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Multi-User SR-LDPC Codes via Coded Demixing with Applications to Cell-Free Systems

Jamison R. Ebert, Jean-Francois Chamberland, Krishna R. Narayanan

TL;DR

It is demonstrated that SR-LDPC codes, when combined with coded demixing techniques, offer a new framework for efficient non-orthogonal multiple access (NOMA) in the context of coordinated multi-user communication channels.

Abstract

Novel sparse regression LDPC (SR-LDPC) codes exhibit excellent performance over additive white Gaussian noise (AWGN) channels in part due to their natural provision of shaping gains. Though SR-LDPC-like codes have been considered within the context of single-user error correction and massive random access, they are yet to be examined as candidates for coordinated multi-user communication scenarios. This article explores this gap in the literature and demonstrates that SR-LDPC codes, when combined with coded demixing techniques, offer a new framework for efficient non-orthogonal multiple access (NOMA) in the context of coordinated multi-user communication channels. The ensuing communication scheme is referred to as MU-SR-LDPC coding. Empirical evidence suggests that, for a fixed SNR, MU-SR-LDPC coding can achieve a target bit error rate (BER) at a higher sum rate than orthogonal multiple access (OMA) techniques such as time division multiple access (TDMA) and frequency division multiple access (FDMA). Importantly, MU-SR-LDPC codes enable a pragmatic solution path for user-centric cell-free communication systems with (local) joint decoding. Results are supported by numerical simulations.

Multi-User SR-LDPC Codes via Coded Demixing with Applications to Cell-Free Systems

TL;DR

It is demonstrated that SR-LDPC codes, when combined with coded demixing techniques, offer a new framework for efficient non-orthogonal multiple access (NOMA) in the context of coordinated multi-user communication channels.

Abstract

Novel sparse regression LDPC (SR-LDPC) codes exhibit excellent performance over additive white Gaussian noise (AWGN) channels in part due to their natural provision of shaping gains. Though SR-LDPC-like codes have been considered within the context of single-user error correction and massive random access, they are yet to be examined as candidates for coordinated multi-user communication scenarios. This article explores this gap in the literature and demonstrates that SR-LDPC codes, when combined with coded demixing techniques, offer a new framework for efficient non-orthogonal multiple access (NOMA) in the context of coordinated multi-user communication channels. The ensuing communication scheme is referred to as MU-SR-LDPC coding. Empirical evidence suggests that, for a fixed SNR, MU-SR-LDPC coding can achieve a target bit error rate (BER) at a higher sum rate than orthogonal multiple access (OMA) techniques such as time division multiple access (TDMA) and frequency division multiple access (FDMA). Importantly, MU-SR-LDPC codes enable a pragmatic solution path for user-centric cell-free communication systems with (local) joint decoding. Results are supported by numerical simulations.
Paper Structure (10 sections, 1 theorem, 29 equations, 4 figures, 1 table)

This paper contains 10 sections, 1 theorem, 29 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Organization and Main Contributions
  3. Notation
  4. Multi-User Single-Cell Model
  5. MU-SR-LDPC Encoding
  6. MU-SR-LDPC Decoding via Coded Demixing
  7. Empirical Results for Single-Cell Setting
  8. Cell-Free MU-SR-LDPC
  9. Conclusion
  10. Proof of Optimal Combining

Key Result

Proposition 1

Let $\mathbf{c} \in \mathbb{R}^{B}$ denote the optimal weights to be used in combining $B$ effective observations. Then, each entry $c_j \in \mathbf{c}$ is given by

Figures (4)

  • Figure 1: This depicts the steps of the SR-LDPC encoding process. Information bits are encoded, the symbols of the LDPC codeword are indexed and stacked, and the resulting vector is compressed through matrix multiplication. The encoding is analogous for every user, each with their own sensing matrix.
  • Figure 2: This notional diagram depicts the operation of the MU-SR-LDPC decoder. The contribution of the estimated state vector enhanced by the Onsager is computed separately for each user (only one block shown). The residual is computed based on the contributions associated with all the users.
  • Figure 3: This plot shows the performance of the MU-SR-LDPC scheme as a function of the sum rate for a varying number of users at $E_b/N_0 = 2.25$ dB.
  • Figure 4: This shows the performance of the MU-SR-LDPC scheme applied to a cell-free setting. There are two base stations and three users; the connectivity graph is $\mathcal{K}_1 = \{0, 1\}$ and $\mathcal{K}_2 = \{1,2\}$. The performance of a single user (SU) in a single cell network with identical parameters is provided as a benchmark.

Theorems & Definitions (1)

  • Proposition 1