Space-Dimension Models of Spectrum Usage for Cognitive Radio Networks.

The dynamic spectrum access (DSA) principle, relying on the cognitive radio (CR) paradigm, allows users to access spectrum over time intervals or spatial areas where it remains unused. Due to the opportunistic nature of DSA/CR, the behavior and performance of DSA/CR networks depends on the perceived spectrum usage pattern. An accurate modeling of spectrum occupancy therefore becomes essential in the context of DSA/CR. In this context, this paper addresses the problem of accurately modeling the spectrum occupancy pattern perceived by DSA/CR users in the spatial domain. A novel spatial modeling approach is introduced to enable a simple yet practical and accurate characterization of spectrum. First, a set of models is proposed to characterize and predict the average level of occupancy perceived by DSA/CR users at various locations based on the knowledge of some simple signal parameters. An extension is then proposed to characterize not only the average occupancy level but the instantaneous channel state perceived simultaneously by DSA/CR users observing the same transmitter from different locations as well. The validity and accuracy of the theoretical models are demonstrated with results from an extensive spectrum measurement campaign. Some illustrative examples of their potential applicability are presented and discussed as well

Efficient Scheduling and Collision Reduction in Hybrid Cognitive Radio Network using SBP

Nowadays, Cognitive Radio Networks have been considered, investigated and planned in an energetic manner. But maximum works focused on separate areas like Allocation, Sensing and Sharing of Spectrum, etc.  In this paper, propose a HCRN with the subsequent characteristics of priority based scheduling based on centralized switching delay aware network and distributed broadcasting protocol to decrease collision and enhance scheduling. For developing Hybrid Cognitive Radio Networks (HCRN) lot of space is still available. The proposed HCRN is simulated and network quality of service is estimated in terms of packet delivery ratio, control overhead, throughput and broadcast delay

Spectrum measurement, sensing, analysis and simulation in the context of cognitive radio

The radio frequency (RF) spectrum is a scarce natural resource, currently regulated locally by national agencies. Spectrum has been assigned to different services and it is very difficult for emerging wireless technologies to gain access due to rigid spectmm policy and heavy opportunity cost. Current spectrum management by licensing causes artificial spectrum scarcity. Spectrum monitoring shows that many frequencies and times are unused. Dynamic spectrum access (DSA) is a potential solution to low spectrum efficiency. In DSA, an unlicensed user opportunistically uses vacant licensed spectrum with the help of cognitive radio. Cognitive radio is a key enabling technology for DSA. In a cognitive radio system, an unlicensed Secondary User (SU) identifies vacant licensed spectrum allocated to a Primary User (PU) and uses it without harmful interference to the PU. Cognitive radio increases spectrum usage efficiency while protecting legacy-licensed systems. The purpose of this thesis is to bring together a group of CR concepts and explore how we can make the transition from conventional radio to cognitive radio. Specific goals of the thesis are firstly the measurement of the radio spectrum to understand the current spectrum usage in the Humber region, UK in the context of cognitive radio. Secondly, to characterise the performance of cyclostationary feature detectors through theoretical analysis, hardware implementation, and real-time performance measurements. Thirdly, to mitigate the effect of degradation due to multipath fading and shadowing, the use of -wideband cooperative sensing techniques using adaptive sensing technique and multi-bit soft decision is proposed, which it is believed will introduce more spectral opportunities over wider frequency ranges and achieve higher opportunistic aggregate throughput.Understanding spectrum usage is the first step toward the future deployment of cognitive radio systems. Several spectrum usage measurement campaigns have been performed, mainly in the USA and Europe. These studies show locality and time dependence. In the first part of this thesis a spectrum usage measurement campaign in the Humber region, is reported. Spectrum usage patterns are identified and noise is characterised. A significant amount of spectrum was shown to be underutilized and available for the secondary use. The second part addresses the question: how can you tell if a spectrum channel is being used? Two spectrum sensing techniques are evaluated: Energy Detection and Cyclostationary Feature Detection. The performance of these techniques is compared using the measurements performed in the second part of the thesis. Cyclostationary feature detection is shown to be more robust to noise. The final part of the thesis considers the identification of vacant channels by combining spectrum measurements from multiple locations, known as cooperative sensing. Wideband cooperative sensing is proposed using multi resolution spectrum sensing (MRSS) with a multi-bit decision technique. Next, a two-stage adaptive system with cooperative wideband sensing is proposed based on the combination of energy detection and cyclostationary feature detection. Simulations using the system above indicate that the two-stage adaptive sensing cooperative wideband outperforms single site detection in terms of detection success and mean detection time in the context of wideband cooperative sensing

On Random Sampling for Compliance Monitoring in Opportunistic Spectrum Access Networks

In the expanding spectrum marketplace, there has been a long term evolution towards more market€“oriented mechanisms, such as Opportunistic Spectrum Access (OSA), enabled through Cognitive Radio (CR) technology. However, the potential of CR technologies to revolutionize wireless communications, also introduces challenges based upon the potentially non€“deterministic CR behaviour in the Electrospace. While establishing and enforcing compliance to spectrum etiquette rules are essential to realization of successful OSA networks in the future, there has only been recent increased research activity into enforcement. This dissertation presents novel work on the spectrum monitoring aspect, which is crucial to effective enforcement of OSA. An overview of the challenges faced by current compliance monitoring methods is first presented. A framework is then proposed for the use of random spectral sampling techniques to reduce data collection complexity in wideband sensing scenarios. This approach is recommended as an alternative to Compressed Sensing (CS) techniques for wideband spectral occupancy estimation, which may be difficult to utilize in many practical congested scenarios where compliance monitoring is required. Next, a low€“cost computational approach to online randomized temporal sensing deployment is presented for characterization of temporal spectrum occupancy in cognitive radio scenarios. The random sensing approach is demonstrated and its performance is compared to CS€“based approach for occupancy estimation. A novel frame€“based sampling inversion technique is then presented for cases when it is necessary to track the temporal behaviour of individual CRs or CR networks. Parameters from randomly sampled Physical Layer Convergence Protocol (PLCP) data frames are used to reconstruct occupancy statistics, taking account of missed frames due to sampling design, sensor limitations and frame errors. Finally, investigations into the use of distributed and mobile spectrum sensing to collect spatial diversity to improve the above techniques are presented, for several common monitoring tasks in spectrum enforcement. Specifically, focus is upon techniques for achieving consensus in dynamic topologies such as in mobile sensing scenarios

Frequency Selective Surface Assisted Dynamic Spectrum Access for the Wireless Indoor Environment

This thesis investigates the impact of the use of Frequency Selective Surfaces (FSS) when applied to walls to improve the performance of indoor wireless communications. FSS controlled spectrum sharing is examined using a point-to-point network topology containing two different types of users, intra-room and inter-room, and considers a system with open spectrum access where all users have equal regulatory status. This approach is used together with FSS walls to smartly control resource assignment inside the building. The FSS filter activation threshold is examined, using a threshold value measured from sensing interference in up to three spectrum bands. It is shown how using this threshold, and different FSS state activation strategies, can significantly improve the way an indoor wireless communications system can control its spectrum resources. Different FSS activation strategies are explored. It is shown how the model where a specific value of FSS threshold is set and used throughout shows much better performance compared to situations where the FSS is either continually on or continually off. This performance can be further improved if a more deterministic value is used. This is achieved by using a sliding window average assessment of performance which aims to minimize the frequency of instantaneous FSS states changes; this means a statistical value is used to determine when to activate the FSS. The result shows that a longer sliding window tends to give a better performance for inter-room users without significantly decreasing the performance of intra-room users. An analytical model of system performance using a two-dimensional Markov Chain is developed. Systems with One Available Spectrum (1AS) and Two Available Spectrums (2AS) have been analysed using a state-transition-rate diagram and global equilibrium expressions for both systems are presented

Measurement and modelling of spectrum occupancy

Based on the conception of spectrum sharing, cognitive Radio as a promising technology for optimizing utilization of the radio spectrum has emerged to revolutionize the next generation wireless communications industry. In order to adopt this technology, the current spectrum allocation strategy has to be reformed and the real spectrum occupancy information has to be systemically investigated. To assess the feasibility of cognitive radio technology, the statistical information of the present spectral occupancy needs to be examined thoroughly, which forms the basis of the spectrum occupancy project. We studied the 100-2500 MHz spectrum with the traditional radio monitoring systems whose technical details have been fully recorded in this thesis. In order to detect the frequency agile signals, a channel sounder, which is capable of scanning 300 MHz spectrum within 4 ms with multiple channel inputs, was used as a dedicated radio receiver in our measurements. The conclusion of the statistical information from the spectrum monitoring experiments shows that the spectrum occupancy range from 100-2500 MHz are low indeed in the measuring locations and period. The average occupancies for most bands are less than 20%. Especially, the average occupancies in the 1 GHz to 2.5GHz spectrum are less than 5%. Time series analysis was initially introduced in spectrum occupancy analysis as a tool to model spectrum occupancy variations with time. For instance, the time series Airline model fits well the GSM band occupancy data. In this thesis, generalized linear models were used as complementarily solutions to model occupancy data into other parameters such as signal amplitude. The validation of the direction of arrival algorithms (EM and SAGE) was verified with the anechoic chamber, by which we can determine the spectrum occupancy in space domain