1,123 research outputs found
Convex Optimisation for Communication Systems
In this thesis new robust methods for the efficient sharing of the radio spectrum for underlay cognitive radio (CR) systems are developed. These methods provide robustness against uncertainties in the channel state information (CSI) that is available to the cognitive radios. A stochastic approach is taken and the robust spectrum sharing methods are formulated as convex optimisation problems. Three efficient spectrum sharing methods; power control, cooperative beamforming and conventional beamforming are studied in detail.
The CR power control problem is formulated as a sum rate maximisation problem and transformed into a convex optimisation problem. A robust power control method under the assumption of partial CSI is developed and also transformed into a convex optimisation problem. A novel method of detecting and removing infeasible constraints from the power allocation problem is presented that results in considerably improved performance. The performance of the proposed methods in Rayleigh fading channels is analysed by simulations.
The concept of cooperative beamforming for spectrum sharing is applied to an underlay CR relay network. Distributed single antenna relay nodes are utilised to form a virtual antenna array that provides increased gains in capacity through cooperative beamforming. It is shown that the cooperative beamforming problems can be transformed into convex optimisation problems. New robust cooperative beamformers under the assumption of partial and imperfect CSI are developed and also transformed into convex optimisation problems. The performance of the proposed methods in Rayleigh fading channels is analysed by simulations.
Conventional beamforming to allow efficient spectrum sharing in an underlay CR system is studied. The beamforming problems are formulated and transformed into convex optimisation problems. New robust beamformers under the assumption of partial and imperfect CSI are developed and also transformed into convex optimisation problems. The performance of the proposed methods in Rayleigh fading channels is analysed by simulations
Cooperative Feedback for Multi-Antenna Cognitive Radio Networks
Cognitive beamforming (CB) is a multi-antenna technique for efficient
spectrum sharing between primary users (PUs) and secondary users (SUs) in a
cognitive radio network. Specifically, a multi-antenna SU transmitter applies
CB to suppress the interference to the PU receivers as well as enhance the
corresponding SU-link performance. In this paper, for a
multiple-input-single-output (MISO) SU channel coexisting with a
single-input-single-output (SISO) PU channel, we propose a new and practical
paradigm for designing CB based on the finite-rate cooperative feedback from
the PU receiver to the SU transmitter. Specifically, the PU receiver
communicates to the SU transmitter the quantized SU-to-PU channel direction
information (CDI) for computing the SU transmit beamformer, and the
interference power control (IPC) signal that regulates the SU transmission
power according to the tolerable interference margin at the PU receiver. Two CB
algorithms based on cooperative feedback are proposed: one restricts the SU
transmit beamformer to be orthogonal to the quantized SU-to-PU channel
direction and the other relaxes such a constraint. In addition, cooperative
feedforward of the SU CDI from the SU transmitter to the PU receiver is
exploited to allow more efficient cooperative feedback. The outage
probabilities of the SU link for different CB and cooperative
feedback/feedforward algorithms are analyzed, from which the optimal
bit-allocation tradeoff between the CDI and IPC feedback is characterized.Comment: 26 pages; to appear in IEEE Trans. Signal Processin
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