Spectrum utilization using game theory

This thesis was submitted for the degree of Master of Philosophy and awarded by Brunel University.Spectrum utilization is the most recent communications issue which takes great deal of attention from communication researchers where most of the efforts have been dedicated for spectral efficient utilization. Spectrum sharing is one of the solutions considered in the problem of lack of available frequency for new communication services which are unlicensed. In this work we propose an optimal method for spectrum utilization to increase spectral efficiency. It considers the problem of spectrum holes found in Primary User's (PU) band and detected using one of the spectral sensing methods. The solution is formulated with the help of Game theory approach in such a way that the primary user who has unoccupied frequency can share it with a group of secondary users (SU) in a competitive way. One of the SUs will be a secondary primary user (SPU), share available frequency from PU then offer his sharing to serve other SUs in different rate of sharing. Each user in the group of secondary users has a chance to be secondary primary user depending on reputation of each SU. Enhancing reputation is the only way for any SU to assure a share in the spectrum where it considered the factor of increasing or decreasing rate of sharing as well as factor of being SPU or an ordinary SU. A theoretical non-cooperative game model is introduced in a comparison with a proposed non-dynamic technique which depends on number of subscribers who occupy frequency in each time period. Multi-users compete on sharing the frequency from one of the users who offers sharing at a time when he has low number of subscribers that occupy his band. It is found that non-dynamic sharing results in inefficient spectrum utilization which is one of the reasons of spectrum scarcity where this resource is allocated in fixed way. Spectrum sharing using game theory solves this problem by its ability to make users compete to gain highest rate of spectrum allocation according to the real requirement of each user at each time interval. The problem of urgent case is also discussed when the primary user comes back to using his band which is the specific band of sharing with the secondary users group. SPU makes it easy to unload the required band from multi-users because PU does not need to request his band from each SU in the group

CYCLOSTATIONARY FEATURES OF PAL TV AND WIRELESS MICROPHONE FOR COGNITIVE RADIO APPLICATIONS

Frequency spectrum being a scarce resource in communication system design, spectrum sharing seems to be the solution to an optimal utilization of frequency spectrum. The traditional fixed frequency allocation is not suitable for futuristic networks that demand more and more spectrum for new wireless services. Cognitive radio is a new emerging technology based on spectrum sharing concept. Spectrum sensing is a vital task in this emerging technology by which it is able to scan the frequency spectrum to identify the unused spectrum bands and utilize them. In this thesis, we discuss spectrum sensing in the context of IEEE 802.22 Wireless Regional Area Network (WRAN). In order to do so, we develop the co-existence scenario with three cases according to geographical positions of primary services and secondary service. In WRAN application, the SUs utilize the unused channel in TV spectrum, which means that the primary users are TV service and other FCC part 74 low power licensed devices. We focus on special case of Analog TV-PAL service and wireless microphone service as part 74 devices. Before discussing the spectrum sensing technique, we propose architecture for sensing receiver. The concept of noise uncertainty is also introduced in this context. The cyclostationarity theory is introduced and we explain the motivation behind using the theory for spectrum sensing and the reason that makes the cyclostationary features detector a powerful detection technique in cognitive radio. We obtain the cyclostationary features of these primary signals using spectral correlation function. Based on these features, we develop two algorithms for spectrum sensing and their performances are evaluated in comparison with energy detector which is considered as the standard simple detector. Given that the cyclostationary features are unique for a particular signal; these features can be used for signals classification. In our case, we use those features to decide if the licensed channel is used by TV service or wireless microphone service. This provides additional information for spectrum management and power control. Implementation issue is very important in cognitive radio generally and spectrum sensing specially, hence we discuss the implementation of cyclostationary features detector and compare its complexity with that of energy detector

Novel QoS-aware proactive spectrum access techniques for cognitive radio using machine learning

Traditional cognitive radio (CR) spectrum access techniques have been primitive and inefficient due to being blind to the occupancy conditions of the spectrum bands to be sensed. In addition, current spectrum access techniques are also unable to detect network changes or even consider the requirements of unlicensed users, leading to a poorer quality of service (QoS) and excessive latency. As user-specific approaches will play a key role in future wireless communication networks, the conventional CR spectrum access should also be updated in order to be more effective and agile. In this paper, a comprehensive and novel solution is proposed to decrease the sensing latency and to make the CR networks (CRNs) aware of unlicensed user requirements. As such, a proactive process with a novel QoS-based optimization phase is proposed, consisting of two different decision strategies. Initially, future traffic loads of the different radio access technologies (RATs), occupying different bands of the spectrum, are predicted using the artificial neural networks (ANNs). Based on these predictions, two strategies are proposed. In the first one, which solely focuses on latency, a virtual wideband (WB) sensing approach is developed, where predicted relative traffic loads in WB are exploited to enable narrowband (NB) sensing. The second one, based on Q -learning, focuses not only on minimizing the sensing latency but also on satisfying other user requirements. The results reveal that the first strategy manages to significantly reduce the sensing latency of the random selection process by 59.6%, while the Q -learning assisted second strategy enhanced the full-satisfaction by up to 95.7%

Spectrum sensing for cognitive radio and radar systems

The use of the radio frequency spectrum is increasing at a rapid rate. Reliable and efficient operation in a crowded radio spectrum requires innovative solutions and techniques. Future wireless communication and radar systems should be aware of their surrounding radio environment in order to have the ability to adapt their operation to the effective situation. Spectrum sensing techniques such as detection, waveform recognition, and specific emitter identification are key sources of information for characterizing the surrounding radio environment and extracting valuable information, and consequently adjusting transceiver parameters for facilitating flexible, efficient, and reliable operation. In this thesis, spectrum sensing algorithms for cognitive radios and radar intercept receivers are proposed. Single-user and collaborative cyclostationarity-based detection algorithms are proposed: Multicycle detectors and robust nonparametric spatial sign cyclic correlation based fixed sample size and sequential detectors are proposed. Asymptotic distributions of the test statistics under the null hypothesis are established. A censoring scheme in which only informative test statistics are transmitted to the fusion center is proposed for collaborative detection. The proposed detectors and methods have the following benefits: employing cyclostationarity enables distinction among different systems, collaboration mitigates the effects of shadowing and multipath fading, using multiple strong cyclic frequencies improves the performance, robust detection provides reliable performance in heavy-tailed non-Gaussian noise, sequential detection reduces the average detection time, and censoring improves energy efficiency. In addition, a radar waveform recognition system for classifying common pulse compression waveforms is developed. The proposed supervised classification system classifies an intercepted radar pulse to one of eight different classes based on the pulse compression waveform: linear frequency modulation, Costas frequency codes, binary codes, as well as Frank, P1, P2, P3, and P4 polyphase codes. A robust M-estimation based method for radar emitter identification is proposed as well. A common modulation profile from a group of intercepted pulses is estimated and used for identifying the radar emitter. The M-estimation based approach provides robustness against preprocessing errors and deviations from the assumed noise model

Wide-band spectrum sensing with convolution neural network using spectral correlation function

Recognition of signals is a spectrum sensing challenge requiring simultaneous detection, temporal and spectral localization, and classification. In this approach, we present the convolution neural network (CNN) architecture, a powerful portrayal of the cyclo-stationarity trademark, for remote range detection and sign acknowledgment. Spectral correlation function is used along with CNN. In two scenarios, method-1 and method-2, the suggested approach is used to categorize wireless signals without any previous knowledge. Signals are detected and classified simultaneously in method-1. In method-2, the sensing and classification procedures take place sequentially. In contrast to conventional spectrum sensing techniques, the proposed CNN technique need not bother with a factual judgment process or past information on the signs’ separating qualities. The method beats both conventional sensing methods and signal-classifying deep learning networks when used to analyze real-world, over-the-air data in cellular bands. Despite the implementation’s emphasis on cellular signals, any signal having cyclo-stationary properties may be detected and classified using the provided approach. The proposed model has achieved more than 90% of testing accuracy at 15 dB

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

Strategies for Devising Automatic Signal Recognition Algorithms in a Shared Radio Environment

In an increasingly congested and complex radio environment interference is to be expected, which poses problems for Automatic Signal Recognition (ASR) systems. This thesis explores strategies for improving ASR performance in the presence of interference. The thesis breaks the overall research question down into a number of subquestions and explores each of these in turn. A Phase-symmetric Cross Recurrence Plot is developed and used to show how a radio signal can be manipulated to separate information about the modulation from the information being carried. The Logarithmic Cyclic frequency Domain Profile is introduced to illustrate how a logarithmic representation can be used for analysing mixtures of signals with very different cyclic frequencies. After defining a canonical ASR system architecture, the concepts of an Ideal Feature and Interference Selectivity are introduced and applied to typical features used in ASR processing. Finally it is shown how these algorithmic developments can be combined in a Bayesian chain implementation that can accommodate a wide variety of feature extraction algorithms. It is concluded that future ASR systems will require features that can handle a wide range of signal types with much higher levels of interference selectivity if they are to achieve acceptable performance in shared spectrum bands. Intelligent segmentation is shown to be a requirement for future ASR systems unless features can be developed that have near ideal performance

Low Complexity Energy-Efficient Collaborative Spectrum Sensing for Cognitive Radio Networks

Clustering approach is considered a management technology that arranged the distributed cognitive radio users into logical groups to improve the sensing performance of the network. A lot of works in this area showed that cluster-based spectrum sensing (CBSS) technique efficiently tackled the trade-off between performance and overhead issue. By employing the tree structure of the cluster, a multilevel hierarchical cluster-based spectrum sensing (MH-CBSS) algorithm was proposed to compromise between the gained performance and incurred overhead. However, the MH-CBSS iterative algorithm incurs high computational requirements. In this thesis, an energy-efficient low computational hierarchical cluster-based algorithm is proposed which reduces the incurred computational burden. This is achieved by predetermining the number of cognitive radios (CRs) in the cluster, which provides an advantage of reducing the number of iterations of the MH-CBSS algorithm. Furthermore, for a comprehensive study, the modified algorithm is investigated over both Rayleigh and Nakagami fading channels. Simulation results show that the detection performance of the modified algorithm outperforms the MH-CBSS algorithm over Rayleigh and Nakagami fading channels. In addition, a conventional energy detection algorithm is a fixed threshold based algorithm. Therefore, the threshold should be selected properly since it significantly affects the sensing performance of energy detector. For this reason, an energy-efficient hierarchical cluster-based cooperative spectrum sensing algorithm with an adaptive threshold is proposed which enables the CR dynamically adapts its threshold to achieve the minimum total cluster error. Besides, the optimal threshold level for minimizing the overall cluster detection error rate is numerically determined. The detection performance of the proposed algorithm is presented and evaluated through simulation results

Hybrid Matched Filter Detection Spectrum Sensing

The radio frequency spectrum is getting more congested day by day due to the growth of wireless devices, applications, and the arrival of fifth generation (5G) mobile communications. This happens because the radio spectrum is a natural resource that has a restricted existence. Access to all devices can be granted, but in a more efficient way. To resolve the issue, cognitive radio technology has come out as a way, because it is possible to sense the radio spectrum in the neighboring. Spectrum sensing has been recognized as an important technology, in cognitive radio networks, to allow secondary users (SUs) to detect spectrum holes and opportunistically access primary licensed spectrum band without harmful interference. This paper considers the Energy Detection (ED) and Matched Filter Detection (MFD) spectrum sensing techniques as the baseline for a study where the so-called Hybrid Matched Filter Detection (Hybrid MFD) was proposed. Apart from an analytical approach, Monte Carlo simulations have been performed in MATLAB. These simulations aimed at understanding how the variation of parameters like the probability of false alarm, the signal-to-noise ratio (SNR) and the number of samples, can affect the probability of miss-detection. Simulation results show that i) higher probability of miss-detection is achieved for the ED spectrum sensing technique when compared to the MFD and Hybrid MFD techniques; ii) More importantly, the proposed Hybrid MFD technique outperforms MFD in terms of the ability to detect the presence of a primary user in licensed spectrum, for a probability of false alarm slightly lower than 0.5, low number of samples and low signal-to-noise ratio.info:eu-repo/semantics/publishedVersio