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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 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.
The line-of-sight (LoS) air-to-ground channel brings both opportunities and challenges in cellular-connected unmanned aerial vehicle (UAV) communications. On one hand, the LoS channels make more cellular base stations (BSs) visible to a UAV as compared to the ground users, which leads to a higher macro-diversity gain for UAV-BS communications. On the other hand, they also render the UAV to impose/suffer more severe uplink/downlink interference to/from the BSs, thus requiring more sophisticated inter-cell interference coordination (ICIC) techniques with more BSs involved. In this paper, we consider the uplink transmission from a UAV to cellular BSs, under spectrum sharing with the existing ground users. To investigate the optimal ICIC design and air-ground performance trade-off, we maximize the weighted sum-rate of the UAV and existing ground users by jointly optimizing the UAV's uplink cell associations and power allocations over multiple resource blocks. However, this problem is non-convex and difficult to be solved optimally. We first propose a centralized ICIC design to obtain a locally optimal solution based on the successive convex approximation (SCA) method. As the centralized ICIC requires global information of the network and substantial information exchange among an excessively large number of BSs, we further propose a decentralized ICIC scheme of significantly lower complexity and signaling overhead for implementation, by dividing the cellular BSs into small-size clusters and exploiting the LoS macro-diversity for exchanging information between the UAV and cluster-head BSs only. Numerical results show that the proposed centralized and decentralized ICIC schemes both achieve a near-optimal performance, and draw important design insights based on practical system setups.
Channel estimation is a practical challenge for intelligent reflecting surface (IRS) aided wireless communication. As the number of IRS reflecting elements or IRS-aided users increases, the channel training overhead becomes excessively high, which results in long delay and low throughput in data transmission. To tackle this challenge, we propose in this paper a new anchor-assisted channel estimation approach, where two anchor nodes, namely A1 and A2, are deployed near the IRS for facilitating its aided base station (BS) in acquiring the cascaded BS-IRS-user channels required for data transmission. Specifically, in the first scheme, the partial channel state information (CSI) on the element-wise channel gain square of the common BS-IRS link for all users is first obtained at the BS via the anchor-assisted training and feedback. Then, by leveraging such partial CSI, the cascaded BS-IRS-user channels are efficiently resolved at the BS with additional training by the users. While in the second scheme, the BS-IRS-A1 and A1-IRS-A2 channels are first estimated via the training by A1. Then, with additional training by A2, all users estimate their individual cascaded A2-IRS-user channels simultaneously. Based on the CSI fed back from A2 and all users, the BS resolves the cascaded BS-IRS-user channels efficiently. In both schemes, the quasi-static channels among the fixed BS, IRS, and two anchors are estimated off-line only, which greatly reduces the real-time training overhead. Simulation results demonstrate that our proposed anchor-assisted channel estimation schemes achieve superior performance as compared to existing IRS channel estimation schemes, under various practical setups. In addition, the first proposed scheme outperforms the second one when the number of antennas at the BS is sufficiently large, and vice versa.
A model-based channel capacity prediction scheme is proposed to provide statistical quality-of-service (QoS) guarantees under a medium or high traffic load for IEEE 802.11 based wireless multimedia networks. The proposed scheme perceives the state of wireless link from the MAC retransmission information and calculates the statistical channel capacity especially in saturated traffic load. Based on the capacity prediction model, resource reservation and QoS routing optimization are carried out. Via a cross-layer design approach, the scheme allocates network resource and forwards data packet by taking into consideration of the interference among flows and the link state. Simulation results show that the proposed scheme can achieve more accurate capacity prediction and higher resource utilization than previous QoS routing schemes, and improve network performance in terms of end to end transmission delay, packet delivery ratio and network throughput.
6DMA (six-dimensional movable antenna) is a new and revolutionizing technology that fully exploits the wireless channel spatial variation at the transmitter/receiver by flexibly adjusting the three-dimensional (3D) positions and 3D rotations of distributed antennas/antenna surfaces (arrays). In this article, we provide an overview of 6DMA for unveiling its great potential in wireless networks, including its motivation and competitive advantages over existing technologies, system/channel modeling, and practical implementation. In particular, we present a variety of 6DMA-enabled performance enhancement in terms of array gain, spatial multiplexing, interference suppression, and geometric gain. Furthermore, we illustrate the main applications of 6DMA in wireless communication and sensing, and elaborate their design challenges as well as promising solutions. Finally, numerical results are provided to demonstrate the significant capacity improvement of 6DMA-aided communication in wireless network.
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.
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.
To address the problems of slow cancellation speed and low cancellation ratio of the current digital domain decomposition algorithm in high hopping speed and dramatically fast environment, this paper proposes an algorithm that uses a look-ahead QR decomposition-based recursive least square approach in the complex domain, aim to achieve fast cancellation and interference cancellation in the digital domain with high cancellation ratio. First, we analyze the iterative boundary of the conventional QRD-RLS algorithm, perform the third-order annihilation-reordering look-ahead, derive the iterative formula of the complex domain look-ahead and the structure of the systolic structure, then analyze the timing relationship of each unit, and finally perform the critical path optimization and build the systolic structure model of the third-order look-ahead. Upon comparison with the QRD-RLS algorithm, this method showcases an enhanced cancellation speed while maintaining a high cancellation ratio. Results from simulations indicate that the cancellation ratio of this method remains consistent with the RLS algorithm.
Intelligent reflecting surface (IRS) is a promising technology for achieving spectrum and energy-efficient wireless networks cost-effectively. Most existing works on IRS have focused on exploiting IRS to enhance the performance of wireless communication or wireless information transmission (WIT), while its potential for boosting the efficiency of radio frequency (RF) wireless energy transmission (WET) still remains largely open. Although IRS-aided WET shares similar characteristics with IRS-aided WIT, they differ fundamentally in terms of design objective, receiver architecture, practical constraints, and so on. In this article, we provide a tutorial overview on how to efficiently design IRS-aided WET systems as well as IRS-aided systems with both WIT and WET, namely, IRS-aided simultaneous wireless information and power transfer (SWIPT) and IRS-aided wireless powered communication network (WPCN), from a communication and signal processing perspective. In particular, we present state-of-the-art solutions to tackle the unique challenges in operating these systems, such as IRS passive reflection optimization, channel estimation, and deployment. In addition, we propose new solution approaches and point out important directions for future research and investigation.
Intracerebral hemorrhage (ICH) is a severe stroke that can adversely affect patient outcomes due to the accompanying inflammatory response. As a result, there is a growing interest in studying inflammation in ICH. We systematically reviewed relevant articles using the Web of Science to understand the literature on this subject. This study aims to provide a comprehensive overview of the field through bibliometric analysis, highlight its current status, identify frontiers, and speculate on future directions. We conducted a bibliometric analysis of the global English literature on inflammation related to ICH research based on the Web of Science from 1993 to the present to address publication trends and research hotspots. A total of 885 publications were included from 1993 to 2023. These articles were authored by 7,375 researchers from 1,639 organizations in 68 countries and published in 571 journals. Collectively, they cited 48,980 references from 5,621 journals. The author who published the most articles was Dr. Zhang, John H. Interestingly, 6 of the top ten most published authors were from China. Regarding the countries that published the most articles, China was at the top of the list, followed by the United States. The most frequently used keywords were "Inflammation", "Neuroinflammation", and "Microglia". Regarding journal publication and reference citations, Stroke was the most published and cited journal. Over the past 30 years, there has been considerable progress in scientific research concerning inflammation in the context of ICH. The pivotal themes of these studies have been immune cells and inflammatory mediators. It is worth noting, however, that most of the published literature on inflammation in ICH pertains to preclinical studies, with comparatively fewer clinical investigations. Several challenges must be addressed to translate these promising research achievements into clinical practice.
