• Corpus ID: 246016447

Clustering-based Joint Channel Estimation and Signal Detection for NOMA

  title={Clustering-based Joint Channel Estimation and Signal Detection for NOMA},
  author={Ayoob Salari and Mahyar Shirvanimoghaddam and Muhammad Basit Shahab and Reza Arablouei and Sarah Johnson},
—We propose a joint channel estimation and signal detection approach for the uplink non-orthogonal multiple access (NOMA) using unsupervised machine learning. We apply a Gaussian mixture model (GMM) to cluster the received signals, and accordingly optimize the decision regions to enhance the symbol error rate (SER) performance. We show that, when the re- ceived powers of the users are sufficiently different, the proposed clustering-based approach achieves an SER performance on a par with that of… 



Clustering-based Joint Channel Estimation and Signal Detection for Grant-free NOMA

It is shown that when the received powers of the users are sufficiently different, the proposed clustering-based approach with no channel state information at the receiver achieves an SER performance similar to that of the conventional maximum likelihood detector with full CSI.

NOMA Joint Channel Estimation and Signal Detection Using Rotational Invariant Codes and GMM-Based Clustering

Simulation results show that the proposed scheme without using any pilot symbol achieves almost the same BER performance as that for the conventional maximum likelihood receiver with full channel state information.

Joint User Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA

A joint user identification, channel estimation, and signal detection scheme based on message passing principles to solve the problem of massive machine-type communication with massive devices and achieves a significant performance improvement over the existing alternatives.

Approximate Message Passing-Based Joint User Activity and Data Detection for NOMA

This letter focuses on joint user activity and data detection in the uplink grant-free non-orthogonal multiple access systems based on approximate message passing (AMP) and expectation maximization

Block Sparse Bayesian Learning Based Joint User Activity Detection and Channel Estimation for Grant-Free NOMA Systems

Simulation results show that the proposed message passing based BSBL algorithm delivers almost the same performance as BOMP with the exact knowledge of active user number and can reach the performance bound for channel estimation.

Training-based and semiblind channel estimation for MIMO systems with maximum ratio transmission

This paper presents two competing schemes for estimating the transmit and receive beamforming vectors of the channel matrix: a training-based conventional least-squares estimation (CLSE) scheme and a closed-form semiblind (CFSB) scheme that employs training followed by information-bearing spectrally white data symbols.

Joint User Activity and Data Detection Based on Structured Compressive Sensing for NOMA

A structured iterative support detection algorithm is proposed by exploiting the inherent structured sparsity of user activity naturally existing in NOMA systems to jointly detect user activity and transmitted data in several continuous time slots and can achieve better performance than conventional solutions.

BER Performance of Uplink NOMA With Joint Maximum-Likelihood Detector

An upper bound of bit-error rate (BER) of uplink non-orthogonal multiple access (NOMA) systems with quadrature phase shift keying (QPSK) modulation in fading channels is derived.

Optimal Training and Pilot Pattern Design for OFDM Systems in Rayleigh Fading

A new and simple pilot pattern is proposed for PSA-OFDM system and its performance is analyzed in terms of the BER and it is shown that this clustered pilot pattern gives better performance than the existing equi-spaced pilot pattern when the channel SNR is moderate, without sacrificing the bandwidth efficiency.

Massive Connectivity With Massive MIMO—Part I: Device Activity Detection and Channel Estimation

  • Liang LiuWei Yu
  • Computer Science
    IEEE Transactions on Signal Processing
  • 2018
It is shown that in the asymptotic massive multiple-input multiple-output regime, both the missed device detection and the false alarm probabilities for activity detection can always be made to go to zero by utilizing compressed sensing techniques that exploit sparsity in the user activity pattern.