Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks

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.Comment: 14 pages, 6 figures, submitted to IEEE JSTSP Special Issue on Machine Learning for Cognition in Radio Communications and Rada

On Peak versus Average Interference Power Constraints for Protecting Primary Users in Cognitive Radio Networks

This paper considers spectrum sharing for wireless communication between a cognitive radio (CR) link and a primary radio (PR) link. It is assumed that the CR protects the PR transmission by applying the so-called interference-temperature constraint, whereby the CR is allowed to transmit regardless of the PR's on/off status provided that the resultant interference power level at the PR receiver is kept below some predefined threshold. For the fading PR and CR channels, the interference-power constraint at the PR receiver is usually one of the following two types: One is to regulate the average interference power (AIP) over all the fading states, while the other is to limit the peak interference power (PIP) at each fading state. From the CR's perspective, given the same average and peak power threshold, the AIP constraint is more favorable than the PIP counterpart because of its more flexibility for dynamically allocating transmit powers over the fading states. On the contrary, from the perspective of protecting the PR, the more restrictive PIP constraint appears at a first glance to be a better option than the AIP. Some surprisingly, this paper shows that in terms of various forms of capacity limits achievable for the PR fading channel, e.g., the ergodic and outage capacities, the AIP constraint is also superior over the PIP. This result is based upon an interesting interference diversity phenomenon, i.e., randomized interference powers over the fading states in the AIP case are more advantageous over deterministic ones in the PIP case for minimizing the resultant PR capacity losses. Therefore, the AIP constraint results in larger fading channel capacities than the PIP for both the CR and PR transmissions

Dynamic Scheduling for Delay Guarantees for Heterogeneous Cognitive Radio Users

We study an uplink multi secondary user (SU) system having statistical delay constraints, and an average interference constraint to the primary user (PU). SUs with heterogeneous interference channel statistics, to the PU, experience heterogeneous delay performances since SUs causing low interference are scheduled more frequently than those causing high interference. We propose a scheduling algorithm that can provide arbitrary average delay guarantees to SUs irrespective of their statistical channel qualities. We derive the algorithm using the Lyapunov technique and show that it yields bounded queues and satisfy the interference constraints. Using simulations, we show its superiority over the Max-Weight algorithm.Comment: Asilomar 2015. arXiv admin note: text overlap with arXiv:1602.0801

Robust Power and Subcarrier Allocation for OFDM-based Cognitive Radio Networks Considering Spectrum Sensing Uncertainties

‎In this paper‎, ‎we address power and subcarrier allocation for cooperative cognitive radio (CR) networks in the presence of spectrum sensing errors‎. ‎First‎, ‎we derive the mutual interference of primary and secondary networks affecting each other by taking into account spectrum sensing errors‎. ‎Then‎, ‎taking into account the interference constraint imposed by the cognitive network to the primary user and the power budget constraint of cognitive network‎, ‎we maximize the achievable data rates of secondary users‎. ‎Besides‎, ‎in a multi secondary user scenario‎, ‎we propose a suboptimal but low complexity power and subcarrier allocation algorithm to solve the formulated optimization problem‎. ‎Our numerical results indicate that the proposed power loading scheme increases the cognitive achievable data rates compared to classical power loading algorithms that do not consider spectrum sensing errors‎

Interference-Assisted Wireless Energy Harvesting in Cognitive Relay Network with Multiple Primary Transceivers

We consider a spectrum sharing scenario, where a secondary network coexists with a primary network of multiple transceivers. The secondary network consists of an energy-constrained decode-and-forward secondary relay which assists the communication between a secondary transmitter and a destination in the presence of the interference from multiple primary transmitters. The secondary relay harvests energy from the received radio-frequency signals, which include the information signal from the secondary transmitter and the primary interference. The harvested energy is then used to decode the secondary information and forward it to the secondary destination. At the relay, we adopt a time switching policy due to its simplicity that switches between the energy harvesting and information decoding over time. Specifically, we derive a closed-form expression for the secondary outage probability under the primary outage constraint and the peak power constraint at both secondary transmitter and relay. In addition, we investigate the effect of the number of primary transceivers on the optimal energy harvesting duration that minimizes the secondary outage probability. By utilizing the primary interference as a useful energy source in the energy harvesting phase, the secondary network achieves a better outage performance.Comment: 6 pages, 5 figures, To be presented at IEEE GLOBECOM 201

A class of constant modulus algorithms for uniform linear arrays with a conjugate symmetric constraint

A class of constant modulus algorithms (CMAs) subject to a conjugate symmetric constraint is proposed for blind beamforming based on the uniform linear array structure. The constraint is derived from the beamformer with an optimum output signal-to-interference-plus-noise ratio (SINR). The effect of the additional constraint is equivalent to adding a second step to the original adaptive algorithms. The proposed approach is general and can be applied to both the traditional CMA and its all kinds of variants, such as the linearly constrained CMA (LCCMA) and the least squares CMA (LSCMA) as two examples. With this constraint, the modified CMAs will always generate a weight vector in the desired form for each update and the number of adaptive variables is effectively reduced by half, leading to a much improved overall performance. (C) 2010 Elsevier B.V. All rights reserved