Highly efficient impulse-radio ultra-wideband cavity-backed slot antenna in stacked air-filled substrate integrated waveguide technology

An impulse-radio ultra-wideband (IR-UWB) cavity-backed slot antenna covering the [5.9803; 6.9989] GHz frequency band of the IEEE 802.15.4a-2011 standard is designed and implemented in an air-filled substrate integrated waveguide (AFSIW) technology for localization applications with an accuracy of at least 3 cm. By relying on both frequency and time-domain optimization, the antenna achieves excellent IR-UWB characteristics. In free-space conditions, an impedance bandwidth of 1.92 GHz (or 29.4%), a total efficiency higher than 89%, a front-to-back ratio of at least 12.1 dB, and a gain higher than 6.3 dBi are measured in the frequency domain. Furthermore, a system fidelity factor larger than 98% and a relative group delay smaller than 100 ps are measured in the time domain within the 3 dB beamwidth of the antenna. As a result, the measured time-of-arrival of a transmitted Gaussian pulse, for different angles of arrival, exhibits variations smaller than 100 ps, corresponding to a maximum distance estimation error of 3 cm. Additionally, the antenna is validated in a real-life worst-case deployment scenario, showing that its characteristics remain stable in a large variety of deployment scenarios. Finally, the difference in frequency-and time-domain performance is studied between the antenna implemented in AFSIW and in dielectric filled substrate integrated waveguide (DFSIW) technology. We conclude that DFSIW technology is less suitable for the envisaged precision IR-UWB localization application

LQG Control and Sensing Co-Design

We investigate a Linear-Quadratic-Gaussian (LQG) control and sensing co-design problem, where one jointly designs sensing and control policies. We focus on the realistic case where the sensing design is selected among a finite set of available sensors, where each sensor is associated with a different cost (e.g., power consumption). We consider two dual problem instances: sensing-constrained LQG control, where one maximizes control performance subject to a sensor cost budget, and minimum-sensing LQG control, where one minimizes sensor cost subject to performance constraints. We prove no polynomial time algorithm guarantees across all problem instances a constant approximation factor from the optimal. Nonetheless, we present the first polynomial time algorithms with per-instance suboptimality guarantees. To this end, we leverage a separation principle, that partially decouples the design of sensing and control. Then, we frame LQG co-design as the optimization of approximately supermodular set functions; we develop novel algorithms to solve the problems; and we prove original results on the performance of the algorithms, and establish connections between their suboptimality and control-theoretic quantities. We conclude the paper by discussing two applications, namely, sensing-constrained formation control and resource-constrained robot navigation.Comment: Accepted to IEEE TAC. Includes contributions to submodular function optimization literature, and extends conference paper arXiv:1709.0882

On the Total Energy Efficiency of Cell-Free Massive MIMO

We consider the cell-free massive multiple-input multiple-output (MIMO) downlink, where a very large number of distributed multiple-antenna access points (APs) serve many single-antenna users in the same time-frequency resource. A simple (distributed) conjugate beamforming scheme is applied at each AP via the use of local channel state information (CSI). This CSI is acquired through time-division duplex operation and the reception of uplink training signals transmitted by the users. We derive a closed-form expression for the spectral efficiency taking into account the effects of channel estimation errors and power control. This closed-form result enables us to analyze the effects of backhaul power consumption, the number of APs, and the number of antennas per AP on the total energy efficiency, as well as, to design an optimal power allocation algorithm. The optimal power allocation algorithm aims at maximizing the total energy efficiency, subject to a per-user spectral efficiency constraint and a per-AP power constraint. Compared with the equal power control, our proposed power allocation scheme can double the total energy efficiency. Furthermore, we propose AP selections schemes, in which each user chooses a subset of APs, to reduce the power consumption caused by the backhaul links. With our proposed AP selection schemes, the total energy efficiency increases significantly, especially for large numbers of APs. Moreover, under a requirement of good quality-of-service for all users, cell-free massive MIMO outperforms the colocated counterpart in terms of energy efficiency