A Coupled Compressive Sensing Scheme for Unsourced Multiple Access

Amalladinne, Vamsi K.; Vem, Avinash; Soma, Dileep Kumar; Narayanan, Krishna; Jean-Francois, Chamberland,

doi:10.48550/arxiv.1806.00138

Cited by 1 publication

(4 citation statements)

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“…The use of slotted transmissions is driven by the need to alleviate the inherent prohibitive computational burden of the underlying index coding problem. More specifically, in the coded/coupled compressive sensing (CCS) scheme [8], the binary message of each user is partitioned into multiple information bit sequences (or chunks). Then binary linear block coding is used to couple the different sequences before using a random Gaussian codebook for actual transmissions over multiple slots.…”

Section: A Background and Motivationmentioning

confidence: 99%

“…Motivated by all the aforementioned facts, we devise in this paper for the first time an algorithmic solution to the URA problem that can accommodate much more active users than the number of receive antenna elements at BS. The proposed scheme exploits the spatial channel statistics to stitch the decoded binary sequences among different slots thereby eliminating completely the need for concatenated coding as was done in all existing works on CCS-based URA [8], [11], [15]. In fact, the strong correlation between the slotwise reconstructed channel vectors pertaining to each active device already provides sufficient information for stitching its decoded sequences across the different slots.…”

Section: B Contributionsmentioning

confidence: 99%

“…Practical approaches have been introduced to alleviate this computational burden, including the slotted transmissions framework also adopted in this paper. Indeed, similar to [8], each active user partitions its B−bit message into L equal-size information bit sequences (or chunks). As apposed to [8], however, our approach does not require concatenated coding to couple the sequences across different slots (i.e., no outer binary encoder).…”

Section: A Slotted Transmission Model and Code Selectionmentioning

confidence: 99%

“…Indeed, similar to [8], each active user partitions its B−bit message into L equal-size information bit sequences (or chunks). As apposed to [8], however, our approach does not require concatenated coding to couple the sequences across different slots (i.e., no outer binary encoder). Therefore, we simply share the bits uniformly between the L slots and there is no need to optimize the sizes of the L sequences.…”

Section: A Slotted Transmission Model and Code Selectionmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: A Background and Motivationmentioning

confidence: 99%