• Corpus ID: 245634877

Statistical Device Activity Detection for OFDM-based Massive Grant-Free Access

  title={Statistical Device Activity Detection for OFDM-based Massive Grant-Free Access},
  author={Yuhang Jia and Ying Cui and Wuyang Jiang},
Existing works on grant-free access, proposed to support massive machine-type communication (mMTC) for the Internet of things (IoT), mainly concentrate on narrow band systems under flat fading. However, little is known about massive grant-free access for wideband systems under frequency-selective fading. This paper investigates massive grant-free access in a wideband system under frequency-selective fading. First, we present an orthogonal frequency division multiplexing (OFDM)-based massive… 

Figures and Tables from this paper



ML and MAP Device Activity Detections for Grant-Free Massive Access in Multi-Cell Networks

Numerical results show the substantial gains of the proposed designs over well-known existing designs and reveal the importance of explicit consideration of inter-cell interference, the value of prior information, and the advantage of AP cooperation in device activity detection.

Covariance Based Joint Activity and Data Detection for Massive Random Access with Massive MIMO

It is made an observation that in the massive multiple-input multiple-output (MIMO) regime, where the BS is equipped with a large number of antennas, a covariance based detection scheme that solves a maximum likelihood estimation problem is more effective than the approximate message passing (AMP) based compressed sensing approach for sequence detection.

Sparse Signal Processing for Grant-Free Massive Connectivity: A Future Paradigm for Random Access Protocols in the Internet of Things

It is argued that massive multiple-input, multiple-output (MIMO) is especially well suited for massive IoT connectivity because the device detection error can be driven to zero asymptotically in the limit as the number of antennas at the base station (BS) goes to infinity by using the multiplemeasurement vector (MMV) compressed sensing techniques.

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.

Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach

An algorithm is designed that jointly detects device activity along with embedded information bits and exploits the structured sparsity introduced by the non-coherent transmission scheme and has superior performance compared with application of the original AMP approach.

Covariance-Based Cooperative Activity Detection for Massive Grant-Free Random Access

Simulation results show that the proposed algorithm is efficient for large-scale activity detection problems while requires shorter pilot sequences compared with the state-of-art algorithms in achieving the same system performance.

MAP-Based Pilot State Detection in Grant-Free Random Access for mMTC

  • Dongdong JiangYing Cui
  • Computer Science
    2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
  • 2020
This paper adopts a general and tractable model for distributions of pilot states (being transmitted or not), namely the multivariate Bernoulli (MVB) model, which can explicitly specify general correlation among device activities and relation among the states of pilots assigned to one device.

Sparse Activity Detection for Massive Connectivity

This paper proposes an AMP algorithm design that exploits the statistics of the wireless channel and provides an analytical characterization of the probabilities of false alarm and missed detection via state evolution and designs the minimum mean squared error denoiser for AMP according to the channel statistics.

Joint Activity Detection and Channel Estimation for IoT Networks: Phase Transition and Computation-Estimation Tradeoff

A structured group sparsity estimation method to simultaneously detect the active devices and estimate the corresponding channels and a smoothing method to solve the high-dimensional structured estimation problem with a given limited time budget is developed.

On Simultaneous Multipacket Channel Estimation and Reception in Random Access for MTC Under Frequency-Selective Fading

  • Jinho Choi
  • Computer Science, Business
    IEEE Transactions on Communications
  • 2018
This paper considers an approach for simultaneous multipacket channel estimation and reception (SMuCER) with a multicarrier system under frequency-selective fading, where the channel estimation becomes challenging due to frequency- selective fading and different round-trip delay between the devices.