Nonparametric regression has proven to be successful in extracting features from limited data in neurological applications. However, due to data scarcity, most brain-computer interfaces still rely on linear classifiers. This work leverages the robustness of the James-Stein theorem in nonparametric regression to harness the potentials of deep learning and foster its successful application in neural engineering with small data sets. We propose a novel method that combines James-Stein regression for feature extraction, and deep neural network for decoding; we refer to the architecture as deep James-Stein neural network (DJSNN). We apply the DJSNN to decode eye movement goals in a memory-guided visual saccades to one of eight target locations. The results demonstrate that the DJSNN outperforms existing methods by a substantial margin, especially at deep cortical sites.

Mission-critical applications require Ultra-Reliable Low Latency (URLLC) wireless connections, where the packet error rate (PER) goes down to 10-9. Fulfillment of the bold reliability figures becomes meaningful only if it can be related to a statistical model in which the URLLC system operates. However, this model is generally not known and needs to be learned by sampling the wireless environment. In this paper, we treat this fundamental problem in the simplest possible communication-theoretic setting: selecting a transmission rate over a dynamic wireless channel in order to guarantee high transmission reliability. We introduce a novel statistical framework for design and assessment of URLLC systems, consisting of three key components: (i) channel model selection; (ii) learning the model using training; and (iii) selecting the transmission rate to satisfy the required reliability. As it is insufficient to specify the URLLC requirements only through PER, two types of statistical constraints are introduced, Averaged Reliability (AR) and Probably Correct Reliability (PCR). The analysis and the evaluations show that adequate model selection and learning are indispensable for designing consistent physical layer that asymptotically behaves as if the channel was known perfectly, while maintaining the reliability requirements in URLLC systems.

The future connectivity landscape, and notably, the 5G wireless systems will feature Ultra-Reliable Low Latency Communication (URLLC). The coupling of high reliability and low latency requirements in URLLC use cases makes the wireless access design very challenging, in terms of both the protocol design and of the associated transmission techniques. This paper aims to provide a broad perspective on the fundamental tradeoffs in URLLC, as well as the principles used in building access protocols. Two specific technologies are considered in the context of URLLC: massive MIMO and multi-connectivity, also termed interface diversity. This paper also touches on the importance of the proper statistical methodology for designing and assessing extremely high-reliability levels.

Ultra-reliable communication (URC) is one of the distinctive features of the upcoming 5G wireless communication, characterized by packet error rates going down to 10-9. In this paper, we analyze the tail of the cumulative distribution function of block fading channels in the regime of extremely rare events, i.e., the ultra-reliable (UR) regime of operation. Our main contribution consists of providing a unified framework for statistical description of wide range of practically important wireless channel models in the UR regime of operation. Specifically, we show that the wireless channel behavior in this regime can be approximated by a simple power law expression, whose exponent and offset depend on the actual channel model. The unification provides a channel-agnostic tool for analyzing and performance optimization of radio systems that operate in the UR regime. Furthermore, the unified model is particularly useful in the emerging measurement campaigns for empirical characterization of wireless channels in the regime of low outages. Finally, the asymptotic analysis can serve as an underlying building block for designing more elaborate, higher-layer technologies for URC. We showcase this by applying the power law results to analyze the performance of receiver diversity schemes and obtain a new simplified expression for maximum ratio combining.

We propose a novel framework for secure and reliable authentication of Distributed Energy Resources to the centralized secondary/tertiary control system of a DC MicroGrid (MG), networked using the IEEE 802.11 wireless interface. The key idea is to perform the authentication using power talk - a powerline communication technique executed by the primary control loops of the power electronic converters. In addition, the scheme also promotes direct and active participation of the control system in the authentication process, a feature not commonly encountered in current networked control systems for MicroGrids. The FLECS®-based simulations verifies the viability of the proposed solution.

We study a communication framework for nonlinear multibus DC MicroGrids based on a deliberate modification of the parameters of the primary control and termed power talk. We assess the case in which the information is modulated in the deviations of reference voltages of the primary control loops and show that the outputs of the power talk communication channels can be approximated through linear combinations of the respective inputs. We show that the coefficients of the linear combinations, representing equivalent channel gains, depend on the virtual resistances of the primary control loops, implying that they can be modified such that effective received signal-to-noise ratio (SNR) is increased. On the other hand, we investigate the constraints that power talk incurs on the supplied power deviations. We show that these constraints translate into constraints on the reference voltages and virtual resistances that are imposed on all units in the system. In this regard, we develop an optimization approach to find the set of controllable virtual resistances that maximize SNR under the constraints on the supplied power deviations.

Power talk is a novel ultra narrow-band powerline communication (UNB-PLC) technique for communication among control units in MicroGrids (MGs). Unlike the existing UNB-PLC solutions, power talk does not require installation of additional dedicated communication hardware and, instead, uses only the power electronic converters through which the control units interface the common bus. This way the communication system has practically the same reliability as the power system. The information is transmitted by modulating the parameters of the primary control, incurring subtle power deviations that can be detected by other units. In this paper, we develop power talk communication strategies for direct-current (DC) MG systems with arbitrary number of control units that carry out all-to-all communication. We investigate two multiple access strategies: time-division multiple access, where only one unit transmits at a time, and full duplex, where all units transmit and receive simultaneously. We apply the concepts of signaling space, where the power talk symbol constellations are constructed, and detection space, where the demodulation of the symbols is performed. The proposed communication technique is challenged by the random changes of the bus parameters due to load variations. To this end, we investigate the performance of power talk when a solution based on training sequences that re-establishes detection spaces is employed. The presented evaluation shows that power talk has a potential to offer an effective and inexpensive solution for reliable communication among units in DC MGs.

We introduce a novel communication strategy for DC Micro Grids (MGs), termed power talk, in which the devices communicate by modulating the power levels in the DC bus. The information is transmitted by varying the parameters that the MG units use to control the level of the common bus voltage, while it is received by processing the bus measurements that units perform. This implies that the communication does not require a dedicated modem, but instead it is piggybacked on top of the power electronics. The communication is challenged by the random fluctuations of the voltage level due to the random load variations in the MG. We develop the corresponding communication model and address the random voltage fluctuations by using coding strategies that transform the MG into some well- known communication channels. The performance analysis shows that it is possible to mitigate the random voltage level variations and communicate reliably over the MG bus.

The location awareness is useful in many wireless networking solutions, such as cellular, ad hoc, self-organizing, cognitive radio networks, and so on. The RSS-based, non-Bayesian source localization is of particular interest, due to the presence of the RSS measurements in all radio devices, as well as the instantaneous estimations without extensive training and learning phases. However, most RSS-based localization algorithms usually neglect problems arising from inherent network topology uncertainty. The positions of some measuring anchors, usually assumed to be precisely known in advance, are often a subject of previous estimation, which propagates errors in the source localization procedure and results in performance degradation. A joint localization framework, termed source position estimation for anchor position uncertainty reduction (SPEAR), has been recently proposed as a possible solution to the problem of RSS-based localization in the presence of network topology uncertainty. The framework simultaneously estimates the positions of the sources and the uncertain anchors and serves as robust tool that provides improved source localization performance and more reliable anchor position estimates. This paper focuses on the theoretical assessment of SPEAR's performance by deriving its fundamental lower bounds using the Cramér-Rao information inequality. The main contribution of this paper is the detailed information-theoretic interpretation of the obtained results. The interpretation provides valuable insight into the essence of the SPEAR localization problem because it identifies different position information components in the observed data, discovers how they are mutually related, and determines how they contribute toward the resulting position information. This paper also provides geometric interpretation of the obtained results and illustrates the principles of distribution of position information in space. The theoretical results as well as their information-theoretic and geometric interpretations can be used as a benchmark for RSS-based location-aware systems that operate in the presence of network topology uncertainty.