Otherwise, when the data is offloaded for edge execution (xti = 1), we denote Pit as the transmit power constrained by the maximum power Pt ≤ Pmax and τtT as the amount of time iii allocated to the ith WD for computation offloading. Here, τit ∈ [0, 1] and Ni=1 τit ≤ 1. The energy consumed on data offloading is Eit,O = PitτitT. Similar to [4] and [8], we neglect the delay on edge computing and result downloading such that the amount of data processed at the edge within the time frame is Dt =WτitTlog 1+Pithti=WτitTlog 1+Eit,Ohti, ∀xt =1, ,Eoit,pit是什么
时间: 2024-01-23 18:02:28 浏览: 327
根据文中的描述,Eit,O表示第i个设备的数据离载能耗,Pit表示传输功率的限制,由最大功率Pt ≤ Pmax决定,τit表示分配给第i个设备的计算离载时间,且τit ∈ [0, 1],并且所有设备的计算离载时间之和不超过1(即Ni=1 τit ≤ 1)。因此,Eit,O = PitτitT表示第i个设备的数据离载能耗。
相关问题
As illustrated in Fig. 1, we consider the general layout of HetNets, where each macro-BS serves multiple UE devices and contains a few backhaul-constrained small BSs within its range. Since the small BSs are backhaul-constrained, when UEs are offloaded to a small BS, the small BS may be overloaded when the number of serving UE devices exceeds its capacity (e.g., the small BS identified by the dotted line in Fig. 1). In this scenario, the overloaded small BS cannot provide the serving UEs with the expected QoS. Therefore, the backhaul capacity must be considered as a limitation in the design of the user association algorithm.翻译
如1所示,我们考虑HetNets的一般布局,其中每个宏基站为多个UE设备提供服务,并在其范围内包含一些受后向链路限制的小型基站。由于小型基站受到后向链路的限制,当UE设备被离载到小型基站时,当服务的UE设备数量超过其容量时(例如,图1中由虚线标识的小型基站),小型基站可能会超载。在这种情况下,超载的小型基站无法为服务的UE设备提供预期的QoS。因此,在设计用户关联算法时必须考虑后向链路容量作为限制因素。
In the proposed algorithm, the access prices of BSs are adjusted according to incomes and load situations in the leaderlevel game, and each UE independently chooses the BS that maximizes its payoff (or minimizes its payment) in the follower-level game. Thus, the algorithm achieves optimization in a distributed manner. When a small BS is overloaded, the price will increase to ensure that the offloaded UE has a higher access payment. For the design of the price, a particle swarm optimization-based pricing strategy (PSO-PS) is proposed to maximize the BS revenue. Finally, we use the resident-oriented Gale-Shapley (GS) algorithm to obtain a stable single-BS association. The main contributions of this paper can be summarized as follows:翻译
在所提出的算法中,基站的接入价格根据领导者层次博弈中的收入和负载情况进行调整,而每个UE在追随者层次博弈中独立选择使其效益最大化(或支付最小化)的基站。因此,该算法以分布式的方式实现了优化。当小型基站超载时,价格将增加,以确保离载的UE具有更高的接入支付。在价格设计方面,提出了一种基于粒子群优化(PSO-PS)的定价策略,以最大化基站的收入。最后,我们使用面向居民的Gale-Shapley(GS)算法来获得稳定的单基站关联。本文的主要贡献可以总结如下:
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