258 research outputs found
Capacity Scaling in MIMO Systems with General Unitarily Invariant Random Matrices
We investigate the capacity scaling of MIMO systems with the system
dimensions. To that end, we quantify how the mutual information varies when the
number of antennas (at either the receiver or transmitter side) is altered. For
a system comprising receive and transmit antennas with , we find
the following: By removing as many receive antennas as needed to obtain a
square system (provided the channel matrices before and after the removal have
full rank) the maximum resulting loss of mutual information over all
signal-to-noise ratios (SNRs) depends only on , and the matrix of
left-singular vectors of the initial channel matrix, but not on its singular
values. In particular, if the latter matrix is Haar distributed the ergodic
rate loss is given by nats. Under
the same assumption, if with the ratio
fixed, the rate loss normalized by converges almost surely to
bits with denoting the binary entropy function. We also quantify and
study how the mutual information as a function of the system dimensions
deviates from the traditionally assumed linear growth in the minimum of the
system dimensions at high SNR.Comment: Accepted for publication in the IEEE Transactions on Information
Theor
Wireless Channel Equalization in Digital Communication Systems
Our modern society has transformed to an information-demanding system, seeking voice, video, and data in quantities that could not be imagined even a decade ago. The mobility of communicators has added more challenges. One of the new challenges is to conceive highly reliable and fast communication system unaffected by the problems caused in the multipath fading wireless channels. Our quest is to remove one of the obstacles in the way of achieving ultimately fast and reliable wireless digital communication, namely Inter-Symbol Interference (ISI), the intensity of which makes the channel noise inconsequential.
The theoretical background for wireless channels modeling and adaptive signal processing are covered in first two chapters of dissertation.
The approach of this thesis is not based on one methodology but several algorithms and configurations that are proposed and examined to fight the ISI problem. There are two main categories of channel equalization techniques, supervised (training) and blind unsupervised (blind) modes. We have studied the application of a new and specially modified neural network requiring very short training period for the proper channel equalization in supervised mode. The promising performance in the graphs for this network is presented in chapter 4.
For blind modes two distinctive methodologies are presented and studied. Chapter 3 covers the concept of multiple cooperative algorithms for the cases of two and three cooperative algorithms. The select absolutely larger equalized signal and majority vote methods have been used in 2-and 3-algoirithm systems respectively. Many of the demonstrated results are encouraging for further research.
Chapter 5 involves the application of general concept of simulated annealing in blind mode equalization. A limited strategy of constant annealing noise is experimented for testing the simple algorithms used in multiple systems. Convergence to local stationary points of the cost function in parameter space is clearly demonstrated and that justifies the use of additional noise. The capability of the adding the random noise to release the algorithm from the local traps is established in several cases
A Very Brief Introduction to Machine Learning With Applications to Communication Systems
Given the unprecedented availability of data and computing resources, there
is widespread renewed interest in applying data-driven machine learning methods
to problems for which the development of conventional engineering solutions is
challenged by modelling or algorithmic deficiencies. This tutorial-style paper
starts by addressing the questions of why and when such techniques can be
useful. It then provides a high-level introduction to the basics of supervised
and unsupervised learning. For both supervised and unsupervised learning,
exemplifying applications to communication networks are discussed by
distinguishing tasks carried out at the edge and at the cloud segments of the
network at different layers of the protocol stack
The Novel Applications of Deep Reservoir Computing in Cyber-Security and Wireless Communication
This chapter introduces the novel applications of deep reservoir computing (RC) systems in cyber-security and wireless communication. The RC systems are a new class of recurrent neural networks (RNNs). Traditional RNNs are very challenging to train due to vanishing/exploding gradients. However, the RC systems are easier to train and have shown similar or even better performances compared with traditional RNNs. It is very essential to study the spatio-temporal correlations in cyber-security and wireless communication domains. Therefore, RC models are good choices to explore the spatio-temporal correlations. In this chapter, we explore the applications and performance of delayed feedback reservoirs (DFRs), and echo state networks (ESNs) in the cyber-security of smart grids and symbol detection in MIMO-OFDM systems, respectively. DFRs and ESNs are two different types of RC models. We also introduce the spiking structure of DFRs as spiking artificial neural networks are more energy efficient and biologically plausible as well
Channel Coding in Molecular Communication
This dissertation establishes and analyzes a complete molecular transmission system from
a communication engineering perspective. Its focus is on diffusion-based molecular communication
in an unbounded three-dimensional fluid medium. As a basis for the investigation
of transmission algorithms, an equivalent discrete-time channel model (EDTCM) is developed
and the characterization of the channel is described by an analytical derivation, a
random walk based simulation, a trained artificial neural network (ANN), and a proof of
concept testbed setup. The investigated transmission algorithms cover modulation schemes
at the transmitter side, as well as channel equalizers and detectors at the receiver side.
In addition to the evaluation of state-of-the-art techniques and the introduction of orthogonal
frequency-division multiplexing (OFDM), the novel variable concentration shift
keying (VCSK) modulation adapted to the diffusion-based transmission channel, the lowcomplex
adaptive threshold detector (ATD) working without explicit channel knowledge,
the low-complex soft-output piecewise linear detector (PLD), and the optimal a posteriori
probability (APP) detector are of particular importance and treated. To improve the
error-prone information transmission, block codes, convolutional codes, line codes, spreading
codes and spatial codes are investigated. The analysis is carried out under various
approaches of normalization and gains or losses compared to the uncoded transmission are
highlighted. In addition to state-of-the-art forward error correction (FEC) codes, novel line
codes adapted to the error statistics of the diffusion-based channel are proposed. Moreover,
the turbo principle is introduced into the field of molecular communication, where extrinsic
information is exchanged iteratively between detector and decoder. By means of an extrinsic
information transfer (EXIT) chart analysis, the potential of the iterative processing is
shown and the communication channel capacity is computed, which represents the theoretical
performance limit for the system under investigation. In addition, the construction of an
irregular convolutional code (IRCC) using the EXIT chart is presented and its performance
capability is demonstrated. For the evaluation of all considered transmission algorithms the
bit error rate (BER) performance is chosen. The BER is determined by means of Monte
Carlo simulations and for some algorithms by theoretical derivation
Dynamic Power Management for Neuromorphic Many-Core Systems
This work presents a dynamic power management architecture for neuromorphic
many core systems such as SpiNNaker. A fast dynamic voltage and frequency
scaling (DVFS) technique is presented which allows the processing elements (PE)
to change their supply voltage and clock frequency individually and
autonomously within less than 100 ns. This is employed by the neuromorphic
simulation software flow, which defines the performance level (PL) of the PE
based on the actual workload within each simulation cycle. A test chip in 28 nm
SLP CMOS technology has been implemented. It includes 4 PEs which can be scaled
from 0.7 V to 1.0 V with frequencies from 125 MHz to 500 MHz at three distinct
PLs. By measurement of three neuromorphic benchmarks it is shown that the total
PE power consumption can be reduced by 75%, with 80% baseline power reduction
and a 50% reduction of energy per neuron and synapse computation, all while
maintaining temporary peak system performance to achieve biological real-time
operation of the system. A numerical model of this power management model is
derived which allows DVFS architecture exploration for neuromorphics. The
proposed technique is to be used for the second generation SpiNNaker
neuromorphic many core system
Rede neural de função de base radial de transmissão de fase complexa para decodificação mimo-ofdm massiva
Orientador: Dalton Soares ArantesDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Os esquemas de transmissão MIMO (multiple-input multiple-output) se tornaram as técnicas escolhidas para aumentar a eficiência espectral em áreas congestionadas. No entanto, o projeto de receptores de baixo custo para canais MIMO continua sendo uma tarefa desafiadora. O detector de máxima verossimilhança pode atingir um desempenho excelente, geralmente o melhor, mas sua complexidade computacional é um fator limitante na implementação prática. Neste trabalho, um novo esquema MIMO é proposto com um algoritmo de decodificação pratico e viável baseado na PTRBFNN (rede neural de função de base radial de transmitância de fase). O esquema proposto atinge uma complexidade computacional bastante competitiva em relação à decodificação de Máxima Verossimilhança, aumentando substancialmente a aplicabilidade do algoritmo. Os resultados da simulação são apresentados para MIMO-OFDM sob desvanecimento Rayleigh em canais sem fio, para que uma comparação de desempenho justa com outras técnicas de referência possa ser estabelecidaAbstract: Multi-Input Multi-Output (MIMO) transmission schemes have become the techniques of choice for increasing spectral efficiency in bandwidth-congested areas. However, the design of cost-effective receivers for MIMO channels remains a challenging task. The maximum likelihood detector can achieve excellent performance, usually the best, but its computational complexity is a limiting factor in practical implementation. In this work, a new MIMO scheme is proposed with a practically feasible decoding algorithm based on the phase transmittance radial basis function neural network (PTRBFNN). The proposed scheme achieves a computational complexity that is quite competitive relative to the Maximum Likelihood decoding, thus substantially increasing the applicability of the algorithm. Simulation results are presented for MIMO-OFDM under wireless Rayleigh fading channels so that a fair performance comparison with other reference techniques can be establishedMestradoTelecomunicações e TelemáticaMestre em Engenharia Elétrica132545/2019-5CNP
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