45 research outputs found
A Fully Bayesian Approach for Massive MIMO Unsourced Random Access
In this paper, we propose a novel fully Bayesian approach for the massive
multiple-input multiple-output (MIMO) massive unsourced random access (URA).
The payload of each user device is coded by the sparse regression codes
(SPARCs) without redundant parity bits. A Bayesian model is established to
capture the probabilistic characteristics of the overall system. Particularly,
we adopt the core idea of the model-based learning approach to establish a
flexible Bayesian channel model to adapt the complex environments. Different
from the traditional divide-and-conquer or pilot-based massive MIMO URA
strategies, we propose a three-layer message passing (TLMP) algorithm to
jointly decode all the information blocks, as well as acquire the massive MIMO
channel, which adopts the core idea of the variational message passing and
approximate message passing. We verify that our proposed TLMP significantly
enhances the spectral efficiency compared with the state-of-the-arts baselines,
and is more robust to the possible codeword collisions
Unsourced Multiuser Sparse Regression Codes achieve the Symmetric MAC Capacity
Unsourced random-access (U-RA) is a type of grant-free random access with a
virtually unlimited number of users, of which only a certain number are
active on the same time slot. Users employ exactly the same codebook, and the
task of the receiver is to decode the list of transmitted messages. Recently a
concatenated coding construction for U-RA on the AWGN channel was presented, in
which a sparse regression code (SPARC) is used as an inner code to create an
effective outer OR-channel. Then an outer code is used to resolve the
multiple-access interference in the OR-MAC. In this work we show that this
concatenated construction can achieve a vanishing per-user error probability in
the limit of large blocklength and a large number of active users at sum-rates
up to the symmetric Shannon capacity, i.e. as long as K_aR <
0.5\log_2(1+K_a\SNR). This extends previous point-to-point optimality results
about SPARCs to the unsourced multiuser scenario. Additionally, we calculate
the algorithmic threshold, that is a bound on the sum-rate up to which the
inner decoding can be done reliably with the low-complexity AMP algorithm.Comment: 7 pages, submitted to ISIT 2020. arXiv admin note: substantial text
overlap with arXiv:1901.0623