A Polar Code Based TIN-SIC Scheme for the Unsourced Random Access in the Quasi-Static Fading MAC

Andreev, Kirill; Marshakov, Evgeny; Frolov, Alexey

doi:10.1109/isit44484.2020.9174247

Cited by 35 publications

(19 citation statements)

References 24 publications

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“…Coded demixing offers an excellent trade-off between performance and complexity. Yet, it is important to note that this approach does not outperform certain spread URA schemes in terms of PUPE, e.g., [7], [8]. Nevertheless, it represents the state-of-the-art in CCS, a family of URA schemes with lower complexity than those cited above.…”

Section: Discussionmentioning

confidence: 94%

“…The main goal of this section is to showcase the potential benefits of coded demixing for single-class URA. We adopt system parameters aligned with the URA literature [1]- [7] to facilitate an easy comparison with other CCS schemes. It is worth mentioning that these parameters are inspired by the LoRaWAN wireless specifications.…”

Section: Design Consideration and Simulationsmentioning

confidence: 99%

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Stochastic Binning and Coded Demixing for Unsourced Random Access

R.¹,

Amalladinne²,

Rini³

et al. 2021

Preprint

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Unsourced random access is a novel communication paradigm designed for handling a large number of uncoordinated users that sporadically transmit very short messages. Under this model, coded compressed sensing (CCS) has emerged as a lowcomplexity scheme that exhibits good error performance. Yet, one of the challenges faced by CCS pertains to disentangling a large number of codewords present on a single factor graph. To mitigate this issue, this article introduces a modified CCS scheme whereby active devices stochastically partition themselves into groups that utilize separate sampling matrices with low cross-coherence for message transmission. At the receiver, ideas from the field of compressed demixing are employed for support recovery, and separate factor graphs are created for message disambiguation in each cluster. This reduces the number of active users on a factor graph, which improves performance significantly in typical scenarios. Indeed, coded demixing reduces the probability of error as the number of groups increases, up to a point. Findings are supported with numerical simulations.

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Section: Discussionmentioning

confidence: 94%

Section: Design Consideration and Simulationsmentioning

confidence: 99%

Stochastic Binning and Coded Demixing for Unsourced Random Access

R.¹,

Amalladinne²,

Rini³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…5). As a reference, we added the converse bound from [31], the 4-fold LDPC-based ALOHA from [11] and 14-fold ALOHA with polar codes from [14] to Fig. 5.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…One can observe a significant (more than 10 dB for t = 5 and K a = 50) improvement compared to t = 0 case. When the number of active users is small, the scheme with t = 5 demonstrates better energy efficiency than a T -fold ALOHA with polar codes from [14], known as the best practical solution for the fading channel with a single antenna at the receiver. When the number of active users grows, the To evaluate the achievability bound for the tree code from Theorem V.2 (t-tree code), one can use the same procedure as the CS-RCB minimum E b /N 0 search.…”

Section: Numerical Resultsmentioning

confidence: 99%

Coded Compressed Sensing with List Recoverable Codes for the Unsourced Random Access

Andreev¹,

Rybin²,

Frolov³

2022

Preprint

Self Cite

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We consider a coded compressed sensing approach for the unsourced random access and replace the outer tree code proposed by Amalladinne et al. with the list recoverable code capable of correcting t errors. A finite-length random coding bound for such codes is derived. The numerical experiments in the single antenna quasi-static Rayleigh fading MAC show that transition to list recoverable codes correcting t errors improves the performance of coded compressed sensing scheme by 7-10 dB compared to the tree code-based scheme. We propose two practical constructions of outer codes. The first is a modification of the tree code. It utilizes the same code structure, and a key difference is a decoder capable of correcting up to t errors. The second is based on the Reed-Solomon codes and Guruswami-Sudan list decoding algorithm. The first scheme provides an energy efficiency very close to the random coding bound when the decoding complexity is unbounded. But for the practical parameters, the second scheme is better and improves the performance of a tree code-based scheme when the number of active users is less than 200.

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“…We note that as a future research direction, generalization of the proposed random signature approach for the case of unknown (and unequal) channel gains can be considered. In this case, these gains can be estimated using clustering techniques [18], and by a proper modification of (4). Employing more sophisticated channel codes with random signatures and IRSA is also a promising research direction.…”

Section: Simulation Resultsmentioning

confidence: 99%

Massive Random Access With Trellis-Based Codes and Random Signatures

Tanc¹,

Duman

2021

IEEE Commun. Lett.

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We investigate unsourced and grant-free massive random access in which all the users employ the same codebook, and the basestation is only interested in decoding the distinct messages sent. To resolve the colliding user packets, a novel approach relying on user transmissions with random-like amplitudes selected from a large number of possible signatures is proposed. The scheme is combined with convolutional coding for error correction. The receiver operates by first identifying the signatures used by the transmitting nodes employing a sparsitybased detection algorithm, and then utilizing a trellis-based decoding algorithm. Despite its simplicity, the proposed solution offers excellent performance.

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A Polar Code Based TIN-SIC Scheme for the Unsourced Random Access in the Quasi-Static Fading MAC

Cited by 35 publications

References 24 publications

Stochastic Binning and Coded Demixing for Unsourced Random Access

Stochastic Binning and Coded Demixing for Unsourced Random Access

Coded Compressed Sensing with List Recoverable Codes for the Unsourced Random Access

Massive Random Access With Trellis-Based Codes and Random Signatures

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