Media-Based MIMO: A New Frontier in Wireless Communications

The idea of Media-based Modulation (MBM), is based on embedding information in the variations of the transmission media (channel state). This is in contrast to legacy wireless systems where data is embedded in a Radio Frequency (RF) source prior to the transmit antenna. MBM offers several advantages vs. legacy systems, including "additivity of information over multiple receive antennas", and "inherent diversity over a static fading channel". MBM is particularly suitable for transmitting high data rates using a single transmit and multiple receive antennas (Single Input-Multiple Output Media-Based Modulation, or SIMO-MBM). However, complexity issues limit the amount of data that can be embedded in the channel state using a single transmit unit. To address this shortcoming, the current article introduces the idea of Layered Multiple Input-Multiple Output Media-Based Modulation (LMIMO-MBM). Relying on a layered structure, LMIMO-MBM can significantly reduce both hardware and algorithmic complexities, as well as the training overhead, vs. SIMO-MBM. Simulation results show excellent performance in terms of Symbol Error Rate (SER) vs. Signal-to-Noise Ratio (SNR). For example, a $4\times 16$ LMIMO-MBM is capable of transmitting $32$ bits of information per (complex) channel-use, with SER $\simeq 10^{-5}$ at $E_b/N_0\simeq -3.5$dB (or SER $\simeq 10^{-4}$ at $E_b/N_0=-4.5$dB). This performance is achieved using a single transmission and without adding any redundancy for Forward-Error-Correction (FEC). This means, in addition to its excellent SER vs. energy/rate performance, MBM relaxes the need for complex FEC structures, and thereby minimizes the transmission delay. Overall, LMIMO-MBM provides a promising alternative to MIMO and Massive MIMO for the realization of 5G wireless networks.Comment: 26 pages, 11 figures, additional examples are given to further explain the idea of Media-Based Modulation. Capacity figure adde

Online reduced complexity parameter estimation technique for equivalent circuit model of lithium-ion battery

For control-oriented battery management applications in electric vehicles, Equivalent Circuit Model (ECM) of battery packs offer acceptable modelling accuracy and simple mathematical equations for including the cell parameters. However, in real-time applications, circuit parameters continuously changes by varying operating conditions and state of the battery and thus, require an online parameter estimator. The estimator must update the battery parameters with less computational complexity suitable for real-time processing. This paper presents a novel Online Reduced Complexity (ORC) technique for the online parameter estimation of the ECM. The proposed technique provides significantly less complexity (hence estimation time) compared to the existing technique, but without compromising the accuracy. We use Trust Region Optimization (TRO) based Least Square (LS) method as an updating algorithm in the proposed technique and validate our results experimentally using Nissan Leaf (pouch) cells and with the help of standard vehicular testing cycles, i.e. the Dynamic Driving Cycle (DDC), and the New European Driving Cycle (NEDC)

A single-phase synchronization technique for grid-connected energy storage system under faulty grid conditions

The control of a single-phase grid-connected energy storage system (ESS) requires a very fast and accurate estimation of grid voltage frequency and phase angle. A phase-locked loop (PLL) based synchronization algorithm usually extracts this information. The operation and control of the entire system are directly affected by the performance of PLL. In this article, a novel advanced single-phase PLL (ASϕPLL) technique with reduced complexity is proposed for the fast and accurate extraction of grid information in an ESS under distorted and abnormal grid conditions, including harmonics, interharmonics, dc offset, and grid faults. The proposed method provides a faster dynamic response, lower frequency overshoot, and accurate estimation under off-nominal grid frequencies with reduced computational complexity in comparison with the existing method. The advanced performance of the proposed ASϕPLL is verified through the simulation and experimental results

An improved pre-filtering moving average filter based synchronization algorithm for single-phase V2G application

The performance and overall operation of grid- connected electric vehicle is directly affected by abnormal grid conditions. In this regard, Moving Average Filter (MAF) provide high noise cancellation capability and require less computational resources. However, the conventional in-loop MAF based synchronization suffers from slower dynamic response. In this paper, an improved pre-filtering MAF based PLL (IPMAFPLL) is proposed where MAF is removed from the control-loop and placed in the pre-filtering stage to improve the dynamic response of system. The phase drift provided by MAF under off-nominal frequency is further mitigated by introducing a compensation factor in the pre-filtering stage. The proposed technique is compared with conventional MAF-PLL and non-adaptive MAF- PLL. The simulation and experimental results show that our proposed approach have lower frequency overshoot and improved performance towards compensating grid harmonics under nominal and off-nominal grid frequencies

Identification of drug resistance mutations in HIV from constraints on natural evolution

Human immunodeficiency virus (HIV) evolves with extraordinary rapidity. However, its evolution is constrained by interactions between mutations in its fitness landscape. Here we show that an Ising model describing these interactions, inferred from sequence data obtained prior to the use of antiretroviral drugs, can be used to identify clinically significant sites of resistance mutations. Successful predictions of the resistance sites indicate progress in the development of successful models of real viral evolution at the single residue level, and suggest that our approach may be applied to help design new therapies that are less prone to failure even where resistance data is not yet available.Comment: 5 pages, 3 figure

Melting of persistent double-stranded polymers

Motivated by recent DNA-pulling experiments, we revisit the Poland-Scheraga model of melting a double-stranded polymer. We include distinct bending rigidities for both the double-stranded segments, and the single-stranded segments forming a bubble. There is also bending stiffness at the branch points between the two segment types. The transfer matrix technique for single persistent chains is generalized to describe the branching bubbles. Properties of spherical harmonics are then exploited in truncating and numerically solving the resulting transfer matrix. This allows efficient computation of phase diagrams and force-extension curves (isotherms). While the main focus is on exposition of the transfer matrix technique, we provide general arguments for a reentrant melting transition in stiff double strands. Our theoretical approach can also be extended to study polymers with bubbles of any number of strands, with potential applications to molecules such as collagen.Comment: 9 pages, 7 figure

Positive Feedback Regulation Results in Spatial Clustering and Fast Spreading of Active Signaling Molecules on a Cell Membrane

Positive feedback regulation is ubiquitous in cell signaling networks, often leading to binary outcomes in response to graded stimuli. However, the role of such feedbacks in clustering, and in spatial spreading of activated molecules, has come to be appreciated only recently. We focus on the latter, using a simple model developed in the context of Ras activation with competing negative and positive feedback mechanisms. We find that positive feedback, in the presence of slow diffusion, results in clustering of activated molecules on the plasma membrane, and rapid spatial spreading as the front of the cluster propagates with a constant velocity (dependent on the feedback strength). The advancing fronts of the clusters of the activated species are rough, with scaling consistent with the Kardar-Parisi-Zhang (KPZ) equation in one dimension. Our minimal model is general enough to describe signal transduction in a wide variety of biological networks where activity in the membrane-proximal region is subject to feedback regulation.Comment: 37 pages, 8 figures. Journal of Chemical Physics (in press

A Novel Solver for an Electrochemical–Thermal Ageing Model of a Lithium-Ion Battery

To estimate the state of health, charge, power, and safety (SoX) of lithium-ion batteries (LiBs) in real time, battery management systems (BMSs) need accurate and efficient battery models. The full-order partial two-dimensional (P2D) model is a common physics-based cell-level LiB model that faces challenges for real-time BMS implementation due to the complexity of its numerical solver. In this paper, we propose a method to discretise the P2D model equations using the Finite Volume and Verlet Integration Methods to significantly reduce the computational complexity of the solver. Our proposed iterative solver uses novel convergence criteria and physics-based initial guesses to provide high fidelity for discretised P2D equations. We also include both the kinetic-limited and diffusion-limited models for Solid Electrolyte Interface (SEI) growth into an iterative P2D solver. With these SEI models, we can estimate the capacity fade in real time once the model is tuned to the cell–voltage curve. The results are validated using three different operation scenarios, including the 1C discharge/charge cycle, multiple-C-rate discharges, and the Lawrence Livermore National Laboratory dynamic stress test. The proposed solver shows at least a 4.5 times improvement in performance with less than 1% error when compared to commercial solvers

Optimal Observer Synthesis for Microgrids With Adaptive Send-on-Delta Sampling Over IoT Communication Networks

State estimation is one of the main challenges in the microgrids, due to the complexity of the system dynamics and the limitations of the communication network. In this regard, a novel real-time event-based optimal state estimator is introduced in this paper, which uses the proposed adaptive send-on-delta (SoD) non-uniform sampling method over wireless sensors networks. The proposed estimator requires low communication bandwidth and incurs lower computational resource cost. The threshold for the SoD sampler is made adaptive based on the average communication link delay, which is computed in a distributed form using the event-based average consensus protocol. The SoD non-uniform signal sampling approach reduces the traffic over the wireless communication network due to the events transmitted only when there is a level crossing in the measurements. The state estimator structure is extended on top of the traditional Kalman filter with the additional stages for the fusion of the received events. The error correction stage is further improved by optimal reconstruction of the signals using projection onto convex sets (POCS) algorithm. Finally, an Internet of things (IoT) experimental platform based on LoRaWAN and IEEE 802.11 (WiFi) protocols is developed to analyse the performance of the state estimator for the IEEE 5 Bus case study microgrid