Periola Ayodele

In this paper, we investigate a multi-user one-way dual-hop multi-relay cognitive radio underlay spectrum sharing network. .The joint optimal resource allocation of the bandwidth and power allocation in underlay Cooperative Cognitive Radio Relay Network (CCRRN) was studied and a convex optimization analytical framework is presented. A combined optimal power and bandwidth allocation (COPBA) scheme for the minimization of the total network power and blocking probability of the call admission control stage of the CCRRN is investigated. Our goal is to jointly optimize the bandwidth and power allocation such that the total transmission power in the CCRRN is minimized without compromising the Quality of Service (QoS) demands of the secondary users (SUs) and the interference constraint thresholds of the primary users (PUs) in the primary networks. The mathematical model and convex optimization problem with the aim of minimizing the total transmission power of the CCRRN was formulated. The ...

Radio astronomy organisations desire to optimise the terrestrial radio astronomy observations by mitigating against interference and enhancing angular resolution. Ground telescopes (GTs) experience interference from intersatellite links (ISLs). Astronomy source radio signals received by GTs are analysed at the high performance computing (HPC) infrastructure. Furthermore, observation limitation conditions prevent GTs from conducting radio astronomy observations all the time, thereby causing low HPC utilisation. This paper proposes mechanisms that protect GTs from ISL interference without permanent prevention of ISL data transmission and enhance angular resolution. The ISL transmits data by taking advantage of similarities in the sequence of observed astronomy sources to increase ISL connection duration. In addition, the paper proposes a mechanism that enhances angular resolution by using reconfigurable earth stations. Furthermore, the paper presents the opportunistic computing scheme (OCS) to enhance HPC utilisation. OCS enables the underutilised HPC to be used to train learning algorithms of a cognitive base station. The performances of the three mechanisms are evaluated. Simulations show that the proposed mechanisms protect GTs from ISL interference, enhance angular resolution, and improve HPC utilisation.

Radio astronomical services (RASs) use the radio spectrum to observe cosmic sources (Srcs). They are protected from interference by establishing national radio quiet zones (NRQZs). However, NRQZs do not prevent interference from satellite networks with intersatellite links. This causes impairment in directly observing cosmic sources. Therefore, current knowledge of the universe is inaccurate being obtained from filtered signals. However, accurate knowledge can be obtained in RASs via a pristine observation of Srcs using cognitive radio (CR) technology. This paper, examines the suitability of CR spectrum sharing models for interference protection of RASs. It proposes an interference protection framework and investigates the transmit opportunities using visibility duration data of RASs. It then examines the end to end delay of the satellite network when the framework is used. Simulation is conducted for a constellation of low earth orbit satellites and the obtained results show that transmit opportunities exist for intersatellite links when the interweave spectrum sharing model is used without causing any interference to RASs or significantly increasing the end to end delay.

—Cognitive radios (CRs) use learning algorithms (LAs) located in the cognition engine (CE) to adapt their behaviour. CRs use LAs for spectrum prediction to enhance their quality of service (QoS). The CR's CE consumes battery power while classifying LAs. The LA classification reduces CR data transmission power and limits CR throughput. This paper proposes a framework to enhance CR QoS in LTE-Advanced (LTE-A). The proposed framework reduces battery power expended in LA classification and increases CR data transmission power. It introduces the radio resource control (RRC); RRC_COGNITIVE state in which the CR pauses LA classification. The framework's performance is evaluated using the CR transmit power and throughput. Simulations show that the proposed framework reduces LA classification power by 65 % on average. The reduction of LA classification power enhances CR transmit power. The CR throughput is enhanced by 23% and 80% when CRs are and are not secondary users (SUs) respectively.

The artificial neural network is an important machine learning algorithm for secondary users (SUs) in cognitive radio networks. An SU equipped with an ANN is able to perform predictive modelling using input samples acquired for a channel sensed to be idle. This input samples are acquired incurring an input sample acquisition time (ISAT) that reduces the data transmission time (DTT) and throughput of SUs in the network. The reduction of the ISAT is therefore important for enhanced throughput. This paper addresses this issue by proposing the addition of a Kullback Leibler divergence (KLD) layer to the neural network based on inspiration from artificial immune systems theory. This layer computes the dissimilairites between previous and current inputs and reduces ISAT. We examine the performance of the neural network with the added KLD layer in three scenarios that consider the achievable SU throughput. The throughput performance of SUs are examined for three scenarios for single and dual mode SUs equipped with ANN and dual mode SUs equipped with recurrent ANNs in the first, second and third scenarios respectively. Performance analysis shows that the addition of the KLD layer improves the DTT and the SU throughput compared to existing scheme.