In the context of dynamic spectrum access (DSA), rendezvous refers to the ability of two or more radios to meet and establish a link on a common channel. In decentralized networks, this is often accomplished by each radio visiting potential channels in random fashion, in a process that we call blind random rendezvous. In this work, we propose the use of sequences that determine the order with which radios visit potentially available channels. Through sequence-based rendezvous, it is possible to: (i) establish an upper bound to the time to rendezvous (TTR); (ii) establish a priority order for channels in which rendezvous occurs; (iii) reduce the expected TTR as compared to random rendezvous. We provide an example of a family of sequences and derive the expected time-torendezvous using this method. We also describe how the method can be adopted when one or more primary users are detected in the channels of interest.

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This paper addresses distributed parameter coordination methods for wireless communication systems. This proposes a method based on a message-passing algorithm, namely min-sum algorithm, on factor graphs for the application of precoder selection. Two particular examples of precoder selection are considered: transmit antenna selection and beam selection. Evaluations on the potential of such an approach in a wireless communication network are provided, and its performance and convergence properties are compared with those of a baseline selfish/greedy approach. Simulation results for the precoder selection examples are presented and discussed, which show that the graph-based technique generally obtains gain in sum rate over the greedy approach at the cost of a larger message size. Besides, the proposed method usually reaches the global optima in an efficient manner. Methods of improving the rate of convergence of the graph-based distributed coordination technique and reducing its associated message size are therefore important topics for wireless communication networks.

In this paper, a new stochastic channel model (SCM) is proposed for fifth-generation (5G) systems. By means of the sum-of-sinusoids (SoS) method to generate spatially consistent random variables (SCRVs), the proposed model extends the 3rd Generation Partnership Project (3GPP)-SCM by considering three important features for accurate simulations in 5G, i.e., support for dual mobility, spatial correlation at both ends of the link and considerable reductions of the required memory consumption when compared with existing models. A typical problem presented in existing channel models, namely the generation of uncorrelated large scale parameters (LSPs) and small scale parameters (SSPs) for close base stations (BSs), is solved, then allowing for more realistic numerical evaluations in most of the 5G scenarios characterized by a large density of BSs and user equipments (UEs) per unit of area, such as ultra-dense networks (UDNs), indoor environments, device-to-device (D2D) and vehicular-to-vehicular (V2V). The proposed model emerges as the first SCM, and therein lower complexity when compared with ray-tracing (RT)based models, that comprises all the following features: support for single and dual mobility with spatial consistency, smooth time evolution, dynamic modeling, large antenna array, frequency range up 100 GHz and bandwidth up to 2 GHz. Some of the features are calibrated for single mobility in selected scenarios and have shown a good agreement with the calibration results found in the literature.

In this paper, we present a method for portfolio selection based on the consideration on deformed exponentials in order to generalize the methods based on the gaussianity of the returns in portfolio, such as the Markowitz model. The proposed method generalizes the idea of optimizing mean-variance and mean-divergence models and allows a more accurate behavior for situations where heavy-tails distributions are necessary to describe the returns in a given time instant, such as those observed in economic crises. Numerical results show the proposed method outperforms the Markowitz portfolio for the cumulated returns with a good convergence rate of the weights for the assets which are searched by means of a natural gradient algorithm.

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Over recent years, wireless indoor positioning systems (WIPS) have attracted considerable research interest. However, high-performance WIPS proposed in the literature requires that the building have at least three access points (APs). This paper proposes an WIPS using a single fifth-generation (5G) Wi-Fi access point. The proposed method uses beam fingerprints and classification models based on KNN (K-nearest neighbor) and Bayes rule. The beam fingerprint is composed of RSS (Received Signal Strength) samples, collected in some 2D locations of the indoor environment for each beam codebook in the off-line phase. In the online phase, RSS samples of the best beams are collected by user equipment (UE) during the beamtracking process, which are then classified based on beam fingerprints into predefined coordinates. Numerical simulations shown that using the best beam samples, it is possible to locate the stationary user's mobile device with average error less than 2.5 m.