The concurrent transmission of cognitive radios (CRs) and primary users (PUs) can achieve high spectral efficiency, provided that the interference power to each PU is limited below a certain threshold. Joint optimization of the precoding and the equalization is widely used to exploit the spatial diversity to mitigate the interference. However, its performance is sensitive to the inevitable imperfect channel state information (CSI). This paper studies the robust transceiver optimization in multiple-input and single-output downlink CR network, aiming at minimizing the worst-case per-user mean square error. The channel uncertainty model is assumed to be ellipsoidal bounded for both channel matrices and short-term channel covariance matrices. Under the uncertainty models for both cases of CSI, we first derive the strict upper bound or equivalent conditions for the constraints, then reformulate the original optimization problem into the semi-definite programming problems which can be efficiently solved. Simulation results demonstrate that the proposed schemes effectively mitigates the performance degradation due to the imperfect CSI. Their robustness with respect to the interference constraint is also validated, which is regarded as the critical part in the design of CR network.
[Show abstract][Hide abstract] ABSTRACT: We consider precoding strategies at the secondary base station (SBS) in a cognitive radio network with interference constraints at the primary users (PUs). Precoding strategies at the SBS which satisfy interference constraints at the PUs in cognitive radio networks have not been adequately addressed in the literature so far. In this paper, we consider two scenarios: i) when the primary base station (PBS) data is not available at SBS, and ii) when the PBS data is made available at the SBS. We derive the optimum MMSE and Tomlinson-Harashima precoding (THP) matrix Alters at the SBS which satisfy the interference constraints at the PUs for the former case. For the latter case, we propose a precoding scheme at the SBS which performs pre-cancellation of the PBS data, followed by THP on the pre-cancelled data. The optimum precoding matrix filters are computed through an iterative search. To illustrate the robustness of the proposed approach against imperfect CSI at the SBS, we then derive robust precoding filters under imperfect CSI for the latter case. Simulation results show that the proposed optimum precoders achieve good bit error performance at the secondary users while meeting the interference constraints at the PUs.
Communications (ICC), 2011 IEEE International Conference on; 07/2011
[Show abstract][Hide abstract] ABSTRACT: This paper addresses the robust transceiver optimization in multiple-input and multiple-output cognitive radio network, where primary users (PUs) and secondary users (SUs) coexist in the same spectrum band. In the design of cognitive system, the performance degradation perceived by PU should be strictly restricted even with imperfect channel state information (CSI) at cognitive transmitter and receivers. Therefore, this work aims at minimizing the sum mean square error of secondary downlink network and strictly limiting the interference caused to PUs with imperfect channel knowledge. Two types of CSI error models are considered: the bounded model and the stochastic model. Since the original optimization problems are non-convex for the joint optimization, firstly it is decomposed into two subproblems to optimize the precoding and equalizers separately, then the iterative algorithms are proposed to solve the subproblems in an alternating way. The challenge is to design the efficiently solvable forms of these subproblems. For the bounded model, Schur complement lemma is utilized to convert the subproblems into convex optimization problems. For the stochastic model, the problem is formulated either according to the stochastic rule or derived for the analytical solutions. The effectiveness and robustness of proposed algorithms are evaluated by the numerical results.
Cognitive Information Processing (CIP), 2010 2nd International Workshop on; 07/2010
[Show abstract][Hide abstract] ABSTRACT: In practice, the imperfect channel state information can degrade the optimization performance of a cognitive system, especially yielding the detrimental violation of the interference constraint perceived by the primary users. This paper investigates the linear transceiver design in cognitive downlink systems with the imperfect channel knowledge, aiming at minimizing the mean square error of the secondary network subject to both the transmit and interference power constraints. Two methods are proposed to solve the optimization problem. The first alternating method decomposes the non-convex primal problem into two subproblems. Each of them is converted to the standard convex optimization forms. The second method is based on the reformulation of the primal problem into a two-loop optimization problem in which a more efficient gradient projected method applies. We also prove the condition to achieve Karush-Kuhn-Tucker optimality. The effectiveness and robustness of the proposed solutions are validated by the simulation result.
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