A unifying convergence analysis of second-order methods for secular equations
Mathematics of Computation
Determinant Maximization with Linear Matrix Inequality Constraints
SIAM Journal on Matrix Analysis and Applications
Convex Optimization
Cognitive radio: an information-theoretic perspective
IEEE Transactions on Information Theory
An Analysis of Uplink OFDMA Optimality
IEEE Transactions on Wireless Communications
Sensing-Throughput Tradeoff for Cognitive Radio Networks
IEEE Transactions on Wireless Communications
IEEE Transactions on Information Theory
Iterative water-filling for Gaussian vector multiple-access channels
IEEE Transactions on Information Theory
Sum power iterative water-filling for multi-antenna Gaussian broadcast channels
IEEE Transactions on Information Theory
Sum-capacity computation for the Gaussian vector broadcast channel via dual decomposition
IEEE Transactions on Information Theory
Achievable rates in cognitive radio channels
IEEE Transactions on Information Theory
On Capacity Under Receive and Spatial Spectrum-Sharing Constraints
IEEE Transactions on Information Theory
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
An introduction to convex optimization for communications and signal processing
IEEE Journal on Selected Areas in Communications
A tutorial on decomposition methods for network utility maximization
IEEE Journal on Selected Areas in Communications
An iterative water-filling algorithm for maximum weighted sum-rate of Gaussian MIMO-BC
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
Secure communication over MISO cognitive radio channels
IEEE Transactions on Wireless Communications
Fading cognitive multiple access channels: outage capacity regions and optimal power allocation
IEEE Transactions on Wireless Communications
IEEE Transactions on Wireless Communications
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Cognitive radio is an emerging technology that shows great promise to dramatically improve the efficiency of spectrum utilization. This paper considers a cognitive radio model, in which the secondary network is allowed to use the radio spectrum concurrently with primary users (PUs) provided that interference from the secondary users (SUs) to the PUs is constrained by certain thresholds. The weighted sum rate maximization problem is studied under interference power constraints and individual transmit power constraints, for a cognitive multiple access channel (C-MAC), in which each SU having a single transmit antenna communicates with the base station having multiple receive antennas. An iterative algorithm is developed to efficiently obtain the optimal solution of the weighted sum rate problem for the C-MAC. It is further shown that the proposed algorithm, although developed for single channel transmission, can be extended to the case of multiple channel transmission. Corroborating numerical examples illustrate the convergence behavior of the algorithm and present comparisons with other existing alternative algorithms.