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In heterogeneous cellular networks (HetNets) with macro base station (MBS) and multiple small BSs (SBSs), cell association of user equipment (UE) affects UE transmission rate and network throughput. Conventional cell association rules are usually based on UE received signal-to-interference-andnoise-ratio (SINR) without being aware of other UE statistical characteristics, such as user movement and distribution. User behaviors can indeed be exploited for improving long term network performance. In this paper, we investigate UE cell association in HetNets by exploiting both individual and clustering user behaviors with aim to maximize long-term system throughput. We model the problem as a stochastic optimization problem, and prove that it is PSPACE-hard. For mathematical tractability, we solve the problem in two steps. In the first step, we investigate UE association for a specific SBS. We use restless multi-armed bandit model to derive association priority index for the SBS. In the second step, we develop an Index Enabled Association (IDEA) policy for making cell association decisions in general HetNets based on the indices derived in the first step. IDEA determines a set of admissible BSs for a UE based on SINR, and then associates the UE with the BS that has the smallest index in the set. We conduct simulation experiments to compare IDEA with other three cell association policies. Numerical results demonstrate the significant advantages of IDEA in typical scenarios.

Network slicing has been viewed as a key enabler for the next-generation software-defined and cloud-based network (e.g., 5G and beyond) to accommodate diverse services in a flexible and costefficient fashion. Network slicing allows a network slice provider (NSP) to operate on a common network infrastructure to create customized isolated logical networks (i.e., network slices) for network slice customers (NSCs), (i.e., service providers). NSP and NSCs are independent operators who pursue profit maximization, while in the literature, only network cost optimization is intensively investigated in terms of service function chain embedding, i.e., virtual network function (VNF) placement and flow routing. Therefore, slices should be dimensioned (i.e., resources allocated to slices) according to the resource availability and the economic mechanism in the network, so as to optimize the resource utilization and improve the profit of NSP/NSCs. In this paper, we study elastic slice dimensioning with resource pricing as a Stackelberg pricing game, in which the NSP sells slices by pricing resources and NSCs adjust their slice's resource demand on VNF capacity and bandwidth, while both are trying to maximize their profit. Then, we formulate optimization problems for the pricing game and find that a closed form solution of the optimal price cannot be obtained for a non-trivial network. Hence, we propose a resource pricing algorithm that aims to maximize the NSP's profit and the network's social welfare. Compared with existing usage-based pricing method and two heuristic methods, our proposed pricing algorithm for slice dimensioning strikes a trade-off between maximizing NSP's profit and other metrics, including the resource utilization. Hence, it will helpfully exploiting the benefits of network slicing.

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An optically clocked track-and-hold (TH) circuit for improved TH linearity and noise performance is presented. Results with fi.=1.0073 GHz and sample rate f,=1.003 GWs show 11.8 SFDR bits and 9.6 SNR bits.

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