Intelligent reflecting surface (IRS) is a new and revolutionizing technology for achieving spectrum and energy efficient wireless networks. By leveraging massive low-cost passive elements that are able to reflect radio-frequency (RF) signals with adjustable phase shifts, IRS can achieve high passive beamforming gains, which are particularly appealing for improving the efficiency of RF-based wireless power transfer. Motivated by the above, we study in the paper an IRS-assisted simultaneous wireless information and power transfer (SWIPT) system. Specifically, a set of IRSs are deployed to assist in the information/power transfer from a multi-antenna access point (AP) to multiple single-antenna information users (IUs) and energy users (EUs), respectively. We aim to minimize the transmit power at the AP via jointly optimizing its transmit precoders and the reflect phase shifts at all IRSs, subject to the quality-of-service (QoS) constraints at all users, namely, the individual signal-to-interference-plus-noise ratio (SINR) constraints at IUs and energy harvesting constraints at EUs. However, this optimization problem is non-convex with intricately coupled variables, for which the existing alternating optimization approach is shown to be inefficient as the number of QoS constraints increases. To tackle this challenge, we first apply proper transformations on the QoS constraints and then propose an efficient iterative algorithm by applying the penalty-based method. Moreover, by exploiting the short-range coverage of IRSs, we further propose a low-complexity algorithm by optimizing the phase shifts of all IRSs in parallel.
For block fading channls, the delay-limited capacity measures the maximum constant-rate achievable over all fading blocks under the availability of complete channel knowledge at both the transmitter and the receiver. In this paper, we consider constant-rate transmissions over MIMO-OFDM block fading channels, under the condition that the channel is known at the receiver but unknown at the transmitter. We consider the well-known V-BLAST architecture that employs independently encoded data for each transmit antenna and present a closedloop extension of V-BLAST whereby the receiver jointly optimizes the rate and power assignments for each transmit antenna and then informs them to the transmitter via a low-rate feedback channel. Simulation results have confirmed that the closed-loop V-BLAST architecture can approach closely the channel delaylimited capacity if a capacity-achieving scalar code is used at each transmit antenna.
In data transmission in a wireless sensor network (WSN), a lot of energy will be consumed, but the energy of WSN is limited.The energy consumption of WSN is optimized to ensure the performance of WSN.This paper first introduced WSN and routing protocol, took Low Energy Adaptive Clustering Hierarchy (LEACH) protocol as the subject, analyzed its workflow and problems, and optimized it with the particle swarm optimization (PSO) algorithm to obtain the PSO-LEACH protocol.The experimental results demonstrated that the Fist Node Die (FND) of the PSO-LEACH protocol appeared in the 946th round and Half Node Die (HND) appeared in the 971st round, both slower than the LEACH protocol, indicating that the PSO-LEACH protocol had a longer life cycle; in the same number of rounds, the energy consumption of the PSO-LEACH protocol was always lower than the LEACH protocol, and the time when all energy was consumed was later than the LEACH protocol; when the energy consumption was the same, the PSO-LEACH protocol received more data than the LEACH protocol.The experimental results show that the improved LEACH protocol can optimize the energy consumption of WSN, which is conducive to energy saving and high efficiency of WSN and can be popularized and applied in practice.
Radio-frequency (RF) enabled wireless energy transfer (WET), as a promising solution to provide cost-effective and reliable power supplies for energy-constrained wireless networks, has drawn growing interests recently. To overcome the significant propagation loss over distance, employing multi-antennas at the energy transmitter (ET) to more efficiently direct wireless energy to desired energy receivers (ERs), termed \emph{energy beamforming}, is an essential technique for enabling WET. However, the achievable gain of energy beamforming crucially depends on the available channel state information (CSI) at the ET, which needs to be acquired practically. In this paper, we study the design of an efficient channel acquisition method for a point-to-point multiple-input multiple-output (MIMO) WET system by exploiting the channel reciprocity, i.e., the ET estimates the CSI via dedicated reverse-link training from the ER. Considering the limited energy availability at the ER, the training strategy should be carefully designed so that the channel can be estimated with sufficient accuracy, and yet without consuming excessive energy at the ER. To this end, we propose to maximize the \emph{net} harvested energy at the ER, which is the average harvested energy offset by that used for channel training. An optimization problem is formulated for the training design over MIMO Rician fading channels, including the subset of ER antennas to be trained, as well as the training time and power allocated. Closed-form solutions are obtained for some special scenarios, based on which useful insights are drawn on when training should be employed to improve the net transferred energy in MIMO WET systems.
This paper studies the optimal power allocation for outage minimization in point-to-point fading channels with the energy-harvesting constraints and channel distribution information (CDI) at the transmitter. Both the cases with non-causal and causal energy state information (ESI) are considered, which correspond to the energy harvesting rates being known and unknown prior to the transmissions, respectively. For the non-causal ESI case, the average outage probability minimization problem over a finite horizon is shown to be non-convex for a large class of practical fading channels. However, the globally optimal "offline" power allocation is obtained by a forward search algorithm with at most $N$ one-dimensional searches, and the optimal power profile is shown to be non-decreasing over time and have an interesting "save-then-transmit" structure. In particular, for the special case of N=1, our result revisits the classic outage capacity for fading channels with uniform power allocation. Moreover, for the case with causal ESI, we propose both the optimal and suboptimal "online" power allocation algorithms, by applying the technique of dynamic programming and exploring the structure of optimal offline solutions, respectively.
Movable antenna (MA) technology is a recent development that fully exploits the wireless channel spatial variation in a confined region by enabling local movement of the antenna. Specifically, the positions of antennas at the transmitter and/or receiver can be dynamically changed to obtain better channel conditions for improving the communication performance. In this article, we first provide an overview of the promising applications for MA-aided wireless communication. Then, we present the hardware architecture and channel characterization for MA systems, based on which the variation of the channel gain with respect to the MA's position is illustrated. Furthermore, we analyze the performance advantages of MAs over conventional fixed-position antennas, in terms of signal power improvement, interference mitigation, flexible beamforming, and spatial multiplexing. Finally, we discuss the main design challenges and their potential solutions for MA-aided communication systems.
Six-dimensional movable antenna (6DMA) is an effective solution for enhancing wireless network capacity through the adjustment of both 3D positions and 3D rotations of distributed antennas/antenna surfaces. Although freely positioning/rotating 6DMA surfaces offers the greatest flexibility and thus highest capacity improvement, its implementation may be challenging in practice due to the drastic architecture change required for existing base stations (BSs), which predominantly adopt fixed-position antenna (FPA) arrays (e.g., sector antenna arrays). Thus, we introduce in this letter a new BS architecture called hybrid fixed and movable antennas (HFMA), which consists of both conventional FPA arrays and position/rotation-adjustable 6DMA surfaces. For ease of implementation, we consider that all 6DMA surfaces can rotate along a circular track above the FPA arrays. We aim to maximize the network capacity via optimizing the rotation angles of all 6DMA surfaces based on the users' spatial distribution. Since this problem is combinatorial and its optimal solution requires prohibitively high computational complexity via exhaustive search, we propose an alternative adaptive Markov Chain Monte Carlo based method to solve it more efficiently. Finally, we present simulation results that show significant performance gains achieved by our proposed design over various benchmark schemes.
No abstract is provided for this article.
Owing to the special structure of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC), the associated capacity region computation and beamforming optimization problems are typically non-convex, and thus cannot be solved directly. One feasible approach is to consider the respective dual multiple-access channel (MAC) problems, which are easier to deal with due to their convexity properties. The conventional BC-MAC duality has been established via BC-MAC signal transformation, and is applicable only for the case in which the MIMO BC is subject to a single transmit sum-power constraint. An alternative approach is based on minimax duality, which can be applied to the case of the sum-power constraint or per-antenna power constraint. In this paper, the conventional BC-MAC duality is extended to the general linear transmit covariance constraint (LTCC) case, which includes sum-power and per-antenna power constraints as special cases. The obtained general BC-MAC duality is applied to solve the capacity region computation for the MIMO BC and beamforming optimization for the multiple-input single-output (MISO) BC, respectively, with multiple LTCCs. The relationship between this new general BC-MAC duality and the minimax duality is also discussed, and it is shown that the general BC-MAC duality leads to simpler problem formulations. Moreover, the general BC-MAC duality is extended to deal with the case of nonlinear transmit covariance constraints in the MIMO BC.
Intelligent reflecting surface (IRS) is envisioned to have abundant applications in future wireless networks by smartly reconfiguring the signal propagation for performance enhancement. Specifically, an IRS consists of a large number of low-cost passive elements each reflecting the incident signal with a certain phase shift to collaboratively achieve beamforming and suppress interference at one or more designated receivers. In this paper, we study an IRS-enhanced point-to-point multiple-input single-output (MISO) wireless system where one IRS is deployed to assist in the communication from a multi-antenna access point (AP) to a single-antenna user. As a result, the user simultaneously receives the signal sent directly from the AP as well as that reflected by the IRS. We aim to maximize the total received signal power at the user by jointly optimizing the (active) transmit beamforming at the AP and (passive) reflect beamforming by the phase shifters at the IRS. We first propose a centralized algorithm based on the technique of semidefinite relaxation (SDR) by assuming the global channel state information (CSI) available at the IRS. Since the centralized implementation requires excessive channel estimation and signal exchange overheads, we further propose a low-complexity distributed algorithm where the AP and IRS independently adjust the transmit beamforming and the phase shifts in an alternating manner until the convergence is reached. Simulation results show that significant performance gains can be achieved by the proposed algorithms as compared to benchmark schemes. Moreover, it is verified that the IRS is able to drastically enhance the link quality and/or coverage over the conventional setup without the IRS.
The AP1000 nuclear steam supply system (NSSS) is designed to accept large load rejections without initiating a reactor trip using the reactor power control system, the pressurizer pressure and water level control systems, the feedwater control system, the steam dump control system and the rapid power reduction (RPR) system. This paper presents the dynamic simulation and study of load rejection transients for the AP1000 NSSS. A real-time NSSS simulation platform is developed in Matlab/Simulink environment with implementation of a nodal core model, a three-region non-equilibrium pressurizer model, a nonlinear steam generator model, a feedwater system model, a main steam system model, and the relevant control systems. Based on the simulation platform, three typical load rejection transients from full power (FP) are simulated, including the 50%FP load rejection without RPR, the large load rejection to 25%FP with RPR and the load rejection to house load. The simulation results show that the NSSS control systems can successfully respond to load rejection transients without reactor trip or operation of the pressurizer or steam generator safety valves. Sustained stable operation can be obtained under the condition of the minimum house load due to the proper control system responses. Science the world's first AP1000 plant is being constructed in China, these simulation and analysis results can make us have a better understanding of the load rejection operations and the special steam dump and RPR control systems for AP1000, and offer certain guidance for the engineering practice.
Wireless communication by leveraging the use of low-altitude unmanned aerial vehicles (UAVs) has received significant interests recently due to its low-cost and flexibility in providing wireless connectivity in areas without infrastructure coverage. This paper studies a UAV-enabled mobile relaying system, where a high-mobility UAV is deployed to assist in the information transmission from a ground source to a ground destination with their direct link blocked. By assuming that the UAV adopts the energy-efficient circular trajectory and employs time-division duplexing (TDD) based decode-and-forward (DF) relaying, we maximize the spectrum efficiency (SE) in bits/second/Hz as well as energy efficiency (EE) in bits/Joule of the considered system by jointly optimizing the time allocations for the UAV's relaying together with its flying speed and trajectory. It is revealed that for UAV-enabled mobile relaying with the UAV propulsion energy consumption taken into account, there exists a trade-off between the maximum achievable SE and EE by exploiting the new degree of freedom of UAV trajectory design.
