A Stochastic Geometry Approach to Energy Efficiency in Relay-Assisted Cellular Networks

Though cooperative relaying is believed to be a promising technology to improve the energy efficiency of cellular networks, the relays' static power consumption might worsen the energy efficiency therefore can not be neglected. In this paper, we focus on whether and how the energy efficiency of cellular networks can be improved via relays. Based on the spatial Poisson point process, an analytical model is proposed to evaluate the energy efficiency of relay-assisted cellular networks. With the aid of the technical tools of stochastic geometry, we derive the distributions of signal-to-interference-plus-noise ratios (SINRs) and mean achievable rates of both non-cooperative users and cooperative users. The energy efficiency measured by "bps/Hz/W" is expressed subsequently. These established expressions are amenable to numerical evaluation and corroborated by simulation results.Comment: 6 pages, 5 figures, accepted by IEEE Globecom'12. arXiv admin note: text overlap with arXiv:1108.1257 by other author

MathNAS: If Blocks Have a Role in Mathematical Architecture Design

Neural Architecture Search (NAS) has emerged as a favoured method for unearthing effective neural architectures. Recent development of large models has intensified the demand for faster search speeds and more accurate search results. However, designing large models by NAS is challenging due to the dramatical increase of search space and the associated huge performance evaluation cost. Consider a typical modular search space widely used in NAS, in which a neural architecture consists of $m$ block nodes and a block node has $n$ alternative blocks. Facing the space containing $n^m$ candidate networks, existing NAS methods attempt to find the best one by searching and evaluating candidate networks directly.Different from the general strategy that takes architecture search as a whole problem, we propose a novel divide-and-conquer strategy by making use of the modular nature of the search space.Here, we introduce MathNAS, a general NAS framework based on mathematical programming.In MathNAS, the performances of the $m*n$ possible building blocks in the search space are calculated first, and then the performance of a network is directly predicted based on the performances of its building blocks. Although estimating block performances involves network training, just as what happens for network performance evaluation in existing NAS methods, predicting network performance is completely training-free and thus extremely fast. In contrast to the $n^m$ candidate networks to evaluate in existing NAS methods, which require training and a formidable computational burden, there are only $m*n$ possible blocks to handle in MathNAS. Therefore, our approach effectively reduces the complexity of network performance evaluation.Our code is available at https://github.com/wangqinsi1/MathNAS.Comment: NeurIPS 202

High genetic abundance of Rpi-blb2/Mi-1.2/Cami gene family in Solanaceae

Relative genomic positions of genes among potato (upper), pepper (middle) and tomato (lower) along chromosome 6. (DOCX 282 kb

Patterns of exon-intron architecture variation of genes in eukaryotic genomes

<p>Abstract</p> <p>Background</p> <p>The origin and importance of exon-intron architecture comprises one of the remaining mysteries of gene evolution. Several studies have investigated the variations of intron length, GC content, ordinal position in a gene and divergence. However, there is little study about the structural variation of exons and introns.</p> <p>Results</p> <p>We investigated the length, GC content, ordinal position and divergence in both exons and introns of 13 eukaryotic genomes, representing plant and animal. Our analyses revealed that three basic patterns of exon-intron variation were present in nearly all analyzed genomes (<it>P </it>< 0.001 in most cases): an ordinal reduction of length and divergence in both exon and intron, a co-variation between exon and its flanking introns in their length, GC content and divergence, and a decrease of average exon (or intron) length, GC content and divergence as the total exon numbers of a gene increased. In addition, we observed that the shorter introns had either low or high GC content, and the GC content of long introns was intermediate.</p> <p>Conclusion</p> <p>Although the factors contributing to these patterns have not been identified, our results provide three important clues: common factor(s) exist and may shape both exons and introns; the ordinal reduction patterns may reflect a time-orderly evolution; and the larger first and last exons may be splicing-required. These clues provide a framework for elucidating mechanisms involved in the organization of eukaryotic genomes and particularly in building exon-intron structures.</p

Mobile edge computing-based data-driven deep learning framework for anomaly detection

5G is anticipated to embed an artificial intelligence (AI)-empowerment to adroitly plan, optimize and manage the highly complex network by leveraging data generated at different positions of the network architecture. Outages and situation leading to congestion in a cell pose severe hazard for the network. High false alarms and inadequate accuracy are the major limitations of modern approaches for the anomaly—outage and sudden hype in traffic activity that may result in congestion—detection in mobile cellular networks. This indicates wasting limited resources that ultimately leads to an elevated operational expenditure (OPEX) and also interrupting quality of service (QoS) and quality of experience (QoE). Motivated by the outstanding success of deep learning (DL) technology, our study applies it for detection of the above-mentioned anomalies and also supports mobile edge computing (MEC) paradigm in which core network (CN)’s computations are divided across the cellular infrastructure among different MEC servers (co-located with base stations), to relief the CN. Each server monitors user activities of multiple cells and utilizes $L$ -layer feedforward deep neural network (DNN) fueled by real call detail record (CDR) dataset for anomaly detection. Our framework achieved 98.8% accuracy with 0.44% false positive rate (FPR)—notable improvements that surmount the deficiencies of the old studies. The numerical results explicate the usefulness and dominance of our proposed detector