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.

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

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.

Due to the rapid development of wireless communications together with the inflexibility of the current spectrum allocation policy, radio spectrum becomes more and more exhausted. One of the critical challenges of wireless communication systems is to efficiently utilize the limited frequency resources to be able to support the growing demand of high data rate wireless services. As a promising solution, cognitive radios have been suggested to deal with the scarcity and under-utilization of radio spectrum. The basic idea behind cognitive radios is to allow unlicensed users, also called secondary users (SUs), to access the licensed spectrum of primary users (PUs) which improves spectrum utilization. In order to not degrade the performance of the primary networks, SUs have to deploy interference control, interference mitigating, or interference avoidance techniques to minimize the interference incurred at the PUs. Cognitive radio networks (CRNs) have stimulated a variety of studies on improving spectrum utilization. In this context, this thesis has two main objectives. Firstly, it investigates the performance of single hop CRNs with spectrum sharing and opportunistic spectrum access. Secondly, the thesis analyzes the performance improvements of two hop cognitive radio networks when incorporating advanced radio transmission techniques. The thesis is divided into three parts consisting of an introduction part and two research parts based on peer-reviewed publications. Fundamental background on radio propagation channels, cognitive radios, and advanced radio transmission techniques are discussed in the introduction. In the first research part, the performance of single hop CRNs is analyzed. Specifically, underlay spectrum access using M/G/1/K queueing approaches is presented in Part I-A while dynamic spectrum access with prioritized traffics is studied in Part I-B. In the second research part, the performance benefits of integrating advanced radio transmission techniques into cognitive cooperative radio networks (CCRNs) are investigated. In particular, opportunistic spectrum access for amplify-and-forward CCRNs is presented in Part II-A where collaborative spectrum sensing is deployed among the SUs to enhance the accuracy of spectrum sensing. In Part II-B, the effect of channel estimation error and feedback delay on the outage probability and symbol error rate (SER) of multiple-input multiple-output CCRNs is investigated. In Part II-C, adaptive modulation and coding is employed for decode-and-forward CCRNs to improve the spectrum efficiency and to avoid buffer overflow at the relay. Finally, a hybrid interweave-underlay spectrum access scheme for a CCRN is proposed in Part II-D. In this work, the dynamic spectrum access of the PUs and SUs is modeled as a Markov chain which then is utilized to evaluate the outage probability, SER, and outage capacity of the CCRN.

Αντικείμενο της παρούσας διδακτορικής διατριβής είναι η μελέτη και ο σχεδιασμός καινοτόμων αλγορίθμων φυσικού επιπέδου σε ασύρματα συστήματα επικοινωνίας που χρησιμοποιούν διαμόρφωση με πολύπλεξη συχνότητας ορθογωνίων φερουσών (Orthogonal Frequency Division Multiplexing - OFDM). Η έρευνα επικεντρώθηκε σε δύο κατηγορίες προβλημάτων, στον σχεδιασμό αλγορίθμων προσαρμοστικής διαμόρφωσης καθώς και αλγορίθμων αντιμετώπισης ισχυρού θορύβου φάσης. Αναπτύχθηκαν αλγόριθμοι εκτίμησης φάσης με γραμμική πολυπλοκότητα, μέσω ενός καινούργιου εναλλακτικού μοντέλου περιγραφής του συστήματος. Το μοντέλο αυτό επιτρέπει την επέκταση των κλασικών αλγορίθμων εκτίμησης της κοινής φάσης με στόχο την εκτίμηση του συνολικού διανύσματος θορύβου φάσης. Επιπλέον, η τεχνική διαγώνιας φόρτωσης (diagonal-loading) προσαρμόστηκε κατάλληλα για τη βελτίωση σύγκλισης της προτεινόμενης λύσης. Τέλος, προτάθηκε και αξιολογήθηκε ένα συνολικό σύστημα OFDM όπου η εκτίμηση του καναλιού, της διαταραχής φάσης και των δεδομένων βασίζονται στο κριτήριο ελαχίστων τετραγώνων, διατηρώντας έτσι τη συνολική πολυπλοκότητα σε χαμηλά επίπεδα. Στο πλαίσιο του σχεδιασμού αλγορίθμων προσαρμοστικής διαμόρφωσης προτείνεται ένα γενικό μοντέλο περιγραφής απόδοσης συστήματος ικανό να περιγράψει τα αναπτυσσόμενα πρωτόκολλα μετάδοσης. Η πρόταση αυτή εντάσσεται στην οικογένεια των τεχνικών ισοδύναμης σηματοθορυβικής απεικόνισης (Εffective SNR Μapping - ESM). Χρησιμοποιώντας τις τεχνικές ESM και κατάλληλους περιορισμούς στην παραμετροποίηση των μεταβλητών μετάδοσης, αναπτύχθηκαν αλγόριθμοι προσαρμοστικής διαμόρφωσης χαμηλής πολυπλοκότητας που ικανοποιούν διαφορετικά κριτήρια βελτιστοποίησης. Επιπρόσθετα, προτείνεται ένα γενικό πλαίσιο για τον σχεδιασμό αλγορίθμων προσαρμοστικής διαμόρφωσης, χρησιμοποιώντας προσεγγιστικά μοντέλα απόδοσης. Ορίστηκαν οι κατάλληλες μετρικές για την ποσοτικοποίηση της σπατάλης ενέργειας που επιφέρει η χρήση προσεγγιστικών μοντέλων. Μελετήθηκε η επίδραση της καθυστέρησης ανατροφοδότησης πληροφορίας καναλιού στους αλγορίθμους και παρήχθησαν κατάλληλα μοντέλα περιγραφής απόδοσης που συμπεριλαμβάνουν το χρόνο καθυστέρησης. Το συνολικό αποτέλεσμα της εργασίας είναι αλγόριθμοι που καταφέρνουν υψηλή απόδοση συστήματος, με χαμηλή πολυπλοκότητα, κάτι το οποίο τους κάνει υλοποιήσιμους σε ρεαλιστικά συστήματα. / The objective of this thesis is to study and develop novel, low complexity physical layer algorithms for Orthogonal Frequency Division Multiplexing (OFDM) based communication systems. The study aims at two algorithmic categories, namely adaptive modulation and coding and compensation of severe phase noise (PHN) errors. A parameterized windowed least-squares (WLS) decision directed phase error estimator is proposed via proper (alternative) system modeling, applied to both channel estimation and data detection stage in OFDM systems. The window is optimized so as to minimize the post-compensation error variance (PCEV) of the residual phase, analytically computed for arbitrary PHN and frequency offset (FO) models. Closed-form expressions for near-optimal windows are derived for zero-mean FO, Wiener and first-order autoregressive PHN models, respectively. Furthermore, the diagonal-loading approach is properly employed, initially proposed for providing robustness to a general class of estimators in the presence of model mismatch, to enhance convergence of the iterative estimation scheme, in those high-SNR regions where the effect of data decision errors dominates performance. In the proposed OFDM scheme, channel, IFO estimation and data equalization are also based on the LS criterion, thus keeping the overall system complexity low. A generic performance description model is proposed and used for AMC algorithmic design, capable of describing most of current and under preparation communication protocols. This model proposition is incorporated to a larger family of performance modelling techniques named Effective SNR Mapping techniques (ESM). Using the ESM techniques and proper parameter adaptation constraints, a number of low-complexity AMC algorithms are developed under a chosen set of optimization scenarios. A framework for the design of AMC algorithms using approximate performance description models is proposed. Specific bounds are derived for quantifying the power loss when using approximate models. The effect of outdated channel state information is also studied by statistically characterizing the effective SNR at the receiver. This description allows parameter adaptation under mobility scenarios. The main value of this collective procedure is the development of low complexity- high performance algorithms, implementable on pragmatic OFDM systems.