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[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.

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.

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.

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.

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.

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.

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

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.

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.