In Orthogonal Frequency Division Multiple Access (OFDMA) systems, resources, including subcarriers, bits and power, need to be adaptively allocated to users in order to improve spectral efficiency,... Show moreIn Orthogonal Frequency Division Multiple Access (OFDMA) systems, resources, including subcarriers, bits and power, need to be adaptively allocated to users in order to improve spectral efficiency, increase capacity, and reduce power consumption, while satisfying the Quality of Service (QoS) requirements for users. Most of the previous works concentrate on satisfying rate and power requirements, however providing delay requirement is also necessary, especially with increasing demand on delay-sensitive applications. We first model the resource allocation problem as a cross-layer optimization problem considering the constraints on bit error rate (BER), data rate, total power, as well as delay. We first develop a nonlinear optimization model, which generally requires high computation complexity. To consider a more realistic scenario, we take into account imperfect Channel State Information (CSI) due to estimation errors or channel feedback delay, and incorporate the imperfect CSI into the optimization problem formulation. We then derive the solution through a dual decomposition method. Due to the duality gap between the original and dual optimizations, we convert the non-linear optimization to an equivalent linear formulation so that an exact solution can be obtained. To further reduce the complexity, we develop a heuristic algorithm to provide a solution close to the optimum. Then, we study the notion of fairness in the context of resource allocation. In particular, cooperative game theory can be applied to OFDMA networks for fair resource allocation. We apply two cooperative games, Non-Transferable Utility (NTU) game and Transferable Utility (TU) game, to provide fairness in OFDMA networks. In NTU game, fairness is achieved by defining appropriate objective function, while in TU game, fairness is provided by forming the appropriate network structure. For NTU game, we analyze the Nash Bargaining Solution (NBS) as a solution of NTU game taking into account CSI and Queue State Information (QSI). In a TU game, we show that coalition among subcarriers to jointly provide rate requirements leads to better performance in terms of power consumpviii tion. We show that although NTU and TU games are modeled as rate adaptive and margin adaptive problems, respectively, but both solutions provide a fair distribution of resources with minimum fairness index of 0.8. Although NBS can provide fairness, the fairness is not from user perspective. In competitive fairness, which is based on auction theory, each user is responsible for his/her own action. A distributed allocation of resources in OFDMA networks is studied through auction theory. A combinatorial auction is formulated in which the usersâ€™ utility enforce the truthful resource demands. Since the original problem is NP hard, a method based on simulated annealing applied to find near-optimum results. Then, we turn our attention toward a more complicated scenario of multicell OFDMA networks. A combinatorial auction, which takes into account the interference from adjacent cells is presented. Auction objective is to minimize the interference, while power of users is limited. Due to the complexity of original problem, we apply a heuristic approach, in which the bids are ordered based on the linear programming approximation of combinatorial auction, and then local improvements are made in the order of bids. Our iterative approach along with the proposed load control scheme provides fair distribution of resources to the users, regardless of their position in the cell. Finally, we propose a comprehensive auction in OFDMA network. We present an auction framework for allocation of subcarriers, in which winner pays monitoring and entry fees, in addition to the price which he is paying for the allocated subcarrier. We prove that in our framework users will avoid bidding for the subcarriers where they have a relatively low chance of winning. We obtain optimal bidding strategy based on Bayesian Nash Equilibrium (BNE) in which users are maximizing their net profit. In a Fractional Frequency Reuse (FFR) implementation of frequency planning, we will find a focal distance which classifies the users into cell-center and cell-edge users. It is shown that the focal distance increases as the interference decreases. Show less