OFDM-Based Massive Grant-Free Transmission Over Frequency-Selective Fading Channels

Zhu, Yiming; Sun, Gangle; Wang, Wenjin; You, Li; Wei, Fan; Wang, Lei; Chen, Yan

doi:10.1109/tcomm.2022.3175178

Cited by 10 publications

(2 citation statements)

References 39 publications

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“…This is achieved by solving for X g using a corrected version of the measurement matrix Sg , and thus providing a general framework for CS-MUD that can be applied in cases where the sensing matrix has errors [58]. Essentially, we can approximate S g by C g Sg ≈ S g , where C g represents the correction term associated with the model error (18) and that can be obtained through statistical training. Since the MTDs are considered to be stationary, it is possible to acquire each C g offline.…”

Section: A Review Of Aemmentioning

confidence: 99%

Joint Activity Detection and Channel Estimation for Clustered Massive Machine Type Communications

Marata,

López,

Hauptmann

et al. 2024

IEEE Trans. Wireless Commun.

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Compressed sensing multi-user detection (CS-MUD) algorithms play a key role in optimizing grant-free (GF) nonorthogonal multiple access (NOMA) for massive machine-type communications (mMTC). However, current CS-MUD algorithms cannot be efficiently parallelized, leading to computationally expensive implementations of joint activity detection and channel estimation (JADCE) as the number of deployed machine-type devices (MTDs) increases. To address this, the present work proposes novel JADCE algorithms that can be applied in parallel for different clusters of MTDs by exploiting the structure of the pilot sequences. These are the approximation error method (AEM)-alternating direction method of multipliers (ADMM), and AEM-sparse Bayesian learning (SBL). Results presented in terms of the normalized mean square error and the probability of miss detection show comparable performance to the conventional algorithms. However, both AEM-ADMM and AEM-SBL algorithms have significantly reduced computational complexity and run times, thus, facilitating network scalability. I. INTRODUCTIOND ETECTION, channel estimation, and data decoding are fundamental operations performed by a receiver in a wireless communication network [1]-[3]. However, the majority of algorithms designed for these operations in previous wireless communication systems, i.e., fourth-generation (4G) and earlier, were tailored exclusively for downlink human-type communications (HTC) [4], [5]. In a turn of events, the new communication standards, i.e., the fifth generation (5G) and beyond (5GB), natively support a new set of devices termed machine-type devices (MTDs), which perform various sensing tasks in the Internet of Things (IoT) paradigm [6]- [8]. Notably, MTDs are energy constrained, yet in some cases, they need to be deployed in remote areas where they cannot be readily charged. For this reason, MTDs are designed to save energy by only switching to active transmission mode after sensing data and remaining in sleep mode in the absence of data. This

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Section: A Review Of Aemmentioning

confidence: 99%

Joint Activity Detection and Channel Estimation for Clustered Massive Machine Type Communications

Marata,

López,

Hauptmann

et al. 2024

IEEE Trans. Wireless Commun.

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“…ñ, m, k,l δ[ξ ñ mk −1]CN h ñ mk l ; 0, σ ñ mk +δ[ξ ñ mk ]δ(h ñ mk l ) ,where ξ ñ mk and σ ñ mk are the (ñ M K + m K + k)-th elements of the state indicator vector ξ ∈ B that the DAD-domain channel elements of different subbands can be processed independently and share the same state indicator and prior variance. The probability density function (PDF) of ξ follows the Bernoulli distribution as[34] …”

mentioning

confidence: 99%

Joint Matrix Completion and Randomized SVD Channel Estimation of the mmWave Massive MIMO

Zhu

Luo

Jin³

et al. 2021

2021 Computing, Communications and IoT Applications (ComComAp)

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In massive multiple-input multiple-output (MIMO) systems, the downlink transmission performance heavily relies on accurate channel state information (CSI). Constrained by the transmitted power, user equipment always transmits sounding reference signals (SRSs) to the base station through frequency hopping, which will be leveraged to estimate uplink CSI and subsequently predict downlink CSI. This paper aims to investigate joint channel estimation and prediction (JCEP) for massive MIMO with frequency hopping sounding (FHS). Specifically, we present a multiple-subband (MS) delay-angle-Doppler (DAD) domain channel model with off-grid basis to tackle the energy leakage problem. Furthermore, we formulate the JCEP problem with FHS as a multiple measurement vector (MMV) problem, facilitating the sharing of common CSI across different subbands. To solve this problem, we propose an efficient Off-Grid-MS hybrid message passing (HMP) algorithm under the constrained Bethe free energy (BFE) framework. Aiming to address the lack of prior CSI in practical scenarios, the proposed algorithm can adaptively learn the hyper-parameters of the channel by minimizing the corresponding terms in the BFE expression. To alleviate the complexity of channel hyperparameter learning, we leverage the approximations of the offgrid matrices to simplify the off-grid hyper-parameter estimation. Numerical results illustrate that the proposed algorithm can effectively mitigate the energy leakage issue and exploit the common CSI across different subbands, acquiring more accurate CSI compared to state-of-the-art counterparts.

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Enhanced MIMO-DCT-OFDM system using cosine domain equaliser

Ramadan

2023

International Journal of Electronics

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OFDM-Based Massive Grant-Free Transmission Over Frequency-Selective Fading Channels

Cited by 10 publications

References 39 publications

Joint Activity Detection and Channel Estimation for Clustered Massive Machine Type Communications

Joint Activity Detection and Channel Estimation for Clustered Massive Machine Type Communications

Joint Matrix Completion and Randomized SVD Channel Estimation of the mmWave Massive MIMO

Enhanced MIMO-DCT-OFDM system using cosine domain equaliser

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