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Maximizing the Number of Users in Clustered MIMO-NOMA Systems Under Rate Constraints

机译:在速率约束下最大化集群MIMO-NOMA系统中的用户数

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Serving multiple users in the same resource block in non-orthogonal multiple access (NOMA) induces interference, which limits the achievable rate for each user. In this paper, the problem of maximization of the number of admissible users is solved in the downlink multiple input multiple output (MIMO)-NOMA systems, under a total transmission power constraint and ergodic user-rate constraints. A closed-form lower bound for the achievable ergodic rate is obtained. A power allocation scheme is derived. Three user admission algorithms are proposed to calculate the maximum number of admissible users for three different user ordering strategies. A comparison between the three ordering strategies is conducted, in terms of the maximum number of admissible users and the capability of admitting far users from the base station (BS). In the first and second strategies, users within each cluster are sorted, based on their channel variances, in a descending order, while in the third strategy, users in all the clusters are sorted in a descending order. In the second strategy, the user clusters are similar unlike the first and third strategies. The results show that the proposed algorithms efficiently compute the maximum number of admissible users. The second strategy achieves the greatest number of admissible users, followed by the first strategy, and then the third strategy, which provides the least number of admissible users. Moreover, the second strategy has the least capability of admitting far users, followed by the third strategy, and then the first strategy, which has the greatest capability of admitting far users.
机译:在非正交多址(NOMA)中为同一资源块提供多个用户,引起干扰,这限制了每个用户的可实现速率。本文在下行链路多输入多输出(MIMO)-NOMA系统中,在总传输功率约束和ergodic用户速率约束下解决了可允许用户数量的最大化问题。获得可实现的ergodic率的闭合形式下限。推导出功率分配方案。建议三个用户准入算法计算三个不同用户订购策略的最大可允许用户数。在最大可接受用户数和录取基站(BS)的最大用户的最大数量的最大数量的最大数量和录取远期用户的能力之间进行了比较。在第一和第二次策略中,每个群集内的用户都根据其频道差异,以下降顺序排序,而在第三次策略中,所有群集中的用户以降序排序。在第二次策略中,用户集群与第一和第三策略不同。结果表明,所提出的算法有效地计算可允许用户数的最大数量。第二次战略实现了最多的可接受用户,其次是第一个策略,然后是第三次策略,提供最少的可接受用户。此外,第二次策略具有承认远期用户的能力最小,其次是第三次策略,然后是第一个策略,这具有承认远远用户的最大能力。

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