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本期推荐
智能算力核心基础系统软件的现状与展望
作者:郑纬民,翟季冬,翟明书
[简介] 智能算力对中国人工智能技术的进步具有重要意义。发展智能算力平台,做好核心基础系统软件尤其重要。梳理了智能算力平台中的10个核心基础系统软件,对这些软件的全球现状进行了详细介绍,并探讨了当前中国算力平台上系统软件栈建设的机遇和挑战。
大模型关键技术与应用
作者:韩炳涛,刘涛
[简介]介绍了自ChatGPT发布以来,大模型关键技术和应用的主要进展。在大模型设计方面,模型规模不断增加,但已有放缓趋势,更长的上下文以及多模态已经成为主流,计算效率明显提升;在模型训练方面,从单纯追求数据数量逐渐转变为关注数据的多样性和质量,特别是如何使用合成数据训练大模型成为主流探索方向,这是迈向通用人工智能(AGI)的关键;在模型推理方面,模型量化和推理引擎优化极大降低了模型使用成本,诸如投机采样等新兴算法逐渐成熟。在应用层,Agent技术获得了重大进展,在克服大模型固有缺陷方面发挥了不可替代的作用。越来越多的企业开始规划、研发以及使用大模型,企业级大模型应用架构日益成熟完善,并以场景、技术、算法三要素为抓手加速大模型商业价值闭环。
Towards Near-Field Communications for 6G: Challenges and Opportunities
LIU Mengu, ZHANG Yang, JIN Yasheng, ZHI Kangda, PAN Cunhua
[Introduction] Extremely large-scale multiple-input multiple-output (XL-MIMO) and terahertz (THz) communications are pivotal candidate technologies for supporting the development of 6G mobile networks. However, these techniques invalidate the common assumptions of far-field plane waves and introduce many new properties. To accurately understand the performance of these new techniques, spherical wave modeling of the near-field communications needs to be applied for future research. Hence, the investigation of near-field communication holds significant importance for the advancement of 6G, which brings many new and open research challenges in contrast to conventional far-field communication. In this paper, we first formulate a general model of the near-field channel and discuss the influence of spatial nonstationary properties on the near-field channel modeling. Subsequently, we discuss the challenges encountered in the near field in terms of beam training, localization, and transmission scheme design, respectively. Finally, we point out some promising research directions for near-field communication.
Impacts of Model Mismatch and Array Scale on Channel Estimation for XL-HRIS-Aided Systems
LU Zhizheng, HAN Yu, JIN Shi
[Introduction] Extremely large-scale hybrid reconfigurable intelligence surface (XL-HRIS), an improved version of the RIS, can receive the incident signal and enhance communication performance. However, as the RIS size increases, the phase variations of the received signal across the whole array are nonnegligible in the near-field region, and the channel model mismatch, which will decrease the estimation accuracy, must be considered. In this paper, the lower bound (LB) of the estimated parameter is studied and the impacts of the distance and signal-to-noise ratio (SNR) on LB are then evaluated. Moreover, the impacts of the array scale on LB and spectral efficiency (SE) are also studied. Simulation results verify that even in extremely large-scale array systems with infinite SNR, channel model mismatch can still limit estimation accuracy. However, this impact decreases with increasing distance.