Block Orthogonal Sparse Superposition Codes for $ \sf{L}^3 $ Communications: Low Error Rate, Low Latency, and Low Power Consumption

TL;DR

The paper addresses the challenge of reliable, low-latency communication with short packets over fading channels by extending Block Orthogonal Sparse Superposition (BOSS) codes to realistic fading scenarios. It introduces two parallelizable decoders: MMSE-A-MAP for fast fading with CSIR, and NSD for non-coherent block fading, achieving strong performance with low complexity. MMSE-A-MAP outperforms CRC-aided polar codes in SISO-OFDM, while NSD matches quasi-ML performance with significantly reduced search space, and both are validated on a software-defined radio testbed for low-power operation. The results indicate BOSS codes as promising candidates for HRLLC and Ambient IoT applications, offering a practical balance between reliability, latency, and energy efficiency.

Abstract

Block orthogonal sparse superposition (BOSS) code is a class of joint coded modulation methods, which can closely achieve the finite-blocklength capacity with a low-complexity decoder at a few coding rates under Gaussian channels. However, for fading channels, the code performance degrades considerably because coded symbols experience different channel fading effects. In this paper, we put forth novel joint demodulation and decoding methods for BOSS codes under fading channels. For a fast fading channel, we present a minimum mean square error approximate maximum a posteriori (MMSE-A-MAP) algorithm for the joint demodulation and decoding when channel state information is available at the receiver (CSIR). We also propose a joint demodulation and decoding method without using CSIR for a block fading channel scenario. We refer to this as the non-coherent sphere decoding (NSD) algorithm. Simulation results demonstrate that BOSS codes with MMSE-A-MAP decoding outperform CRC-aided polar codes, while NSD decoding achieves comparable performance to quasi-maximum likelihood decoding with significantly reduced complexity. Both decoding algorithms are suitable for parallelization, satisfying low-latency constraints. Additionally, real-time simulations on a software-defined radio testbed validate the feasibility of using BOSS codes for low-power transmission.

Figures (7)

• Figure 1: BLER performance comparison of different $(128, 16)$ codes in a seven-tap Rayleigh channel.
• Figure 2: Enhanced performance of CA-BOSS codes with $M \in \{128, 256\}$ and varying block number $G \in \{8, 32, 128\}$.
• Figure 3: Performance of $(M = 64, G = 8, K = 2)$ single-layer BOSS codes under different decoders when the number of receiver antennas are: (a) $N_\text{Rx} = 16$, (b) $N_\text{Rx} = 32$, and (c) $N_\text{Rx} = 64$.
• Figure 4: $(M = 128, G = 8, K = 2)$ single-layer BOSS codes under quasi-ML decoding and NSD.
• Figure 5: $(M = 64, G = 8)$ single-layer BOSS codes with varying sparsity $K \in \{2, 3, 4\}$ when $N_\text{Rx} = 8$.