Intelligent reflecting surfaces (IRSs), active and/or passive, can be densely deployed in complex environments to significantly enhance wireless network coverage for both wireless information transfer (WIT) and wireless power transfer (WPT). In this letter, we study the downlink WIT/WPT from a multi-antenna base station to a single-antenna user over a multi-active/passive IRS (AIRS/PIRS)-enabled wireless link. In particular, we aim to optimize the location of the AIRS with those of the other PIRSs being fixed to maximize the received signal-to-noise ratio (SNR) and signal power at the user in the cases of WIT and WPT, respectively. We derive the optimal solutions for these two cases in closed-form, which reveals that the optimal AIRS deployment is generally different for WIT versus WPT. Furthermore, both analytical and numerical results are provided to show the conditions under which the proposed AIRS deployment strategy yields superior performance to other baseline deployment strategies as well as the conventional all- PIRS enabled WIT/WPT.
This paper develops a unified approach to obtain optimal transmission schedule for an energy-harvesting node with non-ideal circuit power consumption. For both time-invariant and time-varying fading channels, we show that the optimal transmission between any two consecutive channel or energy state changing time, termed epoch, can only take one of the three strategies: (i) no transmission, (ii) transmission with an energy-efficiency (EE) maximizing power over part of the epoch, or (iii) transmission with a power greater than the EE-maximizing power over the whole epoch. Taking into account this structure, we develop efficient algorithms capable of computing the optimal scheduling schemes with a low complexity. The proposed approach can provide the optimal benchmarks for practical schemes in energy-harvesting powered transmissions, and can be employed to develop efficient online scheduling schemes.
This perspective paper proposes a new wireless sensing approach by mounting Intelligent Reflecting Surface (IRS) on the target, namely target-mounted IRS, to assist in the detection of the target.
Data Envelopment Analysis (DEA) is an effective measure tool to evaluate the regional input-output efficiency. After the brief review of existing literature, as well as the principle and models of DEA, this paper evaluates the input-output efficiency of China's 31 provinces (municipalities) via the approach of DEA. The result shows that only four provinces have efficient input-output from 2009-2011 in rural China. Moreover, the average efficiencies values of the three regions are gradually reduce from the east, the center to the west, and the difference is significant. Through the further analysis, the conclusion can be drawn that the whole provincial economic environment and developing level, as well as the reasonable and diversified industrial structure are very important to improve the efficiency and develop the economy.
This paper studies the training design problem for multiple-input single-output (MISO) wireless energy transfer (WET) systems in frequency-selective channels, where the frequency-diversity and energy-beamforming gains can be both reaped to maximize the transferred energy by efficiently learning the channel state information (CSI) at the energy transmitter (ET). By exploiting channel reciprocity, a new two-phase channel training scheme is proposed to achieve the diversity and beamforming gains, respectively. In the first phase, pilot signals are sent from the energy receiver (ER) over a selected subset of the available frequency sub-bands, through which the ET determines a certain number of "strongest" sub-bands with largest antenna sum-power gains and sends their indices to the ER. In the second phase, the selected sub-bands are further trained by the ER, so that the ET obtains a refined estimate of the corresponding MISO channels to implement energy beamforming for WET. A training design problem is formulated and optimally solved, which takes into account the channel training overhead by maximizing the net harvested energy at the ER, defined as the average harvested energy offset by that consumed in the two-phase training. Moreover, asymptotic analysis is obtained for systems with a large number of antennas or a large number of sub-bands to gain useful insights on the optimal training design. Finally, numerical results are provided to corroborate our analysis and show the effectiveness of the proposed scheme that optimally balances the diversity and beamforming gains achieved in MISO WET systems with limited-energy training.
No abstract is provided for this article.
This paper studies the design of a decentralized multiuser multi-antenna (MIMO) system for spectrum sharing over a fixed narrow band, where the coexisting users independently update their transmit covariance matrices for individual transmit-rate maximization via an iterative manner. This design problem was usually investigated in the literature by assuming that each user treats the co-channel interference from all the other users as additional (colored) noise at the receiver, i.e., the conventional single-user decoder (SUD) is applied. This paper proposes a new decoding method for the decentralized multiuser MIMO system, whereby each user opportunistically cancels the co-channel interference from some or all of the other users via applying multiuser detection techniques, thus termed opportunistic multiuser detection (OMD). This paper studies the optimal transmit covariance design for users' iterative maximization of individual transmit rates with the proposed OMD, and demonstrates the resulting capacity gains in decentralized multiuser MIMO systems against the conventional SUD.
In this paper, we study the optimal waveform design for wireless power transfer (WPT) from a multi-antenna energy transmitter (ET) to multiple single-antenna energy receivers (ERs) simultaneously in multi-path frequency-selective channels. First, we propose a refined nonlinear current-voltage model of the diode in the ER rectifier, and accordingly derive new expressions for the output direct current (DC) voltage and corresponding harvested power at the ER. Leveraging this new rectenna model, we first consider the single-ER case and study the multisine-based power waveform design based on the wireless channel to maximize the harvested power at the ER. We propose two efficient algorithms for finding high-quality suboptimal solutions to this non-convex optimization problem. Next, we extend our formulated waveform design problem to the general multi-ER case for maximizing the weighted sum of the harvested powers by all ERs, and propose an efficient difference-of-convex-functions programming (DCP)-based algorithm for solving this problem. Finally, we demonstrate the superior performance of our proposed waveform designs based on the new rectenna model over existing schemes/models via simulations.
The famous Moore's Law states that chip density doubles almost every 18 months. The Semiconductor Industries Association (SIA) roadmap predicts that complementary metal-oxide semiconductor (CMOS) chips will continue to fuel the need for high-performance systems for another 10--15 years. However, new devices and technologies must be introduced to continue the scaling of chips beyond that time-frame. With rapid advancements in electronic materials and devices, many new nanoscale devices have been created, such as resonant tunneling diode (RTD), quantum cellular automata (QCA), single electron transistor (SET), single electron box (SEB), and tunneling phase logic (TPL), which have novel structures and properties. Such devices offer the opportunity to further improve the compactness and speed of very large scale integrated (VLSI) systems. While it is easier to implement Boolean gates using CMOS, it is easier for many nanoscale devices to implement threshold and majority gates. As progress is made in the material and physical understanding of nanoscale devices, research must be done at the logic level to fully harness the potential offered by these devices. The main purpose of the work in this dissertation is to bridge the current wide gap between research on nanoscale devices and research on synthesis methodologies for generating optimized networks utilizing these devices. We present the first comprehensive threshold network synthesis methodology for multi-output multi-level threshold networks starting from Boolean descriptions. It does threshold logic synthesis through binate and unate splitting and by calling a linear program solver. We have implemented the methodology in a tool called TELS, on top of an existing logic synthesis tool, and validated it with a large number of benchmarks. We also present a methodology for efficient majority/minority network synthesis of arbitrary multi-output Boolean functions, which is based on the concept of admissible patterns of majority gates. We have implemented this methodology in a tool called MALS, on top of the same existing logic synthesis tool. We expanded our research from combinational circuits to sequential circuits by addressing the problem of state encoding of finite-state machines targeting threshold/majority logic based implementations. We propose a methodology for state encoding based on an evolutionary algorithm. Besides solving the logic synthesis and state encoding problems for threshold and majority circuits, we also evaluated several factorization methods, targeting threshold/majority logic circuits specifically. Nanotechnology-based circuits are usually more vulnerable to both permanent and transient faults. Therefore, we evaluated threshold/majority totally self-checking (TSC) circuits to impart concurrent error detection capability to such circuits. We establish the efficacy of all the above methods through experimental results on a large number of benchmarks.
Rotatable antenna (RA) is a promising technology that can exploit new spatial degrees-of-freedom (DoFs) by flexibly adjusting the three-dimensional (3D) boresight direction of antennas. In this letter, we investigate an RA-enhanced cell-free system for downlink transmission, where multiple RA-equipped access points (APs) cooperatively serve multiple single-antenna users over the same time-frequency resource. Specifically, we aim to maximize the sum rate of all users by jointly optimizing the AP-user associations and the RA boresight directions. Accordingly, we propose a two-stage strategy to solve the AP-user association problem, and then employ fractional programming (FP) and successive convex approximation (SCA) techniques to optimize the RA boresight directions. Numerical results demonstrate that the proposed RA-enhanced cell-free system significantly outperforms various benchmark schemes.
This paper is concerned with the problem of exploiting spatial multiplexing gain through opportunistic beamforming with multiple random beams. The base station transmits multiple pilot sequences using orthogonal beams, and each user feeds the channel gain of each beam back. Then, the base station selects the subset of users and beams for which the sum-rate is maximized. We aim to exploit spatial multiplexing gain in a controlled fashion, i.e., the number of beams used for data transmission is chosen depending on the available multiuser diversity in the system. Two schemes are proposed: dynamic Orthonormal Random Beamforming with Systematic Beam Selection (ORBF/SBS), for which the number of beams used for data transmission is dynamically changed from one fading block to another so that the sum-rate is maximized; and static ORBF/SBS, which selects a fixed number of beams for a given signal-to-noise ratio and user number. The static scheme achieves the sum-rate of the dynamic one up to a small gap with much reduced computational and feedback complexity. We derive an approximate expression for the sum-rate achieved by the dynamic scheme, and provide insight into the optimal data stream number in the static scheme by means of asymptotical results. Computer simulations are provided to evaluate the performance of the proposed schemes.
