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... propose a load and spillage algorithm that jointly updates the SINR assignment and power. This algorithm however confines the optimality by considering a reduced feasible SINR region. The authors in =-=[3]-=- propose algorithms to coordinate the secondary users by sensing a feedback signal from the primary users’ interference temperature condition to reduce outage, but do not address the joint optimal SIN...

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...udget constraints and interference temperature constraints in cognitive radio networks. Throughput maximization for wireless cognitive radio networks is relatively new and challenging. The authors in =-=[5]-=- considered throughput maximization for secondary users over non-overlapping channels. Throughput maximization in interference channels is considerably harder, as it is a nonconvex problem. In [6], th...

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...y. The authors in [25] proposed a robust distributed uplink power allocation algorithm in a cognitive radio network to maximize the social utility of secondary users that are admitted. The authors in =-=[26]-=- proposed a power control algorithm to maximize the throughput of the secondary users while protecting the primary users. Phunchongharn et al. proposed power control algorithms for transmission under ...

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...uted underlay scenarios, the binary feedback has been used to enable CRs apply Reinforcement Learning procedures, like Q-Learning and BushMosteller Learning, to regulate the aggregated interference to the PU [21] and additionally reach a throughput optimization objective [12]. Formulating this problem as a repeated PC game and employing Game Theory to analyse it [12] has been a critical contribution to explain the behaviour of such a system and prove the convergence of decentralized learning methods. Also, pricing distributed PC schemes have been developed under outage probability constraints [11]. As far as the centralized underlay research work is concerned, a central decision maker, the CBS, must learn the interference channel gains, elaborate an intelligent selection of the operational parameters of the SUs, such as their transmit power, and communicate it to them. Even though distributed PC underlay scenarios have been investigated thoroughly, the centralized PC problem combined with interference channel gain learning is still an unexplored area. Remarkably, the most sophisticated and fast methods suitable for the CBS learning the interference channel gains of multiple SUs with th...

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...tralized gradual removal algorithm that removes users to increase the maximum achievable SINR in the system. In [17], Rasti et al. proposed a distributed temporarily removal algorithm, in which users stop transmission once their instantaneous power exceed certain threshold. Matskani et al. [13] and Mitliagkas et al. [18] proposed removing users based on convex relaxation to obtain an approximate solution to the system capacity. Besides, Kang et al. in [19] proposed an optimal power allocation strategy for the secondary user under the primary user outage probability constraint. Huang et al. in [20] designed a distributed power control algorithm to maximize the throughput of secondary users and protect the primary user’s quality-of-service. Anandkumar et al. in [21] proposed policies to obtain the optimal throughput transmission for secondary users. Phunchongharn et al. considered the channel gain uncertainty to design power control algorithms to tackle the system capacity violation in [22]. Halldorsson et al. gave algorithms based on a novel linear programming formulation for capacity problems, with constant-factor performance guarantees for several capacity and throughput problems in [...

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... under average IPCs. Thus, average IPCs should be used in practice to protect both PU and SU systems. Besides using IPCs, PU outage probability constraint (OPC) can be used to protect PU transmission =-=[83,84]-=-, where the outage probability of the PU transmission shouldnotbebelowagiventhreshold.UndertheOPC and average/peak TPCs, optimal power allocation strategies have been developed to maximize the ergodic...

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...d and may significantly differ from reality. 2) Channel Reciprocity: Another method for dealing with the channel uncertainty is to estimate the reciprocal channel by overhearing feedback from the PRs =-=[151]-=-. Authors in [151] propose a framework that incorporates the inherent feedback information in typical two-way PU communication links such as power control feedback in Code Division Multiple Access (CD...

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... The problem of sum rate maximization for the cognitive radio network is relatively new and challenging. The sum rate maximization for the secondary users under non-interfering channels is studied in =-=[9]-=-, but this problem becomes nonconvex under interference channels. A message-passing algorithm is proposed to suboptimally solve such nonconvex problem in [10]. The work in [11] characterizes the solut...