Non-uniform deployment of power beacons in wireless powered communication networks

© 2002-2012 IEEE. In wireless powered communication networks (WPCNs), base station (BS) and power beacons (PBs) can offer supplement power for uplink transmission of user equipments (UEs). However, the aggregate power consumption of massively deployed PBs may exceed that of a BS. We propose a non-uniform deployment scheme for PBs in WPCNs, where a cell is divided into inner and outer areas, such that BS and PBs can cooperate to power UEs. To be more specific, a BS located in the center of a cell provides downlink power supply for the inner area UEs and uplink information decoding for all the UEs in the cell; while the PBs power UEs in the outer area. With multiple antennas, maximum ratio transmission and maximum ratio combining are adopted for downlink energy beamforming and uplink information reception. Considering a finite area of the network, we derive the distribution of the distance from a non-center-located UE to its nearest PB in the outer area. An optimization problem is formulated to minimize total average power consumption while satisfying BS average transmission power constraint and coverage probability threshold. Moreover, coverage probability is derived for performance evaluation. The numerical results show that the power consumption of the proposed scheme is reduced significantly compared to PB-only WPCNs

Rate-delay analysis of radio access network slices

Based on wireless network virtualization, radio access network (RAN) slicing is developed to provide services for the different users' requirements. Moreover, the users' sum data rate and delay are two significant metrics to guarantee quality of services. In this paper, we first establish an optimization problem to maximize the downlink sum rate while guaranteeing users' delay for RAN slices, where the base stations and user equipments are randomly distributed. Then we analyze the performance tradeoff between the sum rate maximization and delay tolerance. With the aid of Lyapunov optimization, the upper bounds of the achievable rate and delay are derived, through which the existence of tradeoff in performance is obvious and verified by numerical results

Energy-efficiency versus delay tradeoff in wireless networks virtualization

This paper studies the issues on wireless networks virtualization in terms of two important performance metrics, i.e., energy efficiency (EE) and delay. Different from existing works on physical layer, we aim to achieve a good tradeoff between EE and delay in wireless networks virtualization using cross-layer stochastic optimization approach. In particular, we formulate a cross-layer problem using fractional programming and Lyapunov optimization method. The EE and delay tradeoff solution is given explicitly by deriving their analytical bounds that are verified by simulation results

Model-driven learning for generic MIMO downlink beamforming with uplink channel information

Accurate downlink channel information is crucial to the beamforming design, but it is difficult to obtain in practice. This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, when only the uplink channel information is available. Our main contribution is to propose a model-driven learning technique that exploits the structure of the optimal downlink beamforming to design an effective hybrid learning strategy with the aim to maximize the sum rate performance. This is achieved by jointly considering the learning performance of the downlink channel, the power and the sum rate in the training stage. The proposed approach applies to generic cases in which the uplink channel information is available, but its relation to the downlink channel is unknown and does not require an explicit downlink channel estimation. We further extend the developed technique to massive multiple-input multiple-output scenarios and achieve a distributed learning strategy for multicell systems without an inter-cell signalling overhead. Simulation results verify that our proposed method provides the performance close to the state of the art numerical algorithms with perfect downlink channel information and significantly outperforms existing data-driven methods in terms of the sum rate

Energy efficiency and delay optimization for edge caching aided video streaming

In this paper, we design a computing, communication and caching scheme for edge caching-based video streaming in order to improve the network performance. Firstly, we optimize the system's energy efficiency and delay with the aid of network function virtualization. Then, a dynamic edge caching decision is developed, and based on Lyapunov optimization, an alternating resource optimization algorithm is proposed for allocating the optimal subcarrier and power resources, video caching and computing resources. Our numerical results show that the proposed scheme outperforms both the traditional caching scheme as well as the least frequently used (LFU)-40% regime, and strikes a compelling tradeoff between the energy efficiency and delay

Open-source multi-access edge computing for 6G: opportunities and challenges

Multi-access edge computing (MEC) is capable of meeting the challenging requirements of next-generation networks, e.g., 6G, as a benefit of providing computing and caching capabilities in the close proximity of the users. However, the traditional MEC architecture relies on specialized hardware and its bespoke software functions are closely integrated with the hardware, hence it is too rigid for supporting the rapidly evolving scenarios in the face of the demanding requirements of 6G. As a remedy, we conceive the compelling concept of open-source-defined cellular networking and intrinsically amalgamate it with MEC, which is defined by open-source software running on general-purpose hardware platforms. Specifically, an open-source-defined MEC (OpenMEC) scheme is presented relying on a pair of core principles: the decoupling of the MEC functions and resources from each other with the aid of network function virtualization (NFV); as well as the reconfiguration of the disaggregated MEC functions and resources into customized edge instances. This philosophy allows operators to adaptively customize their users’ networks. Then, we develop improved networking functions for OpenMEC decoupling and discuss both its key components as well as the process of OpenMEC reconfiguration. The typical use cases of the proposed OpenMEC scheme are characterized with the aid of a small-scale test network. Finally, we discuss some of the potential open-source-related technical challenges when facing 6G

Open-source multi-access edge computing for 6G: opportunities and challenges

Multi-access edge computing (MEC) is capable of meeting the challenging requirements of next-generation networks, e.g., 6G, as a benefit of providing computing and caching capabilities in the close proximity of the users. However, the traditional MEC architecture relies on specialized hardware and its bespoke software functions are closely integrated with the hardware, hence it is too rigid for supporting the rapidly evolving scenarios in the face of the demanding requirements of 6G. As a remedy, we conceive the compelling concept of open-source-defined cellular networking and intrinsically amalgamate it with MEC, which is defined by open-source software running on general-purpose hardware platforms. Specifically, an open-source-defined MEC (OpenMEC) scheme is presented relying on a pair of core principles: the decoupling of the MEC functions and resources from each other with the aid of network function virtualization (NFV); as well as the reconfiguration of the disaggregated MEC functions and resources into customized edge instances. This philosophy allows operators to adaptively customize their users’ networks. Then, we develop improved networking functions for OpenMEC decoupling and discuss both its key components as well as the process of OpenMEC reconfiguration. The typical use cases of the proposed OpenMEC scheme are characterized with the aid of a small-scale test network. Finally, we discuss some of the potential open-source-related technical challenges when facing 6G

Evaluation of Appropriate Reference Genes for Gene Expression Normalization during Watermelon Fruit Development

<div><p>Gene expression analysis in watermelon (<i>Citrullus lanatus</i>) fruit has drawn considerable attention with the availability of genome sequences to understand the regulatory mechanism of fruit development and to improve its quality. Real-time quantitative reverse-transcription PCR (qRT-PCR) is a routine technique for gene expression analysis. However, appropriate reference genes for transcript normalization in watermelon fruits have not been well characterized. The aim of this study was to evaluate the appropriateness of 12 genes for their potential use as reference genes in watermelon fruits. Expression variations of these genes were measured in 48 samples obtained from 12 successive developmental stages of parthenocarpic and fertilized fruits of two watermelon genotypes by using qRT-PCR analysis. Considering the effects of genotype, fruit setting method, and developmental stage, geNorm determined <i>clathrin adaptor complex subunit</i> (<i>ClCAC</i>), <i>β-actin</i> (<i>ClACT</i>), and <i>alpha tubulin 5</i> (<i>ClTUA5</i>) as the multiple reference genes in watermelon fruit. Furthermore, <i>ClCAC</i> alone or together with <i>SAND family protein</i> (<i>ClSAND</i>) was ranked as the single or two best reference genes by NormFinder. By using the top-ranked reference genes to normalize the transcript abundance of <i>phytoene synthase</i> (<i>ClPSY1</i>), a good correlation between lycopene accumulation and <i>ClPSY1</i> expression pattern was observed in ripening watermelon fruit. These validated reference genes will facilitate the accurate measurement of gene expression in the studies on watermelon fruit biology.</p></div

Lycopene accumulation and expression profiles of <i>ClPSY1</i> during fruit ripening.

<p>Geometric mean was calculated for the two or multiple reference genes, and used for normalization. <i>Cl18SrRNA</i> was used as control. The results are depicted as mean ± SE (<i>n</i> = 6).</p

Boxplot analysis of expression variations of the tested reference genes in all 48 samples.

<p>The line across the box is the median. The boxes are 25/75 percentiles. Whisker caps are the minimum and maximum values.</p