Implementação e ambiente de validação em lógica programável de um decodificador LDPC

A concepção de um circuito integrado envolve uma sequência algorítmica de passos a serem cumpridos para transformar uma ideia em “silício”. De forma simplificada, um desses passos é a implementação de uma determinada lógica usando linguagens apropriadas para esta finalidade. Fundamentalmente é de suma importância efetivar testes e simulações nessa lógica, propiciando ao desenvolvedor menor risco financeiro, pois é uma oportunidade de encontrar defeitos e assim realizar novos e rápidos ciclos de projeto na lógica gerada. Com o intuito de realizar testes que demandariam excessivo tempo computacional de simulação na lógica em questão, é possível realizar a prototipação em lógica programável, em Field Programmable Gate Array (FPGA) e assim, fisicamente exercitar os circuitos digitais nela contida. Porém, para se realizar esta, é necessária a implementação não só do módulo de lógica em questão como também de uma infraestrutura adjacente para estimular o bloco e gerenciar os testes. Neste trabalho é proposta uma arquitetura para executar esses estímulos em um decodificador de correção de erros com estratégia LDPC. Para tal, é efetuada a implementação deste mesmo bloco, que fora anteriormente descrito pelo autor em HDL, juntamente com módulos de gerenciamento dos estímulos para exercitar e coletar os resultados.The conception of an integrated circuit involves an algorithmic sequence of steps to be followed to transform an idea into “silicon”. In a simplified way, one of these steps is the implementation of a certain logic, using languages appropriate for this task. Fundamentally, it is crucial to carry tests and simulations in this logic, providing the developer with less financial risk, as it is an opportunity to find defects and thus carry out new and fast design cycles in the generated logic. To carry out tests that would require excessive computational simulation time in the logic in question, it is possible to perform prototyping in programmable logic, in Field Programmable Gate Array (FPGA), and therefore, physically exercise the digital circuits contained therein. However, to perform, it is necessary to implement the logic module in question and adjacent infrastructure to stimulate the block and manage the tests. An architecture is proposed to execute these stimuli in an error correction decoder with the LDPC strategy in this work. To this end, the implementation of this same block is carried out, which was previously described by the author in HDL, together with modules for managing the stimuli to exercise and collect the results

A tutorial on the characterisation and modelling of low layer functional splits for flexible radio access networks in 5G and beyond

The centralization of baseband (BB) functions in a radio access network (RAN) towards data processing centres is receiving increasing interest as it enables the exploitation of resource pooling and statistical multiplexing gains among multiple cells, facilitates the introduction of collaborative techniques for different functions (e.g., interference coordination), and more efficiently handles the complex requirements of advanced features of the fifth generation (5G) new radio (NR) physical layer, such as the use of massive multiple input multiple output (MIMO). However, deciding the functional split (i.e., which BB functions are kept close to the radio units and which BB functions are centralized) embraces a trade-off between the centralization benefits and the fronthaul costs for carrying data between distributed antennas and data processing centres. Substantial research efforts have been made in standardization fora, research projects and studies to resolve this trade-off, which becomes more complicated when the choice of functional splits is dynamically achieved depending on the current conditions in the RAN. This paper presents a comprehensive tutorial on the characterisation, modelling and assessment of functional splits in a flexible RAN to establish a solid basis for the future development of algorithmic solutions of dynamic functional split optimisation in 5G and beyond systems. First, the paper explores the functional split approaches considered by different industrial fora, analysing their equivalences and differences in terminology. Second, the paper presents a harmonized analysis of the different BB functions at the physical layer and associated algorithmic solutions presented in the literature, assessing both the computational complexity and the associated performance. Based on this analysis, the paper presents a model for assessing the computational requirements and fronthaul bandwidth requirements of different functional splits. Last, the model is used to derive illustrative results that identify the major trade-offs that arise when selecting a functional split and the key elements that impact the requirements.This work has been partially funded by Huawei Technologies. Work by X. Gelabert and B. Klaiqi is partially funded by the European Union's Horizon Europe research and innovation programme (HORIZON-MSCA-2021-DN-0) under the Marie Skłodowska-Curie grant agreement No 101073265. Work by J. Perez-Romero and O. Sallent is also partially funded by the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under Grant Agreements No. 101096034 (VERGE project) and No. 101097083 (BeGREEN project) and by the Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 under ARTIST project (ref. PID2020-115104RB-I00). This last project has also funded the work by D. Campoy.Peer ReviewedPostprint (author's final draft

When Machine Learning Meets Information Theory: Some Practical Applications to Data Storage

Machine learning and information theory are closely inter-related areas. In this dissertation, we explore topics in their intersection with some practical applications to data storage. Firstly, we explore how machine learning techniques can be used to improve data reliability in non-volatile memories (NVMs). NVMs, such as flash memories, store large volumes of data. However, as devices scale down towards small feature sizes, they suffer from various kinds of noise and disturbances, thus significantly reducing their reliability. This dissertation explores machine learning techniques to design decoders that make use of natural redundancy (NR) in data for error correction. By NR, we mean redundancy inherent in data, which is not added artificially for error correction. This work studies two different schemes for NR-based error-correcting decoders. In the first scheme, the NR-based decoding algorithm is aware of the data representation scheme (e.g., compression, mapping of symbols to bits, meta-data, etc.), and uses that information for error correction. In the second scenario, the NR-decoder is oblivious of the representation scheme and uses deep neural networks (DNNs) to recognize the file type as well as perform soft decoding on it based on NR. In both cases, these NR-based decoders can be combined with traditional error correction codes (ECCs) to substantially improve their performance. Secondly, we use concepts from ECCs for designing robust DNNs in hardware. Non-volatile memory devices like memristors and phase-change memories are used to store the weights of hardware implemented DNNs. Errors and faults in these devices (e.g., random noise, stuck-at faults, cell-level drifting etc.) might degrade the performance of such DNNs in hardware. We use concepts from analog error-correcting codes to protect the weights of noisy neural networks and to design robust neural networks in hardware. To summarize, this dissertation explores two important directions in the intersection of information theory and machine learning. We explore how machine learning techniques can be useful in improving the performance of ECCs. Conversely, we show how information-theoretic concepts can be used to design robust neural networks in hardware

Journal of Telecommunications and Information Technology, 2018, nr 3

kwartalni

Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services

This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book