A Compact RF/Photonic Antenna using a Quantum Dot Mode Locked Laser as a Source

The research presented here is focused on achieving an active compact RF/Photonic antenna module based on a broadband antenna design integrated with a quantum dot mode-locked laser (QDMLL). A two-section QDMLL is used to produce pulsed microwaves signals to feed the radiating antenna. To realize the microwave signal radiation generated by the QDMLL, several possible MLL-integrated-antennas are proposed. The prototype integrated antenna is fully described, including the design, fabrication, and characterization of the antenna performance. Additionally, this work deals with the improvement of the radiation efficiency and functionality of the integrated module. An impedance matching network is designed to match the QDMLL to a bowtie slot antenna. The RF/Photonic integrated prototype is tested and analyzed over a wide frequency range. Finally a QDMLL-integrated-phased antenna array is designed to achieve beam steering. By manipulating the applied voltage bias of each QDMLL, one can achieve beam steering without the use of external RF phase shifters yielding a more compact design of an RF/photonic antenna on a chip. The 2-element integrated prototype is presented and discussed. Beam-steering is fully demonstrated via both simulation and measurements

Prototyping and Experimentation of a Closed-Loop Wireless Power Transmission with Channel Acquisition and Waveform Optimization

A systematic design of adaptive waveform for Wireless Power Transfer (WPT) has recently been proposed and shown through simulations to lead to significant performance benefits compared to traditional non-adaptive and heuristic waveforms. In this study, we design the first prototype of a closed-loop wireless power transfer system with adaptive waveform optimization based on Channel State Information acquisition. The prototype consists of three important blocks, namely the channel estimator, the waveform optimizer, and the energy harvester. Software Defined Radio (SDR) prototyping tools are used to implement a wireless power transmitter and a channel estimator, and a voltage doubler rectenna is designed to work as an energy harvester. A channel adaptive waveform with 8 sinewaves is shown through experiments to improve the average harvested DC power at the rectenna output by 9.8% to 36.8% over a non-adaptive design with the same number of sinewaves.Comment: accepted for publication in IEEE WPTC 201

Signal and System Design for Wireless Power Transfer : Prototype, Experiment and Validation

A new line of research on communications and signals design for Wireless Power Transfer (WPT) has recently emerged in the communication literature. Promising signal strategies to maximize the power transfer efficiency of WPT rely on (energy) beamforming, waveform, modulation and transmit diversity, and a combination thereof. To a great extent, the study of those strategies has so far been limited to theoretical performance analysis. In this paper, we study the real over-the-air performance of all the aforementioned signal strategies for WPT. To that end, we have designed, prototyped and experimented an innovative radiative WPT architecture based on Software-Defined Radio (SDR) that can operate in open-loop and closed-loop (with channel acquisition at the transmitter) modes. The prototype consists of three important blocks, namely the channel estimator, the signal generator, and the energy harvester. The experiments have been conducted in a variety of deployments, including frequency flat and frequency selective channels, under static and mobility conditions. Experiments highlight that a channeladaptive WPT architecture based on joint beamforming and waveform design offers significant performance improvements in harvested DC power over conventional single-antenna/multiantenna continuous wave systems. The experimental results fully validate the observations predicted from the theoretical signal designs and confirm the crucial and beneficial role played by the energy harvester nonlinearity.Comment: Accepted to IEEE Transactions on Wireless Communication

Learning-Based Adaptive User Selection in Millimeter Wave Hybrid Beamforming Systems

We consider a multi-user hybrid beamforming system, where the multiplexing gain is limited by the small number of RF chains employed at the base station (BS). To allow greater freedom for maximizing the multiplexing gain, it is better if the BS selects and serves some of the users at each scheduling instant, rather than serving all the users all the time. We adopt a two-timescale protocol that takes into account the mmWave characteristics, where at the long timescale an analog beam is chosen for each user, and at the short timescale users are selected for transmission based on the chosen analog beams. The goal of the user selection is to maximize the traditional Proportional Fair (PF) metric. However, this maximization is non-trivial due to interference between the analog beams for selected users. We first define a greedy algorithm and a "top-k" algorithm, and then propose a machine learning (ML)-based user selection algorithm to provide an efficient trade-off between the PF performance and the computation time. Throughout simulations, we analyze the performance of the ML-based algorithms under various metrics, and show that it gives an efficient trade-off in performance as compared to counterparts.Comment: Accepted for publication in IEEE International Conference on Communications (ICC), 202

Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning

The design of codes for feedback-enabled communications has been a long-standing open problem. Recent research on non-linear, deep learning-based coding schemes have demonstrated significant improvements in communication reliability over linear codes, but are still vulnerable to the presence of forward and feedback noise over the channel. In this paper, we develop a new family of non-linear feedback codes that greatly enhance robustness to channel noise. Our autoencoder-based architecture is designed to learn codes based on consecutive blocks of bits, which obtains de-noising advantages over bit-by-bit processing to help overcome the physical separation between the encoder and decoder over a noisy channel. Moreover, we develop a power control layer at the encoder to explicitly incorporate hardware constraints into the learning optimization, and prove that the resulting average power constraint is satisfied asymptotically. Numerical experiments demonstrate that our scheme outperforms state-of-the-art feedback codes by wide margins over practical forward and feedback noise regimes, and provide information-theoretic insights on the behavior of our non-linear codes. Moreover, we observe that, in a long blocklength regime, canonical error correction codes are still preferable to feedback codes when the feedback noise becomes high.Comment: To appear in International Conference on Machine Learning (ICML) 202