Abstract

This article proposes Convolutional Neural Network based Auto Encoder (CNN-AE) to predict location dependent rate and coverage probability of a network from its topology. We train the CNN utilising BS location data of India, Brazil, Germany and the USA and compare its performance with stochastic geometry (SG) based analytical models. In comparison to the best-fitted SGbased model, CNN-AE improves the coverage and rate prediction errors by a margin of as large as 40% and 25% respectively. As an application, we propose a low complexity, provably convergent algorithm that, using trained CNN-AE, can compute locations of new BSs that need to be deployed in a network in order to satisfy pre-defined spatially heterogeneous performance goals. Index Terms—Network Performance Prediction, Convolutional Neural Network, Stochastic Geometry, Network Design

Abstract

This paper considers beamforming for a reconfigurable intelligent surface (RIS)-aided multiple input single output (MISO) communication system in the presence of Rician multipath fading. Our aim is to jointly optimize the transmit beamformer and RIS phase shift matrix for maximizing the mean signal-to-noise (SNR) of the combined signal received over direct and indirect links. While numerical solutions are known for such optimization problems, this is the first paper to derive closedform expressions for the optimal beamformer and the phase shifter for a closely related problem. In particular, we maximize a carefully constructed lower bound of the mean SNR, which is more conducive to analytical treatment. Further, we show that effective channel gain under optimal beamforming follows Rice distribution. Next, we use these results to characterize a closedform expression for the outage probability under the proposed beamforming scheme, which is subsequently employed to derive an analytical expression for the ergodic capacity. Finally, we numerically demonstrate the efficacy of the proposed beamformer solution in comparison with the existing algorithmically obtained optimal solution for the exact mean SNR maximization. Index Terms—RIS, Optimal Beamforming, Rician Channel, Outage Analysis, Ergodic Capacity, and Maximum Mean SNR.

Abstract

This paper proposes a buffer-assisted amplify- and-forward (AF) unmanned aerial vehicle (UAV) relay for communication between two ground nodes without a direct link. These communications are essential in disaster rescue areas where fairness plays a significant role. To achieve fairness, we aim to maximize the minimum information rate. The traditional way of prefixed scheduling the time slots to transfer and receive at the UAV does not guarantee that the communication system uses high signal-to-noise ratio (SNR) communication links instead of low SNR links. Therefore, we choose to employ a buffer at the UAV to store the information and transfer it to the destination nodes in the high SNR links. The pairing of time slots is necessary here since we are using a buffer, and we do not know in which time slot data is transmitted to the destination node after it is received at the UAV. Consequently, we formulate a fairness maximization problem by jointly optimizing the trajectory and power control. Unfortunately, this formula- tion results in a non-convex problem. We propose a solution based on the principles of the minorize-maximize (MM) algorithm and linear programming relaxation techniques to solve the fairness problem and pairing of time slots. Numerical results demonstrate that the trajectory, power control, and paired slots favor the UAV and ground nodes to communicate in the high SNR channel links, thus maintaining fairness.

Abstract

State estimation in non-linear models is performed by tracking the posterior distribution recursively. A plethora of algorithms have been proposed for this task. Among them, the Gaussian particle filter uses a weighted set of particles to construct a Gaussian approximation to the posterior. In this paper, we propose to use invertible particle flow methods, derived under the Gaussian boundary conditions for a flow equation, to generate a proposal distribution close to the posterior. The resultant particle flow Gaussian particle filter (PFGPF) algorithm retains the asymptotic properties of Gaussian particle filters, with the potential for improved state estimation performance in highdimensional spaces. We compare the performance of PFGPF with the particle flow filters and particle flow particle filters in two challenging numerical simulation examples. Index Terms—Particle filters, particle flow filters, Gaussian particle filters, particle flow particle filters.

Abstract

Sparse Bayesian learning (SBL) has been successful in direction of arrival (DOA) estimation due to its robustness and high resolution using a few snapshots. Most wideband SBL algorithms make the simplifying assumption that distinct sources have the same power spectrum across frequency bands. However, this assumption may not be true in practice (for example speech signals). We analyze three wideband signal models and compare variants of wideband SBL (called SBL1, SBL2, and SBL3) with different assumptions on source signal power spectrum. The localization performance of SBL algorithms is compared with wideband processing of conventional beamforming (CBF) and multiple signal classification (MUSIC). The experimental validation is presented using simulated data and experimental LOCATA data. This comparative study shows that SBL3 which simultaneously enforces sparsity and models frequency-dependent signal spectrum shows superior performance in most scenarios. Index Terms—Compressive sensing, Sparse Bayesian learning, DOA estimation, MUSIC, CBF.

Abstract

Sound source localization is crucial in acoustic sensing and monitoring-related applications. In this paper, we do a comprehensive analysis of improvement in sound source localization by combining the direction of arrivals (DOAs) with their derivatives which quantify the changes in the positions of sources over time. This study uses the SALSA-Lite feature with a convolutional recurrent neural network (CRNN) model for predicting DOAs and their first-order derivatives. An update rule is introduced to combine the predicted DOAs with the estimated derivatives to obtain the final DOAs. The experimental validation is done using TAU-NIGENS Spatial Sound Events (TNSSE) 2021 dataset. We compare the performance of the networks predicting DOAs with derivative vs. the one predicting only the DOAs at low SNR levels. The results show that combining the derivatives with the DOAs improves the localization accuracy of moving sources. Index Terms— Deep learning, Microphone array, SALSALite, Sound event localization and detection (SELD).

Abstract

Several studies have reported that numerical magnitudes biases temporal judgments, i.e., large numerical magnitude, were perceived to last longer than small numerical magnitude. However, these predictions have been predominantly verified only when the large and small numerical magnitudes were presented in an intermixed fashion where numerical magnitudes varied randomly from trial to trial. We conducted two experiments (Blockedmagnitude and Mixed-Magnitude) using a temporal bisection paradigm to investigate whether numerical context affects temporal processing in a sub-second timescale. The numbers were presented with varying durations. Participants were asked to judge whether the presented durations were shorter or longer. The results suggest that the temporal judgments were affected when small and large numbers were randomly presented in an intermixed manner. However, such effects disappeared when the number magnitudes were presented separately. These results indicate the modulation of attention in number-time interaction, and such crosstalk may not require a generalized magnitude system.

Abstract

A theory of magnitude (ATOM) suggests that a generalized magnitude system in the brain processes magnitudes such as space, time, and numbers. Numerous behavioral and neurocognitive studies have provided support to ATOM theory. However, the evidence for common magnitude processing primarily comes from the studies in which numerical and temporal information are presented visually. Our current understanding of such cross-dimensional magnitude interactions is limited to visual modality only. However, it is still unclear whether the ATOM-framework accounts for the integration of cross-modal magnitude information. To examine the crossmodal influence of numerical magnitude on temporal processing of the tone, we conducted three experiments using a temporal bisection task. We presented the numerical magnitude information in the visual domain and the temporal information in the auditory either simultaneously with duration judgment task (Experiment-1), before duration judgment task (Experiment2), and before duration judgment task but with numerical magnitude also being task-relevant (Experiment-3). The results suggest that the numerical information presented in the visual domain affects temporal processing of the tone only when the numerical magnitudes were task-relevant and available while making a temporal judgment (Experiments-1 and 3). However, numerical information did not interfere with temporal information when presented temporally separated from the duration information (Experiments2). The findings indicate that the influence of visual numbers on temporal processing in cross-modal settings may not arise from the common magnitude system but instead from general cognitive mechanisms like attention and memory.

Abstract

n the rapidly growing computing world, the data is being generated every minute, and it has been a big challenge to manage and maintain privacy of the data when it is required to share among different locations. In this regard the privacy preserving data mining is playing a major role to design and develop the methods and models to preserve privacy of data in data owners perspectives, when ever the necessity of exchanging the information between multiple parties to be done. There has been lot of research going on in the field of computational intelligence, machine learning and soft computing domains and some good number of algorithms and secured models are being developed by the researchers. We have worked on the random forest classification as the best example model to preserve privacy of two parties when a combined classification to be done in order to build a privacy preserving global random …

Abstract

Preserving privacy of data in data mining domain has been a challenging objective to achieve. There has been lot of progress in applying secure algorithms in order to achieve privacy preserving of sensitive information. In distributed data mining perspective, there is necessity to share the information among the different parties aiming for combined outcomes. Privacy preserving methods and algorithms helps in preserving privacy of data when it is compulsory to share the information between the collaborated parties to perform various data mining operations like Classification and Clustering. It is always important to address the privacy issues of an individual when the data is shared among different parties to achieve a common goal. It is observed that there has been great development in addressing the privacy issues and designing privacy preserving algorithms applied to achieve a secured model. Adopting neural …