Robust Worst-Case Interference Control in Underlay Cognitive Radio Networks

Abstract

We investigate the problem of power allocation to secondary users (SUs) in underlay cognitive radio networks where the channel state information (CSI) pertaining to the link between a SU's transmitter and a primary user (PU) receiver is uncertain. To keep the interference of SUs to PUs below a given threshold under any realization of uncertainty in CSI, we utilize the robust optimization theory where uncertainty in CSI is defined by a bounded distance between its estimated and exact values, demonstrate that the convexity of allocating power to SUs is preserved, and show that it can be solved in a computationally efficient manner. Considering worst-case interference is a very conservative approach that although protects PUs to the maximum extent, may cause undesirable and significant reductions in the SUs' throughput as well, which at many instances may not be necessary. Hence, when permissible, it is worthwhile to trade-off between the robust worst-case interference control to PUs and the SUs' throughput. In doing so, we maintain the probability of violating the interference to PUs below a given threshold by applying the differential norm (D-norm) and the chance constrained approaches, both of which can be solved very efficiently via a water-filling like formula. Simulation results show the effectiveness of our proposed approach.Index Terms-Chance constrained approach, cognitive radio networks, differential norm (D-norm), robustness-optimality trade-off, robust worst-case interference control, uncertainty region.

A Robust Distributed Power Control Algorithm for Minimum Interference to Primary Users in Underlay Cognitive Radio Networks

“…We also compare the performance of robust distributed power control (RDPC) algorithm with that of distributed power control algorithm (DPC) [12] and the traditional iterative water filling algorithm (IWFA) [15].…”

“…In order to guarantee communication quality of PUs and SUs, our proposed power control scheme not only considers that the transmit power of each SU should not exceed its maximum power, but also takes the minimum SINR at secondary receivers into account. Simulation results show that the RDPC under the worst case condition is superior to the distributed power control algorithm (DPC) [12] the traditional iterative water filling algorithm (IWFA) [15] in time-varying channel environment.…”

Energy-Efficient Robust Resource Provisioning in Virtualized Wireless Networks

“…To immunize the performance of VWN against the uncertainty in the CSI values, we apply the worst-case optimization theory, which has been widely applied in the resource allocation in wireless networks, e.g., [12]- [15]. In this context, the uncertain parameter is modeled as an estimated value plus an error that is modeled as a bounded value in the specific region and the performance of network is maximized under the worst condition of error.…”

“…In this context, the uncertain parameter is modeled as an estimated value plus an error that is modeled as a bounded value in the specific region and the performance of network is maximized under the worst condition of error. It is well-known that the worstcase approach is capable to preserve the instantaneous VWN performance against the uncertain parameters, while it suffers from high computational complexity and total throughput reduction due to its conservative view of worst-case error [12], [13].…”

Robust power allocation in two-tier heterogeneous networks

“…In general, there are two approaches to model channel uncertainty, i.e., stochastic approach that assumes statistical knowledge of uncertainty (e.g., a given stochastic distribution) and formulates the problem in a probabilistic manner, and worst-case robust optimization approach where fluctuations of the uncertain component are restricted to be within a bounded and convex set [11]. Due to the stochastic variations in channel gains, the worst-cast approach is more appealing to guarantee MUEs' interference threshold when CSI errors are bounded [12]. Plus the tractability of the worst-case formulation owing to ellipsoid model's neat form, we employ worst-case robust optimization theory to deal with channel uncertainty.…”