Sparse IDMA: A Joint Graph-Based Coding Scheme for Unsourced Random Access

Pradhan, Asit Kumar; Amalladinne, Vamsi K.; Vem, Avinash; Narayanan, Krishna R.; Jean-Francois, Chamberland,

doi:10.48550/arxiv.1906.05410

Cited by 5 publications

(16 citation statements)

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“…Although the coding architecture has not been fully optimized, its performance is excellent and complexity remains manageable. Original AMP+Tree [5] Enhanced AMP+Tree Sparse IDMA [6] Fig. 3: The figure compares the performance of the proposed scheme with existing schemes.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

On Approximate Message Passing for Unsourced Access with Coded Compressed Sensing

Amalladinne¹,

Pradhan²,

Rush³

et al. 2020

Preprint

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Sparse regression codes with approximate message passing (AMP) decoding have gained much attention in recent times. The concepts underlying this coding scheme extend to unsourced access with coded compressed sensing (CCS), as first pointed out by Fengler, Jung, and Caire. More specifically, their approach uses a concatenated coding framework with an inner AMP decoder followed by an outer tree decoder. In the original implementation, these two components work independently of each other, with the tree decoder acting on the static output of the AMP decoder. This article introduces a novel framework where the inner AMP decoder and the outer tree decoder operate in tandem, dynamically passing information back and forth to take full advantage of the underlying CCS structure. The enhanced architecture exhibits significant performance benefit over a range of system parameters. Simulation results are provided to demonstrate the performance benefit offered by the proposed access scheme over existing schemes in the literature.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

On Approximate Message Passing for Unsourced Access with Coded Compressed Sensing

Amalladinne¹,

Pradhan²,

Rush³

et al. 2020

Preprint

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“…Further extensions of the CSS framework for URA were also made in [11] where a low-complexity algorithm based on chirps for CS decoding was introduced. A number of other algorithmic solutions to the unsourced random access problem were also reported in [12]- [14]. However, all the aforementioned works assume a single receive antenna at the BS and it was only recently that the use of large-scale antenna arrays in the context of URA has been investigated in [15].…”

Section: A Background and Motivationmentioning

confidence: 99%

Massive Unsourced Random Access Based on Uncoupled Compressive Sensing: Another Blessing of Massive MIMO

Shyianov¹,

Bellili²,

Mezghani³

et al. 2020

Preprint

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We put forward a new algorithmic solution to the massive unsourced random access (URA) problem, by leveraging the rich spatial dimensionality offered by large-scale antenna arrays. This paper makes an observation that spatial signature is key to URA in massive connectivity setups. The proposed scheme relies on a slotted transmission framework but eliminates completely the need for concatenated coding that was introduced in the context of the coupled compressive sensing (CCS) paradigm. Indeed, all existing works on CCS-based URA rely on an inner/outer tree-based encoder/decoder to stitch the slotwise recovered sequences. This paper takes a different path by harnessing the nature-provided correlations between the slot-wise reconstructed channels of each user in order to put together its decoded sequences. The required slot-wise channel estimates and decoded sequences are first obtained through the powerful hybrid generalized approximate message passing (HyGAMP) algorithm which systematically accommodates the multiantenna-induced group sparsity. Then, a channel correlation-aware clustering framework based on the expectation-maximization (EM) concept is used together with the Hungarian algorithm to find the slotwise optimal assignment matrices by enforcing two clustering constraints that are very specific to the problem at hand. Stitching is then readily accomplished by associating the decoded sequences to their respective users according to the ensuing assignment matrices. Exhaustive computer simulations reveal that the proposed scheme outperforms by far the only known work from the open literature, which investigates the use of large-scale antenna arrays in the context of massive URA. More precisely, it will be seen that our scheme can accommodate a very large number of active users with much higher total spectral efficiency while using a remarkably smaller number of receive antennas and achieving a low decoding error probability.

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“…The decoding algorithm performs multiple iterations on the received signal by canceling the contribution of successfully decoded signals in the spirit of successive interference cancellation. Through a numerical study, we demonstrate that the proposed approach outperforms the schemes in [8,9] in pertinent regimes.…”

Section: Introductionmentioning

confidence: 97%

“…Ever since the introduction of the unsourced MAC challenge, there has been significant effort in designing coding schemes that operate close to the FBL bound while maintaining low computational complexity. Proposed schemes achieve this by either splitting the payload [2][3][4], sparsifying collisions [5][6][7][8][9], or a combination thereof [10]. Schemes that rely on splitting the payload take a compressed sensing view of the unsourced MAC problem, and they use a divide-and-conquer approach to limit complexity.…”

Section: Introductionmentioning

confidence: 99%

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Polar Coding and Random Spreading for Unsourced Multiple Access

Pradhan¹,

Amalladinne²,

Narayanan³

et al. 2019

Preprint

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This article presents a novel transmission scheme for the unsourced, uncoordinated Gaussian multiple access problem. The proposed scheme leverages notions from single-user coding, random spreading, minimum-mean squared error (MMSE) estimation, and successive interference cancellation. Specifically, every message is split into two parts: the first fragment serves as the argument to an injective function that determines which spreading sequence should be employed, whereas the second component of the message is encoded using a polar code. The latter coded bits are then spread using the sequence determined during the first step. The ensuing signal is transmitted through a Gaussian multiple-access channel (GMAC). On the receiver side, active sequences are detected using a correlation-based energy detector, thereby simultaneously recovering individual signature sequences and their generating information bits in the form of preimages of the sequence selection function. Using the set of detected active spreading sequences, an MMSE estimator is employed to produce log-likelihood ratios (LLRs) for the second part of the messages corresponding to these detected users. The LLRs associated with each detected user are then passed to a list decoder of the polar code, which performs single-user decoding to decode the second portion of the message. This decoding operation proceeds iteratively by subtracting the interference due to the successfully decoded messages from the received signal, and repeating the above steps on the residual signal. At this stage, the proposed algorithm outperforms alternate existing lowcomplexity schemes when the number of active uses is below 225.

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Sparse IDMA: A Joint Graph-Based Coding Scheme for Unsourced Random Access

Cited by 5 publications

References 0 publications

On Approximate Message Passing for Unsourced Access with Coded Compressed Sensing

On Approximate Message Passing for Unsourced Access with Coded Compressed Sensing

Massive Unsourced Random Access Based on Uncoupled Compressive Sensing: Another Blessing of Massive MIMO

Polar Coding and Random Spreading for Unsourced Multiple Access

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