Power vs. Spectrum 2-D Sensing in Energy Harvesting Cognitive Radio Networks

Energy harvester based cognitive radio is a promising solution to address the shortage of both spectrum and energy. Since the spectrum access and power consumption patterns are interdependent, and the power value harvested from certain environmental sources are spatially correlated, the new power dimension could provide additional information to enhance the spectrum sensing accuracy. In this paper, the Markovian behavior of the primary users is considered, based on which we adopt a hidden input Markov model to specify the primary vs. secondary dynamics in the system. Accordingly, we propose a 2-D spectrum and power (harvested) sensing scheme to improve the primary user detection performance, which is also capable of estimating the primary transmit power level. Theoretical and simulated results demonstrate the effectiveness of the proposed scheme, in term of the performance gain achieved by considering the new power dimension. To the best of our knowledge, this is the first work to jointly consider the spectrum and power dimensions for the cognitive primary user detection problem

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

From Sensing to Predictions and Database Technique: A Review of TV White Space Information Acquisition in Cognitive Radio Networks

Strategies to acquire white space information is the single most significant functionality in cognitive radio networks (CRNs) and as such, it has gone some evolution to enhance information accuracy. The evolution trends are spectrum sensing, prediction algorithm and recently, geo-location database technique. Previously, spectrum sensing was the main technique for detecting the presence/absence of a primary user (PU) signal in a given radio frequency (RF) spectrum. However, this expectation could not materialized as a result of numerous technical challenges ranging from hardware imperfections to RF signal impairments. To convey the evolutionary trends in the development of white space information, we present a survey of the contemporary advancements in PU detection with emphasis on the practical deployment of CRNs i.e. Television white space (TVWS) networks. It is found that geo-location database is the most reliable technique to acquire TVWS information although, it is financially driven. Finally, using financially driven database model, this study compared the data-rate and spectral efficiency of FCC and Ofcom TV channelization. It was discovered that Ofcom TV channelization outperforms FCC TV channelization as a result of having higher spectrum bandwidth. We proposed the adoption of an all-inclusive TVWS information acquisition model as the future research direction for TVWS information acquisition techniques

Spectrum prediction in dynamic spectrum access systems

Despite the remarkable foreseen advancements in maximizing network capacities, the in-expansible nature of radio spectrum exposed outdated spectrum management techniques as a core limitation. Fixed spectrum allocation inefficiency has generated a proliferation of dynamic spectrum access solutions to accommodate the growing demand for wireless, and mobile applications. This research primarily focuses on spectrum occupancy prediction which equip dynamic users with the cognitive ability to identify and exploit instantaneous availability of spectrum opportunities. The first part of this research is devoted to identifying candidate occupancy prediction techniques suitable for SOP scenarios are extensively analysed, and a theoretical based model selection framework is consolidated. The performance of single user Bayesian/Markov based techniques both analytically and numerically. Understanding performance bounds of Bayesian/Markov prediction allows the development of efficient occupancy prediction models. The third and fourth parts of this research investigates cooperative decision and data-based occupancy prediction. The expected cooperative prediction accuracy gain is addressed based on the single user prediction model. Specifically, the third contributions provide analytical approximations of single user, as well as cooperative hard fusion based spectrum prediction. Finally, the forth contribution shows soft fusion is superior and more robust compared to hard fusion cooperative prediction in terms of prediction accuracy. Throughout this research, case study analysis is provided to evaluate the performance of the proposed approaches. Analytical approaches and Monte-Carlo simulation are compared for the performance metric of interest. Remarkably, the case study analysis confirmed that the statistical approximation can predict the performance of local and hard fusion cooperative prediction accurately, capturing all the essential aspects of signal detection performance, temporal dependency of spectrum occupancy as well as the finite nature of the network

Intelligent Design in Wireless System

We are living in an era full of data services, and the advancement in statistical learning encourages the development of intelligent system design algorithms based on practical data. In our work, we plan to study two potential applications with intelligent design in wireless systems based on statistical and machine learning techniques. The first application we study is the spectrum sensing problem in energy harvesting based cognitive radio networks, which is a promising solution to address the shortage of both spectrum and energy. Since the spectrum access and power consumption pattern are interdependent, and the power value harvested from certain environmental sources are spatially correlated, the new power dimension could provide additional information to enhance the spectrum sensing accuracy. In our work, the Markovian behavior of the primary users is considered, based on which we adopt a hidden input Markov model to specify the primary vs. secondary dynamics in the system. Accordingly, we propose a 2-D spectrum vs. power (harvested) sensing scheme to improve the primary user detection performance, which is also capable of estimating the primary transmit power level. Theoretical and simulated results demonstrate the effectiveness of the proposed scheme, in terms of the performance gain achieved by considering the new power dimension. To the best of our knowledge, this is the first work to jointly consider the spectrum and power dimensions for the cognitive primary user detection problem. The second work is about spatio-temporal base station traffic prediction with machine learning. Accurate prediction of user traffic in cellular networks is crucial to improve the system performance in terms of energy efficiency and resource utilization. However, existing work mainly considers the temporal traffic correlations within each cell while neglecting the spatial correlation across neighboring cells. In this work, machine learning models that jointly explore the spatio-temporal correlations are proposed, where a multitask learning approach is adopted to explore the commonalities and differences across cells in improving the prediction performance. Base on real data, we demonstrate the benefits of joint learning over spatial and temporal dimensions

Cross-layer design for multimedia applications in cognitive radio networks.

Ph. D. University of KwaZulu-Natal, Durban 2015.The exponential growth in wireless services and the current trend of development in wireless communication technologies have resulted into an overcrowded radio spectrum band in such a way that it can no longer meet the ever increasing requirements of wireless applications. In contrary however, literature surveys indicate that a large amount of the licensed radio spectrum bands are underutilized. This has necessitated the need for efficient ways to be implemented for spectrum sharing among different systems, applications and services in dynamic wireless environment. Cognitive radio (CR) technology emerges as a way to improve the overall efficiency of radio spectrum utilization by allowing unlicensed users (also known as secondary user) to utilize a licensed band when it is vacant. Multimedia applications are being targeted for CR networks. However, the performance and success of CR technology will be determined by the quality of service (QoS) perceived by secondary users. In order to transmit multimedia contents which have stringent QoS requirements over the CR networks, many technical challenges have to be addressed that are constrained by the layered protocol architecture. Cross-layer design has shown a promise as an approach to optimize network performance among different layers. This work is aimed at addressing the question on how to provide QoS guarantee for multimedia transmission over CR networks in terms of throughput maximization while ensuring that the interference to primary users is avoided or minimized. Spectrum sensing is a fundamental problem in cognitive radio networks for the protection of primary users and therefore the first part of this work provides a review of some low complex spectrum sensing schemes. A cooperative spectrum sensing scheme where multi-users are independently performing spectrum sensing is also developed. In order to address a hidden node problem, a cooperate relay based on amplify-and-forward technique (AF) is formulated. Usually the performance of a spectrum sensor is evaluated using receiver operating characteristic (ROC) curve which provides a trade-off between the probability of miss detection and the probability of false alarm. Due to hardware limitations, the spectrum sensor can not sense the whole range of radio spec- trum which results into partial information of the channel state. In order to model a media access control(MAC) protocol which is able to make channel access decision under partial information about the state of the system we apply a partially observable Markov decision process (POMDP) technique as a suitable tool in making decision under uncertainty. A throughput optimization MAC scheme in presence of spectrum sensing errors is then devel- oped using the concept of cross-layer design which integrates the design of spectrum sensing at physical layer (PHY) and sensing and access strategies at MAC layer in order to maximize the overall network throughput. A problem is formulated as a POMDP and the throughput performance of the scheme is evaluated using computer simulations under greedy sensing algorithm. Simulation results demonstrate an improved overall throughput performance. Further more, multiple channels with multiple secondary users having random message ar- rivals are considered during simulation and the throughput performance is evaluated under greedy sensing scheme which forms a benchmark for cross-layer MAC scheme in presence of spectrum sensing errors. By realizing that speech communication is still the most dom- inant and common service in wireless application, we develop a cross-layer MAC scheme for speech transmission in CR networks. The design is aimed at maximizing throughput of secondary users by integrating the design of spectrum sensing at PHY, quantization param- eter of speech traffic at application layer (APP), together with strategy for spectrum access at MAC layer with the main goal to improve the QoS perceived by secondary users in CR networks. Simulation results demonstrate throughput performance improvement and hence QoS is improved. One of the main features of the modern communication systems is the parameterized operation at different layers of the protocol stack. The feature aims at providing them with the capability of adapting to the rapidly changing traffic, channel and system conditions. Another interesting research problem in this thesis is the combination of individual adap- tation mechanisms into a cross-layer that can maximize their effectiveness. We propose a joint cross-layer design MAC scheme that integrates the design of spectrum sensing at PHY layer, access at MAC layer and APP information in order to improve the QoS for video transmission in CR networks. The end-to-end video distortion which is considered as an APP parameter resides in the video encoder. This is integrated in the state space and the problem is formulated as a constrained POMDP. H.264 coding algorithm which is one of the high efficient video coding standards is considered. The objective is to minimize this end-to- end video distortion while maximizes the overall network throughput for video transmission in CR networks. The end-to-end video distortion has signifficant effects to the QoS the per- ceived by the user and is viewed as the cost in the overall system design. Given the target system throughput, the packet loss ration when the system is in the state i and a composite action is taken in time slot t, the system immediate cost is evaluated. The expected total cost for overall end-to-end video distortion over the total time slots is then computed. A joint optimal policy which minimizes the expected total end-to-end distortion in total time slots is computed iteratively. The minimum expected cost (which also known as the value function) is also evaluated iteratively for the total time slots. The throughput performance of the proposed scheme is evaluated through computer simulation. In order to study the throughput performance of the proposed scheme, we considered four simulation scenarios namely simulation scenario A, simulation scenario B, simulation scenario C, and simulation scenario D. These simulation scenarios enabled us to study the throughput performance of the proposed scheme by by computer simulations. In the simulation scenario A, the av- erage throughput performance as a function of time horizon is studied. The throughput performance under channel access decision based on belief vector and that of channel access decision based on the end-to-end distortion are compared. Simulation results show that the channel access decision based on end-to-end distortion outperforms that of channel access decision based on a belief vector. In the simulation scenario B we aimed at studying the spectral efficiency as a function of prescribed collision probability. The simulation results show that, at large values of collision probability the overall spectral efficiency performs poorly. However, there is an optimal value of collision probability of which the spectral efficiency approaches that of the perfect channel access decision. In the simulation scenario C, we aimed at studying the average throughput performance and the spectral efficiency both as a function of prescribed collision probability. The simulation results show that both average throughput and the spectral efficiency are highly affected by the increase in collision probability. However, there is an optimal prescribed collision probability which achieves the maximum average throughput and maximum spectral efficiency

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future