Exploiting Multi-Antennas for Opportunistic Spectrum Sharing in Cognitive Radio Networks

In cognitive radio (CR) networks, there are scenarios where the secondary (lower priority) users intend to communicate with each other by opportunistically utilizing the transmit spectrum originally allocated to the existing primary (higher priority) users. For such a scenario, a secondary user usually has to trade off between two conflicting goals at the same time: one is to maximize its own transmit throughput; and the other is to minimize the amount of interference it produces at each primary receiver. In this paper, we study this fundamental tradeoff from an information-theoretic perspective by characterizing the secondary user's channel capacity under both its own transmit-power constraint as well as a set of interference-power constraints each imposed at one of the primary receivers. In particular, this paper exploits multi-antennas at the secondary transmitter to effectively balance between spatial multiplexing for the secondary transmission and interference avoidance at the primary receivers. Convex optimization techniques are used to design algorithms for the optimal secondary transmit spatial spectrum that achieves the capacity of the secondary transmission. Suboptimal solutions for ease of implementation are also presented and their performances are compared with the optimal solution. Furthermore, algorithms developed for the single-channel transmission are also extended to the case of multi-channel transmission whereby the secondary user is able to achieve opportunistic spectrum sharing via transmit adaptations not only in space, but in time and frequency domains as well.Comment: Extension of IEEE PIMRC 2007. 35 pages, 6 figures. Submitted to IEEE Journal of Special Topics in Signal Processing, special issue on Signal Processing and Networking for Dynamic Spectrum Acces

MIMO Transceiver Optimization With Linear Constraints on Transmitted Signal Covariance Components

This correspondence revisits the joint transceiver optimization problem for multiple-input multiple-output (MIMO) channels. The linear transceiver as well as the transceiver with linear precoding and decision feedback equalization are considered. For both types of transceivers, in addition to the usual total power constraint, an individual power constraint on each antenna element is also imposed. A number of objective functions including the average bit error rate, are considered for both of the above systems under the generalized power constraint. It is shown that for both types of systems the optimization problem can be solved by first solving a class of MMSE problems (AM-MMSE or GM-MMSE depending on the type of transceiver), and then using majorization theory. The first step, under the generalized power constraint, can be formulated as a semidefinite program (SDP) for both types of transceivers, and can be solved efficiently by convex optimization tools. The second step is addressed by using results from majorization theory. The framework developed here is general enough to add any finite number of linear constraints to the covariance matrix of the input