In wireless packet-forwarding networks, the nodes or users are always selfish to maximize their utilities in nature. Selfish users would not like to help others for forward each others¡¦ packets, which will cause the network performance degrades severely. To solve the packet-forwarding problem, we propose a novel coalitional game approach based orthogonal decode-and-forward (ODF) relaying scheme to encourage the selfish users for cooperation. In the game-theoretic analysis, we study the properties and stability of the coalitions thoroughly. Furthermore, we prove that the cohesive behavior can be obtained by the aspect of outage probability indeed in this game. Simulation results show that the proposed ODF coalitional game can enforce cooperation exactly and it is always beneficial to form the cooperative groups for all users.

We first present a non-cooperative auction game to solve the bandwidth allocation problem for non-cooperative channel access in a wireless network. The Nash equilibrium is obtained as a solution of the game. To address this problem of bandwidth sharing under unknown information, we further develop a Bayesian auction game model and then Bayesian Nash equilibrium is then obtained. Next, we present a framework based on coalitional game for cooperative channel access for carry-and-forward-based data delivery. Each mobile node helps others to carry and then forward their data. A coalitional game is proposed to find a stable coalition structure for this cooperative data delivery. We next present static and dynamic coalitional games for carry-and-forward-based data delivery when the behavior of each mobile node is unknown by others. In the dynamic game, each mobile node can update its beliefs about other mobile nodes’ types when the static coalitional game is played repeatedly.

The focus of this thesis is on cooperative spectrum sensing and related security issues in multi-channel cognitive radio networks (MCCRNs). We first study the channel assignment for cooperative spectrum sensing in MCCRNs to maximize the number of available channels. In centralized implementation, a heuristic scheme is proposed along with a greedy scheme to reduce the reported information from the cognitive radios (CRs). In distributed scenario, a novel scheme with multi-round operation is designed following the coalitional game theory. Next, we focus on the physical layer security issues for cooperative spectrum sensing in MCCRNs, caused by Byzantine attacks. New counterattacks are proposed to combat attacks comprising coalition head and CRs as Byzantine attackers, which target to reduce the number of available channels for sensing in distributed MCCRNs. First, a new secure coalition head selection is proposed, by using statistical properties of the exchanged SNRs in the coalitions. Then, an iterative algorithm is proposed to block out attackers, if they continue attacking the system. The important problem of key management is considered next, and an energy-efficient identity-based and a certificate-based distributed key management schemes are proposed. First, a new elliptic curve cryptography (ECC)-based distributed private key generation scheme is proposed to combat the single point of failure problem along with novel distributed private key generator (DPKG) selection schemes to preserve security and energy-efficiency. Because of its importance in the proposed identity-based key management scheme, we further propose a low-complexity DPKG assignment, based on multi-objective programming, which can capture DPKG fairness in addition to energy-efficiency. Finally, a more powerful and intelligent distributed cooperative Byzantine attack on the proposed multi-channel cooperative spectrum sensing is proposed, where attackers collude by applying coalitional game theory to maximize the number of invaded channels in a distributed manner. As a remedy, a hierarchical identity-based key management scheme is proposed, in which CRs can only play on a certain number of requested channels and channel access for sensing is limited to the honest CRs selected in the coalitional game. Simulation results show that the proposed schemes can significantly improve cooperative spectrum sensing and secure the system against Byzantine attacks.

We first present a non-cooperative auction game to solve the bandwidth allocation problem for non-cooperative channel access in a wireless network. The Nash equilibrium is obtained as a solution of the game. To address this problem of bandwidth sharing under unknown information, we further develop a Bayesian auction game model and then Bayesian Nash equilibrium is then obtained. Next, we present a framework based on coalitional game for cooperative channel access for carry-and-forward-based data delivery. Each mobile node helps others to carry and then forward their data. A coalitional game is proposed to find a stable coalition structure for this cooperative data delivery. We next present static and dynamic coalitional games for carry-and-forward-based data delivery when the behavior of each mobile node is unknown by others. In the dynamic game, each mobile node can update its beliefs about other mobile nodes’ types when the static coalitional game is played repeatedly.

Wireless communications are important in our highly connected world. The amount of data being transferred in cellular networks is steadily growing, and consequently more capacity is needed. This thesis considers the problem of downlink capacity improvement from the perspective of multicell coordination. By employing multiple antennas at the transmitters and receivers of a multicell network, the inherent spatial selectivity of the users can be exploited in order to increase the capacity through linear precoding and receive filtering. For the coordination between cells, distributed algorithms are often sought due to their low implementation complexity and robustness. In this context, the thesis considers two problem domains: base station clustering and coordinated precoding. Base station clustering corresponds to grouping the cell base stations into disjoint clusters in order to reduce the coordination overhead. This is needed in intermediate-sized to large networks, where the overhead otherwise would be overwhelmingly high. Two solution methods for the clustering problem are proposed: an optimal centralized method, as well as a heuristic distributed method. The optimal method applies to a family of throughput models and exploits the structure of the model to find bounds that can be used to focus the search for the optimal clustering into promising territories. The distributed method instead uses notions from coalitional game theory, where the base stations are modelled as rational and intelligent players in a game. By letting the players make individual deviations that benefit them in the game, i.e.\@ switching clusters, a distributed coalition formation algorithm is obtained. Coordinated precoding is the act of finding the linear precoders and receive filters that maximize the network performance, given a base station clustering. Four specific challenges are studied in this problem domain. First, coordinated precoding under intercluster interference is considered. The channels of the intercluster links are not explicitly estimated due to overhead reasons, and these links thus lead to intercluster interference. By exploiting the known statistics of the intercluster channels, a robust and distributed coordinated precoding algorithm is developed. Second, coordinated precoding under imperfect channel state information is considered. Relying on the channel reciprocity under time-division duplex operation, a distributed estimation framework is proposed. Given the estimated channels, a robust and distributed coordinated precoding algorithm is then derived. Third, coordinated precoding under imperfect radio hardware is considered. By modelling the radio frequency distortion noises, a distributed coordinated precoding method that accounts for the imperfections is proposed. Fourth, joint coordinated precoding and discrete rate selection is considered. By bounding and linearizing an originally intractable optimization problem, a heuristic algorithm is derived which selects the transmit rate from a finite set and simultaneously forms the linear precoders and receive filters. / Trådlös kommunikation är ett viktigt verktyg i dagens ständigt uppkopplade värld. Datamängden som överförs i mobilnätverk ökar stadigt och därmed behovet av mer kapacitet. För att öka kapaciteten i nedlänken så utvecklar denna avhandling nya metoder för koordinering av multicellnätverk. Med flerantenniga sändare och mottagare så kan den spatiala selektiviteten hos mottagarna utnyttjas för att separera dem, vilket ger en ökad kapacitet. För denna koordinering är distribuerade algoritmer ofta att föredra eftersom de är robusta och har låg implementeringskomplexitet. I detta sammanhang undersöker denna avhandling två problemområden: basstationsgruppering och samordnad förkodning. Basstationsgruppering innebär att basstationerna delas in i disjunkta grupper, vilket minskar overheadkostnaden för samordningen. Detta är framför allt nödvändigt i medelstora till stora nätverk, eftersom overheadkostnaden för koordineringen av dessa annars skulle bli för stor. Två lösningar för basstationsgruppering presenteras: dels en optimal och centraliserad metod samt dels en heuristisk och distribuerad metod. Den optimala och centraliserade metoden kan hantera en familj av modeller för den totala datatakten och utnyttjar strukturen i modellen för att fokusera sökandet efter den optimala grupperingen mot lovande områden. Den heuristiska och distribuerade metoden bygger på spelteori för koalitioner och modellerar basstationerna som rationella och intelligenta spelare i ett spel. En distribuerad algoritm för koalitionsformering härleds genom att låta spelarna göra individuella förflyttningar, dvs. byta grupp, när det gynnar dem under spelets regler. Vid samordnad förkodning använder de flerantenniga sändarna och mottagarna linjära förkodare och mottagningsfilter för att maximera nätverkets prestanda. Inom detta problemområde undersöks fyra olika specifika problem. Först undersöks problemet när det finns störningar mellan basstationsgrupperna. För att hålla nere mängden overhead så skattas inte kanalerna mellan grupperna, vilket ger upphov till störningar hos mottagarna. Genom att utnyttja den kända statistiska informationen för dessa okända kanaler kan en robust och distribuerade samordningsmetod för förkodningen utvecklas. Därnäst undersöks problemet då kanalkännedomen är bristfällig i allmänhet. Reciprociteten som uppstår vid tidsdelningsduplexning utnyttjas och flera distribuerade skattningsmetoder härleds. Givet den skattade kanalkännedomen föreslås en robust metod för samordnad förkodning. Därnäst undersöks problemet med samordnad förkodning då radiohårdvaran är bristfällig. En modell för det distortionsbrus som skapas av den bristfälliga hårdvaran används för att föreslå en robust distribuerad metod för samordnad förkodning för detta scenario. Slutligen undersöks valet av diskret datatakt med simultan samordnad förkodning. En heuristisk algoritm utvecklas som löser ett begränsat optimeringsproblem. Algoritmen väljer sänddatatakten från en ändlig mängd och bestämmer simultant de linjära förkodarna och mottagningsfiltrena. / <p>QC 20160407</p>