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首页> 外文期刊>IEEE Transactions on Vehicular Technology >MOCA: Multiobjective Cell Association for Device-to-Device Communications
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MOCA: Multiobjective Cell Association for Device-to-Device Communications

机译:MOCA:用于设备到设备通信的多目标小区关联

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It is widely accepted that device-to-device (D2D) communication is envisaged to become the key enabler of direct localized communication between mobile nodes in future wireless networks. However, little attention has been paid to an important aspect that can potentially affect both the performance of D2D and cellular transmissions, which is that of the D2D cell association. In this paper, a multiobjective cell association (MOCA) optimization framework for orchestrating a large number of D2D links in a multicell network is introduced. To this end, and without loss of generality, a differentiated Fractional Frequency Reuse (FFR) scheme is considered as the interference-limiting method, especially for cell-edge users, and we assume the provision of different resource pools for D2D and cellular users, which can vary according to their location. Under this assumption, we develop a set of integer linear programming optimization formulations for D2D links, part of which fall within the coverage area of different neighboring base stations. The main purpose is to achieve improved network traffic balancing via an efficient cell association scheme. Furthermore, we provide an iterative randomized resource allocation algorithm (i-RRA ), which roots its logic on the differentiated FFR model in order to increase the overall network throughput. A wide set of numerical investigations demonstrate the benefits offered by MOCA, as well as the throughput gains that can be achieved through i-RRA, compared to existing solutions.
机译:人们普遍认为,设备到设备(D2D)通信将成为未来无线网络中移动节点之间直接本地通信的关键推动力。但是,很少关注可能会影响D2D和蜂窝传输(即D2D小区关联)的性能的重要方面。本文介绍了一种用于在多小区网络中协调大量D2D链接的多目标小区关联(MOCA)优化框架。为此,在不失一般性的前提下,差分分数频率复用(FFR)方案被认为是一种干扰限制方法,尤其是对于小区边缘用户而言,并且我们假设为D2D和蜂窝用户提供不同的资源池,视位置而定。在此假设下,我们为D2D链路开发了一组整数线性规划优化公式,其中一部分落在不同相邻基站的覆盖范围内。主要目的是通过有效的小区关联方案来实现改进的网络流量平衡。此外,我们提供了一种迭代随机资源分配算法(i-RRA),该算法将其逻辑植根于差分FFR模型,以提高整体网络吞吐量。大量的数值研究表明,与现有解决方案相比,MOCA的优势以及通过i-RRA可以实现的吞吐量提高。

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