Simultaneous Information and Power Transfer with Transmitters with Hardware Impairments

We investigate the performance of a communication system with simultaneous wireless information and power transfer capabilities under non-ideal transmitter hardware. We adopt an experimentally validated additive noise model in which the level of the noise at an antenna is proportional to the signal power at that antenna. We consider the linear precoder design problem and focus on the problem of minimizing the mean-square error under energy harvesting constraints. This set-up, in general, constitutes a non-convex formulation. For the single antenna information user case, we provide a tight convex relaxation, i.e. a convex formulation from which an optimal solution for the original problem can be constructed. For the general case, we propose a block coordinate descent technique to solve the resulting non-convex problem. Our numerical results illustrate the effect of hardware impairments on the system

MIMO Linear Precoder Design with Non-Ideal Transmitters

We investigate the linear precoder design problem for multiple-input multiple-output (MIMO) channels under non-ideal transmitter hardware. We consider two different non-ideal hardware models: i) an additive noise model in which the level of the noise at an antenna is proportional to the signal power at that antenna, ii) an additive precoder error model. We focus on the problem of minimizing mean-square error at the receiver under transmit power constraints at the transmitter. For the first hardware impairment model, this scenario leads to a non-convex formulation for which we propose a block-coordinate descent technique. The proposed method has a convergence guarantee and provides rank-constrained solutions. For the second model, analytical expressions for the optimum designs are provided. We compare the performance of our hardware impairment aware designs with that of designs developed with ideal hardware assumptions. Our results suggest that significant gains can be obtained by the proposed designs for sufficiently high signal-to-noise ratio values

Linear Precoder Design for Simultaneous Information and Energy Transfer over Two-User MIMO Interference Channels

Communication strategies that utilize wireless media for simultaneous information and power transfer offer a promising perspective for efficient usage of energy resources. With this motivation, we focus on the design of optimal linear precoders for interference channels utilizing such strategies. We formulate the problem of minimizing the total minimum mean-square error while keeping the energy harvested at the energy receivers above given levels. Our framework leads to a non-convex problem formulation. For point-to-point multiple-input multiple-output channels, we provide a characterization of the optimal solutions under a constraint on the number of transmit antennas. For the general interference scenario, we propose two numerical approaches, one for the single antenna information receivers case, and the other for the general case. We also investigate a hybrid signalling scheme, where the transmitter sends a superposition of two signals: a deterministic signal optimized for energy transfer and an information carrying signal optimized for information and energy transfer. It is illustrated that if hybrid signalling is not incorporated into the transmission scheme, interference can be detrimental to the system performance when the number of antennas at the receivers is low.}

Feasibility of Ambient RF Energy Harvesting for Self-Sustainable M2M Communications Using Transparent and Flexible Graphene Antennas

Lifetime is a critical parameter in ubiquitous, battery-operated sensors for machine-to-machine (M2M) communication systems, an emerging part of the future Internet of Things. In this practical article, the performance of radio frequency (RF) to DC energy converters using transparent and flexible rectennas based on graphene in an ambient RF energyharvesting scenario is evaluated. Full-wave EM simulations of a dipole antenna assuming the reported state-of-the-art sheet resistance for few-layer, transparent graphene yields an estimated ohmic efficiency of 5 %. In the power budget calculation, the low efficiency of transparent graphene antennas is an issue because of the relatively low amount of available ambient RF energy in the frequency bands of interest, which together sets an upper limit on the harvested energy available for the RF-powered device. Using a commercial diode rectifier and an off-the-shelf wireless system for sensor communication, the graphene-based solution provides only a limited battery lifetime extension. However, for ultra-low-power technologies currently at the research stage, more advantageous ambient energy levels, or other use cases with infrequent data transmission, graphene-based solutions may be more feasible

Impact of Communication Frequency on Remote Control of Automated Vehicles

This paper investigates the impact of the communication frequency on the remote control of automated vehicles. In particular, we consider a remote controller, which receives vehicles\u27 state information and issues control commands based on a model predictive control (MPC) framework, to steer the vehicles to reach their respective target position intervals at given specific times. We present a framework where both state information (from the vehicles to the controller) and control actions (from the controller to the vehicles) are communicated through a wireless network. Due to limited communication resources and possible channel impairments, information is not necessarily always provided to the destination (either the controller or the vehicles). Herein, we particularly focus on the communications to the controller and investigate the effect of frequency and last instant of communication. Our results quantify the impact of these factors on the system performance, and subsequently, underline the need for an efficient resource allocation scheme

NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics

NeuroBench:Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics

Optimal representation of non-stationary random fields with finite numbers of samples: A linear MMSE framework

In this article we consider the representation of a finite-energy non-stationary random field with a finite number of samples. We pose the problem as an optimal sampling problem where we seek the optimal sampling interval under the mean-square error criterion, for a given number of samples. We investigate the optimum sampling rates and the resulting trade-offs between the number of samples and the representation error. In our numerical experiments, we consider a parametric non-stationary field model, the Gaussian-Schell model, and present sampling schemes for varying noise levels and for sources with varying numbers of degrees of freedom. We discuss the dependence of the optimum sampling interval on the problem parameters. We also study the sensitivity of the error to the chosen sampling interval. \ua9 2013 Elsevier Inc