Spectrum sensing is a significant issue in cognitive radio networks which enables estimation of the frequency spectrum and hence provides frequency reuse. In the large-scale cognitive radio networks, secondary users cannot share a common spectrum since the coverage area of primary users is limited. In this study, the authors suggest a diffusion adaptive learning algorithm based on correntropy cooperation policy, which first categorises received data of secondary users into several groups, and then learns a common spectrum inside each group. The mean-square performance of proposed algorithm is analysed and supported by simulations. Experimental results show that, in a multitask cognitive network, the proposed algorithm can achieve a better mean-square deviation learning performance both in transient and steady-state regimes in comparison with other conventional algorithms.
In this paper, a new scheme is suggested for cooperative beamforming (BF) and relay selection in CR networks, where a pair of secondary users communicates with each other assisted by some multiple antenna relay nodes. The goal of the algorithm is to maximize signal to interference plus noise ratio of secondary user receiver subject to limited interference caused for primary user receiver and power constraints of relay nodes. The relay selection and BF optimization problem is solved separately by employing convex semidefinite programming through rank-one relaxation. It is shown that our proposed algorithm outperforms conventional relay selection and BF schemes.CR system based on decode-and-forward protocol. The dual decomposition technique is adopted to obtain an asymptotically optimal subcarrier pairing, relay selection, and power allocation. The optimization problem is based on maximization of sum rate subject to interference limit for PU and power constraints in SU-Tx and relay nodes. In [13], the problem of relay and power allocation for OFDM-based CR systems with single antenna are considered where the capacity of SU employing relays is maximized subject to total transmission power constraint and interference limit for PU. Because of high computational complexity of the optimization problem, three suboptimal schemes are presented. The authors in [14] investigate the problem of resource (subcarrier and power) allocation in an OFDMA-based relayed cellular CR network in which a base station services some user equipment via multiple relay stations (RSs). The resource allocation problem must decide for each symbol which subcarrier at which RSs and at what power level would relay. The objective function is maximization of network capacity. Joint relay selection and power allocation are investigated in [15] to maximize system throughput with limited interference to PUs in CR network. The authors suggest an optimal scheme based on dual method and then propose a suboptimal algorithm to reduce computational complexity. In [16], the authors investigate the problem of optimal power allocation and relay selection in CR network where a pair of cognitive (secondary) transceivers communicate with each other using some two-way relay nodes. The goal of the proposed power allocation and relay selection algorithm is to maximize the achievable rate subject to interference constraint of PU and power constraints of cognitive transceivers and relay nodes. A power control and beamforming (BF) scheme is proposed for an overlay CR network in [17]. The suggested scheme minimizes the total transmitted power of the network while guaranteeing each user's signal to interference plus noise ratio (SINR) requirement. The authors suggested an iterative algorithm based on second-order cone programming and investigated the overall power saving of the network. Their results show that the improvement of the algorithm in power saving is considerable.In the conventional relay interweave CR networks, the end-to-end SU communication is carried out in t...
Adaptive networks solve distributed optimization problems in which all agents of the network are interested to collaborate with their neighbors to learn a similar task. Collaboration is useful when all agents seek a similar task. However, in many applications, agents may belong to different clusters that seek dissimilar tasks. In this case, nonselective collaboration will lead to damaging results that are worse than noncooperative solution. In this paper, we contribute in problems that several clusters of interconnected agents are interested in learning multiple tasks. To address multitask learning problem, we consider an information theoretic criterion called correntropy in a distributed manner providing a novel adaptive combination policy that allows agents to learn which neighbors they should cooperate with and which other neighbors they should reject. In doing so, the proposed algorithm enables agents to recognize their clusters and to achieve improved learning performance compared with noncooperative strategy. Stability analysis in the mean sense and also a closed-form relation determining the network error performance in steady-state mean-square-deviation is derived. Simulation results illustrate the theoretical findings and match well with theory. KEYWORDSadaptive network, correntropy criterion, distributed processing, multitask learning 1 1232
In this paper, we investigate the transient performance of the proposed distributed multitask learning algorithm that is developed based on maximum correntropy criterion. In the first stage, we derive the proposed multitask learning algorithm in which the correntropy-based combination matrix determines which sensors should collaborate together and which sensors should stop the collaboration. In the second stage, according to the variance relation of the error vector, we derive a closed-form relation that shows the transition of mean-square-deviation learning performance. We also find the lower and upper bounds of the step size that ensure the stability of the multitask learning algorithm. The theoretical finding of the transient performance is shown to fit a well match with simulation results. KEYWORDS adaptive network, correntropy criterion, distributed signal processing, multitask learning, transient analysis Int J Adapt Control Signal Process. 2018;32:229-247.wileyonlinelibrary.com/journal/acs
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