Integer and combinatorial optimization
Integer and combinatorial optimization
Interior point algorithms: theory and analysis
Interior point algorithms: theory and analysis
Convex Optimization
Spectrum sharing in wireless networks via QoS-aware secondary multicast beamforming
IEEE Transactions on Signal Processing
Strong Duality for the CDT Subproblem: A Necessary and Sufficient Condition
SIAM Journal on Optimization
Capacity and power allocation for spectrum-sharing communications in fading channels
IEEE Transactions on Wireless Communications
Cognitive radio: an information-theoretic perspective
IEEE Transactions on Information Theory
Cognitive multiple access channels: optimal power allocation for weighted sum rate maximization
IEEE Transactions on Communications
Robust cognitive beamforming with bounded channel uncertainties
IEEE Transactions on Signal Processing
Convex approximation techniques for joint multiuser downlink beamforming and admission control
IEEE Transactions on Wireless Communications
How much spectrum sharing is optimal in cognitive radio networks?
IEEE Transactions on Wireless Communications
Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency
IEEE Communications Magazine
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Joint Beamforming and Power Allocation for Multiple Access Channels in Cognitive Radio Networks
IEEE Journal on Selected Areas in Communications
| Hi-index | 0.01 |
We consider a network of cognitive users (also referred to as secondary users (SUs)) coexisting and sharing the spectrum with primary users (PUs) in an underlay cognitive radio network (CRN). Specifically, we consider a CRN wherein the number of SUs requesting channel access exceeds the number of available frequency bands and spatial modes. In such a setting, we propose a joint fast optimal resource allocation and beam-forming algorithm to accommodate maximum possible number of SUs while satisfying quality of service (QoS) requirement for each admitted SU, transmit power limitation at the secondary network basestation (SNBS) and interference constraints imposed by the PUs. Recognizing that the original user maximization problem is a nondeterministic polynomial-time hard (NP), we use a mixed-integer programming framework to formulate the joint user maximization and beamforming problem. Subsequently, an optimal algorithm based on branch and bound (BnB) method has been proposed. In addition, we propose a suboptimal algorithm based on BnB method to reduce the complexity of the proposed algorithm. Specifically, the suboptimal algorithm has been developed based on the first feasible solution it achieves in the fast optimal BnB method. Simulation results have been provided to compare the performance of the optimal and suboptimal algorithms.