Enhanced Multi-Parameter Cognitive Architecture for Future Wireless Communications

The very original concept of cognitive radio (CR) raised by Mitola targets at all the environment parameters, including those in physical layer, MAC layer, application layer as well as the information extracted from reasoning. Hence the first CR is also referred to as "full cognitive radio". However, due to its difficult implementation, FCC and Simon Haykin separately proposed a much more simplified definition, in which CR mainly detects one single parameter, i.e., spectrum occupancy, and is also called as "spectrum sensing cognitive radio". With the rapid development of wireless communication, the infrastructure of a wireless system becomes much more complicated while the functionality at every node is desired to be as intelligent as possible, say the self-organized capability in the approaching 5G cellular networks. It is then interesting to re-look into Mitola's definition and think whether one could, besides obtaining the "on/off" status of the licensed user only, achieve more parameters in a cognitive way. In this article, we propose a new cognitive architecture targeting at multiple parameters in future cellular networks, which is a one step further towards the "full cognition" compared to the most existing CR research. The new architecture is elaborated in detailed stages, and three representative examples are provided based on the recent research progress to illustrate the feasibility as well as the validity of the proposed architecture.Comment: 15 pages, 6 figures, IEEE Communications Magazin

Cognitive Radios: A Survey of Methods for Channel State Prediction

This paper discusses the need for Cognitive Radio ability in view of the physical scarcity of wireless spectrum for communication. A background of the Cognitive Radio technology is presented and the aspect of 'channel state prediction' is focused upon. Hidden Markov Models (HMM) have been traditionally used to model the wireless channel behavior but it suffers from certain limitations. We discuss few techniques of channel state prediction using machine-learning methods and will extend the Conditional Random Field (CRF) procedure to this field.Comment: 10 pages, 5 figure

Dynamic Profit Maximization of Cognitive Mobile Virtual Network Operator

We study the profit maximization problem of a cognitive virtual network operator in a dynamic network environment. We consider a downlink OFDM communication system with various network dynamics, including dynamic user demands, uncertain sensing spectrum resources, dynamic spectrum prices, and time-varying channel conditions. In addition, heterogenous users and imperfect sensing technology are incorporated to make the network model more realistic. By exploring the special structural of the problem, we develop a low-complexity on-line control policies that determine pricing and resource scheduling without knowing the statistics of dynamic network parameters. We show that the proposed algorithms can achieve arbitrarily close to the optimal profit with a proper trade-off with the queuing delay

Internet of Things for Residential Areas: Toward Personalized Energy Management Using Big Data

Intelligent management of machines, particularly in a residence area, has been of interest for many years. However, such system design has always been limited to simple control of machines from a local area or remotely from the Internet. In this report, for the first time, an intelligent system is proposed, where not only provides intelligent control ability of machines to user, but also utilizes big data and optimization techniques to provide promotional offers to the user to optimize energy consumption of machines. Since a high traffic communication is involved among the machines and the optimization-big data core of system, the communication core of the proposed system is designed based on cloud, where many challenging issues such as spectrum assignment and resource management are involved. To deal with that, the communication network in the home area network (HAN) is designed based on the cognitive radio system, where a new spectrum assignment method based on the ant colony optimization (ACO) algorithm is proposed to perform spectrum assignment to the machines in the HAN. Performance evaluation of the proposed spectrum assignment method shows its performance in fair spectrum assignment among machines.Comment: Draft of technical report. Limited version under preparation for submissio

Smart Radio Spectrum Management for Cognitive Radio

Today's wireless networks are characterized by fixed spectrum assignment policy. The limited available spectrum and the inefficiency in the spectrum usage necessitate a new communication paradigm to exploit the existing wireless spectrum opportunistically. Cognitive radio is a paradigm for wireless communication in which either a network or a wireless node changes its transmission or reception parameters to communicate efficiently avoiding interference with licensed or unlicensed users. In this work, a fuzzy logic based system for spectrum management is proposed where the radio can share unused spectrum depending on some parameters like distance, signal strength, node velocity and availability of unused spectrum. The system is simulated and is found to give satisfactory results.Comment: 13 pages, 11 figure

Optimal Channel Sensing Strategy for Cognitive Radio Networks with Heavy-Tailed Idle Times

In Cognitive Radio Network (CRN), the secondary user (SU) opportunistically access the wireless channels whenever they are free from the licensed / Primary User (PU). Even after occupying the channel, the SU has to sense the channel intermittently to detect reappearance of PU, so that it can stop its transmission and avoid interference to PU. Frequent channel sensing results in the degradation of SU's throughput whereas sparse sensing increases the interference experienced by the PU. Thus, optimal sensing interval policy plays a vital role in CRN. In the literature, optimal channel sensing strategy has been analysed for the case when the ON-OFF time distributions of PU are exponential. However, the analysis of recent spectrum measurement traces reveals that PU exhibits heavy-tailed idle times which can be approximated well with Hyper-exponential distribution (HED). In our work, we deduce the structure of optimal sensing interval policy for channels with HED OFF times through Markov Decision Process (MDP). We then use dynamic programming framework to derive sub-optimal sensing interval policies. A new Multishot sensing interval policy is proposed and it is compared with existing policies for its performance in terms of number of channel sensing and interference to PU.Comment: 20 pages (single column), 7 figures, Submitted to IEEE Transactions on Cognitive Communications and Networking for possible publicatio

Algorithms for Dynamic Spectrum Access with Learning for Cognitive Radio

We study the problem of dynamic spectrum sensing and access in cognitive radio systems as a partially observed Markov decision process (POMDP). A group of cognitive users cooperatively tries to exploit vacancies in primary (licensed) channels whose occupancies follow a Markovian evolution. We first consider the scenario where the cognitive users have perfect knowledge of the distribution of the signals they receive from the primary users. For this problem, we obtain a greedy channel selection and access policy that maximizes the instantaneous reward, while satisfying a constraint on the probability of interfering with licensed transmissions. We also derive an analytical universal upper bound on the performance of the optimal policy. Through simulation, we show that our scheme achieves good performance relative to the upper bound and improved performance relative to an existing scheme. We then consider the more practical scenario where the exact distribution of the signal from the primary is unknown. We assume a parametric model for the distribution and develop an algorithm that can learn the true distribution, still guaranteeing the constraint on the interference probability. We show that this algorithm outperforms the naive design that assumes a worst case value for the parameter. We also provide a proof for the convergence of the learning algorithm.Comment: Published in IEEE Transactions on Signal Processing, February 201

Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning

The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum- and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems

Spectrum Sensing Framework based on Blind Source Separation for Cognitive Radio Environments

El uso eficiente del espectro se ha convertido en un área de investigación activa, debido a la escasez de este recurso y a su subutilización. En un escenario en el que el espectro es un recurso compartido como en la radio cognitiva (CR), los espacios sin uso dentro de las bandas de frecuencias con licencia podrían ser detectados y posteriormente utilizados por un usuario secundario a través de técnicas de detección y sensado del espectro. Generalmente, estas técnicas de detección se utilizan a partir de un conocimiento previo de las características de canal. En el presente trabajo se propone un enfoque de detección ciega del espectro basado en análisis de componentes independientes (ICA) y análisis de espectro singular (SSA). La técnica de detección se valida a través de simulación, y su desempeño se compara con metodologías propuestas por otros autores en la literatura. Los resultados muestran que el sistema propuesto es capaz de detectar la mayoría de las fuentes con bajo consumo de tiempo, un aspecto que cabe resaltar para aplicaciones en línea con exigencias de tiempo.The efficient use of spectrum has become an active research area, due to its scarcity and underutilization. In a spectrum sharing scenario as Cognitive Radio (CR), the vacancy of licensed frequency bands could be detected by a secondary user through spectrum sensing techniques. Usually, this sensing approaches are performed with a priori knowledge of the channel features. In the present work, a blind spectrum sensing approach based on Independent Component Analysis and Singular Spectrum Analysis is proposed. The approach is tested and compared with other outcomes. Results show that the proposed scheme is capable of detect most of the sources with low time consumption, which is a remarkable aspect for online applications with demanding time issues

Power Control and Multiuser Diversity for the Distributed Cognitive Uplink

This paper studies optimum power control and sum-rate scaling laws for the distributed cognitive uplink. It is first shown that the optimum distributed power control policy is in the form of a threshold based water-filling power control. Each secondary user executes the derived power control policy in a distributed fashion by using local knowledge of its direct and interference channel gains such that the resulting aggregate (average) interference does not disrupt primary's communication. Then, the tight sum-rate scaling laws are derived as a function of the number of secondary users $N$ under the optimum distributed power control policy. The fading models considered to derive sum-rate scaling laws are general enough to include Rayleigh, Rician and Nakagami fading models as special cases. When transmissions of secondary users are limited by both transmission and interference power constraints, it is shown that the secondary network sum-rate scales according to \frac{1}{\e{}n_h}\log\logp{N}, where $n_h$ is a parameter obtained from the distribution of direct channel power gains. For the case of transmissions limited only by interference constraints, on the other hand, the secondary network sum-rate scales according to \frac{1}{\e{}\gamma_g}\logp{N}, where $\gamma_g$ is a parameter obtained from the distribution of interference channel power gains. These results indicate that the distributed cognitive uplink is able to achieve throughput scaling behavior similar to that of the centralized cognitive uplink up to a pre-log multiplier \frac{1}{\e{}}, whilst primary's quality-of-service requirements are met. The factor \frac{1}{\e{}} can be interpreted as the cost of distributed implementation of the cognitive uplink