Millimeter-wave (mmWave) communication and network densification hold great promise for achieving high-rate communication in next-generation wireless networks. Cloud radio access network (CRAN), in which low-complexity remote radio heads (RRHs) coordinated by a central unit (CU) are deployed to serve users in a distributed manner, is a cost-effective solution to achieve network densification. However, when operating over a large bandwidth in the mmWave frequencies, the digital fronthaul links in a CRAN would be easily saturated by the large amount of sampled and quantized signals to be transferred between RRHs and the CU. To tackle this challenge, we propose in this paper a new architecture for mmWave-based CRAN with advanced lens antenna arrays at the RRHs. Due to the energy focusing property, lens antenna arrays are effective in exploiting the angular sparsity of mmWave channels, and thus help in substantially reducing the fronthaul rate and simplifying the signal processing at the multi-antenna RRHs and the CU, even when the channels are frequency-selective. We consider the uplink transmission in a mmWave CRAN with lens antenna arrays and propose a low-complexity quantization bit allocation scheme for multiple antennas at each RRH to meet the given fronthaul rate constraint. Further, we propose a channel estimation technique that exploits the energy focusing property of the lens array and can be implemented at the CU with low complexity. Finally, we compare the proposed mmWave CRAN using lens antenna arrays with a conventional CRAN using uniform planar arrays at the RRHs, and show that the proposed design achieves significant throughput gains, yet with much lower complexity.
No abstract is provided for this article.
In upstream vectored DSL transmission, the far-end crosstalk (FEXT) can be completely cancelled by using zero- forcing generalized decision-feedback equalizers (ZF-GDFE). When the spatially correlated alien crosstalk is present, the achievable data rates of DSL lines with ZF-GDFE depend on their decoding orders at each DMT tone. Given a weighted sum-rate maximization problem, the optimal orderings for all DMT tones can be found by the Lagrange dual decomposition method. However, the computational complexity of such approach grows with the factorial of the number of users, which makes the optimal search infeasible with a large number of vectored lines. This paper presents a modified greedy algorithm (MGA) that performs close to the optimal search of decoding orders. The complexity of MGA is only proportional to the cube of the number of users, which is the same as it of QR decomposition. With a significant reduction of complexity, MGA is a promising technique for practical DSL systems.
This letter studies a coexisting wireless information and power transfer system, where a multi-antenna energy transmitter (ET) delivers power wirelessly to its intended receiver by reusing the spectrum owned by an existing information transmitter (IT). We propose a new cognitive wireless power transfer technique, namely information helping, where the ET assists the IT in the wireless information transmission in order to gain the spectrum access for its own power delivery. The optimal beamforming of the ET is obtained to maximize the achievable rate-power region at the information receiver (IR) and energy receiver (ER). Simulation results validate the performance gain of the proposed design under the coexisting setup compared against other benchmark schemes without information helping.
Passenger train remote monitor system consists of monitoring centre,data transmission net and termination aboard the car,adopting GPRS and internet as media,data transmission between monitoring centre and termination is implemented.After the design scheme for the whole system is presented,the hardware and software design schemes of the termination are introduced mainly.The front machine of termination controls GPRS model communicating with monitoring centre by sending AT instructions via one serial port,and MCU monitors state of GPRS model via the other serial port of the model,once abnormal state appears,MCU resets the model,then keeps termination on-line all the time.The application shows that this system has good performance.
This study investigates microbial population dynamics in granular sequencing batch reactors (GSBR). The experimental results of DGGE fingerprint of sludge demonstrated that the microbial community structure of sludge shifted significantly during granulation period and nutrient removal improvement period. After reactor performance and physical characteristics of sludge reached steady state, microbial population of sludge became relatively stable. The high similarity of microbial community structure between co-existed flocculated sludge and granular sludge in GSBR at different operation phases indicated that similar microbial consortium could exist in compact aggregated form or in amorphous flocculated form. Therefore, strong selection pressure was still required to wash out flocs to maintain the stability of reactor operation. In addition, it was found that substrate type had considerable impact on microbial species selection and enrichment in granular sludge. The clone library of granular sludge showed that microbial species in divisions of α-Proteobacteria, β-Proteobacteria, γ-Proteobacteria and Bacteroidetes existed within acetate-fed granule communities and Thauera spp. from β-Proteobacteria accounted for 49% of the total clones in the whole clone library. It is thus speculated that Thauera spp. are important for the formation of acetate-fed granules under the conditions used in this study, maintaining the integrity of granules or substrate degradation.
Intelligent reflecting surface (IRS) is a cost-effective solution for achieving high spectrum and energy efficiency in future wireless networks by leveraging massive low-cost passive elements that are able to reflect the signals with adjustable phase shifts. Prior works on IRS mainly consider continuous phase shifts at reflecting elements, which are practically difficult to implement due to the hardware limitation. In contrast, we study in this paper an IRS-aided wireless network, where an IRS with only a finite number of phase shifts at each element is deployed to assist in the communication from a multi-antenna access point (AP) to multiple single-antenna users. We aim to minimize the transmit power at the AP by jointly optimizing the continuous transmit precoding at the AP and the discrete reflect phase shifts at the IRS, subject to a given set of minimum signal-to-interference-plus-noise ratio (SINR) constraints at the user receivers. The considered problem is shown to be a mixed-integer non-linear program (MINLP) and thus is difficult to solve in general. To tackle this problem, we first study the single-user case with one user assisted by the IRS and propose both optimal and suboptimal algorithms for solving it. Besides, we analytically show that as compared to the ideal case with continuous phase shifts, the IRS with discrete phase shifts achieves the same squared power gain in terms of asymptotically large number of reflecting elements, while a constant proportional power loss is incurred that depends only on the number of phase-shift levels. The proposed designs for the single-user case are also extended to the general setup with multiple users among which some are aided by the IRS. Simulation results verify our performance analysis as well as the effectiveness of our proposed designs as compared to various benchmark schemes.
Few-shot learning (FSL) is one of the significant and hard problems in the field of image classification. However, in contrast to the rapid development of the visible light dataset, the progress in SAR target image classification is much slower. The lack of unified benchmark is a key reason for this phenomenon, which may be severely overlooked by the current literature. The researchers of SAR target image classification always report their new results on their own datasets and experimental setup. It leads to inefficiency in result comparison and impedes the further progress of this area. Motivated by this observation, we propose a novel few-shot SAR image classification benchmark (FewSAR) to address this issue. FewSAR consists of an open-source Python code library of 15 classic methods in three categories for few-shot SAR image classification. It provides an accessible and customizable testbed for different few-shot SAR image classification task. To further understanding the performance of different few-shot methods, we establish evaluation protocols and conduct extensive experiments within the benchmark. By analyzing the quantitative results and runtime under the same setting, we observe that the accuracy of metric learning methods can achieve the best results. Meta-learning methods and fine-tuning methods perform poorly on few-shot SAR images, which is primarily due to the bias of existing datasets. We believe that FewSAR will open up a new avenue for future research and development, on real-world challenges at the intersection of SAR image classification and few-shot deep learning. We will provide our code for the proposed FewSAR at https://github.com/solarlee/FewSAR.
In order to make a difference in transaction- oriented distributed environments, performance management efforts must be focused on those components that play a critical role in end-to-end system performance. This paper overviews a flexible approach to identifying these components and quantifying their criticality, through a statistical extension to the classic critical path algorithm for DAGs. A preliminary case study in a real-world service-oriented Grid is used to demonstrate the approach.
No abstract is provided for this article.
To determine propagation characteristics in buildings for fifth-generation (5G) wireless communication, it is necessary to obtain the complex permittivity of the construction materials at millimeter-wave band. Backward reconstruction of complex permittivity of construction materials in free space at millimeter-waves remains a challenging and up to date complex problem. A procedure for the estimation of the complex permittivity of construction materials based on Particle Swarm Optimization (PSO) method is presented in this paper. The complex permittivity is extracted from the transmission and reflection coefficients for perpendicular polarization over 40-50 GHz, and verified by reflection coefficients for parallel polarization. This method is relatively precise and fast which gives a good estimation of complex permittivity.
Massive multiple-input-multiple-output (MIMO) techniques have been recently advanced to tremendously improve the performance of wireless communication networks. However, the use of very large antenna arrays at the base stations brings new issues, such as the significantly increased hardware and signal processing costs. In order to reap the performance gains of massive MIMO and yet reduce its cost, this paper proposes a novel system design by integrating an electromagnetic (EM) lens with the large antenna array, termed the EM-lens enabled MIMO. The EM lens has the capability of focusing the power of an incident wave to a small area of the antenna array, whereas the location of the focal area varies with the angle of arrival (AoA) of the wave. Hence, in scenarios where the arriving signals from geographically separated users have different AoAs, the EM-lens enabled receiver provides two new benefits, namely, energy focusing and spatial interference rejection. By taking into account the effects of imperfect channel estimation via pilot-assisted training, in this paper, we analytically show that the average received signal-to-noise ratio in both the single-user and multiuser uplink transmissions can be improved by the EM-lens enabled system. Furthermore, we demonstrate that the proposed design makes it possible to considerably reduce the hardware and signal processing costs with only slight degradations in performance. To this end, two complexity/cost reduction schemes are proposed, which are small-MIMO processing with parallel receiver filtering applied over subgroups of antennas to reduce the computational complexity, and channel covariance based antenna selection to reduce the required number of radio frequency chains. Numerical results are provided to corroborate our analysis and show the great potential advantages of our proposed EM-lens enabled MIMO system for next generation cellular networks.
