Line-of-sight (LoS) millimeter wave (mmWave) multiple-input multiple-output (MIMO) is a promising approach for providing the ultra-high speed point to point links required for wireless backhaul in picocellular networks. The combination of large bandwidth and spatial multiplexing can sustain 100+ Gbps links over 10s to 100s of meters, while the antenna form factors required for providing the necessary spatial degrees of freedom remain small due to the small carrier wavelength. However, the large bandwidth makes the system susceptible to geometric misalignments: relatively small misalignments can cause multi-symbol delay spread across the receiver aperture. Furthermore, as the signaling bandwidth approaches the limits of hardware (i.e., analog-to-digital converter (ADC)) capabilities, the temporal oversampling typically used to overcome intersymbol interference becomes infeasible. In this paper, we investigate an architecture for joint space-time equalization with spatial oversampling by introducing additional receive antennas while maintaining symbol-rate sampling. We consider linear space-time equalization, controlling complexity by employing an adaptive time window at each receiver. We illustrate tradeoffs between the size of the adaptive window and the spatial oversampling factor via analysis and simulation for a 4-stream, 128 Gbps, 100 m LoS link at 130 GHz with both horizontal and vertical misalignment of the 2D planar arrays. For example, we show, via a signal space analysis accompanied by simulation results, that error floors can be avoided via 2X spatial oversampling and a temporal window of 5.

Deep Neural Networks (DNNs) are vulnerable to adversarial attacks: carefully constructed perturbations to an image can seriously impair classification accuracy, while being imperceptible to humans. The most effective current defense is to train the network using adversarially perturbed examples. In this paper, we investigate a radically different, neuro-inspired defense mechanism, aiming to reject adversarial perturbations before they reach a classifier DNN, using an encoder with characteristics commonly observed in biological vision, followed by a decoder restoring image dimensions that can be cascaded with standard CNN architectures. Unlike adversarial training, all training is based on clean images. Our experiments on the CFAR-10 and a subset of Imagenet datasets show performance competitive with state-of-the-art adversarial training, and point to the promise of bottom-up neuro-inspired techniques for the design of robust neural networks.

The problem of optimal power allocation among light emitting diode (LED) transmitters in a visible light positioning system is considered for the purpose of improving localization performance of visible light communication (VLC) receivers. Specifically, the aim is to minimize the Cramér-Rao lower bound (CRLB) on the localization error of a VLC receiver by optimizing LED transmission powers in the presence of practical constraints, such as individual and total power limitations and illuminance constraints. The formulated optimization problem is shown to be convex and thus can efficiently be solved via standard tools. We also investigate the case of imperfect knowledge of localization parameters and develop robust power allocation algorithms by taking into account both overall system uncertainty and individual parameter uncertainties related to the location and orientation of the VLC receiver. In addition, we address the total power minimization problem under predefined accuracy requirements to obtain the most energy-efficient power allocation vector for a given CRLB level. Numerical results illustrate the improvements in localization performance achieved by employing the proposed optimal and robust power allocation strategies over the conventional uniform and non-robust approaches.

Visible light communication (VLC) is an emerging paradigm that enables multiple functionalities to be accomplished concurrently, including illumination, high-speed data communications, and localization. Based on the VLC technology, visible light positioning (VLP) systems aim to estimate locations of VLC receivers by utilizing light-emitting diode (LED) transmitters at known locations. VLP presents a viable alternative to radio frequency (RF)-based positioning systems by providing inexpensive and accurate localization services. In this paper, we consider the problem of localization in visible light systems and provide an extensive survey of various location estimation techniques, accompanied by discussions of their relative merits and demerits within the context of accuracy and computational complexity. In addition, we investigate a cooperative VLP system architecture in which VLC receiver units are able to communicate with each other for the purpose of cooperation, and present a low-complexity, iterative localization algorithm to demonstrate the benefits of cooperation in VLP systems. Finally, we investigate optimal strategies for power allocation among LED transmitters to maximize the localization accuracy subject to power and illumination constraints.

In this study, the optimal channel switching problem is investigated for average capacity maximization in the presence of channel switching delays. First, the optimal strategy is obtained and the corresponding average capacity is derived when channel switching is performed among a given number of channels. Then, it is proved that channel switching among more than two different channels is not optimal. Also, the maximum average capacity achieved by the optimal channel switching strategy is expressed as a function of the channel switching delay parameter and the average and peak power limits. Then, scenarios in which the optimal strategy corresponds to the use of a single channel or to channel switching between two channels are described. Numerical examples are presented for showing the effects of channel switching delays.

The optimal channel switching problem is studied for average capacity maximization in the presence of additive white Gaussian noise channels and channel switching delays. First, an optimization problem is formulated for the maximization of the average channel capacity, considering channel switching delays and constraints on average and peak powers. Then, an equivalent optimization problem is obtained to facilitate theoretical investigations. The optimal strategy is derived and the corresponding average capacity is specified when channel switching is performed among a given number of channels. Based on this result, it is shown that channel switching among more than two different channels is not optimal. In addition, the maximum average capacity achieved by the optimal channel switching strategy is formulated as a function of the channel switching delay parameter and the average and peak power limits. Then, scenarios under which the optimal strategy corresponds to the exclusive use of a single channel or to channel switching between two channels are described. Furthermore, sufficient conditions are obtained to determine when the optimal single channel strategy outperforms the optimal channel switching strategy. The numerical results are presented to provide the examples of the theoretical results and to illustrate the effects of channel switching delays.

In this study, an optimal channel switching strategy is proposed for average capacity maximization in the presence of average and peak power constraints. Necessary and sufficient conditions are derived to determine when the proposed optimal channel switching strategy can or cannot outperform the optimal single channel strategy, which performs no channel switching. Also, it is obtained that the optimal channel switching strategy can be realized by channel switching between, at most, two different channels. In addition, a low-complexity optimization problem is derived to obtain the optimal channel switching strategy. Furthermore, based on some necessary conditions that need to be satisfied by the optimal channel switching solution, an alternative approach is proposed for calculating the optimal channel switching strategy. Numerical examples are provided to exemplify the derived theoretical results and to provide intuitive explanations.

Optimal channel switching is proposed for average capacity maximization in the presence of average and peak power constraints. A necessary and sufficient condition is derived in order to determine when the proposed optimal channel switching approach can or cannot outperform the optimal single channel approach, which performs no channel switching. Also, it is stated that the optimal channel switching solution can be realized by channel switching between at most two different channels. In addition, a low-complexity optimization problem is derived in order to obtain the optimal channel switching solution. Numerical examples are provided to exemplify the derived theoretical results.