Autonomous computing, communication, and collaboration among devices have their importance to transform the digital world. Emerging technology device-to-device (D2D) communication has a great impet...

Device-to-Device Communication Based IoT System: Benefits and Challenges

One of the fundamental research areas in wireless communications is the development of realistic models that can efficiently and accurately describe the wireless propagation channel. Most of the proposed models disregard the three dimensional character of the signal spread or use techniques with excessive computational complexity. In this paper, we develop a simple 3-D geometric scattering model for the uplink of a macrocell mobile environment that provides the statistics of Angle-of-Arrival (AoA) of the multipath components. The model extends the 2-D geometrical-based single bounce macrocell (GBSBM) model. Explicit closed-form expressions are derived for the statistics of the AoA of the multipaths in the azimuth and elevation planes. Analysis of the results exhibits the advantages of our proposal compared to 2-D and 3-D ones published in the literature. Comparisons with experimental data confirm its validity. Interesting conclusions for the effective evaluation of mobile communication systems have been derived. Moreover, an application of the model to mobile location estimation has been developed and evaluated.

A Simple 3-D Geometric Channel Model for Macrocell Mobile Communications

The application of cooperative communication (CC) in wireless networks makes it possible for the terminals to hear and aid the transfer of each other’s information to the required destinations. CC utilizes the advantage of the broadcast nature of wireless communications to achieve its objectives. The advantages enjoyed by networks that employ the principles of CC include: improvement of connectivity, improvement in energy utilization, spectrum efficiency, counteracting of the limited per device complexity, and a boost in communication reliability among devices in the network. When CC is compared with other techniques that perform functions similar to it like multiple input multiple output, it has an upper hand due to the fact that CC has a hardware that is feasible and its deployment is more flexible. The implementation of CC in machine to machine (M2M) in this paper is based on the use of relays and clustering protocols. Popular protocols of relays and clustering are presented as solutions, and detailed emerging research challenges that include but are not limited to: escalating complex scheduling design, increased message overheads, increased intra and inter network interferences, increased end-to-end latency, and redundancy are discussed. Future works that includes critical application of full duplex relays, utilization of non-regenerative mode of relay operation, cross layer optimization, application of clustering scheme with reconciling quality-of-service properties, and designing of hybrid clustering techniques are proposed. They are aimed at improving on the challenges and hence foster efficiency in M2M network resources.

Cooperative Communications in Machine to Machine (M2M): Solutions, Challenges and Future Work

The authors propose novel spectrum sensing and utilisation schemes for cognitive radio networks using the chirp-z transform. To improve the spectral efficiency, a dispersed chirp-z transform is introduced with the energy detection method. The dispersed chirp-z transform enables one to analyse the dispersed frequency spectrum of any frequency range of interest. The analysis is first focused on the sensing part, including the derivations of closed-form expressions for the optimal detection thresholds minimising the total error rate over additive white Gaussian noise (AWGN), Rayleigh, Rician and log-normally distributed fading channels. Then, the performance analysis of the proposed system is investigated for efficient spectrum utilisation over AWGN and fading channels by presenting the receiver operating characteristics. Conventional and segmented chirp-z transform-based spectrum utilisation techniques are also introduced for performance comparison purposes. Finally, the theoretical framework and the optimal threshold derivations through simulations are verified. The analyses reveal that the proposed schemes have a considerable potential to improve the non-cooperative spectrum sensing and utilisation performance.

Dispersed chirp-z transform-based spectrum sensing and utilisation in cognitive radio networks

In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this position paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS we propose to combine the learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared to past and current research efforts in this area, the technical approach depicted in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication networks.

COBANETS: A new paradigm for cognitive communications systems

Cognitive radio offers the promise of intelligent radios that can learn from and adapt to their environment. To date, most cognitive radio research has focused on policy-based radios that are hard-coded with a list of rules on how the radio should behave in certain scenarios. Some work has been done on radios with learning engines tailored for very specific applications. This article describes a concrete model for a generic cognitive radio to utilize a learning engine. The goal is to incorporate the results of the learning engine into a predicate calculusbased reasoning engine so that radios can remember lessons learned in the past and act quickly in the future. We also investigate the differences between reasoning and learning, and the fundamentals of when a particular application requires learning, and when simple reasoning is sufficient. The basic architecture is consistent with cognitive engines seen in AI research. The focus of this article is not to propose new machine learning algorithms, but rather to formalize their application to cognitive radio and develop a framework from within which they can be useful. We describe how our generic cognitive engine can tackle problems such as capacity maximization and dynamic spectrum access.

Cognitive wireless networks

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Cognitive wireless networks