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Wideband adaptive full response multilevel transceivers and equalizersWong, Choong Hin January 1999 (has links)
No description available.
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[en] PERFORMANCE ANALYSIS OF LINKS WITH ADAPTIVE MODULATION APPLIED TO WCDMA/HSDPA SYSTEMS / [pt] ANÁLISE DE DESEMPENHO DE ENLACES COM MODULAÇÃO ADAPTATIVA APLICADA A SISTEMAS WCDMA/HSDPARODRIGO JUREMA DE ASSIS CORREA 05 December 2003 (has links)
[pt] Este trabalho é um estudo sobre o desempenho de um enlace
típico do padrão UMTS/WCDMA para sistemas celulares de
terceira geração, utilizando a técnica denominada modulação
adaptativa. Esta técnica consiste em escolher, para
cada quadro de bits transmitido, a modulação mais adequada
às condições do canal. Os aspectos básicos do problema
foram formulados analiticamente considerando a transmissão
com modulações PSK e QAM coerentes em um canal com
desvanecimento plano e efeito Doppler. Para considerar a
aplicação a um modelo mais realista, foi desenvolvido um
programa de simulação capaz de modelar esse mesmo problema
considerando os principais componentes do sistema HSDPA
(High Speed Data Packet Access), um sub-sistema do
UMTS/WCDMA padronizado para transmissão de dados em alta
velocidade no enlace de descida. O desempenho do enlace foi
avaliado para diversas situações através da taxa de erro de
bit, da taxa de transmissão média e da vazão obtida na
transmissão, procurando-se investigar a influência de
parâmetros do sistema nesse desempenho. / [en] This work is a study about the performance of a typical
UMTS/WCDMA link for third generation mobile communications
systems which uses the adaptive modulation technique. This
technique consists of choosing, for each transmitted
frame, the most efficient modulation according to the
channel conditions. The basic aspects of the problem were
analytically examined considering transmissions with PSK
and QAM coherent techniques in a flat fading channel
with Doppler effect. A simulator was developed in order to
consider the application in a more realistic model. This
simulator was capable of modeling this problem considering
the main aspects of the HSDPA (High Speed Data Packet
Access) system, which is a subsystem of UMTS/WCDMA that is
standardized for high-speed data transmission in the
downlink. The link level performance was evaluated for many
different situations through average transmission bit error
rate and throughput, investigating the influence of the
system parameters in this performance.
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Performance Analysis of Adaptive Loading OFDM Under Nakagami Fading ChannelChan, Cheng-che 31 July 2005 (has links)
In this thesis, we investigate the performance of adaptive loading orthogonal frequency division multiplexing (OFDM) under Nakagami fading with maximal ratio combining (MRC) diversity at receiver. We not only expound the principles and structures of the system, but also analyze its performance of the lower bound on the average capacity under Nakagami fading. First, we defined the lower bound on the average capacity under Nakagami fading with ideal MRC diversity. Then, we fixed the values of bit error rate. A maximum rate adaptive loading strategy is derived for uncoded quadrature-amplitude-modulation modulated OFDM. Simple lower bound expressions are provided for average spectral efficiency of the maximum rate adaptive loading OFDM under Nakagami fading channel. Finally, the numerical results will be also shown.
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Stochastic optimization algorithms for adaptive modulation in software defined radioMisra, Anup 05 1900 (has links)
Adaptive modulation has been actively researched as a means to increase spectral efficiency of wireless communications systems. In general, analytic closed form models have been derived for the performance of the communications system as a function of the control parameters.
However, in systems where general error correction coding is employed, it may be difficult to derive closed form performance functions of the communications systems. In addition, in closed form optimization, real time adaptation is not possible. Systems designed with deterministic state optimization are developed offline for a certain set of parameters and hardwired into mobile devices.
In this thesis we present stochastic learning algorithms for adaptive modulation design. The algorithms presented allow for adaptive modulation system design in-dependent of error correction coding and modulation constellation requirements. In real time, the performance of the system is measured and stochastic approximation techniques are used to learn the optimal transmission parameters of the system.
The technique is applied to Software Defined Radio (SDR) platforms, an emerging wireless technology which is currently being researched as a means of designing intelligent communications devices. The fundamental property that sets SDR apart from traditional radios is that the communications parameters are controlled in software, allowing for real-time control of physical layer communications.
Our treatment begins by modeling the time evolution of the adaptive modulation process as a general state space Markov chain. We show the existence and uniqueness of the invariant measure and model performance functions as expectations with respect to the invariant measure. We consider constrained and unconstrained throughput optimization. We show that the cost functions considered are convex. Next we present stochastic approximation algorithms that are used to estimate the gradient of the cost function given only noisy estimates.
We conclude by presenting simulation results obtained by the presented method. The learning based method is able to achieve the maximum throughput as dictated by exhaustive Monte Carlo simulation of the communications system, which provide an upper bound on performance. In addition, the learning algorithm is able to optimize communications under various error correction schemes. The tracking abilities of the algorithm are also demonstrated. We see that the proposed method is able to track optimal throughput settings as constraints are changed in time.
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Subcarrier Allocation for OFDM System with Adaptive ModulationLin, Cheng-cheng 30 July 2010 (has links)
Orthogonal frequency division multiplexing¡]OFDM¡^systems play an important
role in modern wireless communications due to following advantages: bandwidth
saving¡Bcombat with frequency selective fading channel and high throughput. The
performance of wireless communications is often degraded by fading channel .
adaptive modulation and subcarrier allocation are proposed to overcome the degration
to meet the quality of servie¡]QoS¡^. Lagrange method and heuristics method, two of
the subcarrier allocation technology under multi-user OFDM, can achieve the goal
that maximizing bit rate with minimizing transmitted power. However, significantly
high complexity of either Lagrange method or heuristics method makes the
implementation difficult. Zhang and Letaief proposed a method of making subcarriers
detected one by one to reduce the complexity. However, in piratical, an OFDM system
accommodates hundred of , or even thousand of subcarriers, so the method can be
improved.
In this thesis, we propose a subcarrier allocation method. The users that are not
satisfied with the QOS requirement are named demander, and the users satisfied with
the QOS requirement are named supplier. In the proposed subcarrier allocation
method, we evaluate the number of subcarriers that demanders need and remove the
subcarriers from supplier to directly compensate demander. Then the system has
lower complexity due to less iterations.
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Stochastic optimization algorithms for adaptive modulation in software defined radioMisra, Anup 05 1900 (has links)
Adaptive modulation has been actively researched as a means to increase spectral efficiency of wireless communications systems. In general, analytic closed form models have been derived for the performance of the communications system as a function of the control parameters.
However, in systems where general error correction coding is employed, it may be difficult to derive closed form performance functions of the communications systems. In addition, in closed form optimization, real time adaptation is not possible. Systems designed with deterministic state optimization are developed offline for a certain set of parameters and hardwired into mobile devices.
In this thesis we present stochastic learning algorithms for adaptive modulation design. The algorithms presented allow for adaptive modulation system design in-dependent of error correction coding and modulation constellation requirements. In real time, the performance of the system is measured and stochastic approximation techniques are used to learn the optimal transmission parameters of the system.
The technique is applied to Software Defined Radio (SDR) platforms, an emerging wireless technology which is currently being researched as a means of designing intelligent communications devices. The fundamental property that sets SDR apart from traditional radios is that the communications parameters are controlled in software, allowing for real-time control of physical layer communications.
Our treatment begins by modeling the time evolution of the adaptive modulation process as a general state space Markov chain. We show the existence and uniqueness of the invariant measure and model performance functions as expectations with respect to the invariant measure. We consider constrained and unconstrained throughput optimization. We show that the cost functions considered are convex. Next we present stochastic approximation algorithms that are used to estimate the gradient of the cost function given only noisy estimates.
We conclude by presenting simulation results obtained by the presented method. The learning based method is able to achieve the maximum throughput as dictated by exhaustive Monte Carlo simulation of the communications system, which provide an upper bound on performance. In addition, the learning algorithm is able to optimize communications under various error correction schemes. The tracking abilities of the algorithm are also demonstrated. We see that the proposed method is able to track optimal throughput settings as constraints are changed in time.
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Fast Power Allocation Algorithms for Adaptive MIMO Systems.Chung, Jong-Sun January 2009 (has links)
Recent research results have shown that the MIMO wireless communication architecture is a promising approach to achieve high bandwidth efficiencies.
MIMO wireless channels can be simply defined as a link for which both the
transmitting and receiving ends are equipped with multiple antenna elements.
Adaptive modulation and power allocation could be used to further improve the performance of MIMO systems.
This thesis focuses on developing a fast and high performance power allocation algorithm. Three power allocation algorithms are proposed in this thesis and their performances are compared in various system sizes and transceiver architectures. Among the three algorithms proposed in this thesis, the fast algorithm may be considered as the best power allocation algorithm since the performance of the fast algorithm is almost as good as the fullsearch (optimal)algorithm and the mean processing time is considerably less than the fullsearch algorithm. The fast algorithm achieves about 97.6% agreement with the optimal throughput on average. In addition, the time taken to find the power scaling factors using the fullsearch algorithm is about 2300 times longer than the processing time of the fast algorithm in a 6 x 6 system when the SNR is 20dB.
As an extension to the power allocation process, excess power allocation methods are introduced. Excess power is the unused power during the power
allocation process. The power allocation algorithm allocates power to each received SNR to maximize the throughput of the system whereas the excesspower allocation distributes the excess power to each SNR to improve both the instantaneous and temporal behavior of the system. Five different excess power allocation methods are proposed in this thesis. These methods were simulated in the Rayleigh fading channel with different Doppler frequencies, fD = 10Hz,50Hz and 100Hz, where the ACF of the channel coefficients are given by the Jakes' model. The equal BER improvement method showed a slightly better performance than the other methods. The equal BER improvement method enables the system to maintain the power scaling factors without sacrificing QoS for 19.6 ms on average when the maximum Doppler shift is 10Hz.
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Stochastic optimization algorithms for adaptive modulation in software defined radioMisra, Anup 05 1900 (has links)
Adaptive modulation has been actively researched as a means to increase spectral efficiency of wireless communications systems. In general, analytic closed form models have been derived for the performance of the communications system as a function of the control parameters.
However, in systems where general error correction coding is employed, it may be difficult to derive closed form performance functions of the communications systems. In addition, in closed form optimization, real time adaptation is not possible. Systems designed with deterministic state optimization are developed offline for a certain set of parameters and hardwired into mobile devices.
In this thesis we present stochastic learning algorithms for adaptive modulation design. The algorithms presented allow for adaptive modulation system design in-dependent of error correction coding and modulation constellation requirements. In real time, the performance of the system is measured and stochastic approximation techniques are used to learn the optimal transmission parameters of the system.
The technique is applied to Software Defined Radio (SDR) platforms, an emerging wireless technology which is currently being researched as a means of designing intelligent communications devices. The fundamental property that sets SDR apart from traditional radios is that the communications parameters are controlled in software, allowing for real-time control of physical layer communications.
Our treatment begins by modeling the time evolution of the adaptive modulation process as a general state space Markov chain. We show the existence and uniqueness of the invariant measure and model performance functions as expectations with respect to the invariant measure. We consider constrained and unconstrained throughput optimization. We show that the cost functions considered are convex. Next we present stochastic approximation algorithms that are used to estimate the gradient of the cost function given only noisy estimates.
We conclude by presenting simulation results obtained by the presented method. The learning based method is able to achieve the maximum throughput as dictated by exhaustive Monte Carlo simulation of the communications system, which provide an upper bound on performance. In addition, the learning algorithm is able to optimize communications under various error correction schemes. The tracking abilities of the algorithm are also demonstrated. We see that the proposed method is able to track optimal throughput settings as constraints are changed in time. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate
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Game Theory and Adaptive Modulation for Cognitive RadiosSharma, Guarav 10 1900 (has links)
ITC/USA 2008 Conference Proceedings / The Forty-Fourth Annual International Telemetering Conference and Technical Exhibition / October 27-30, 2008 / Town and Country Resort & Convention Center, San Diego, California / In a multi-user cognitive radio network, there arises a need for coordination among the network users for efficient utilization of the available electromagnetic spectrum. While adaptive modulation alone helps cognitive radios actively determine the channel quality metric for the next transmission, Game theory combined with an adaptive modulation system helps them achieve mutual coordination among channel users and avoids any possible confusion about transmitting/receiving through a channel in the future. This paper highlights how the concepts of game theory and adaptive modulation can be incorporated in a cognitive radio framework to achieve better communication for telemetry applications.
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A HARDWARE PLATFORM FOR COGNITIVE RADIOPratt, Jason 10 1900 (has links)
ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Cognitive radio is a reasonably new branch of research aimed at more fully utilizing the RF
spectrum. This is accomplished by allowing wireless communication systems to dynamically
choose a frequency band, and a modulation technique, based on the current state of the RF
spectrum as perceived by the cognitive radio network. This paper will give a brief introduction
of cognitive radio networks, and describe a hardware platform designed at the IFT/UMR
Telemetry Learning Center. The test-bed will accommodate future research into cognitive
networks, by allowing the user to dynamically change both its carrier frequency and modulation
technique through software. A general description of the design of the platform is provided.
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