MULTI USER COOPERATION SPECTRUM SENSING IN WIRELESS COGNITIVE RADIO NETWORKS

With the rapid proliferation of new wireless communication devices and services, the demand for the radio spectrum is increasing at a rapid rate, which leads to making the spectrum more and more crowded. The limited available spectrum and the inefficiency in the spectrum usage have led to the emergence of cognitive radio (CR) and dynamic spectrum access (DSA) technologies, which enable future wireless communication systems to exploit the empty spectrum in an opportunistic manner. To do so, future wireless devices should be aware of their surrounding radio environment in order to adapt their operating parameters according to the real-time conditions of the radio environment. From this viewpoint, spectrum sensing is becoming increasingly important to new and future wireless communication systems, which is designed to monitor the usage of the radio spectrum and reliably identify the unused bands to enable wireless devices to switch from one vacant band to another, thereby achieving flexible, reliable, and efficient spectrum utilisation. This thesis focuses on issues related to local and cooperative spectrum sensing for CR networks, which need to be resolved. These include the problems of noise uncertainty and detection in low signal to noise ratio (SNR) environments in individual spectrum sensing. In addition to issues of energy consumption, sensing delay and reporting error in cooperative spectrum sensing. In this thesis, we investigate how to improve spectrum sensing algorithms to increase their detection performance and achieving energy efficiency. To this end, first, we propose a new spectrum sensing algorithm based on energy detection that increases the reliability of individual spectrum sensing. In spite of the fact that the energy detection is still the most common detection mechanism for spectrum sensing due to its simplicity. Energy detection does not require any prior knowledge of primary signals, but has the drawbacks of threshold selection, and poor performance due to noise uncertainty especially at low SNR. Therefore, a new adaptive optimal energy detection algorithm (AOED) is presented in this thesis. In comparison with the existing energy detection schemes the detection performance achieved through AOED algorithm is higher. Secondly, as cooperative spectrum sensing (CSS) can give further improvement in the detection reliability, the AOED algorithm is extended to cooperative sensing; in which multiple cognitive users collaborate to detect the primary transmission. The new combined approach (AOED and CSS) is shown to be more reliable detection than the individual detection scheme, where the hidden terminal problem can be mitigated. Furthermore, an optimal fusion strategy for hard-fusion based cognitive radio networks is presented, which optimises sensing performance. Thirdly, the need for denser deployment of base stations to satisfy the estimated high traffic demand in future wireless networks leads to a significant increase in energy consumption. Moreover, in large-scale cognitive radio networks some of cooperative devices may be located far away from the fusion centre, which causes an increase in the error rate of reporting channel, and thus deteriorating the performance of cooperative spectrum sensing. To overcome these problems, a new multi-hop cluster based cooperative spectrum sensing (MHCCSS) scheme is proposed, where only cluster heads are allowed to send their cluster results to the fusion centre via successive cluster heads, based on higher SNR of communication channel between cluster heads. Furthermore, in decentralised CSS as in cognitive radio Ad Hoc networks (CRAHNs), where there is no fusion centre, each cognitive user performs the local spectrum sensing and shares the sensing information with its neighbours and then makes its decision on the spectrum availability based on its own sensing information and the neighbours’ information. However, cooperation between cognitive users consumes significant energy due to heavy communications. In addition to this, each CR user has asynchronous sensing and transmission schedules which add new challenges in implementing CSS in CRAHNs. In this thesis, a new multi-hop cluster based CSS scheme has been proposed for CRAHNs, which can enhance the cooperative sensing performance and reduce the energy consumption compared with other conventional decentralised cooperative spectrum sensing modes

An energy efficient reinforcement learning based cooperative channel sensing for cognitive radio sensor networks

In Cognitive Radio (CR), the conventional narrow band spectrum sensing requires either random channel sensing order or predefined channel sensing sequence to sense all channels in a specified spectrum band in order to detect vacant channels. This may be inefficient in energy constraint devices networks such as Cognitive Radio Wireless Sensor Network (CR-WSN). In this paper, we propose a Reinforcement Learning based clustered Cooperative Channel Sensing (RL-CCS) that learns channels’ dynamic behaviors in terms of channel availability, sensing energy cost, and channel impairment to achieve optimal sensing sequence and optimal set of channels. The problem of selecting optimal policy is formulated as a Markov Decision Problem (MDP) to determine optimal solutions that minimize sensing energy while improving Primary User (PU) detection and channel utilization in CR-WSN. Simulation results show convergence and adaptability of the algorithm to dynamic environment in achieving optimal solutions. The results also indicate that with optimal channel sensing sequence and optimal sets of channels, sensing energy cost savings of 15.14% per channel sensing cycle can be achieved while improving PU detection accuracy and channel utilization compared to the sensing sequence based on Greedy search approach. Performance comparison of the proposed algorithm with other benchmark schemes indicates viability of our proposed approach over the other schemes in minimizing sensing energy and improving PU detection performance

A hybrid double-threshold based cooperative spectrum sensing over fading channels

This paper investigates double-threshold based energy detector for cooperative spectrum sensing mechanisms in cognitive wireless radio networks. We first propose a hybrid double-threshold based energy detector (HDTED) to improve the sensing performance at secondary users (SUs) by exploiting both the local binary/energy decisions and global binary decisions feedback from the fusion centre (FC). Significantly, we derive closed-form expressions and bounds for the probabilities of missed detection and false alarm considering a practical scenario where all channel links suffer from Rayleigh fading and background noise. The derived expressions not only show the improved performance achieved with the HDTED scheme but also enable us to analyse the impacts of the number of the SUs and the fading channels on the cooperative spectrum sensing performance. Furthermore, based on the derived bounds, we propose an optimal SU selection algorithm for forwarding the local decisions to the FC, which helps reduce the number of forwarding bits for a lower-complexity signaling. Finally, numerical results are provided to demonstrate the validity of the analytical findings

A hybrid double-threshold based cooperative spectrum sensing over fading channels

This paper investigates double-threshold based energy detector for cooperative spectrum sensing mechanisms in cognitive wireless radio networks. We first propose a hybrid double-threshold based energy detector (HDTED) to improve the sensing performance at secondary users (SUs) by exploiting both the local binary/energy decisions and global binary decisions feedback from the fusion centre (FC). Significantly, we derive closed-form expressions and bounds for the probabilities of missed detection and false alarm considering a practical scenario where all channel links suffer from Rayleigh fading and background noise. The derived expressions not only show the improved performance achieved with the HDTED scheme but also enable us to analyse the impacts of the number of the SUs and the fading channels on the cooperative spectrum sensing performance. Furthermore, based on the derived bounds, we propose an optimal SU selection algorithm for forwarding the local decisions to the FC, which helps reduce the number of forwarding bits for a lower-complexity signaling. Finally, numerical results are provided to demonstrate the validity of the analytical findings

Performance Evaluation of Cognitive Radio Spectrum Sensing Techniques through a Rayleigh Fading Channel

In recent years, there has been a steep rise in the demand for bandwidth due to a sharp increase in the number of devices connected to the wireless network. Coupled with the expected commercialization of 5G services and massive adoption of IoT, the upsurge in the number of devices connected to the wireless network will continue to grow exponentially into billions of devices. To accommodate the associated demand for wireless spectrum as we step into this new era of wireless connectivity, traditional methods of spectrum utilization based on fixed and static allocation are no longer adequate. New innovative forms that support dynamic assignment of spectrum space on as-per-need basis are now paramount. Cognitive radio has emerged as one of the most promising techniques that allow flexible usage of the scarce spectrum resource. Cognitive radio allows unlicensed users to opportunistically access spectrum bands assigned to primary users when these spectrum bands are idle. As such, cognitive radio reduces the gap between spectrum scarcity and spectrum underutilization. The most critical function of cognitive radio is spectrum sensing, which establishes the occupation status of a spectrum band, paving the way for a cognitive radio to initiate transmission if the band is idle. The most common and widely used methods for spectrum sensing are energy detection, matched filter detection, cyclostationary feature detection and cooperative based spectrum sensing. This dissertation investigates the performance of these spectrum-sensing techniques through a Rayleigh fading channel. In a wireless environment, a Rayleigh fading channel models the propagation of a wireless signal where there is no dominant line of sight between the transmitter and receiver. Understanding the performance of spectrum sensing techniques in a real world simulation environment is important for both industry and academia, as this allows for the optimal design of cognitive radio systems capable of efficiently executing their function. MATLAB software provides an experimental platform for the fusion of various Rayleigh fading channel parameters that mimic real world wireless channel characteristics. In this project, a MATLAB environment test bed is used to simulate the performance for each spectrum sensing technique across a range of signal-to-noise values, through a Rayleigh fading channel with a given set of parameters for channel delay, channel gain and Doppler shift. Simulation results are presented as plots for probability of detection versus signal-tonoise ratio, receiver operating characteristics (ROC) curves and complementary ROC curves. A detailed performance analysis for each spectrum sensing technique then follows, with comparisons done to determine the technique that offers the best relative performance