Advances in wireless communication and microelectronics have led to the development of low-power compact sensor nodes (popularly called motes) that are capable of sensing, computing, and communication. A large number of these nodes can be deployed over some area of interest to form a multi-hop network, commonly referred to as a wireless sensor network (WSN). Typical applications of WSNs include, environment and process monitoring in industrial installations, forest fire detection, structural health monitoring, etc. In such applications where the variables to be measured are slowly varying, or the events to be monitored are rare, continuous sensing is unnecessary. Instead, the nodes, in order to conserve their battery power, can sleep-wake cycle whereby each node is allowed to independently alternate between an ON state and a low power OFF state. Sleep-wake cycling, while increasing the network lifetime, renders the network disconnected a large fraction of the time; however, connectivity can be established over time by transporting packets in a store-and-forward manner, whereby packets are held by a forwarding node until a suitable node wakes up in its neighborhood that can serve to forward the packet towards the destination. We are concerned with sleep-wake cycling multi-hop wireless networks whose main task is to carry sporadic alarms packets from sensing nodes to a sink node. Our objective is to design simple local-information based routing solutions for such networks. With this in mind, we propose a relay selection problem that arises at a forwarding node (which is currently holding the alarm packet) while choosing a next-hop relay node. The forwarder, as and when the relays wake-up, evaluating the goodness of a relay based on a “reward” metric (e.g., a function of the relay’s progress towards sink, and the power required to get the packet across), has to decide whether to forward to this relay or to wait for future ones (i.e., to stop or continue). The forwarder’s objective is to choose a relay so as to minimize a combination of the average delay incurred and the average reward achieved. A basic version of our relay selection problem is equivalent to the basic asset selling problem studied in the operations research literature. After reviewing the solution to the basic problem we will proceed to study a model with full information, referred to as the completely observable (CO) model, where the number of relays is exactly known to the forwarder. Formulating the problem as a Markov decision process (MDP) we will characterize the solution to the CO model in terms of recursively-computable threshold functions. Next, we consider the partially observable (PO) model where only a belief (probability mass function) on the number of relays is known. Hence, the PO model falls within the realm of partially observable MDPs. After incorporating our model into this framework we will characterize the solution in terms of stopping sets, which is the set of all belief states where it is optimal to stop. Our main contribution here is to obtain inner and outer bounds for the stopping sets. We next propose a variant where the relays, upon waking up, do not reveal their rewards immediately, but instead the forwarder can choose to probe the relay to know its reward, incurring a probing cost. Thus, to the existing set of stop and continue actions, we have added a new probe action. This model is motivated by the efforts required to learn the channel gains (by probing) in a wireless system. A key result we prove here is that the solution is characterized in terms of stage independent thresholds. Finally, we study a model comprising two forwarders which are competing against each other to choose a next-hop relay (one for each). Here, a relay is allowed to offer possibly different reward to each forwarder. We will first consider a complete information case where the reward pair of a relay is known to both the forwarders. Using stochastic game theory we will characterize the solution to this model in terms of Nash equilibrium policy pairs (NEPPs). We obtain results illustrating the structure of NEPPs. Next, we study a partial information model where each forwarder gets to observe only its reward value. Towards obtaining the solution for this model, we will first formulate a Bayesian game which is effectively played by both the forwarders at each stage. Next, for this Bayesian game we prove the existence of Nash equilibrium strategies within the class of threshold strategies. This result will enable us to construct NEPPs for the partial information model. Although our primary contribution from the thesis is the theoretical study of the above mentioned variants of the basic relay selection model, we have also conducted extensive simulations to study the end-to-end performance obtained by applying the solution to these models at each hop en-route to the sink in a sleep-wake cycling WSN.

Cooperative relaying is a promising diversity achieving technique to provide reliable transmission, high throughput and extensive coverage for wireless networks in a variety of applications. Two-way relaying is a spectrally efficient protocol, providing one solution to overcome the half-duplex loss in one-way relay channels. Moreover, incorporating the multiple-input-multiple-output (MIMO) technology can further improve the spectral efficiency and diversity gain. A lot of related work has been performed on the two-way relay network (TWRN), but most of them assume perfect channel state information (CSI). In a realistic scenario, however, the channel is estimated and the estimation error exists. So in this thesis, we explicitly take into account the CSI error, and investigate its impact on the performance of amplify-and-forward (AF) TWRN where either multiple distributed single-antenna relays or a single multiple-antenna relay station is exploited. For the distributed relay network, we consider imperfect self-interference cancellation at both sources that exchange information with the help of multiple relays, and maximal ratio combining (MRC) is then applied to improve the decision statistics under imperfect signal detection. The system performance degradation in terms of outage probability and average bit-error rate (BER) are analyzed, as well as their asymptotic trend. To further improve the spectral efficiency while maintain the spatial diversity, we utilize the maximum minimum (Max-Min) relay selection (RS), and examine the impact of imperfect CSI on this single RS scheme. To mitigate the negative effect of imperfect CSI, we resort to adaptive power allocation (PA) by minimizing either the outage probability or the average BER, which can be cast as a Geometric Programming (GP) problem. Numerical results verify the correctness of our analysis and show that the adaptive PA scheme outperforms the equal PA scheme under the aggregated effect of imperfect CSI. When employing a single MIMO relay, the problem of robust MIMO relay design has been dealt with by considering the fact that only imperfect CSI is available. We design the MIMO relay based upon the CSI estimates, where the estimation errors are included to attain the robust design under the worst-case philosophy. The optimization problem corresponding to the robust MIMO relay design is shown to be nonconvex. This motivates the pursuit of semidefinite relaxation (SDR) coupled with the randomization technique to obtain computationally efficient high-quality approximate solutions. Numerical simulations compare the proposed MIMO relay with the existing nonrobust method, and therefore validate its robustness against the channel uncertainty. / Graduate

Cooperative relay systems have emerged as promising techniques to boost the performance of wireless systems. Recent studies have confirmed that interferences, co-channel interferences (CCIs) and self-interferences, have a huge impact on cooperative relay systems and can cause significant performance degradation. Two problems were observed in this research. Firstly, previous studies on performance analysis of Amplify-and-Forward (AF) relay systems in presence of CCIs have only focused on a specific interference channel model. However, in practical design scenarios, such an assumption is not a realistic proposition. Secondly, analyses of overheads introduced by a time-based relay selection protocol in distributed cooperative systems have been based on an over-pessimistic assumption where all packets involved in a collision are destroyed. Nevertheless, collisions due to the protocol overheads cause the system performance to be degraded but this does not mean that the failure of end-to-end transmission certainly occurs. The thesis aims to analyse the performance of practical cooperative relay systems in the presence of CCIs and self-interferences, by developing exact mathematical methods. A new unified mathematical method for AF relay systems in presence of a random number of arbitrary non-identical CCIs was developed. The obtained new approach derived in terms of a moment generating function of the aggregate interferences' power led to the derivation of new explicit expressions. The new results greatly simplify evaluation of average error rates over diverse practical interference scenarios. Moreover, a new exact mathematical analysis for distributed cooperative relay systems employing a time-based relay selection protocol based on an accurate interference model was presented. This approach led to the derivation of new exact expressions for the spectral efficiency which accounts for both self-interferences and the protocol overheads as well as for different fading scenarios and arbitrary relay locations. This approach provided several advantages over direct approaches, one of which is that it significantly simplified averaging-out the joint random variables involved.

Relay-assisted cooperative communication exploits spatial diversity to combat wireless fading, and is an appealing technology for next generation wireless systems. Several relay cooperation protocols have been proposed in the literature. In amplify-and-forward (AF)relaying, which is the focus of this thesis, the relay amplifies the signal it receives from the source and forwards it to the destination. AF has been extensively studied in the literature on account of its simplicity since the relay does not need to decode the received signal. We propose a novel optimal relaying policy for two-hop AF cooperative relay systems. In this, an average power-constrained relay adapts its gain and transmit power to minimize the fading-averaged symbol error probability (SEP) at the destination. Next, we consider a generalization of the above policy in which the relay operates as an underlay cognitive radio (CR). This mode of communication is relevant because it promises to address the spectrum shortage constraint. Here, the relay adapts its gain as a function of its local channel gain to the source and destination and also the primary such that the average interference it causes to the primary receiver is also constrained. For both the above policies, we also present near-optimal, simpler relay gain adaptation policies that are easy to implement and that provide insights about the optimal policies. The SEPs and diversity order of the policies are analyzed to quantify their performance. These policies generalize the conventional fixed-power and fixed-gain AF relaying policies considered in cooperative and CR literature, and outperform them by 2.0-7.7 dB. This translates into significant energy savings at the source and relay, and motivates their use in next generation wireless systems.

Cette thèse étudie les communications directes entre les mobiles, appelées communications D2D, en tant que technique prometteuse pour améliorer les futurs réseaux cellulaires. Cette technologie permet une communication directe entre deux terminaux mobiles sans passer par la station de base. La modélisation, l'évaluation et l'optimisation des différents aspects des communications D2D constituent les objectifs fondamentaux de cette thèse et sont réalisés principalement à l'aide des outils mathématiques suivants: la théorie des files d'attente, l'optimisation de Lyapunov et les processus de décision markovien partiellement observable POMDP. Les résultats de cette étude sont présentés en trois parties. Dans la première partie, nous étudions un schéma de sélection entre mode cellulaire et mode D2D. Nous dérivons les régions de stabilité des scénarios suivants: réseaux cellulaires purs et réseaux cellulaires où les communications D2D sont activées. Une comparaison entre ces deux scénarios conduit à l'élaboration d'un algorithme de sélection entre le mode cellulaire et le mode D2D qui permet d'améliorer la capacité du réseau. Dans la deuxième partie, nous développons un algorithme d'allocation de ressources des communications D2D. Les utilisateurs D2D sont en mesure d'estimer leur propre qualité de canal, cependant la station de base a besoin de recevoir des messages de signalisation pour acquérir cette information. Sur la base de cette connaissance disponibles au niveau des utilisateurs D2D, une approche d'allocation des ressources est proposée afin d'améliorer l'efficacité énergétique des communications D2D. La version distribuée de cet algorithme s'avère plus performante que celle centralisée. Dans le schéma distribué des collisions peuvent se produire durant la transmission de l'état des canaux D2D ; ainsi un algorithme de réduction des collisions est élaboré. En outre, la mise en œuvre des algorithmes centralisé et distribué dans un réseau cellulaire, type LTE, est décrite en détails. Dans la troisième partie, nous étudions une politique de sélection des relais D2D mobiles. La mobilité des relais représente un des principaux défis que rencontre toute stratégie de sélection de relais. Le problème est modélisé par un processus contraint de décision markovien partiellement observable qui prend en compte le dynamisme des relais et vise à trouver la politique de sélection de relais qui optimise la performance du réseau cellulaire sous des contraintes de coût. / This thesis considers Device-to-Device (D2D) communications as a promising technique for enhancing future cellular networks. Modeling, evaluating and optimizing D2D features are the fundamental goals of this thesis and are mainly achieved using the following mathematical tools: queuing theory, Lyapunov optimization and Partially Observed Markov Decision Process (POMDP). The findings of this study are presented in three parts. In the first part, we investigate a D2D mode selection scheme. We derive the queuing stability regions of both scenarios: pure cellular networks and D2D-enabled cellular networks. Comparing both scenarios leads us to elaborate a D2D vs cellular mode selection design that improves the capacity of the network. In the second part, we develop a D2D resource allocation algorithm. We observe that D2D users are able to estimate their local Channel State Information (CSI), however the base station needs some signaling exchange to acquire this information. Based on the D2D users' knowledge of their local CSI, we provide an energy efficient resource allocation framework that shows how distributed scheduling outperforms centralized one. In the distributed approach, collisions may occur between the different CSI reporting; thus, we propose a collision reduction algorithm. Moreover, we give a detailed description on how both centralized and distributed algorithms can be implemented in practice. In the third part, we propose a mobile relay selection policy in a D2D relay-aided network. Relays' mobility appears as a crucial challenge for defining the strategy of selecting the optimal D2D relays. The problem is formulated as a constrained POMDP which captures the dynamism of the relays and aims to find the optimal relay selection policy that maximizes the performance of the network under cost constraints.