Dynamic Resource Allocation for Multiple-Antenna Wireless Power Transfer

We consider a point-to-point multiple-input-single-output (MISO) system where a receiver harvests energy from a wireless power transmitter to power itself for various applications. The transmitter performs energy beamforming by using an instantaneous channel state information (CSI). The CSI is estimated at the receiver by training via a preamble, and fed back to the transmitter. The channel estimate is more accurate when longer preamble is used, but less time is left for wireless power transfer before the channel changes. To maximize the harvested energy, in this paper, we address the key challenge of balancing the time resource used for channel estimation and wireless power transfer (WPT), and also investigate the allocation of energy resource used for wireless power transfer. First, we consider the general scenario where the preamble length is allowed to vary dynamically. Taking into account the effects of imperfect CSI, the optimal preamble length is obtained online by solving a dynamic programming (DP) problem. The solution is shown to be a threshold-type policy that depends only on the channel estimate power. Next, we consider the scenario in which the preamble length is fixed. The optimal preamble length is optimized offline. Furthermore, we derive the optimal power allocation schemes for both scenarios. For the scenario of dynamic-length preamble, the power is allocated according to both the optimal preamble length and the channel estimate power; while for the scenario of fixed-length preamble, the power is allocated according to only the channel estimate power. The analysis results are validated by numerical simulations. Encouragingly, with optimal power allocation, the harvested energy by using optimized fixed-length preamble is almost the same as the harvested energy by employing dynamic-length preamble, hence allowing a low-complexity WPT system to be implemented in practice.Comment: 30 pages, 6 figures, Submitted to the IEEE Transactions on Signal Processin

Throughput Optimization for Massive MIMO Systems Powered by Wireless Energy Transfer

This paper studies a wireless-energy-transfer (WET) enabled massive multiple-input-multiple-output (MIMO) system (MM) consisting of a hybrid data-and-energy access point (H-AP) and multiple single-antenna users. In the WET-MM system, the H-AP is equipped with a large number $M$ of antennas and functions like a conventional AP in receiving data from users, but additionally supplies wireless power to the users. We consider frame-based transmissions. Each frame is divided into three phases: the uplink channel estimation (CE) phase, the downlink WET phase, as well as the uplink wireless information transmission (WIT) phase. Firstly, users use a fraction of the previously harvested energy to send pilots, while the H-AP estimates the uplink channels and obtains the downlink channels by exploiting channel reciprocity. Next, the H-AP utilizes the channel estimates just obtained to transfer wireless energy to all users in the downlink via energy beamforming. Finally, the users use a portion of the harvested energy to send data to the H-AP simultaneously in the uplink (reserving some harvested energy for sending pilots in the next frame). To optimize the throughput and ensure rate fairness, we consider the problem of maximizing the minimum rate among all users. In the large-$M$ regime, we obtain the asymptotically optimal solutions and some interesting insights for the optimal design of WET-MM system. We define a metric, namely, the massive MIMO degree-of-rate-gain (MM-DoRG), as the asymptotic UL rate normalized by $\log(M)$. We show that the proposed WET-MM system is optimal in terms of MM-DoRG, i.e., it achieves the same MM-DoRG as the case with ideal CE.Comment: 15 double-column pages, 6 figures, 1 table, to appear in IEEE JSAC in February 2015, special issue on wireless communications powered by energy harvesting and wireless energy transfe

Hinge solitons in three-dimensional second-order topological insulators

A second-order topological insulator in three dimensions refers to a topological insulator with gapless states localized on the hinges, which is a generalization of a traditional topological insulator with gapless states localized on the surfaces. Here we theoretically demonstrate the existence of stable solitons localized on the hinges of a second-order topological insulator in three dimensions when nonlinearity is involved. By means of systematic numerical study, we find that the soliton has strong localization in real space and propagates along the hinge unidirectionally without changing its shape. We further construct an electric network to simulate the second-order topological insulator. When a nonlinear inductor is appropriately involved, we find that the system can support a bright soliton for the voltage distribution demonstrated by stable time evolution of a voltage pulse.Comment: 11 pages, 6 figure

Optimization of Fast-Decodable Full-Rate STBC with Non-Vanishing Determinants

Full-rate STBC (space-time block codes) with non-vanishing determinants achieve the optimal diversity-multiplexing tradeoff but incur high decoding complexity. To permit fast decoding, Sezginer, Sari and Biglieri proposed an STBC structure with special QR decomposition characteristics. In this paper, we adopt a simplified form of this fast-decodable code structure and present a new way to optimize the code analytically. We show that the signal constellation topology (such as QAM, APSK, or PSK) has a critical impact on the existence of non-vanishing determinants of the full-rate STBC. In particular, we show for the first time that, in order for APSK-STBC to achieve non-vanishing determinant, an APSK constellation topology with constellation points lying on square grid and ring radius \sqrt{m^2+n^2} (m,n\emph{\emph{integers}}) needs to be used. For signal constellations with vanishing determinants, we present a methodology to analytically optimize the full-rate STBC at specific constellation dimension.Comment: Accepted by IEEE Transactions on Communication

ShapeScaffolder:Structure-Aware 3D Shape Generation from Text

We present ShapeScaffolder, a structure-based neural network for generating colored 3D shapes based on text input. The approach, similar to providing scaffolds as internal structural supports and adding more details to them, aims to capture finer text-shape connections and improve the quality of generated shapes. Traditional text-to-shape methods often generate 3D shapes as a whole. However, humans tend to understand both shape and text as being structure-based. For example, a table is interpreted as being composed of legs, a seat, and a back; similarly, texts possess inherent linguistic structures that can be analyzed as dependency graphs, depicting the relationships between entities within the text. We believe structure-aware shape generation can bring finer text-shape connections and improve shape generation quality. However, the lack of explicit shape structure and the high freedom of text structure make cross-modality learning challenging. To address these challenges, we first build the structured shape implicit fields in an unsupervised manner. We then propose the part-level attention mechanism between shape parts and textual graph nodes to align the two modalities at the structural level. Finally, we employ a shape refiner to add further detail to the predicted structure, yielding the final results. Extensive experimentation demonstrates that our approaches outperform state-of-the-art methods in terms of both shape fidelity and shape-text matching. Our methods also allow for part-level manipulation and improved part-level completeness.</p