We formulate throughput maximizing, max-min fair, weighted max-min fair, and proportionally fair scheduling problems for cognitive radio networks managed by a centralized cognitive base station. We propose a very general scheduling model accomplishing goals such as making frequency, time slot, and data rate allocation to secondary users with possibly multiple antennas, in a heterogenous multi-channel and multi-user scenario. Moreover, our schedulers ensure that reliable communication between the cognitive base station and secondary users are maintained, no collisions occur among secondary users, and primary users in the service area of the cognitive base station are not disturbed. Two distinctive features of our fair schedulers are that they provide joint temporal and throughput fairness, and take throughput values experienced by secondary users in the recent past, referred to as window size, into account and use this information in the current scheduling decision. We also propose a heuristic algorithm for our fair schedulers and demonstrate through simulations that our proposed heuristic yields very close solutions to the values obtained from the optimization softwares. Furthermore, we make extensive simulations to evaluate our schedulers' performance in terms of both total throughput and fairness for varying number of secondary users, frequencies, antennas, and window size.
Most of the scheduling works about underlay spectrum sharing in the literature focused on rate and power allocation without emphasizing other criteria such as frequency allocation, possibly having multiple antennas, ensuring reliable communication, and joint temporal throughput fairness. The work in @cite_27 proposed a joint scheduling and power control scheme for centralized CRNs. Authors in @cite_7 focused on capacity maximization in multi-hop CRNs. None of the works in @cite_27 @cite_37 focused on issues such as fairness or multiple antennas.
@cite_27 @cite_37 @cite_7
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In this paper, we investigate a joint scheduling and power control for an infrastructure-based cognitive radio network (CRN) in coexistence with a cellular primary radio network (PRN). The PRN uses a set of licensed nonoverlapping orthogonal frequency channels for transmission. This set of channels is also accessed in an opportunistic manner by a set of cognitive radio base stations (CR-BSs) to support secondary users (SUs). The problem is formulated to maximize the spectrum utilization of SUs without causing excessive interference to active primary users (PUs) of the PRN. In addition, all the serviced SUs must meet a certain Quality of Service (QoS), such as satisfying a predefined signal to interference noise ratio (SINR). A centralized solution for joint scheduling and power control is derived to make the global accessing decision for all unserved SUs. With the assumption that the knowledge of all subscribers is available, a coordinator of the CRN can use the joint scheduling and power control algorithm to maximize the spectrum utilization of serviced SUs by solving a mixed-integer linear programming (MILP) with an NP-hard complexity. To avoid the NP-hard complexity, we propose a suboptimal heuristic greedy algorithm that can be obtained at a much lower complexity based on the coloring interference graph among unserved SUs effected by serviced SUs and active PUs. Its superior performance over the existing algorithms is demonstrated through simulations. In this paper, we study resource allocation for a multicarrier-based cognitive radio (CR) network. More specifically, we investigate the problem of secondary users’ energy-efficiency (EE) maximization problem under secondary total power and primary interference constraints. First, assuming cooperation among the secondary base stations (BSs), a centralized approach is considered to solve the EE optimization problem for the CR network where the primary and secondary users are using either orthogonal frequency-division multiplexing (OFDM) or filter bank based multicarrier (FBMC) modulations. We propose an alternating-based approach to solve the joint power-subcarrier allocation problem. More importantly, in the first place, subcarriers are allocated using a heuristic method for a given feasible power allocation. Then, we conservatively approximate the nonconvex power control problem and propose a joint successive convex approximation-Dinkelbach algorithm (SCADA) to efficiently obtain a solution to the nonconvex power control problem. The proposed algorithm is shown to converge to a solution that coincides with the stationary point of the original nonconvex power control subproblem. Moreover, we propose a dual decomposition-based decentralized version of the SCADA. Second, under the assumption of no cooperation among the secondary BSs, we propose a fully distributed power control algorithm from the perspective of game theory. The proposed algorithm is shown to converge to a Nash-equilibrium (NE) point. Moreover, we propose a sufficient condition that guarantees the uniqueness of the achieved NE. Extensive simulation analyses are further provided to highlight the advantages and demonstrate the efficiency of our proposed schemes. Cognitive radio (CR) networking achieves high utilization of the scarce spectrum resources without causing any performance degradation to the licensed users. Since the spectrum availability varies over time and space, the infrastructure-based CR networks are required to have a dynamic inter-cell spectrum sharing capability. This allows fair resource allocation as well as capacity maximization and avoids the starvation problems seen in the classical spectrum sharing approaches. In this paper, a joint spectrum and power allocation framework is proposed that addresses these concerns by (i) opportunistically negotiating additional spectrum based on the licensed user activity (exclusive allocation), and (ii) having a share of reserved spectrum for each cell (common use sharing). Our algorithm accounts for the maximum cell capacity, minimizes the interference caused to neighboring cells, and protects the licensed users through a sophisticated power allocation method. Simulation results reveal that the proposed inter-cell spectrum sharing framework achieves better fairness and higher network capacity than the conventional spectrum sharing methods. A resource allocation framework is presented for spectrum underlay in cognitive wireless networks. We consider both interference constraints for primary users and quality of service (QoS) constraints for secondary users. Specifically, interference from secondary users to primary users is constrained to be below a tolerable limit. Also, signal to interference plus noise ratio (SINR) of each secondary user is maintained higher than a desired level for QoS insurance. We propose admission control algorithms to be used during high network load conditions which are performed jointly with power control so that QoS requirements of all admitted secondary users are satisfied while keeping the interference to primary users below the tolerable limit. If all secondary users can be supported at minimum rates, we allow them to increase their transmission rates and share the spectrum in a fair manner. We formulate the joint power rate allocation with proportional and max-min fairness criteria as optimization problems. We show how to transform these optimization problems into a convex form so that their globally optimal solutions can be obtained. Numerical results show that the proposed admission control algorithms achieve performance very close to that of the optimal solution. Also, impacts of different system and QoS parameters on the network performance are investigated for the admission control, and rate power allocation algorithms under different fairness criteria.