This study investigates the problem of learning linear block codes optimized for Belief-Propagation decoders significantly improving performance compared to the state-of-the-art. Our previous research is extended with an enhanced system design that facilitates a more effective learning process for the parity check matrix. We simplify the input dataset, restrict the number of parameters to learn and improve the gradient back-propagation within the model. We also introduce novel optimizers specifically designed for discrete-valued weights. Based on conventional gradient computation, these optimizers provide discrete weights updates, enabling finer control and improving explainability of the learning process. Through these changes, we consistently achieve improved code performance, provided appropriately chosen hyper-parameters. To rigorously evaluate the performance of learned codes in the context of short to medium block lengths, we propose a comprehensive code performance assessment framework. This framework enables a fair comparison between our learning methodology and random search approaches, ensuring statistical significance in our results. The proposed model pave the way for a new approach to the efficient learning of linear block codes tailored to specific decoder structures.

In 5G New Radio (NR), beam management entails periodic and continuous transmission and reception of control signals in the form of synchronization signal blocks (SSBs), used to perform initial access and/or channel estimation. However, this procedure demands continuous energy consumption, which is particularly challenging to handle for low-cost, low-complexity, and battery-constrained devices, such as RedCap devices to support mid-market Internet of Things (IoT) use cases. In this context, this work aims at reducing the energy consumption during beam management for RedCap devices, while ensuring that the desired Quality of Service (QoS) requirements are met. To do so, we formalize an optimization problem in an Indoor Factory (InF) scenario to select the best beam management parameters, including the beam update periodicity and the beamwidth, to minimize energy consumption based on users' distribution and their speed. The analysis yields the regions of feasibility, i.e., the upper limit(s) on the beam management parameters for RedCap devices, that we use to provide design guidelines accordingly.

The growing number of Internet of Thing (IoT) and Ultra-Reliable Low Latency Communications (URLCC) use cases in next generation communication networks calls for the development of efficient Forward Error Correction (FEC) mechanisms. These use cases usually imply using short to mid-sized information blocks and requires low-complexity and/or fast decoding procedures. This paper investigates the joint learning of short to mid block-length coding schemes and associated Belief-Propagation (BP) like decoders using Machine Learning (ML) techniques. An interpretable auto-encoder (AE) architecture is proposed, ensuring scalability to block sizes currently challenging for ML-based linear block code design approaches. By optimizing a coding scheme w.r.t. the targeted decoder, the proposed system offers a good complexity/performance trade-off compared to various codes from literature with length up to 128 bits.

The Internet of Things (IoT), describing the Machine Type Communications (MTC), is in the center of many new technical studies and industrial projects. It is also a central topic for the future 5G technologies standardization. Pending the new generation of cellular networks, the 3rd Generation Partnership Project (3GPP) has already standardized in May 2016, an evolution of the 2G and 4G networks to include IoT features. EC-GSM-IoT, the standard for the 2G technologies including the MTC, uses several technical evolutions to obtain a 20 dB improvement of the legacy Maximum Coupling Loss (MCL) of GPRS. One of them is the use of blind repetitions, and of combination techniques in the receiver. In a previous work [1], the Bit Error Rate (BER) performance of several time diversity combining mechanisms in a classical GSM receiver were studied in simulation. The present paper focuses on two of these combination techniques and leads a novel analytical study of their performances under a time variant multipath Rayleigh fading channel. The impact of the time correlation of the channels on the average Signal to Noise and Interference Ratio (SINR) is enlightened. Simulation results are compared to the theoretical curves and confirm the analytical work. This analytical investigation allows us to foresee the consequences on the final BER of the system. To the best of our knowledge, this is the first analytical study of the performance of such time diversity combining mechanisms under time variant multipath Rayleigh fading channels.