Abstract
Unsourced random access (URA) has emerged as a pragmatic framework for next-generation distributed sensor networks. Within URA, concatenated coding structures are often employed to ensure that the central base station can accurately recover the set of sent codewords during a given transmission period. Many URA algorithms employ independent inner and outer decoders, which can help reduce computational complexity at the expense of a decay in performance. In this article, an enhanced decoding algorithm is presented for a concatenated coding structure consisting of a wide range of inner codes and an outer tree-based code. It is shown that this algorithmic enhancement has the potential to simultaneously improve error performance and decrease the computational complexity of the decoder. This enhanced decoding algorithm is applied to two existing URA algorithms, and the performance benefits of the algorithm are characterized. Findings are supported by numerical simulations.
Highlights
Compressed Sensing withMassive machine-type communication is a rapidly growing class of wireless communications that aim to connect tens of billions of unattended devices to wireless networks
Before quantifying the gains obtained by applying this algorithmic enhancement to coded compressed sensing (CCS), we define appropriate measures of performance and computational complexity
Being a Unsourced random access (URA) scheme, the performance of CCS is evaluated with respect to the per-user probability of error (PUPE), which is given by the following: Pe =
Summary
Massive machine-type communication (mMTC) is a rapidly growing class of wireless communications that aim to connect tens of billions of unattended devices to wireless networks. The sporadic and bursty nature of sensor transmissions makes them highly inefficient under current estimation/enrollment/scheduling procedures typical of cellular networks [1,2] The combination of these challenges necessitates the design of novel physical and medium access control (MAC) layer protocols to efficiently handle the demands of these wireless devices. In [3], Polyanskiy provides finite block length achievability bounds for short block lengths typical of URA applications using random Gaussian coding and maximum likelihood (ML) decoding These bounds are derived in the absence of complexity constraints and, are impractical for deployment in real-world networks. We present a novel framework for sharing information between a wide class of inner codes and a tree-based outer code This approach significantly improves PUPE performance and reduces the computational complexity of the scheme. With the enhanced decoding algorithm, the number of antennas required to achieve a fixed PUPE is reduced by 23% in certain regimes, and the decoding runtime is reduced by 70–90%
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