Stall Pattern Avoidance in Polynomial Product Codes

Product codes are a concatenated error-correction scheme that has been often considered for applications requiring very low bit-error rates, which demand that the error floor be decreased as much as possible. In this work, we consider product codes constructed from polynomial algebraic codes, and propose a novel low-complexity post-processing technique that is able to improve the error-correction performance by orders of magnitude. We provide lower bounds for the error rate achievable under post processing, and present simulation results indicating that these bounds are tight.Comment: 4 pages, 2 figures, GlobalSiP 201

Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming

Hybrid beamforming is a promising technique to reduce the complexity and cost of massive multiple-input multiple-output (MIMO) systems while providing high data rate. However, the hybrid precoder design is a challenging task requiring channel state information (CSI) feedback and solving a complex optimization problem. This paper proposes a novel RSSI-based unsupervised deep learning method to design the hybrid beamforming in massive MIMO systems. Furthermore, we propose i) a method to design the synchronization signal (SS) in initial access (IA); and ii) a method to design the codebook for the analog precoder. We also evaluate the system performance through a realistic channel model in various scenarios. We show that the proposed method not only greatly increases the spectral efficiency especially in frequency-division duplex (FDD) communication by using partial CSI feedback, but also has near-optimal sum-rate and outperforms other state-of-the-art full-CSI solutions.Comment: Submitted to IEEE Transactions on Wireless Communication

Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning

Cell-free massive MIMO (CF-mMIMO) systems represent a promising approach to increase the spectral efficiency of wireless communication systems. However, near-optimal beamforming solutions require a large amount of signaling exchange between access points (APs) and the network controller (NC). In this letter, we propose two unsupervised deep neural networks (DNN) architectures, fully and partially distributed, that can perform decentralized coordinated beamforming with zero or limited communication overhead between APs and NC, for both fully digital and hybrid precoding. The proposed DNNs achieve near-optimal sum-rate while also reducing computational complexity by 10-24x compared to conventional near-optimal solutions.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notic

Low-Latency LDPC Decoding Achieved by Code and Architecture Co-Design

International audienceA novel low-density parity-check decoder architecture is presented that can achieve a high data throughput while retaining the flexibility to decode a wide range of quasi-cyclic codes. The proposed architecture allows to combine multiple message-update schedules, providing an additional degree of freedom to jointly optimize the code and decoder architecture. Protograph-based code constructions are introduced that exploit this added degree of freedom in order to maximize data throughput, and that are also optimized to reduce the complexity of the required parallel data accesses.For some examples and under an ideal pipeline speedup assumption, the proposed architecture and code designs reduce decoding latency by a factor of $3.2\times$ compared to a decoder using a strict sequential schedule