A Joint Graph Based Coding Scheme for the Unsourced Random Access Gaussian Channel

Abstract

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.

Massive Access for Future Wireless Communication Systems

“…Polyanskiys framework has attracted a wide research interest from different aspects recently. For AWGN channel, Calderbank et al [29], Fengler et al [30], and Pradhan [31] use binary chirp code, sparse regression code, and short blocklength LDPC code as inter code in Figure 5 [34]. It is revealed in [34] that by leveraging the inherent randomization introduced by the channel, it is more easily to approach the optimal performance via a practical coding scheme.…”

“…It is revealed in [34] that by leveraging the inherent randomization introduced by the channel, it is more easily to approach the optimal performance via a practical coding scheme. Figure 6 plots the E b /N 0 required by Ordentlich-Polyanskiy scheme, sparse regression code scheme [30], CS-based coding scheme [31], and relevant benchmarks for k = 100 bits per user, n = 30000 channel uses, P e = 0.05 error probability, and different number of active users K a . We observe from Figure 6 that the TIN scheme and the traditional ALOHA scheme result in very poor energy efficiency in the massive access regime whereas Odentlich-Polyanskiy scheme is still effective when K a = 300.…”

Index Modulation–Aided Mixed Massive Random Access

“…In Pradhan et al (2019), another scheme was proposed for Gaussian channels, where the transmit data are partitioned into two parts. The former is transmitted using a CS code and also acts to determine the corresponding interleaving pattern.…”

Detecção de Atividade para Canal de Acesso Aleatório Massivo Gaussiano Utilizando Sensoriamento Compressivo

“…Neste artigo, mostrou-se que métodos existentes na época, como ALOHA e Treat Interference as Noise, eram ineficientes para uma grande quantidade de usuários, em comparação ao resultado proposto. Em busca de atingir o resultado teórico apresentado com uma complexidade realizável, vários artigos propuseram métodos grantless, isto é, em que os usuários transmitem seus dados sem coordenação, com resultados progressivamente melhores [2], [3], [4], [5].…”

“…Em busca de melhorar a eficiência do sistema, neste artigo, propomos a utilização de métodos de sensoriamento compressivo para a etapa de sinalização de atividade. A relação entre o problema de acesso aleatório e sensoriamento compressivo já havia sido observada em [1] e é a base de artigos como [4], [3], no entanto, há limitações de complexidade quanto ao seu uso quando consideramos uma mensagem completa. No entanto, ao utilizar sensoriamento compressivo apenas no primeiro estágio, a codificação e decodificação se tornam realizáveis.…”