Intelligent reflecting surface (IRS) is a promising solution to enhance the wireless communication capacity both cost-effectively and energy-efficiently, by properly altering the signal propagation via tuning a large number of passive reflecting units. In this paper, we aim to characterize the fundamental capacity limit of IRS-aided point-to-point multiple-input multiple-output (MIMO) communication systems with multi-antenna transmitter and receiver in general, by jointly optimizing the IRS reflection coefficients and the MIMO transmit covariance matrix. We consider narrowband transmission under frequency-flat fading channels, and develop an efficient alternating optimization algorithm to find a locally optimal solution by iteratively optimizing the transmit covariance matrix or one of the reflection coefficients with the others being fixed. Numerical results show that our proposed algorithm achieves substantially increased capacity compared to traditional MIMO channels without the IRS, and also outperforms various benchmark schemes.
In this paper, we study the trajectory design for a cellular-connected unmanned aerial vehicle (UAV) with given initial and final locations, while communicating with the ground base stations (GBSs) along its flight. We consider delay-limited communications between the UAV and its associated GBSs, where a given signal-to-noise ratio (SNR) target needs to be satisfied at the receiver. However, in practice, due to various factors such as quality-of-service (QoS) requirement, GBSs' availability and UAV mobility constraints, the SNR target may not be met at certain time periods during the flight, each termed as an outage duration. In this paper, we aim to optimize the UAV trajectory to minimize its mission completion time, subject to a constraint on the maximum tolerable outage duration in its flight. To tackle this non-convex problem, we first transform it into a more tractable form and thereby reveal some useful properties of the optimal trajectory solution. Based on these properties, we then further simplify the problem and propose efficient algorithms to check the feasibility of the problem as well as to obtain its optimal and high-quality suboptimal solutions, by leveraging graph theory and convex optimization techniques. Numerical results show that our proposed trajectory designs outperform the conventional method based on dynamic programming, in terms of both performance and complexity.
Although prior works have exploited the UAV's mobility to enhance the wireless communication performance under different setups, the fundamental capacity limits of UAV-enabled/aided multiuser communication systems have not yet been characterized. To fill this gap, we consider in this paper a UAV-enabled two-user broadcast channel (BC), where a UAV flying at a constant altitude is deployed to send independent information to two users at different fixed locations on the ground. We aim to characterize the capacity region of this new type of BC over a given UAV flight duration, by jointly optimizing the UAV's trajectory and transmit power/rate allocations over time, subject to the UAV's maximum speed and maximum transmit power constraints. First, to draw essential insights, we consider two special cases with asymptotically large/low UAV flight duration/speed, respectively. For the former case, it is shown that a simple hover-fly-hover (HFH) UAV trajectory with time division multiple access (TDMA) based orthogonal multiuser transmission is capacity-achieving, while in the latter case, the UAV should hover at a fixed location that is nearer to the user with larger achievable rate and in general superposition coding (SC) based non-orthogonal transmission with interference cancellation at the receiver of the nearer user is required. Next, we consider the general case with finite UAV speed and flight duration. We show that the optimal UAV trajectory should follow a general HFH structure, i.e., the UAV successively hovers at a pair of initial and final locations above the line segment of the two users each with a certain amount of time and flies unidirectionally between them at the maximum speed, and SC is generally needed.
In magnetic resonant coupling (MRC) enabled multiple-input multiple-output (MIMO) wireless power transfer (WPT) systems, multiple transmitters (TXs) each with one single coil are used to enhance the efficiency of simultaneous power transfer to multiple single-coil receivers (RXs) by constructively combining their induced magnetic fields at the RXs, a technique termed "magnetic beamforming". In this paper, we study the optimal magnetic beamforming design in a multi-user MIMO MRC-WPT system. We introduce the multi-user power region that constitutes all the achievable power tuples for all RXs, subject to the given total power constraint over all TXs as well as their individual peak voltage and current constraints. We characterize each boundary point of the power region by maximizing the sum-power deliverable to all RXs subject to their minimum harvested power constraints. For the special case without the TX peak voltage and current constraints, we derive the optimal TX current allocation for the single-RX setup in closed-form as well as that for the multi-RX setup. In general, the problem is a non-convex quadratically constrained quadratic programming (QCQP), which is difficult to solve. For the case of one single RX, we show that the semidefinite relaxation (SDR) of the problem is tight. For the general case with multiple RXs, based on SDR we obtain two approximate solutions by applying time-sharing and randomization, respectively. Moreover, for practical implementation of magnetic beamforming, we propose a novel signal processing method to estimate the magnetic MIMO channel due to the mutual inductances between TXs and RXs. Numerical results show that our proposed magnetic channel estimation and adaptive beamforming schemes are practically effective, and can significantly improve the power transfer efficiency and multi-user performance trade-off in MIMO MRC-WPT systems.
This paper addresses the design issues of the multiantenna-based cognitive radio (CR) system that is able to concurrently operate with the licensed primary-radio (PR) system. We propose a practical CR transmission strategy consisting of three major stages, namely, environment learning, channel training, and data transmission. In the environment-learning stage, the CR transceivers both listen to the PR transmission and apply blind algorithms to estimate the spaces that are orthogonal to the channels from the PR. Assuming time-division duplex (TDD)-based transmission for the PR, cognitive beamforming is then designed and applied at CR transceivers to restrict the interference to/from the PR during the subsequent channel-training and data-transmission stages. In the channel-training stage, the CR transmitter sends training signals to the CR receiver, which applies the linear-minimum-mean-square-error (LMMSE)-based estimator to estimate the effective channel. Considering imperfect estimations in both learning and training stages, we derive a lower bound on the ergodic capacity that is achievable for the CR in the data-transmission stage. From this capacity lower bound, we observe a general learning/training/throughput tradeoff associated with the proposed scheme, pertinent to transmit power allocation between the training and transmission stages, as well as time allocation among the learning, training, and transmission stages. We characterize the aforementioned tradeoff by optimizing the associated power and time allocation to maximize the CR ergodic capacity.
An intelligent reflecting surface (IRS) can adaptively adjust the phase shifts of its reflecting units to strengthen the desired signal and/or suppress the undesired signal. In this letter, we investigate an IRS-aided secure wireless communication system where a multi-antenna access point (AP) sends confidential messages to a single-antenna user in the presence of a single-antenna eavesdropper. In particular, we consider the challenging scenario where the eavesdropping channel is stronger than the legitimate communication channel and they are also highly correlated in space. We maximize the secrecy rate of the legitimate communication link by jointly designing the AP's transmit beamforming and the IRS's reflect beamforming. While the resultant optimization problem is difficult to solve, we propose an efficient algorithm to obtain high-quality suboptimal solution for it by applying the alternating optimization, and semidefinite relaxation methods. Simulation results show that the proposed design significantly improves the secrecy communication rate for the considered setup over the case without using the IRS, and outperforms a heuristic scheme.
The inter-turn insulation of stator winding for wind turbine generator (WTG) usually suffers the repetitive impulse over-voltages when the converter is connected to the winding terminals. The long-term effect of repetitive impulse over-voltages may lead to the development of inter-turn insulation defects and/or the occurrence of inter-turn insulation fault due to partial discharge (PD) activity. In this paper, the detecting method of inter-turn PD in the stator winding of WTG under the induced impulse voltage was investigated. A typical form-wound winding specimen for simulating the inter-turn insulation system of WTG was prepared. An exciting system for inducing high impulse voltage was established. The induced impulse voltage on inter-turn insulations of specimen can be with the peak value up to 1200 V and rise time less than 0.5 μs. The high frequency current sensor with the band width of 1.5~ 45 MHz was designed for detecting PD activity in inter-turn insulation. The numerically simulating model for this test system was also provided. A series of tests under the different induced impulse voltage levels were carried out in the laboratory. Corresponding to the different induced inter-turn voltages, the output signal of the current sensors were recorded and analyzed. The presently test results indicate that the inducted impulse voltage can excite the PD activity in the inter-turn insulation of specimen.
This paper investigates the influence of tunnel geometry on the capacity of multiple-input multiple-output (MIMO) wireless communication system in underground environment. Due to the geometry of underground tunnels, such as mine and transportation tunnels, there are sufficient scattering objects, and MIMO system can take great advantage of such geometry. Through the experimental evaluations, it is shown MIMO systems in underground tunnel with complicated geometry can achieve an obvious improvement on channel capacities.
In the energy internet, the technologies of multi-energy storage (MES) and generalized demand response (GDR) have been widely applied, which make a great contribution to meet development needs of modern power systems. In order to analyze the effect of adequate cooperation style between GDR and MES, a joint optimal operation strategy of GDR-MES is proposed in integrated electricity-gas system (IEGS) in this paper. According to the actual operation characteristics of MES equipment and GDR implementation in the IEGS, GDR models based on energy prices and MES models including battery stations and gas storage tanks are respectively established to maximize operation benefits of IEGS operators. The validity of the presented model is verified by an example of regional IEGS operators, and the results show that the joint optimal operation strategy of GDR-MES can increase the benefits of IEGS operators and create a better energy-consumption fashion for the whole system.
No abstract is provided for this article.
In this letter, we consider an intelligent reflecting surface (IRS)-aided wireless communication system, where an active or passive IRS is employed to assist the communication between an access point and a user. First, we consider the downlink/uplink communication separately and optimize the IRS placement for rate maximization with an active or passive IRS. We show that the active IRS should be deployed closer to the receiver with the IRS's decreasing amplification power; while in contrast, the passive IRS should be deployed near either the transmitter or receiver. Moreover, with optimized IRS placement, the passive IRS is shown to outperform its active counterpart when the number of reflecting elements is sufficiently large and/or the active-IRS amplification power is too small. Next, we optimize the IRS placement for both active and passive IRSs to maximize the weighted sum-rate of uplink and downlink communications. We show that in this case, the passive IRS is more likely to achieve superior rate performance. This is because the optimal active-IRS placement needs to balance the rate performance in the uplink and downlink, while deploying the passive IRS near the transmitter or receiver is optimal regardless of the uplink or downlink.
Conventional multi-beam forming with fixed-position antenna (FPA) arrays needs to trade-off between maximizing the beamforming gain over desired directions and minimizing the interference power over undesired directions. In this letter, we study the enhanced multi-beam forming with a linear movable-antenna (MA) array by exploiting the new degrees of freedom (DoFs) via antennas' position optimization. Specifically, we jointly optimize the antenna position vector (APV) and antenna weight vector (AWV) to maximize the minimum beamforming gain over multiple desired directions, subject to a given constraint on the maximum interference power over undesired directions. We propose an efficient alternating optimization algorithm to find a suboptimal solution by iteratively optimizing one of the APV and AWV with the other being fixed. Numerical results show that the proposed multi-beam forming design with MA arrays can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of both beamforming gain and interference suppression.
The emerging Connected Vehicle (CV) technology is widely expected to greatly enhance traffic safety and efficiency by enabling vehicles, pedestrians, and infrastructures to communicate with one another. As a promising CV application, CV-based traffic signal control aims to improve the traffic efficiency at intersections by dynamically optimizing traffic signal control plans based on the mobility information submitted by surrounding CVs. Effective CV-based traffic control relies on accurate estimation of the queue length i.e., the number of vehicles waiting at intersections, to determine the optimal traffic signal control plans. Despite significant efforts on accurate queue length estimation, the robustness of queue length estimation has so far received very limited attention. A recent study has demonstrated that it is possible for malicious CVs to significantly manipulate the queue length estimation by reporting false mobility data, which can cause severe traffic congestion. To tackle this challenge, we introduce a robust queue length estimation mechanism that first utilizes the mobility data reported by all the CVs waiting in the queue to calculate multiple preliminary queue length estimates. Then, the robust statistical methods are adopted to derive a resulting estimated queue length whose accuracy is kept at an acceptable level even though there exist multiple malicious CVs in the queue. The simulation results confirm the effectiveness of the proposed mechanism.
This paper studies the achievable rate region of the K-user Gaussian multiple-input single-output interference channel (MISO-IC) with the interference treated as noise, when improper or circularly asymmetric complex Gaussian signaling is applied. The transmit optimization with improper Gaussian signaling involves not only the signal covariance matrix as in the conventional proper or circularly symmetric Gaussian signaling, but also the signal pseudo-covariance matrix, which is conventionally set to zero in proper Gaussian signaling. By exploiting the separable rate expression with improper Gaussian signaling, we propose a separate transmit covariance and pseudo-covariance optimization algorithm, which is guaranteed to improve the users' achievable rates over the conventional proper Gaussian signaling. In particular, for the pseudo-covariance optimization, we establish the optimality of rank-1 pseudo-covariance matrices, given the optimal rank-1 transmit covariance matrices for achieving the Pareto boundary of the rate region. Based on this result, we are able to greatly reduce the number of variables in the pseudo-covariance optimization problem and thereby develop an efficient solution by applying the celebrated semidefinite relaxation (SDR) technique. Finally, we extend the result to the Gaussian MISO broadcast channel (MISO-BC) with improper Gaussian signaling or so-called widely linear transmit precoding.
