UE indices to form so-called virtual cells, which leads to better initial solutions. We assess the performance of our algorithm in terms of root-meansquared-symbol error, bit error rate, and mutual information, and we demonstrate that JED significantly reduces the pilot overhead compared to orthogonal training, which enables reliable communication with short packets to a large number of UEs.

Section: James-stein Estimator and Median Absolute Deviationmentioning

confidence: 99%

Joint Channel Estimation and Data Detection in Cell-Free Massive MU-MIMO Systems

Song

Goldstein

IEEE Trans. Wireless Commun.

et al. 2022

“…For massive singleinput multiple-output (SIMO) wireless systems, an efficient JED algorithm and VLSI design has been proposed in [17]. For massive MU-MIMO systems, suitable algorithms have been proposed only recently [18]- [21]. These methods have been designed mostly for Rayleigh fading channels and/or for systems in which the number of basestation (BS) antennas are significantly larger than the number of UEs.…”

Section: B Relevant Prior Artmentioning

confidence: 99%

Minimizing Pilot Overhead in Cell-Free Massive MIMO Systems via Joint Estimation and Detection

Song¹,

2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

Zhang³

et al. 2020

We propose a joint channel estimation and data detection (JED) algorithm for cell-free massive multiuser (MU) multiple-input multiple-output (MIMO) systems. Our algorithm yields improved reliability and reduced latency while minimizing the pilot overhead of coherent uplink transmission. The proposed JED method builds upon a novel non-convex optimization problem that we solve approximately and efficiently using forwardbackward splitting. We use simulation results to demonstrate that our algorithm achieves robust data transmission with more than 3× reduced pilot overhead compared to orthogonal training in a 128 antenna cell-free massive MU-MIMO system in which 128 users transmit data over 128 time slots.

“…A soft-output algorithm that approximates JED using iterative estimation and detection (IED) has been proposed recently in [7], which alternates between channel estimation and data detection. Similar alternating optimization methods have been used before for IED in [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Soft-Output Joint Channel Estimation and Data Detection using Deep Unfolding

Song

2021 IEEE Information Theory Workshop (ITW)

Zhang

et al. 2021

We propose a novel soft-output joint channel estimation and data detection (JED) algorithm for multiuser (MU) multiple-input multiple-output (MIMO) wireless communication systems. Our algorithm approximately solves a maximum aposteriori JED optimization problem using deep unfolding and generates soft-output information for the transmitted bits in every iteration. The parameters of the unfolded algorithm are computed by a hyper-network that is trained with a binary cross entropy (BCE) loss. We evaluate the performance of our algorithm in a coded MU-MIMO system with 8 basestation antennas and 4 user equipments and compare it to state-ofthe-art algorithms separate channel estimation from soft-output data detection. Our results demonstrate that our JED algorithm outperforms such data detectors with as few as 10 iterations.