Investigation of Radio Frequency Licensed Spectrum Utilization in Nigeria: A Study of Rumuokwuta, Port Harcourt, Nigeria

This study was carried out to investigate the spectrum utilization of the licensed Radio Frequency (RF) spectrum in Rumuokwuta, Port Harcourt. An outdoor measurement of spectrum occupancy was carried out in a high-rise building situated at Rumuokwuta urban area in Port Harcourt, Nigeria using RF explorer spectrum analyzer and a personal computer laptop system. Spectrum activities in the band of 240-960 MHz were monitored for 24 hours. The frequency band was subdivided into 24 sub bands each with a span size of 30 MHz. Scanning of bands was made efficient using a python script that scans a range, analyzed the frequencies and signal strengths for 112 data points, saves data in CSV file format, scans the next range until the 24 ranges were scanned. The process was repeated to achieve 15 iterations. With a noise floor of - 110dBm, a threshold of -95dBm was used to determine the presence of signal, hence the spectrum occupancy of measured bands. Results showed that out of the 24 investigated sub bands; only one band was completely occupied with spectrum occupancy of 100%. 12 bands were partially occupied while 11 were completely free. The average spectrum occupancy for the whole band was obtained as 11.64%. This showed good location for dynamic spectrum access and cognitive radio deployment, especially in Television White Space (TVWS).

Spectrum Occupancy Model Based on Empirical Data for FM Radio Broadcasting in Suburban Environments

It is well-known that the analog FM radio channels in suburban areas are underutilized. Before reallocating the unused channels for other applications, a regulator must analyze the spectrum occupancy. Many researchers proposed the spectrum occupancy models to find vacant spectrum. However, the existing models do not analyze each channel individually. This paper proposes an approach consisting (i) a spectrum measurement strategy, (ii) an appropriate decision threshold, and (iii) criteria for channel classification, to find the unused channels. The measurement strategy monitors each channel’s activity by capturing the power levels of the passband and the guardband separately. The decision threshold is selected depending on the monitored channel’s activity. The criteria classifies the channels based on the passband’s and guardband’s duty cycles. The results show that the proposed channel classification can identify 42 unused channels. If the power levels of wholebands (existing model) were analyzed instead of passband’s and guardband’s duty cycles, only 24 unoccupied channels were found. Furthermore, we propose the interference criteria, based on relative duty cycles across channels, to classify the abnormally used channels into interference sources and interference sinks, which have 16 and 15 channels, respectively. This information helps the dynamic spectrum sharing avoid or mitigate the interferences.

Optimization of Spectrum Sensing Schemes in Cognitive Sensor Networks

The currently dominant spectrum allocation policy is reported to be inefficient. Cognitive radio, therefore, has been proposed in the literature to improve the spectrum usage efficiency. This dissertation proposes the optimization of spectrum sensing schemes in cognitive sensor networks. The modeling of the spectrum occupancy is a prerequisite for cognitive radio analysis. We describe the radio spectrum occupancy as a continuous- time Markov chain, and mathematically define the model by deriving the transition rate matrix and the probability state vector. The dissertation addresses an important aspect of spectrum sensing that has been often overlooked in the literature. While the cognitive radio is supposed to be aware of its surroundings, existing work does not consider the characteristics of unlicensed users for finding the optimum sensing period. In this work, we propose an application- specific method that finds the optimal sensing period according to the characteristics of both secondary and primary networks. According to the unlicensed user’s state in the Markov chain, two optimization problems are formulated to derive the optimum sensing periods. The secondary network’s throughput and power consumption are also studied and the corresponding parameters are derived. By numerical and simulation analyses, it is elaborated that the proposed method increases the secondary network’s throughput by up to 11% and significantly decreases the power consumption of the secondary network by as low as 33% of the non-hybrid approach. In addition, we study cooperative spectrum sensing in cognitive sensor networks and address two important issues. First, an optimization problem is solved to obtain the minimum required number of cognitive users. Second, we define a metric for sensing ac- curacy and propose a novel energy-aware secondary user selection method that identifies the most eligible cognitive users through a probability-based approach. The network’s lifetime is compared at several sensing accuracy thresholds and the trade-off between sensing accuracy and network lifetime is studied. Finally, the effects of several fusion rules on the proposed method are studied through simulation and numerical analyses. It is discussed that the Majority rule has the best performance among the examined rules. i

Optimization of Spectrum Sensing Schemes in Cognitive Sensor Networks

The currently dominant spectrum allocation policy is reported to be inefficient. Cognitive radio, therefore, has been proposed in the literature to improve the spectrum usage efficiency. This dissertation proposes the optimization of spectrum sensing schemes in cognitive sensor networks. The modeling of the spectrum occupancy is a prerequisite for cognitive radio analysis. We describe the radio spectrum occupancy as a continuous- time Markov chain, and mathematically define the model by deriving the transition rate matrix and the probability state vector. The dissertation addresses an important aspect of spectrum sensing that has been often overlooked in the literature. While the cognitive radio is supposed to be aware of its surroundings, existing work does not consider the characteristics of unlicensed users for finding the optimum sensing period. In this work, we propose an application- specific method that finds the optimal sensing period according to the characteristics of both secondary and primary networks. According to the unlicensed user’s state in the Markov chain, two optimization problems are formulated to derive the optimum sensing periods. The secondary network’s throughput and power consumption are also studied and the corresponding parameters are derived. By numerical and simulation analyses, it is elaborated that the proposed method increases the secondary network’s throughput by up to 11% and significantly decreases the power consumption of the secondary network by as low as 33% of the non-hybrid approach. In addition, we study cooperative spectrum sensing in cognitive sensor networks and address two important issues. First, an optimization problem is solved to obtain the minimum required number of cognitive users. Second, we define a metric for sensing ac- curacy and propose a novel energy-aware secondary user selection method that identifies the most eligible cognitive users through a probability-based approach. The network’s lifetime is compared at several sensing accuracy thresholds and the trade-off between sensing accuracy and network lifetime is studied. Finally, the effects of several fusion rules on the proposed method are studied through simulation and numerical analyses. It is discussed that the Majority rule has the best performance among the examined rules. i