This paper derives the distribution of the residual self-interference (SI) power in an analog post-mixer canceler adopted in a wireless in-band full-duplex communication system. We focus on the amount of uncanceled SI power due to SI channel estimation errors. Closed form expressions are provided for the distribution of the residual SI power when Rician and Rayleigh fading SI channels are considered. Moreover, the distribution of the residual SI power is derived for low and high channel gain dynamics, by considering the cases when the SI channel gain is time-invariant and time-variant. While for time-invariant channels the residual SI power is exponentially distributed, for time-variant channels the exponential distribution is not a valid assumption. Instead, the distribution of the residual SI power can be approximated by a product distribution. Several Monte Carlo simulation results show the influence of the channel dynamics on the distribution of the residual SI power. Finally, the accuracy of the theoretical approach is assessed through the comparison of numerical and simulated results, which confirm its effectiveness. INDEX TERMSIn-band full-duplex radio systems, residual self-interference power, stochastic modeling, performance analysis.

In this paper, we characterize the wireless interference of a mobile ad hoc network, where the nodes move according to the random waypoint model. The interferers are assumed to be located within an interference region that is defined as a circular region centered in a fixed node located at a given point of the mobility scenario. The main contribution of this paper is the characterization of the aggregate interference caused to the fixed node by mobile interferers located within the interference region. The distribution of the interference is analyzed taking into account the stochastic nature of the path loss due to the mobility of the nodes, as well as fast fading and shadowing effects. The derivation of the characteristic function of the aggregate interference is used in two different estimators, which successfully characterize the interference using only a small set of samples. The theoretical approach is validated through simulations, which confirm its effectiveness. Finally, we assess the accuracy of the proposed estimators, demonstrating the practical value of this paper.

Radio spectrum sensing (SS) has been an active topic of research over the past years due to its importance to cognitive radio (CR) systems. However, in CR networks (CRNs) with multiple primary users (PUs), the secondary users (SUs) can often detect PUs that are located outside the sensing range, due to the level of the aggregated interference caused by the PUs. This effect, known as spatial false alarm (SFA), degrades the performance of CRNs because it decreases the SUs' medium access probability. This paper characterizes the SFA effect in a CRN, identifying possible actions to attenuate it. Adopting energy-based sensing (EBS) in each SU, this paper starts to characterize the interference caused by multiple PUs located outside a desired sensing region. The interference formulation is then used to write the probabilities of detection and false alarm, and closed-form expressions are presented and validated through simulation. The first remark to be made is that the SFA can be neglected, depending on the path-loss factor and the number of samples collected by the energy detector to decide the spectrum's occupancy state. However, it is shown that by increasing the number of samples needed to increase the sensing accuracy, the SUs may degrade their throughput, namely, if SUs are equipped with a single radio that is sequentially used for sensing and transmission (split-phase operation). Assuming this scenario, this paper ends by providing a bound for the maximum throughput achieved in a CRN with multiple active PUs and for a given level of PUs' detection inside the SUs' sensing region. The results presented in this paper show the impact of path loss and EBS parameterization on SUs' throughput and are particularly useful to guide the design and parameterization of multihop CRNs, including future ad hoc CRNs considering multiple PUs.

This work presents an effective tool to predict the future trajectories of vehicles when its current and previous locations are known. We propose a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) prediction scheme due to its adequacy to learn from sequential data. To fully learn the vehicles' mobility patterns, during the training process we use a dataset that contains real traces of 442 taxis running in the city of Porto, Portugal, during a full year. From experimental results, we observe that the prediction process is improved when more information about prior vehicle mobility is available. Moreover, the computation time is evaluated for a distinct number of prior locations considered in the prediction process. The results exhibit a prediction performance higher than 89%, showing the effectiveness of the proposed LSTM network.

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