Power Control and Cooperative Sensing in Cognitive Radio

The traditional ways of spectrum management is inefficient as large portions of useable spectrum is left idle most periods of the day hence the call for more dynamic spectrum management techniques. Cognitive Radio (CR) is considered a viable means to vastly improve the efficiency of spectrum since it allows unlicensed users access to licenced spectrum as long as the quality of service is not downgraded. This research investigates the major problems associated with designing CRs. An in-depth analysis shows that the two major problems that hinders the successful design of CR systems are that of spectrum sensing (How the device detects the Primary User (PU)) and Power Control (which focuses on the level of transmit power of CR devices so as not to induce interference to PUs). To solve the problem of power control in this research, we consider a single cell scenario where N CR terminals are operating in a network with a Cognitive base station (CBS) together with one PU along with its Primary Base station (PBS). In the scenario, CR devices will generally seek to improve quality of service by increasing it’s transmit power. This increase introduces interference to the PU. To mitigate this, the CR devices are modelled as players of a non-cooperative game where offending devices are penalised till a Nash equilibrium level is achieved. At this point, the players can no longer influence the state of the game no matter the strategy they chose to play. The work is extended to cover CR internet of things devices by exploiting the adequate path loss exponent for the operational environment. The power control algorithm is compared with two other known power control algorithms and it outperforms them in average power, average SNR and rate of convergence. Spectrum sensing in CRs has been shown in literature to improve when done cooperatively rather than individually. To this end, this research focuses on cooperative sensing which allows the radios to make decision on their channel state based on the combine results of individual radios. The channel is modelled as a frame- by frame structure of equal length using the slotted aloha access contention technique. Each frame has a fixed length and is made up of sensing, prediction and transmission periods. It is seen observed that longer sensing periods results in better sensing results but considerable lower throughput. The scenario researched involves a CR network with K CRs and M sub-channels. It is assumed that the conditions of all sub-channels are equal, and each CR randomly chooses any one to sense and the throughput is measured. The interference caused to the PU are measured by collisions in the system. This are of two types: (1) Collisions with PUs due to missed detections and (2) collisions with other CRs due to access contention. Whenever there is a collision, the packet is withheld by the system and transmission is stopped. The throughput is a measure of successful packet transmissions. The derived algorithm improved the throughput by detecting the optimal sensing period. Using the K-of-M fusion decision rule, the sensing algorithm guarantees that optimal throughput can be achieved when 50% of the cognitive radio correctly detects the state of the spectrum. Cognitive radio throughput will be of very grave importance. Especially in spectrums like TVWSs and radar systems. A throughput model with power control is presented. The aim is to improve the throughput in interweave scenarios

A survey on MAC protocols for complex self-organizing cognitive radio networks

Complex self-organizing cognitive radio (CR) networks serve as a framework for accessing the spectrum allocation dynamically where the vacant channels can be used by CR nodes opportunistically. CR devices must be capable of exploiting spectrum opportunities and exchanging control information over a control channel. Moreover, CR nodes should intelligently coordinate their access between different cognitive radios to avoid collisions on the available spectrum channels and to vacate the channel for the licensed user in timely manner. Since inception of CR technology, several MAC protocols have been designed and developed. This paper surveys the state of the art on tools, technologies and taxonomy of complex self-organizing CR networks. A detailed analysis on CR MAC protocols form part of this paper. We group existing approaches for development of CR MAC protocols and classify them into different categories and provide performance analysis and comparison of different protocols. With our categorization, an easy and concise view of underlying models for development of a CR MAC protocol is provided

Performance Analysis of Cognitive Radio Systems with Imperfect Channel Knowledge

An analytical framework is established to characterize the effects such as time allocation and variation, arising due to the incorporation of imperfect channel knowledge, that are detrimental to the performance of the cognitive radio systems. In order to facilitate hardware deployment of a cognitive radio system, received power-based estimation, a novel channel estimation technique is employed for the channels existing between the primary and the secondary systems, thus fulfilling low-complexity and versatility requirements

Cooperative Spectrum Sharing in Cognitive Radio Networking

Driven by the massive growth in communications data traffic as well as flourishing users' demands, we need to fully utilize the existing scarce spectrum resource. However, there have been several studies and reports over the years showing that a large portion of licensed spectrum is actually underutilized in both temporal and spatial domains. Moreover, aiming at facing the dilemma among the fixed spectrum allocation, the ever enormous increasing traffic demand and the limited spectrum resource, cognitive radio (CR) was proposed by Mitola to alleviate the under usage of spectrum. Thus, cognitive radio networking (CRN) has emerged as a promising paradigm to improve the spectrum efficiency and utilization by allowing secondary users (SUs) to utilize the spectrum hole of primary users (PUs). By using spectrum sensing, SUs can opportunistically access spectrum holes for secondary transmission without interfering the transmissions of the PUs and efficient spectrum utilization by multiple PUs and SUs requires reliable detection of PUs. Nevertheless, sensing errors such as false alarm and misdetection are inevitable in practical networks. Hence, the assumption that SUs always obtain the exact channel availability information is unreasonable. In addition, spectrum sensing must be carried out continuously and the SU must terminate its transmission as soon as it senses the re-occupancy by a PU. As a better alternative of spectrum sensing, cooperation has been leveraged in CRN, which is referred as cooperative cognitive radio networking (CCRN). In CCRN, in order to obtain the transmission opportunities, SUs negotiate with the PUs for accessing the spectrum by providing tangible service for PUs. In this thesis, we study cluster based spectrum sharing mechanism for CCRN and investigate on exploiting the cooperative technique in heterogeneous network. First, we develop cooperation protocols for CRN. Simultaneous transmission can be realized through quadrature signalling method in our proposed cooperation protocol. The optimal power allocation has been analyzed and closed-form solution has been derived for amplify and forward mode. Second, we study a cluster based spectrum sharing mechanism. The spectrum sharing is formulated as a combinatorial non-linear optimization problem which is NP-hard. Afterwards, we solve this problem by decomposing it into cluster allocation and time assignment, and we show that the result is close to the optimal solution. Third, we propose a macrocell-femtocell network cooperation scheme for heterogeneous networks under closed access mode. The cooperation between the femtocell network and macrocell network is investigated. By implementing the cooperation, not only the macrocell users' (MUEs') and femtocell users' (FUEs') utility can be improved compared with the non-cooperation case, but also the energy consumption as well as the interference from the femtocell network to the macrocell network can be reduced

Performance analysis of spectrum sensing techniques for future wireless networks

In this thesis, spectrum sensing techniques are investigated for cognitive radio (CR) networks in order to improve the sensing and transmission performance of secondary networks. Specifically, the detailed exploration comprises of three areas, including single-node spectrum sensing based on eigenvalue-based detection, cooperative spectrum sensing under random secondary networks and full-duplex (FD) spectrum sensing and sharing techniques. In the first technical chapter of this thesis, eigenvalue-based spectrum sensing techniques, including maximum eigenvalue detection (MED), maximum minimum eigenvalue (MME) detection, energy with minimum eigenvalue (EME) detection and the generalized likelihood ratio test (GLRT) eigenvalue detector, are investigated in terms of total error rates and achievable throughput. Firstly, in order to consider the benefits of primary users (PUs) and secondary users (SUs) simultaneously, the optimal decision thresholds are investigated to minimize the total error rate, i.e. the summation of missed detection and false alarm rate. Secondly, the sensing-throughput trade-off is studied based on the GLRT detector and the optimal sensing time is obtained for maximizing the achievable throughput of secondary communications when the target probability of detection is achieved. In the second technical chapter, the centralized GLRT-based cooperative sensing technique is evaluated by utilizing a homogeneous Poisson point process (PPP). Firstly, since collaborating all the available SUs does not always achieve the best sensing performance under a random secondary network, the optimal number of cooperating SUs is investigated to minimize the total error rate of the final decision. Secondly, the achievable ergodic capacity and throughput of SUs are studied and the technique of determining an appropriate number of cooperating SUs is proposed to optimize the secondary transmission performance based on a target total error rate requirement. In the last technical chapter, FD spectrum sensing (FDSS) and sensing-based spectrum sharing (FD-SBSS) are investigated. There exists a threshold pair, not a single threshold, due to the self-interference caused by the simultaneous sensing and transmission. Firstly, by utilizing the derived expressions of false alarm and detection rates, the optimal decision threshold pair is obtained to minimize total error rate for the FDSS scheme. Secondly, in order to further improve the secondary transmission performance, the FD-SBSS scheme is proposed and the collision and spectrum waste probabilities are studied. Furthermore, different antenna partitioning methods are proposed to maximize the achievable throughput of SUs under both FDSS and FD-SBSS schemes

Interference mitigation in cognitive femtocell networks

“A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of Philosophy”.Femtocells have been introduced as a solution to poor indoor coverage in cellular communication which has hugely attracted network operators and stakeholders. However, femtocells are designed to co-exist alongside macrocells providing improved spatial frequency reuse and higher spectrum efficiency to name a few. Therefore, when deployed in the two-tier architecture with macrocells, it is necessary to mitigate the inherent co-tier and cross-tier interference. The integration of cognitive radio (CR) in femtocells introduces the ability of femtocells to dynamically adapt to varying network conditions through learning and reasoning. This research work focuses on the exploitation of cognitive radio in femtocells to mitigate the mutual interference caused in the two-tier architecture. The research work presents original contributions in mitigating interference in femtocells by introducing practical approaches which comprises a power control scheme where femtocells adaptively controls its transmit power levels to reduce the interference it causes in a network. This is especially useful since femtocells are user deployed as this seeks to mitigate interference based on their blind placement in an indoor environment. Hybrid interference mitigation schemes which combine power control and resource/scheduling are also implemented. In a joint threshold power based admittance and contention free resource allocation scheme, the mutual interference between a Femtocell Access Point (FAP) and close-by User Equipments (UE) is mitigated based on admittance. Also, a hybrid scheme where FAPs opportunistically use Resource Blocks (RB) of Macrocell User Equipments (MUE) based on its traffic load use is also employed. Simulation analysis present improvements when these schemes are applied with emphasis in Long Term Evolution (LTE) networks especially in terms of Signal to Interference plus Noise Ratio (SINR)

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial