Homogeneous Test-bed for Cognitive Radio

In the current frequency allocation scheme, the radio spectrum is found to be heavily underutilized in time, frequency and space dimensions or any of their combination. To improve spectrum utilization, the unused contiguous or non-contiguous portion of the radio spectrum (spectrum hole) can be accessed opportunistically using cognitive radio technology provided it is interference free to the local users of the network. To reliably detect the spectrum holes, which is necessary to limit the interference, cognitive radio is required to have high time and frequency resolutions to detect radio technologies (e.g. GSM 900, 2.4 GHz WLAN) at the packet level in the transmitted channel to avoid misinterpretation of occupancy states in time and frequency. In addition, having high sensitivity and instantaneous dynamic range can enable cognitive radio to detect weak received signals and their detection in the presence of strong received signals. Besides these requirements, a large sensing bandwidth can increase the chances to find spectrum holes in multiple radio technologies concurrently. A chirp channel sounder receiver has been developed according to the aforementioned requirements with a bandwidth of 750 MHz to provide reliable detection of received signals in two frequency ranges; 1) 250 MHz to 1 GHz, 2) 2.2 GHz to 2.95 GHz. The developed receiver is capable of finding spectrum holes having a duration of 204.8 μs and a transmitted channel bandwidth up to 200 kHz. To explore the spectrum holes in the space dimensions, six chirp channel sounder receivers have been developed to form a homogeneous test-bed, which can be deployed and controlled independently. To experimentally validate the ability of the built receiver, short term spectrum occupancy measurements have been conducted to monitor 2.4 GHz WLAN traffic from a real wireless network to quantify the spectrum utilization and duration of spectrum holes in the time domain. It has been found that the radio spectrum is underutilized and empirical distribution of the duration of the spectrum hole can be modelled using lognormal and gamma distributions for prediction using a two state continuous time semi-Markov model. To experimentally validate the receiver’s capabilities in both the supported frequency ranges, long term spectrum occupancy measurements with 750 MHz sensing bandwidth have been performed and received signals have been detected at frame or packet level to quantify spectrum utilization. It has been found that the radio spectrum is highly underutilized at the measurement location and exhibits significant amount of spectrum holes in both time and frequency. To experimentally validate the functionalities of the homogeneous test-bed, short term spectrum occupancy have been performed to monitor 2.4 GHz WLAN traffic from a real wireless network. The experiment has been conducted using multiple receivers to quantify the amount of cooperation individual or multiple cognitive radio users can provide for reliable detection of spectrum holes in time, frequency and space. It has been found that the space dimension influences strongly the statistics of cooperation parameters

A Step Towards Enhancing Spectrum Utilization by Implementing a Spectrum Sensing Cognitive Radio Using an RTL-SDR

In this paper, a spectrum sensing cognitive radio using the RTL-SDR interfaced with Simulink in MATLAB was developed. It performed spectrum sensing and signal prediction between the ranges of 25MHz to 1.5GHz, the tuner range of the RTL-SDR. The RF spectrum occupancy was explored by choosing specific centre frequencies between 25MHz to 1.5GHz in real time using the RTL-SDR. Tests were carried out in real time to ascertain the workability and efficiency of the RTL-SDR cognitive radio. The efficiency of the cognitive radio reduced as the false alarm probability increased. The cognitive radio’s efficiency also reduced in high noise signal floors

Efficient spectrum occupancy prediction exploiting multidimensional correlations through composite 2D-LSTM models

In cognitive radio systems, identifying spectrum opportunities is fundamental to efficiently use the spectrum. Spectrum occupancy prediction is a convenient way of revealing opportunities based on previous occupancies. Studies have demonstrated that usage of the spectrum has a high correlation over multidimensions, which includes time, frequency, and space. Accordingly, recent literature uses tensor-based methods to exploit the multidimensional spectrum correlation. However, these methods share two main drawbacks. First, they are computationally complex. Second, they need to re-train the overall model when no information is received from any base station for any reason. Different than the existing works, this paper proposes a method for dividing the multidimensional correlation exploitation problem into a set of smaller sub-problems. This division is achieved through composite two-dimensional (2D)-long short-term memory (LSTM) models. Extensive experimental results reveal a high detection performance with more robustness and less complexity attained by the proposed method. The real-world measurements provided by one of the leading mobile network operators in Turkey validate these results

Spectral analysis for long-term robotic mapping

This paper presents a new approach to mobile robot mapping in long-term scenarios. So far, the environment models used in mobile robotics have been tailored to capture static scenes and dealt with the environment changes by means of ‘memory decay’. While these models keep up with slowly changing environments, their utilization in dynamic, real world environments is difficult. The representation proposed in this paper models the environment’s spatio-temporal dynamics by its frequency spectrum. The spectral representation of the time domain allows to identify, analyse and remember regularly occurring environment processes in a computationally efficient way. Knowledge of the periodicity of the different environment processes constitutes the model predictive capabilities, which are especially useful for long-term mobile robotics scenarios. In the experiments presented, the proposed approach is applied to data collected by a mobile robot patrolling an indoor environment over a period of one week. Three scenarios are investigated, including intruder detection and 4D mapping. The results indicate that the proposed method allows to represent arbitrary timescales with constant (and low) memory requirements, achieving compression rates up to 106 . Moreover, the representation allows for prediction of future environment’s state with ∼ 90% precision

Deep Learning Meets Cognitive Radio: Predicting Future Steps

Learning the channel occupancy patterns to reuse the underutilised spectrum frequencies without interfering with the incumbent is a promising approach to overcome the spectrum limitations. In this work we proposed a Deep Learning (DL) approach to learn the channel occupancy model and predict its availability in the next time slots. Our results show that the proposed DL approach outperforms existing works by 5%. We also show that our proposed DL approach predicts the availability of channels accurately for more than one time slot