有限框架中带有强制访问和重试功能的插槽-Aloha 性能分析

Diogo Pereira;Rodolfo Oliveira;Daniel Benevides da Costa;Hyong Kim
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摘要

在最近的介质访问控制(MAC)协议中,例如在 IEEE 802.11ad 和 IEEE 802.11ay 中采用的协议,站点必须访问构成帧的 $N_{S}\gt 0 $ 连续时隙中的一个。这可以看作是传统插槽式网络(SA)的一种变体,在这种变体中,一个站不是以概率 p 访问一个插槽,而是以概率 $(1-p)^{N_{S}}$ 在有限帧中不访问,在一个帧中总是至少访问一次,也就是说,站必须在一个帧的插槽中访问。此外,如果上一次尝试不成功,站点还可以在帧中进行多次重试。鉴于目前缺乏对强制接入和重试的 SA 方案(SAMAR)的建模工作和性能评估,我们在这封信中评估了在由有限个时隙组成的帧中能成功接入信道的台站的预期数量。与 SA 不同,SAMAR 的性能分析因其枚举性而具有挑战性。我们提出了一种创新的 SAMAR 性能递归模型,表明 SAMAR 可以达到比以渐近最优接入概率为参数的 SA 更高的性能。对比分析表明了 SAMAR 性能超过 SA 性能的运行区域,这对确定 SAMAR 框架的时隙数至关重要。
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Performance Analysis of Slotted-Aloha With Mandatory Access and Retries in a Finite Frame
In recent medium access control (MAC) protocols, such as the ones adopted in IEEE 802.11ad and IEEE 802.11ay, stations mandatorily access one of the $N_{S} \gt 0 $ consecutive slots forming a frame. This can be seen as a variant of the traditional slotted-aloha (SA), where instead of accessing a slot with probability p and not accessing in the finite frame with probability $(1-p)^{N_{S}}$ , a station always accesses at least once in a frame, i.e., stations mandatorily access in a frame’s slot. Additionally, a station can also perform multiple retries in the frame when the previous attempt did not succeed. Given the lack of modeling efforts and performance evaluation for SA schemes with mandatory access and retries (SAMAR), in this letter, we evaluate the expected number of stations that can successfully access the channel in a frame composed of a finite number of slots. Contrarily to SA, the analysis of SAMAR performance is challenging due to its enumerative nature. We propose an innovative recursive model of SAMAR performance, showing that SAMAR can achieve higher performance than that of SA parameterized with asymptotically optimal access probabilities. A comparative analysis shows the operational region where SAMAR overpasses SA’s performance, which is of crucial importance to define the number of slots of the SAMAR frame.
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Table of Contents IEEE Networking Letters Author Guidelines IEEE COMMUNICATIONS SOCIETY IEEE Communications Society Optimal Classifier for an ML-Assisted Resource Allocation in Wireless Communications
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