This article introduces a novel communication paradigm for the unsourced, uncoordinated Gaussian multiple access problem. The major components of the envisioned framework are as follows. The encoded bits of every message are partitioned into two groups. The first portion is transmitted using a compressive sensing scheme, whereas the second set of bits is conveyed using a multi-user coding scheme. The compressive sensing portion is key in sidestepping some of the challenges posed by the unsourced aspect of the problem. The information afforded by the compressive sensing is employed to create a sparse random multi-access graph conducive to joint decoding. This construction leverages the lessons learned from traditional IDMA into creating low-complexity schemes for the unsourced setting, while also accounting for inherent randomness. Under joint message-passing decoding, the proposed scheme offers good performance at a low computational complexity. Findings are supported by numerical simulations, and results are compared to existing alternatives.

This article introduces a novel communication paradigm for the unsourced, uncoordinated Gaussian multiple access problem. The major components of the envisioned framework are as follows. The encoded bits of every message are partitioned into two groups. The first portion is transmitted using a compressive sensing scheme, whereas the second set of bits is conveyed using a multi-user coding scheme. The compressive sensing portion is key in sidestepping some of the challenges posed by the unsourced aspect of the problem. The information afforded by the compressive sensing is employed to create a sparse random multi-access graph conducive to joint decoding. This construction leverages the lessons learned from traditional IDMA into creating low- complexity schemes for the unsourced setting and its inherent randomness. Under joint message- passing decoding, the proposed scheme offers superior performance compared to existing low- complexity alternatives. Findings are supported by numerical simulations.

This work introduces a novel coding paradigm for the unsourced multiple access channel model. The envisioned framework builds on a select few key components. First, the transmission period is partitioned into a sequence of sub-blocks, thereby yielding a slotted structure. Second, messages are split into two parts. A portion of the data is encoded using spreading sequences or codewords that are designed to be recovered by a compressed sensing type decoder. In addition to being an integral part of the data, the information bits associated with this first part also determine the parameters of the low-density parity check code employed during the subsequent stages of the communication process. The other portion of the message is encoded using the aforementioned low-density parity check code. The data embedded in this latter stage is decoded using a joint message passing algorithm designed for the T-user binary input real adder channel. Finally, devices repeat their codeword in multiple sub-blocks, with the transmission pattern being a deterministic function of message content independent of the identity of the device. When combined with successive interference cancellation, the ensuing communication infrastructure offers significant performance improvement compared to coding schemes recently published in the literature for unsourced random access.

This article introduces a novel paradigm for the unsourced multiple-access communication problem. This divide-and-conquer approach leverages recent advances in compressive sensing and forward error correction to produce a computationally efficient algorithm. Within the proposed framework, every active device first partitions its data into several subblocks, and subsequently adds redundancy using a systematic linear block code. Compressive sensing techniques are then employed to recover sub-blocks, and the original messages are obtained by connecting pieces together using a low-complexity tree-based algorithm. Numerical results suggest that the proposed scheme outperforms other existing practical coding schemes. Measured performance lies approximately 4.3 dB away from the Polyanskiy achievability limit, which is obtained in the absence of complexity constraints.

We propose a novel coding scheme for the unsourced multiple access channel model introduced by Polyanskiy [1]. This new paradigm is composed of four main ingredients: (i) the transmission period is partitioned into sub-blocks, thereby instituting a slotted framework; (ii) The message (data) is split into two parts and one part chooses an interleaver for a low density parity check (LDPC) type code. This part of the message is encoded using spreading sequences or codewords that are designed to be decoded by a compressed sensing type decoder; (iii) The other part of the message is encoded using a low density parity check (LDPC) type code and decoded using a joint message passing decoding algorithm designed for the T-user binary input real adder channel; (iv) users repeat their codeword in multiple sub-blocks, with the transmission pattern being a deterministic function of message content and independent of the identity of the user. When this coding scheme is combined with serial interference cancellation, the ensuing communication infrastructure can offer significant performance improvements compared to the recently proposed coding scheme in [2] and results in the best performing coding scheme to date.

Future wireless access points may have to support sporadic transmissions from a massive number of unattended machines. Recently, there has been a lot of interest in the design of massive uncoordinated multiple access schemes for such systems based on clever enhancements to slotted ALOHA. A close connection has been established between the design of the multiple access scheme and the design of low density generator matrix codes. Based on this connection, optimal multiple access schemes have been designed based on slotted ALOHA and successive interference cancellation, assuming that the number of users in the network is known at the transmitters. In this paper, we extend this work and consider the design of universal uncoordinated multiple access schemes that are agnostic to the number of users in the network. We design Markov chain based transmission policies and numerical results show that substantial improvement to slotted ALOHA is possible.

In [1], [2], two schemes have been proposed to recover the support of a K-sparse N-dimensional signal from noisy linear measurements. Both schemes use left-regular sparse-graph code based sensing matrices and a simple peeling-based decoding algorithm. Both the schemes require O(K logN) measurements and the first scheme require O(N logN) computations whereas the second scheme requires O(K logN) computations (sub-linear time complexity when K is sub-linear in N). We show that by replacing the left-regular ensemble with left and right regular ensemble, we can reduce the number of measurements required of these schemes to the optimal order of O(K log N/K) with decoding complexities of O(K log N/K) and O(N log N/K), respectively.

For compound LDGM/LDPC codes with maximum a posteriori (MAP) processing, Wainwright and Martinian showed that the information-theoretic rate regions of the Wyner-Ziv (WZ) and Gelfand-Pinsker (GP) problems are achievable. For the same ensemble, these rates do not appear to be achievable with message-passing guided decimation (GD). Fortunately, spatially-coupled (SC) codes seem to provide an elegant remedy when iterative decoding falls short of MAP decoding. In particular, Aref et al. recently introduced SC LDGM codes that approach the rate-distortion region with belief-propagation guided decimation (BPGD). In this paper, we show that SC compound LDGM/LDPC codes with BPGD can approach the rate regions of the WZ and GP problems.

We propose a class of lattices constructed using Construction D where the underlying linear codes are nested binary spatially-coupled low-density parity-check codes (SC-LDPC) codes with uniform left and right degrees. By leveraging recent results on the optimality of spatially-coupled codes for binary input memoryless channels and Forney et al.'s earlier results on the optimality of construction D, we show that the proposed lattices achieve the Poltyrev limit under multistage belief propagation decoding. Lattice codes constructed from these lattices are shown to provide excellent performance for the three user symmetric interference channel. They can also be naturally used in applications such as integer-forcing and compute-and-forward.