Robust Energy Efficiency Maximization in Cognitive Radio Networks: the Worst-Case Optimization Approach

Abstract

Energy efficiency (EE) is very crucial for future wireless communication systems, especially for cognitive radio networks (CRNs). The EE performance relies on channel state information (CSI) of channels. Besides, the interference from secondary users (SUs) to primary users (PUs) also closely depends on CSI in underlay CRNs. However, available works on EE usually assume that CSI is perfect, which is often inaccurate in practical systems. Thus, in this paper we investigate the robust EE maximization problem in underlay CRNs with multiple SUs and PUs. Assuming CSI error to be bounded, we consider that all channels lie in some bounded uncertainty regions. From the perspective of worst-case optimization, we formulate it as the max-min problem with infinite constraint, which is nontrivial even without this constraint. This is because that the outer-maximization problem is non-convex and the inner-minimization problem is a concave minimization problem known as NP-hard in general. We propose a scheme to handle this problem via the fractional programming and global optimization techniques. Particularly, we efficiently solve this problem in two special cases. Simulation results validate that our proposed scheme can improve the worst-case EE of SUs distinctly and strictly guarantee the quality-of-service (QoS) of PUs under all parameters' uncertainties.Index Terms-Cognitive radio networks, energy efficiency, worst-case optimization, general norm uncertainty.

Slow adaptive energy‐efficient resource allocation in OFDMA HetNets considering demand uncertainty

“…Subject to: Equations 2, 4, 5, 6, 7, and 8 (9) In the nominal problem formulation, it is assumed that the exact values of all users' demands, ie,…”

“…• Nominal EERA (N-EERA): Nominal EERA corresponds to the optimization problem (9) where parameters R f,u s are assumed to be perfectly known at femtocells. • Distributionally robust EERA (DR-EERA): This approach corresponds to problem (16) where the effect of uncertainty is taken into account.…”

“…This modeling methodology immunizes mathematical optimization models against worst‐case realization of uncertain parameter in a bounded uncertainty set. Some works utilize this approach for designing robust resource allocation . However, using this approach to model uncertainty leads to conservative results.…”

“…Some works utilize this approach for designing robust resource allocation. [9][10][11][12] However, using this approach to model uncertainty leads to conservative results. On the other hand, in the stochastic optimization, 13,14 uncertainties are modeled as random variables with known distributions.…”

Distributed power control in crowdsensing‐based femtocell networks with QoS provisioning

“…Hence, how to improve robustness of the control system has been a hot topic. In previous works, [27][28][29][30][31] uncertainties of channel state information were considered for wireless communication model. In the work of Yun and Caramanis, 30 the uncertain random variable is the total arrival rate, and the uncertainty of distance was discussed in the work of Ye and Ordonez.…”