Wideband Spectrum Sensing in Cognitive Radio Networks

Spectrum sensing is an essential enabling functionality for cognitive radio networks to detect spectrum holes and opportunistically use the under-utilized frequency bands without causing harmful interference to legacy networks. This paper introduces a novel wideband spectrum sensing technique, called multiband joint detection, which jointly detects the signal energy levels over multiple frequency bands rather than consider one band at a time. The proposed strategy is efficient in improving the dynamic spectrum utilization and reducing interference to the primary users. The spectrum sensing problem is formulated as a class of optimization problems in interference limited cognitive radio networks. By exploiting the hidden convexity in the seemingly non-convex problem formulations, optimal solutions for multiband joint detection are obtained under practical conditions. Simulation results show that the proposed spectrum sensing schemes can considerably improve the system performance. This paper establishes important principles for the design of wideband spectrum sensing algorithms in cognitive radio networks

Energy Efficient Power Allocation In Cognitive Radio Network

Energy efficient wireless cellular networks have been the subject of intense research in recent years. Green radio networks are the demand of new era of communication system. In order to improve the energy of cognitive radio network, the technique for power allocation in transmission link is proposed in this paper. Recently, many technological and network issues like interference and resource allocation or power optimization in cognitive radio network have been studied but only in terms of spectrum sensing. Different from the other work, the optimization of power in order to maximize the energy with consideration of imperfect spectrum sensing is proposed. Simulation result shows that for every value of power levels energy will be improved

Un nuevo esquema de agrupación para redes sensoras inalámbricas de radio cognitivas heterogéneas

Introduction: This article is the product of the research “Learning-based Spectrum Analysis and Prediction in Cognitive Radio Sensor Networks”, developed at Sejong University in the year 2019. Problem: Most of the clustering schemes for distributed cognitive radio-enabled wireless sensor networks consider homogeneous cognitive radio-enabled wireless sensors. Many clustering schemes for such homogeneouscognitive radio-enabled wireless sensor networks waste resources and suffer from energy inefficiency because of the unnecessary overheads. Objective: The objective of the research is to propose a node clustering scheme that conserves energy and prolongs network lifetime. Methodology: A heterogeneous cognitive radio-enabled wireless sensor network in which only a few nodes have a cognitive radio module and the other nodes are normal sensor nodes. Along with the hardware cost, theproposed scheme is efficient in energy consumption. Results: We simulated the proposed scheme and compared it with the homogeneous cognitive radio-enabled wireless sensor networks. The results show that the proposed scheme is efficient in terms of energyconsumption. Conclusion: The proposed node clustering scheme performs better in terms of network energy conservation and network partition. Originality: There are heterogeneous node clustering schemes in the literature for cooperative spectrum sensing and energy efficiency, but to the best of our knowledge, there is no study that proposes a non-cognitiveradio-enabled sensor clustering for energy conservation along with cognitive radio-enabled wireless sensors. Limitations: The deployment of the proposed special device for cognitive radio-enabled wireless sensors is complicated and requires special hardware with better battery powered cognitive sensor nodes

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

Efficient radio resource management for future generation heterogeneous wireless networks

The heterogeneous deployment of small cells (e.g., femtocells) in the coverage area of the traditional macrocells is a cost-efficient solution to provide network capacity, indoor coverage and green communications towards sustainable environments in the future fifth generation (5G) wireless networks. However, the unplanned and ultra-dense deployment of femtocells with their uncoordinated operations will result in technical challenges such as severe interference, a significant increase in total energy consumption, unfairness in radio resource sharing and inadequate quality of service provisioning. Therefore, there is a need to develop efficient radio resource management algorithms that will address the above-mentioned technical challenges. The aim of this thesis is to develop and evaluate new efficient radio resource management algorithms that will be implemented in cognitive radio enabled femtocells to guarantee the economical sustainability of broadband wireless communications and users' quality of service in terms of throughput and fairness. Cognitive Radio (CR) technology with the Dynamic Spectrum Access (DSA) and stochastic process are the key technologies utilized in this research to increase the spectrum efficiency and energy efficiency at limited interference. This thesis essentially investigates three research issues relating to the efficient radio resource management: Firstly, a self-organizing radio resource management algorithm for radio resource allocation and interference management is proposed. The algorithm considers the effect of imperfect spectrum sensing in detecting the available transmission opportunities to maximize the throughput of femtocell users while keeping interference below pre-determined thresholds and ensuring fairness in radio resource sharing among users. Secondly, the effect of maximizing the energy efficiency and the spectrum efficiency individually on radio resource management is investigated. Then, an energy-efficient radio resource management algorithm and a spectrum-efficient radio resource management algorithm are proposed for green communication, to improve the probabilities of spectrum access and further increase the network capacity for sustainable environments. Also, a joint maximization of the energy efficiency and spectrum efficiency of the overall networks is considered since joint optimization of energy efficiency and spectrum efficiency is one of the goals of 5G wireless networks. Unfortunately, maximizing the energy efficiency results in low performance of the spectrum efficiency and vice versa. Therefore, there is an investigation on how to balance the trade-off that arises when maximizing both the energy efficiency and the spectrum efficiency simultaneously. Hence, a joint energy efficiency and spectrum efficiency trade-off algorithm is proposed for radio resource allocation in ultra-dense heterogeneous networks based on orthogonal frequency division multiple access. Lastly, a joint radio resource allocation with adaptive modulation and coding scheme is proposed to minimize the total transmit power across femtocells by considering the location and the service requirements of each user in the network. The performance of the proposed algorithms is evaluated by simulation and numerical analysis to demonstrate the impact of ultra-dense deployment of femtocells on the macrocell networks. The results show that the proposed algorithms offer improved performance in terms of throughput, fairness, power control, spectrum efficiency and energy efficiency. Also, the proposed algorithms display excellent performance in dynamic wireless environments

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

Collaborative spectrum sensing in cognitive radio networks

The radio frequency (RF) spectrum is a scarce natural resource, currently regulated by government agencies. With the explosive emergence of wireless applications, the demands for the RF spectrum are constantly increasing. On the other hand, it has been reported that localised temporal and geographic spectrum utilisation efficiency is extremely low. Cognitive radio is an innovative technology designed to improve spectrum utilisation by exploiting those spectrum opportunities. This ability is dependent upon spectrum sensing, which is one of most critical components in a cognitive radio system. A significant challenge is to sense the whole RF spectrum at a particular physical location in a short observation time. Otherwise, performance degrades with longer observation times since the lagging response to spectrum holes implies low spectrum utilisation efficiency. Hence, developing an efficient wideband spectrum sensing technique is prime important. In this thesis, a multirate asynchronous sub-Nyquist sampling (MASS) system that employs multiple low-rate analog-to-digital converters (ADCs) is developed that implements wideband spectrum sensing. The key features of the MASS system are, 1) low implementation complexity, 2) energy-efficiency for sharing spectrum sensing data, and 3) robustness against the lack of time synchronisation. The conditions under which recovery of the full spectrum is unique are presented using compressive sensing (CS) analysis. The MASS system is applied to both centralised and distributed cognitive radio networks. When the spectra of the cognitive radio nodes have a common spectral support, using one low-rate ADC in each cognitive radio node can successfully recover the full spectrum. This is obtained by applying a hybrid matching pursuit (HMP) algorithm - a synthesis of distributed compressive sensing simultaneous orthogonal matching pursuit (DCS-SOMP) and compressive sampling matching pursuit (CoSaMP). Moreover, a multirate spectrum detection (MSD) system is introduced to detect the primary users from a small number of measurements without ever reconstructing the full spectrum. To achieve a better detection performance, a data fusion strategy is developed for combining sensing data from all cognitive radio nodes. Theoretical bounds on detection performance are derived for distributed cognitive radio nodes suffering from additive white Gaussian noise (AWGN), Rayleigh fading, and log-normal fading channels. In conclusion, MASS and MSD both have a low implementation complexity, high energy efficiency, good data compression capability, and are applicable to distributed cognitive radio networks