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首页> 外文期刊>IEEE Transactions on Communications >Content Pushing Over Multiuser MISO Downlinks With Multicast Beamforming and Recommendation: A Cross-Layer Approach
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Content Pushing Over Multiuser MISO Downlinks With Multicast Beamforming and Recommendation: A Cross-Layer Approach

机译：利用多播波束形成和建议在多用户MISO下行链路上推送内容：一种跨层方法

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摘要

Proactive caching is recognized as a promising approach to handle the rapid growth of data traffic, thereby attracting much attention recently. As a key performance metric of caching, the hit ratio is determined by demand probabilities of users for content items and caching decisions. Because the recommendation system is capable of shaping user demands, the joint caching and recommendation holds the potential of improving the hit ratio substantially. In this paper, joint pushing and recommendation (JPR) schemes are presented for multiuser multiple-input single-output (MISO) systems, in which content items are pushed over MISO downlinks with multicast beamforming. Aiming at maximizing the effective throughput, we formulate a multi-stage stochastic programming problem under the constraints of transmit power and quality of experience (QoE). Since the formulated problem is intractable, suboptimal online JPR policies are presented based on the convex-concave procedure and branch-and-bound methods. Simulations show that presented JPR policies are capable of attaining significant effective throughput gains.
机译：主动缓存被认为是处理数据流量快速增长的一种有前途的方法，因此最近引起了很多关注。作为缓存的关键性能指标，命中率由用户对内容项和缓存决策的需求概率确定。由于推荐系统能够调整用户需求，因此联合缓存和推荐具有大幅提高命中率的潜力。在本文中，提出了针对多用户多输入单输出（MISO）系统的联合推送和推荐（JPR）方案，其中，内容项通过多播波束成形在MISO下行链路上进行推送。为了最大化有效吞吐量，我们在发射功率和体验质量（QoE）的约束下，提出了一个多阶段随机规划问题。由于提出的问题是棘手的，因此基于凸凹过程和分支定界方法提出了次优的在线JPR策略。仿真表明，提出的JPR策略能够获得显着的有效吞吐量增长。

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