Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks

in IEEE Journal of Selected Topics in Signal Processing (2017)

In this paper, a sequential probing method for interference constraint learning is proposed to allow a centralized Cognitive Radio Network (CRN) accessing the frequency band of a Primary User (PU) in an ... [more ▼]

In this paper, a sequential probing method for interference constraint learning is proposed to allow a centralized Cognitive Radio Network (CRN) accessing the frequency band of a Primary User (PU) in an underlay cognitive scenario with a designed PU protection specification. The main idea is that the CRN probes the PU and subsequently eavesdrops the reverse PU link to acquire the binary ACK/NACK packet. This feedback indicates whether the probing-induced interference is harmful or not and can be used to learn the PU interference constraint. The cognitive part of this sequential probing process is the selection of the power levels of the Secondary Users (SUs) which aims to learn the PU interference constraint with a minimum number of probing attempts while setting a limit on the number of harmful probing-induced interference events or equivalently of NACK packet observations over a time window. This constrained design problem is studied within the Active Learning (AL) framework and an optimal solution is derived and implemented with a sophisticated, accurate and fast Bayesian Learning method, the Expectation Propagation (EP). The performance of this solution is also demonstrated through numerical simulations and compared with modified versions of AL techniques we developed in earlier work. [less ▲]

Interference Constraint Active Learning with Uncertain Feedback for Cognitive Radio Networks

in IEEE Transactions on Wireless Communications (2017), 16(7), 4654-4668

In this paper, an intelligent probing method for interference constraint learning is proposed to allow a centralized cognitive radio network (CRN) to access the frequency band of a primary user (PU) in an ... [more ▼]

In this paper, an intelligent probing method for interference constraint learning is proposed to allow a centralized cognitive radio network (CRN) to access the frequency band of a primary user (PU) in an underlay cognitive communication scenario. The main idea is that the CRN probes the PU and subsequently eavesdrops the reverse PU link to acquire the binary ACK/NACK packet. This feedback is implicit channel state information of the PU link, indicating whether the probinginduced interference is harmful or not. The intelligence of this sequential probing process lies in the selection of the power levels of the secondary users, which aims to minimize the number of probing attempts, a clearly active learning (AL) procedure, and expectantly the overall PU QoS degradation. The enhancement introduced in this paper is that we incorporate the probability of each feedback being correct into this intelligent probing mechanism by using a multivariate Bayesian AL method. This technique is inspired by the probabilistic bisection algorithm and the deterministic cutting plane methods (CPMs). The optimality of this multivariate Bayesian AL method is proven and its effectiveness is demonstrated through numerical simulations. Computationally cheap CPM adaptations are also presented, which outperform existing AL methods. [less ▲]

Active Interference Constraint Learning with Uncertain Feedback for Cognitive Radio Networks

in Proceedings of IEEE International Conference on Communications (ICC) 2016 (2016)

In this paper, an intelligent probing method for interference constraint learning is proposed to allow a centralized Cognitive Radio Network (CRN) to access the frequency band of a Primary User (PU ... [more ▼]

In this paper, an intelligent probing method for interference constraint learning is proposed to allow a centralized Cognitive Radio Network (CRN) to access the frequency band of a Primary User (PU) operating based on an Adaptive Coding and Modulation (ACM) protocol. The main idea is that the CRN probes the PU and subsequently applies a Modulation and Coding Classification (MCC) technique to acquire the Modulation and Coding scheme (MCS) of the PU. This feedback is an implicit channel state information (CSI) of the PU link, indicating how harmful the probing induced interference is. The intelligence of this sequential probing process lies on the selection of the power levels of the Secondary Users (SUs) which aims to minimize the number of probing attempts, a clearly Active Learning (AL) procedure, and consequently the overall PU QoS degradation. The enhancement introduced in this work is that we incorporate the probability of each feedback being correct into this intelligent probing mechanism by using a univariate Bayesian Nonparametric AL method, the Probabilistic Bisection Algorithm (PBA). An adaptation of the PBA is implemented for higher dimensions and its effectiveness as an uncertainty driven AL method is demonstrated through numerical simulations. [less ▲]

Power control in cognitive radio networks using cooperative modulation and coding classification

in Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (2015), 156

In this paper, a centralized Power Control (PC) scheme aided by interference channel gain learning is proposed to allow a Cognitive Radio (CR) network to access the frequency band of a Primary User (PU ... [more ▼]

In this paper, a centralized Power Control (PC) scheme aided by interference channel gain learning is proposed to allow a Cognitive Radio (CR) network to access the frequency band of a Primary User (PU) operating based on an Adaptive Coding and Modulation (ACM) protocol. The main idea is the CR network to constantly probe the band of the PU with intelligently designed aggregated interference and sense whether the Modulation and Coding scheme (MCS) of the PU changes in order to learn the interference channels. The coordinated probing is engineered by the Cognitive Base Station (CBS), which assigns appropriate CR power levels in a binary search way. Subsequently, each CR applies a Modulation and Coding Classification (MCC) technique and sends the sensing information through a control channel to the CBS, where all the MCC information is combined using a fusion rule to acquire an MCS estimate of higher accuracy and monitor the probing impact to the PU MCS. After learning the normalized interference channel gains towards the PU, the CBS selects the CR power levels to maximize total CR network throughput while preserving the PU MCS and thus its QoS. The effectiveness of the proposed technique is demonstrated through numerical simulations. © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015. [less ▲]

Automatic Modulation Classification for Adaptive Power Control in Cognitive Satellite Communications

in Proceedings of 7th Advanced Satellite Multimedia Systems Conference (ASMS) and 13th Signal Processing for Space Communications Workshop (SPSC) 2014 (2014)