ITC/USA 2005 Conference Proceedings / The Forty-First Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2005 / Riviera Hotel & Convention Center, Las Vegas, Nevada / To correctly demodulate a signal sent through a multiple-input multiple-output (MIMO) channel, a receiver may use training to learn the channel parameters. The choice of training parameters can significantly impact system performance. Training too often yields low throughput while training infrequently produces poor channel estimates and increased transmission errors. Previous work on optimal training parameters has focused on the block fading Rayleigh model. This work examines a more general case; finding the training parameters that maximize throughput for a continuously varying channel. Training parameters that maximize a lower bound on channel capacity are determined via simulation, and general guidelines are presented for selecting optimal training parameters.

Cette thèse traite le problème de l’allocation des ressources physiques dans les réseaux ad hoc mobiles en contexte multi-utilisateurs. Nous considérons qu’un noeud du réseau, appelé gestionnaire des ressources (GR) a pour tâche d’effectuer cette allocation de ressources, et que pour ce faire, les autres noeuds lui communiquent des informations relatives aux canaux de propagations de leurs liens de communications. Ce modèle de réseaux induit un délai entre le moment où les noeuds envoient leurs informations au GR et le moment où le GR leur envoie leur allocation de ressource, ce qui rend impossible l’utilisation d’informations de canal instantanées pour effectuer l’allocation. Ainsi, nous considérons que le GR ne dispose que d’informations statistiques relatives aux canaux des différents liens de communications. De plus, nous supposons que chaque lien utilise le mécanisme de l’ARQ Hybride (HARQ). Dans ce contexte, la thèse comporte deux objectifs principaux: i) Proposer des procédures d’estimation de la statistique du canal de propagation, et plus particulièrement du facteur K du canal de Rice avec et sans effet de masquage. ii) Proposer et étudier des algorithmes d’allocation de ressources basés sur les statistiques du canal et prenant en compte l’utilisation de l’HARQ ainsi que de schéma de modulation et de codage pratique. En particulier, on cherche à maximiser des grandeurs relatives à l’efficacité énergétique du système. Les ressources à allouer à chaque lien sont une énergie de transmission et une proportion de la bande de fréquence. / This thesis addresses the Resource Allocation (RA) problem in multiuser mobile ad hoc networks. We assume that there is a node in the network, called the resource manager (RM), whose task is to allocate the resource and thus the other nodes send him there channel state information (CSI). This network model induces a delay between the time the nodes send the RM their CSI and the time the RM sends them their RA, which renders impossible the use of instantaneous CSI. Thus, we assume that only statistical CSI is available to perform the RA. Moreover, we assume that an Hybrid ARQ (HARQ) mechanism is used on all the links. In this context, the objective of the thesis is twofold: i) Propose procedures to estimate the statistical CSI, and more precisely to estimate the Rician K factor with and without shadowing. ii) Propose and analyse new RA algorithms using statistical CSI and taking into account the use of HARQ and practical modulation and coding schemes. We aim to maximize energy efficiency related metrics. The resource to allocate are per-link transmit energy and bandwidth proportion.

Traditional wireless networks are regulated by a fixed spectrum assignment policy. This results in situations where most of the allocated radio spectrum is not utilized. In order to address this spectrum underutilization, cognitive radio (CR) has emerged as a promising solution. Spectrum sensing is an essential component in CR networks to discover spectrum opportunities. The most common spectrum sensing techniques are energy detection, matched filtering or cyclostationary feature detection, which aim to maximize the probability of detection subject to a certain false alarm rate. Besides probability of detection, detection delay is also a crucial criterion in spectrum sensing. In an interweave CR network, quick detection of the absence of primary user (PU), which is the owner of the licensed spectrum, allows good utilization of unused spectrum, while quick detection of PU transmission is important to avoid any harmful interference. This thesis consider quickest spectrum sensing, where the aim is to detect the PU with minimal detection delay subject to a certain false alarm rate. In the earlier chapters of this thesis, a single antenna cognitive user (CU) is considered and we study quickest spectrum sensing performance in Gaussian channel and classical fading channel models, including Rayleigh, Rician, Nakagami-m and a long-tailed channel. We prove that the power of the complex received signal is a sufficient statistic and derive the probability density function (pdf) of the received signal amplitude for all of the fading cases. The novel derivation of the pdfs of the amplitude of the received signal for the Rayleigh, Rician and Nakagami-m channels uses an approach which avoids numerical integration. We also consider the event of a mis-matched channel, where the cumulative sum (CUSUM) detector is designed for a specific channel, but a different channel is experienced. This scenario could occur in CR network as the channel may not be known and hence the CUSUM detector may be experiencing a different channel. Simulations results illustrate that the average detection delay depends greatly on the channel but very little on the nature of the detector. Hence, the simplest time-invariant detector can be employed with minimal performance loss. Theoretical expressions for the distribution of detection delay for the time-invariant CUSUM detector, with single antenna CU are developed. These are useful for a more detailed analysis of the quickest spectrum sensing performance. We present several techniques to approximate the distribution of detection delay, including deriving a novel closed-form expression for the detection delay distribution when the received signal experiences a Gaussian channel. We also derive novel approximations for the distribution of detection delay for the general case due to the absence of a general framework. Most of the techniques are general and can be applied to any independent and identically distributed (i.i.d) channel. Results show that different signal-to-noise ratio (SNR) and detection delay conditions require different methods in order to achieve good approximations of the detection delay distributions. The remarkably simple Brownian motion approach gives the best approximation for longer detection delays. In addition, results show that the type of fading channel has very little impact on long detection delays. In later chapters of this thesis, we employ multiple receive antennas at the CU. In particular, we study the performance of multi-antenna quickest spectrum sensing when the received signal experiences Gaussian, independent and correlated Rayleigh and Rician channels. The pdfs of the received signals required to form the CUSUM detector are derived for each of the scenarios. The extension into multiple antennas allows us to gain some insight into the reduction in detection delay that multiple antennas can provide. Results show that the sensing performance increases with an increasing Rician K-factor. In addition, channel correlation has little impact on the sensing performance at high SNR, whereas at low SNR, increasing correlation between channels improves the quickest spectrum sensing performance. We also consider mis-matched channel conditions and show that the quickest spectrum sensing performance at a particular correlation coefficient or Rician K-factor depends heavily on the true channel irrespective of the number of antennas at the CU and is relatively insensitive to the channel used to design the CUSUM detector. Hence, a simple multi-antenna time-invariant detector can be employed. Based on the results obtained in the earlier chapters, we derive theoretical expressions for the detection delay distribution when multiple receive antennas are employed at the CU. In particular, the approximation of the detection delay distribution is based on the Brownian motion approach.