Joint Coherent and Non-Coherent Detection and Decoding Techniques for Heterogeneous Networks

Marata, Leatile; López, Onel L. Alcaraz; Djelouat, Hamza; Leinonen, Markus; Alves, Hirley; Juntti, Markku

doi:10.1109/twc.2022.3206608

Cited by 7 publications

(11 citation statements)

References 65 publications

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“…Despite the potential benefit of capturing the clusters of MTDs using orthogonal pilot subspaces to facilitate efficient parallel implementation of JADCE algorithms, this has not been explored in the literature. Motivated by the work [40],…”

Section: A Related Literaturementioning

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 Related Literaturementioning

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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“…To this end, the pilot transmission phase is realized by assigning to each UE i, ∀i ∈ N , a unique unit-norm pilot sequence ϕ i ∈ C τp , and a transmit power p i inversely proportional to its average channel gain in order to to reduce the disparity in the channels gain amongst the UEs. [29], [30]. Consequently, the received signal during the pilot transmission phase Y ∈ C τp×M is given by…”

Section: A System Modelmentioning

confidence: 99%

“…is the key to obtain a sparse solution to (29). A conventional choice for a tractable sparsitypromoting prior p(γ) is the log-sum penalty prior N i=1 log(γ i + ϵ 0 ) as it resembles most closely the canonical ℓ 0 -norm when ϵ 0 → 0.…”

Section: B Cluster-sparsity Promoting Prior Via Log-summentioning

confidence: 99%

“…Before we derive the ADMM based solution for (29), we make two technical choices: 1) the binary nature of γ renders the objective function in (29) intractable for large N . Thus, to overcome this challenge, we note that finding the index set {i | γ i ̸ = 0, i ∈ N } is equivalent to finding the index set {i | ∥x i ∥ > 0, i ∈ N }.…”

Section: Proposed Admm Solutionmentioning

confidence: 99%

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Spatial Correlation Aware Compressed Sensing for User Activity Detection and Channel Estimation in Massive MTC

Djelouat

Leinonen

Juntti

2022

IEEE Trans. Wireless Commun.

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Grant-free access is considered as a key enabler for massive machine-type communications (mMTC) as it promotes energy-efficiency and small signalling overhead. Due to the sporadic user activity in mMTC, joint user identification and channel estimation (JUICE) is a main challenge. This paper addresses the JUICE in single-cell mMTC with single-antenna users and a multi-antenna base station (BS) under spatially correlated fading channels. In particular, by leveraging the sporadic user activity, we solve the JUICE in a multi measurement vector compressed sensing (CS) framework under two different cases, with and without the knowledge of prior channel distribution information (CDI) at the BS. First, for the case without prior information, we formulate the JUICE as an iterative reweighted 2,1-norm minimization problem. Second, when the CDI is known to the BS, we exploit the available information and formulate the JUICE from a Bayesian estimation perspective as a maximum a posteriori probability (MAP) estimation problem. For both JUICE formulations, we derive efficient iterative solutions based on the alternating direction method of multipliers (ADMM). The numerical experiments show that the proposed solutions achieve higher channel estimation quality and activity detection accuracy with shorter pilot sequences compared to existing algorithms.

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“…In wireless communications, compressed sensing has been exploited for solving problems related to sparse estimation/detection and support identification [147], [153]- [155]. The latter relates to a partial recovering of s, specifically, of the indexes of the non-zero elements of s, and it is particularly relevant here as it can mimic the multi-user detection problem in massive MTC systems with grant-free and sporadic random access.…”

Section: Compressed Sensingmentioning

confidence: 99%

Statistical Tools and Methodologies for URLLC- A Tutorial

López¹,

Shehab²,

Mahmood³

et al. 2022

Preprint

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<p>Ultra-reliable low-latency communication (URLLC) constitutes a key service class of the fifth generation and beyond cellular networks. Notably, designing and supporting URLLC poses a herculean task due to the fundamental need of identifying and accurately characterizing the underlying statistical models in which the system operates, e.g., interference statistics, channel conditions, and the behavior of protocols. In general, multi-layer end-to-end approaches considering all the potential delay and error sources and proper statistical tools and methodologies are inevitably required for providing strong reliability and latency guarantees. This paper contributes to the body of knowledge in the latter aspect by providing a tutorial on several statistical tools and methodologies that are useful for designing and analyzing URLLC systems. Specifically, we overview the frameworks related to i) reliability theory, ii) short packet communications, iii) inequalities, distribution bounds, tail approximations, and risk-assessment tools, iv) rare events simulation, v) large-scale tools such as stochastic geometry, clustering, compressed sensing, and mean-field games, vi) queuing theory and information freshness, and vii) machine learning. Throughout the paper, we briefly review the state-of-the-art works using the addressed tools and methodologies, and their link to URLLC systems. Moreover, we discuss novel application examples focused on physical and medium access control layers. Finally, key research challenges and directions are highlighted to elucidate how URLLC analysis/design research may evolve in the coming years.</p>

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Joint Coherent and Non-Coherent Detection and Decoding Techniques for Heterogeneous Networks

Cited by 7 publications

References 65 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

Spatial Correlation Aware Compressed Sensing for User Activity Detection and Channel Estimation in Massive MTC

Statistical Tools and Methodologies for URLLC- A Tutorial

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