In this dissertation, adaptive power control for single and multiuser opportunistic systems is investigated. First, a new adaptive power-controlled diversity combining scheme for single user systems is proposed, upon which is extended to the multiusers case. In the multiuser case, we first propose two new threshold based parallel multiuser scheduling schemes without power control. The first scheme is named on-off based scheduling (OOBS) scheme and the second scheme is named switched based scheduling (SBS) scheme. We then propose and study the performance of thresholdbased power allocation algorithms for the SBS scheme. Finally, we introduce a unified analytical framework to determine the joint statistics of partial sums of ordered RVs with i.i.d. and then the impact of interference on the performance of parallel multiuser scheduling is investigated based on our unified analytical framework.

In this thesis, we introduce a signal-to-noise ratio (SNR) estimator and analyze the performance degradation of the SNR estimator due to the synchronization error in the orthogonal frequency division multiplexing (OFDM) systems. This SNR estimator through the expectation-maximization (EM) algorithm is used in the adaptive modulation. When the synchronization is imperfectly done, the synchronization error reduces the performance of the OFDM systems and the accuracy of the SNR estimator. We investigate the estimation offset of the SNR estimator with the synchronization error. Simulation results demonstrate that the theoretical analyses are correct. In addition, the simulation results show that the more synchronization errors cause the more estimation errors of the SNR estimator. And the estimation errors are not decreased by the iteration of the EM algorithm.

In Earth-to-Space communications, well-known propagation effects such as path loss and atmospheric loss can lead to fluctuations in the strength of the communications link between a satellite and its ground station. Additionally, a less-often considered effect of shadowing due to the geometry of the satellite and its solar panels can also lead to link degradation. As a result of these anticipated channel impairments, NASA's communication links have been traditionally designed to handle the worst-case impact of these effects through high link margins and static, lower rate, modulation formats. This thesis first characterizes the propagation environment experienced by a software-defined radio on the NASA SCaN Testbed through a full link-budget analysis. Then, the following chapters propose, design, and model a link adaptation algorithm to provide an improved trade-off between data rate and link margin through varying the modulation format as the received signal-to-noise ratio fluctuates. / Master of Science

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 / Adaptive modulation is a communication technique capable of maximizing throughput while guaranteeing a fixed symbol error rate (SER). However, this technique requires instantaneous channel state information at the transmitter. This can be obtained by predicting channel states at the receiver and feeding them back to the transmitter. Existing algorithms used to predict single-input single-output (SISO) channels with recurrent neural networks (RNN) are extended to multiple-input multiple-output (MIMO) channels for use with adaptive modulation and their performance is demonstrated in several examples.

L’OFDM est une technique de modulation multiporteuses largement utilisée dans des applications de communications filaires et sans-fils comme le DVB-T/T2, le Wifi, et la 4G, grâce à sa robustesse contre les canaux sélectifs en fréquence en comparaison avec la modulation monoporteuse. Cependant, le signal OFDM souffre de grandes variations d’amplitude. Les fluctuations de l’enveloppe du signal OFDM génèrent des distorsions non-linéaires quand on introduit le signal dans un équipement non-linéaire comme l’amplificateur de puissance. Réduire les variations du signal améliore le rendement de l’amplificateur, réduit la consommation énergétique et diminue les émissions de CO2 des transmissions numériques.Le PAPR (rapport de la puissance crête sur la puissance moyenne) est une variable aléatoire qui a été introduite pour mesurer les variations du signal. Il existe plusieurs systèmes multiporteuses basés sur différentes bases de modulation et filtres de mise en forme. Nous prouvons d’abord dans ces travaux que le PAPR dépend de cette structure de modulation. Ensuite, nous étudions le comportement du PAPR vis-à-vis des formes d’ondes utilisées dans la modulation. Le problème de réduction du PAPR est ainsi formulé en un problème d’optimisation. Par ailleurs, une condition nécessaire pour construire des formes d’ondes avec un meilleur PAPR que l’OFDM est développée. Cette condition est notamment satisfaite par des bases en ondelettes. Enfin, une nouvelle forme d’onde en paquets d’ondelettes adaptative est proposée, permettant des gains significatifs en PAPR, tout en maintenant les avantages des modulations multiporteuses. / OFDM is a multicarrier modulation system widely used in wireline and wireless applications such as DVB-T/T2, Wifi, and 4G, due to its resilience against frequency selective channels compared with the single carrier modulation systems. However, the OFDM signal suffers from large amplitude variations. The fluctuations of the OFDM envelope generate non-linear distortions when we introduce the signal into a non-linear device like the power amplifier. Reducing the variations of the signal improves the power amplifier efficiency, reduces the energy consumption and decreases CO2 emissions.The peak-to-average power ratio (PAPR) has been introduced as a random variable that measures the power variations of the signal. There exist several multicarrier modulation systems based on different modulation basis and shaping filters. We first prove in this work that the PAPR depends on this modulation structure. Moreover, the behaviour of the PAPR regarding to the modulation waveforms is analysed and the PAPR reduction problem is formulated as an optimization problem. Furthermore, a necessary condition for designing waveforms with better PAPR than OFDM is developed. This necessary condition is particularly satisfied by wavelet basis. Finally, a new adaptive wavelet packet waveform is proposed, allowing significant gain in terms of PAPR, while keeping the advantages of multicarrier modulations.

Emerging 4G standards, such as WiMAX and LTE have adopted the proven technique of Adaptive Modulation and Coding (AMC) to dynamically react to channel fluctuations while maintaining bit-error rate targets of the transmission. This scheme makes use of the estimated channel state indication (CSI) to efficiently utilize channel capacity for next transmission, but it brings with it the stale CSI problem due to the frequently channel fluctuations. As its objective, this thesis focuses on mitigating the vicious effect of stale CSI by proposing a novel framework that incorporate AMC with layered transmission through Superposition Coding (SPC) is introduced. A layered multi-step finite-state Markov chain model (FSMC) is developed under this framework, to effectively assist the system in selecting the optimal modulation and coding scheme as well as the power allocated for each layer in every multi-resolution unicast transmission. Extensive simulations are conducted to verify the proposed framework and compare its performance with other counterparts. The effects of changing key parameters, such as the complexity factor and step size, are also investigated to get close to real world performance. Results demonstrate that the proposed framework can achieve better spectrum efficiency than similar counterparts, due to its improved robustness to the stale CSI problem for each multi-resolution modulated transmission, also these show that the performance of two-layer scheme is good enough for layer allocation, without need of more layers.

One of the major applications driving wireless network services is video streaming, which is based on the ability to simultaneously multicast the same video contents to a group of users, thus reducing the bandwidth consumption. On the other hand, due to slow progress in battery technology, the investigation of power saving technologies becomes important. IEEE 802.16e (also known as Mobile WiMAX) is currently the international MAC (medium access control) standard for wireless metropolitan area networks. However, in 802.16e, the power saving class for multicast traffic is designed only for best-effort-based management operations. On the other hand, SMBC-AMC adopts the concepts of ¡§multicast superframe¡¨ and ¡§logical broadcast channel¡¨ to support push-based multicast applications. However, SMBC-AMC requires that (1) the number of frames in each logical broadcast channel must be equal, (2) all mobile stations must have the same duty cycle, and (3) the base station must use the same modulation to send data in a frame. These imply that SMBC-AMC is too inflexible to reach high multicast energy throughput. In this thesis, we propose cross-layer energy efficient multicast scheduling algorithms, called EEMS-AMC, for scalable video streaming. The goal of EEMS-AMC is to find a multicast data scheduling such that the multicast energy throughput of a WiMAX network is maximum. Specifically, EEMS-AMC has the following attractive features: (1) By means of admission control and the restriction of the multicast superframe length, EEMS-AMC ensures that the base layer data of all admitted video streams can be delivered to mobile stations in timeliness requirements. (2) EEMS-AMC adopts the greedy approach to schedule the base layer data such that the average duty cycle of all admitted stations can approach to the theoretical minimum. (3) EEMS-AMC uses the metric ¡§potential multicast throughput¡¨ to find the proper modulation for each enhancement layer data and uses the metric ¡§multicast energy throughput gain¡¨ to find the near-optimal enhancement layer data scheduling. Simulation results show that EEMS-AMC significantly outperforms SMBC-AMC in terms of average duty cycle, multicast energy throughput, multicast packet loss rate, and normalized total utility.

<p>This thesis comprises two parts: the first part presents channel-adaptive techniques to achieve high spectral efficiency in a single user multiple-input multiple-output (MIMO) system; the second part exhibits a programmable and reconfigurable software-defined-radio orkbench(SDR-WB) in the Matlab/Octave environment that accommodates a variety of wireless applications.</p><p>In an attempt to achieve high spectral efficiency, an adaptive modulation technique is applied at the transmitter to vary the data rate depending on the channel state information (CSI). To further enhance the spectral efficiency, adaptive power allocation schemes are applied in the spatial domain to adjust the power on every transmit antenna. We analyze several power control schemes subject to a peak power constraint to maximize the spectral efficiency given an instantaneous target bit-error-rate (BER). A novel power allocation trategy is proposed to achieve high spectral efficiency with relatively low complexity. In addition, adaptive techniques that switch across different MIMO schemes enables even higher spectral efficiency by choosing the scheme with the highest spectral efficiency. We propose a new method to switch between spatial multiplexing with zero-forcing (ZF) detection and orthogonal space-time block coding (OSTBC). This is done by exploiting closed form expressions of the spectral efficiencies--discrete rate spectral efficiency--and finding the crossing points of the two curves. The proposed adaptation scheme adds limited complexity to the transmitter since it requires only statistical information of the channel, which does not change as time evolves.</p><p>Software Defined Radio (SDR) has received more and more interest recently as a promising multi-band multi-standard solution for transceiver design. In order to support as many wireless applications as possible, we build up a programmable and reconfigurable workbench, namely SDR-WB, in Matlab/Octave environment. The workbench is functionally modularized into generic blocks to facilitate fast development and verification of new algorithms and architectures. The modulation formats that are currently supported by the SDR-WB are MIMO, Orthogonal frequency-division multiplexing (OFDM), MIMO-OFDM, DS-CDMA and Filtered Multitone (FMT).</p>

Link adaptation is an important component of contemporary wireless networks that require high spectral efficiency and service a variety of network applications/configurations. By exploiting information about the wireless channel, link adaptation strategically selects wireless communication transmission parameters in real-time to optimize performance. Link adaptation in practice has proven challenging due to impairments outside system models and analytical intractability in modern broadband networks with multiple antennas (MIMO), orthogonal frequency division multiplexing (OFDM), forward error correction, and bit-interleaving. The objective of this dissertation is to provide simple and flexible link adaptation algorithms with few link model assumptions that are amenable to modern wireless networks. First, a complete design and analysis of supervised learning for link adaptation in MIMO-OFDM is provided. This includes the construction of a publicly available data set, a new frame error rate bound leading to a new feature set, and IEEE 802.11n performance evaluation to verify that my design outperforms existing link quality metrics. Next, two supervised learning classification algorithms are designed to exploit information collected from packets transmitted and received over standard links in real time: database learning with nearest neighbor classifiers and support vector machines. Algorithms are also proposed to preserve diversity of feature sets in the database and to allow learning algorithms to seek out more information about the network. Finally, link adaptation with supervised learning is applied to MIMO-OFDM systems where the modulation order may be adapted per-stream. This leads to the analysis of the ordered SNR per stream and its connection to the cumulative distribution function of SNR on each stream. Decoupled link adaptation algorithms, which significantly reduce the complexity of non-uniform link adaptation algorithms, are proposed. New analysis of non-uniform link adaptation shows that the performance of decoupled link adaptation algorithms converge to the performance of joint (optimal) link adaptation algorithms as the number of modulation and coding options per-stream increase. This guides the construction of future standards to reduce the complexity of link adaptation in MIMO-OFDM. / text

Wireless Sensor Network (WSN) is a widely researched technology that has applications in a broad variety of fields ranging from medical, industrial, automotive and pharmaceutical to even office and home environments. It is composed of a network of self-organizing sensor nodes that operate in complex environments without human intervention for long periods of time. The energy available to these nodes, usually in the form of a battery, is very limited. Consequently, energy saving algorithms that maximize the network lifetime are sought-after. Link adaptation polices can significantly increase the data rate and effectively reduce energy consumption. In this sense, they have been studied for power optimization in WSNs in recent research proposals. In this thesis, we first examine the Adaptive Modulation (AM) schemes for flat-fading channels, with data rate and transmit power varied to achieve minimum energy consumption. Its variant, Adaptive Modulation with Idle mode (AMI), is also investigated. An Adaptive Sleep with Adaptive Modulation (ASAM) algorithm is then proposed to dynamically adjust the operating durations of both the transmission and sleep stages based on channel conditions in order to minimize energy consumption. Furthermore, adaptive power allocation schemes are developed to improve energy efficiency for multi-hop relay networks. Experiments indicate that a notable reduction in energy consumption can be achieved by jointly considering the data rate and the transmit power in WSNs. The proposed ASAM algorithm considerably improves node lifetime relative to AM and AMI. Channel conditions play an important role in energy consumption for both AM and ASAM protocols. In addition, the number of modulation stages is also found to substantially affect energy consumption for ASAM. Node lifetime under different profiles of traffic intensity is also investigated. The optimal power control values and optimal power allocation factors are further derived for single-hop networks and multi-hop relay networks, respectively. Results suggest that both policies are more suitable for ASAM than for AM. Finally, the link adaptation techniques are evaluated based on the power levels of commercial IEEE 802.15.4-compliant transceivers, and ASAM consistently outperforms AM and AMI in terms of energy saving, resulting in substantially longer node lifetime. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2011-02-08 18:26:29.222