下行异构网络下的D2D节能通信

Martinus Erico Noktafianus Hindarto, N. Rahma, N. Adriansyah, Vinsensius Sigit Widhi Prabowo
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引用次数: 0

摘要

蜂窝用户(CU)的增加导致了基站(BS)的数据流量拥塞。设备到设备(D2D)通信可用于减少BS上的流量。D2D通信是设备之间的直接通信,不使用中央BS来减少交通拥堵。CU和D2D用户将共享相同的资源块(Resource Block, RB),但有一个缺点会导致干扰。本研究采用下行方向异构网络(HetNet)系统模型来减轻干扰问题。资源分配采用贪心算法,为提高其性能,将两个小基站(SB)合并,记为SB1(第一SB)和SB2(第二SB),从而将算法转化为带有SB1SB2的贪心算法。将添加SB1SB2的贪心算法与常规贪心算法(不添加SB1和SB2)进行对比分析。此外,作者在本研究中操纵了宏蜂窝基站(MB)的半径,以检验其对系统性能的影响。仿真结果表明,SB1SB2算法的总数据速率较低,为1.62 × 108 bps,频谱效率为9.02 bps/Hz,总公平性(CU和D2D)为0.4095。同时,在功耗效率方面,采用SB1SB2的贪心算法的值更高,达到10.40%。然而,两种算法的资源分配都不是最佳解决方案。这样得到的性能参数就不是最优的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy Efficient D2D Communication Under Downlink Heterogeneous Network
The increase in cellular users (CU) caused data traffic congestion on the Base Station (BS). Device to Device (D2D) communication can be used to reduce the traffic on BS. D2D communication is direct communication between devices without using a central BS to reduce traffic congestion. CU and D2D users will share the same Resource Block (RB), but there is a drawback that causes interference. This study employs a downlink direction Heterogeneous Network (HetNet) system model to mitigate interference issues. Resource allocation is performed using a greedy algorithm, and to enhance its performance, two small cell base stations (SB) were incorporated, denoted as SB1 (1st SB) and SB2 (2nd SB), thereby transforming the algorithm into a greedy algorithm with SB1SB2. A comparative analysis was conducted between the greedy algorithm with SB1SB2 and the conventional greedy algorithm (without the addition of SB1 and SB2). Furthermore, the authors manipulated the radius of the macro cell base station (MB) in this study to examine its impact on system performance. The results obtained from the simulation are greedy with the SB1SB2 algorithm getting a lower value on the sum data rate with a value of 1.62 × 108 bps, spectral efficiency with a value of 9.02 bps/Hz, total fairness (CU and D2D) with a value of 0.4095. Meanwhile, in terms of power efficiency, the greedy algorithm with SB1SB2 has a higher value of 10.40%. However, resource allocation in both algorithms is not the best solution. So that the resulting performance parameters are less than optimal.
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