6,113 research outputs found

    Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement

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    We propose an algorithm to automate fault management in an outdoor cellular network using deep reinforcement learning (RL) against wireless impairments. This algorithm enables the cellular network cluster to self-heal by allowing RL to learn how to improve the downlink signal to interference plus noise ratio through exploration and exploitation of various alarm corrective actions. The main contributions of this paper are to 1) introduce a deep RL-based fault handling algorithm which self-organizing networks can implement in a polynomial runtime and 2) show that this fault management method can improve the radio link performance in a realistic network setup. Simulation results show that our proposed algorithm learns an action sequence to clear alarms and improve the performance in the cellular cluster better than existing algorithms, even against the randomness of the network fault occurrences and user movements.Comment: (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

    Partially Blind Handovers for mmWave New Radio Aided by Sub-6 GHz LTE Signaling

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    For a base station that supports cellular communications in sub-6 GHz LTE and millimeter (mmWave) bands, we propose a supervised machine learning algorithm to improve the success rate in the handover between the two radio frequencies using sub-6 GHz and mmWave prior channel measurements within a temporal window. The main contributions of our paper are to 1) introduce partially blind handovers, 2) employ machine learning to perform handover success predictions from sub-6 GHz to mmWave frequencies, and 3) show that this machine learning based algorithm combined with partially blind handovers can improve the handover success rate in a realistic network setup of colocated cells. Simulation results show improvement in handover success rates for our proposed algorithm compared to standard handover algorithms.Comment: (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

    The formation of mixed germanium–cobalt carbonyl clusters: an electrospray mass spectrometric study, and the structure of a high-nuclearity [Ge₂Co₁₀(CO)₂₄]²⁻ anion

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    The reaction of [µ₄-Ge{Co₂(CO)₇}₂] with [Co(CO)₄]⁻ has been monitored by electrospray mass spectrometry to detect the cluster anions generated. Conditions giving known mixed Ge–Co carbonyl clusters were established, and a new high nuclearity cluster anion, [Ge₂Co₁₀(CO)₂₄]²⁻ was detected. Conditions for its formation were optimised and it was subsequently isolated as its [Et₄N]⁺ salt and characterised by single-crystal X-ray crystallography. The Ge₂Co₁₀ cluster core has a novel geometry with the two germanium atoms in semi-encapsulated positions, forming seven formal Ge–Co bonds. There are also eighteen formal Co–Co bonds. Corresponding reactions of [µ₄-Si{Co₂(CO)₇}₂] with [Co(CO)₄]⁻ were also investigated

    Complex Block Floating-Point Format with Box Encoding For Wordlength Reduction in Communication Systems

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    We propose a new complex block floating-point format to reduce implementation complexity. The new format achieves wordlength reduction by sharing an exponent across the block of samples, and uses box encoding for the shared exponent to reduce quantization error. Arithmetic operations are performed on blocks of samples at time, which can also reduce implementation complexity. For a case study of a baseband quadrature amplitude modulation (QAM) transmitter and receiver, we quantify the tradeoffs in signal quality vs. implementation complexity using the new approach to represent IQ samples. Signal quality is measured using error vector magnitude (EVM) in the receiver, and implementation complexity is measured in terms of arithmetic complexity as well as memory allocation and memory input/output rates. The primary contributions of this paper are (1) a complex block floating-point format with box encoding of the shared exponent to reduce quantization error, (2) arithmetic operations using the new complex block floating-point format, and (3) a QAM transceiver case study to quantify signal quality vs. implementation complexity tradeoffs using the new format and arithmetic operations.Comment: 6 pages, 9 figures, submitted to Asilomar Conference on Signals, Systems, and Computers 201

    ‘I enjoy supporting people to reach their full potential’: former joiner Brian Evans was driven to become a learning disability nurse after the death of his son

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    I am a clinical nurse specialist for learning disabilities at University Hospitals of Morecambe Bay NHS Trust. In my role I support individuals, families, carers and hospital staff to ensure care and treatment is person-centred and designed to meet the specific needs of the patient. This includes identifying reasonable adjustments, ensuring the legislative principles of the Mental Capacity Act 2005 are followed and supporting the person and carers to develop a hospital passport

    Why we need to urgently redouble our efforts

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    Authored section within the Manifesto for Scotland's landscape, prepared by the Landscape Institute Scotland and published at the Scottish Parliament in November 2017. The document reviews the diversity of Scotland's landscapes, the importance of good design, the contribution of green spaces and public spaces for health, tourism and economic prosperity together with the importance of stewardship and management in the face of climate change and concludes with a statement by Professor Evans from the Glasgow School of Art
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