Intelligent reflecting surface (IRS) provides a promising technology that can improve the energy efficiency of wireless communications, by building the controllable wireless propagation environment with the utilization of massive passive reflecting elements. Motivated by this, we apply the IRS to assist the data collection based on collaborative beamforming in wireless sensor networks (WSNs). With the objective to maximize the network lifetime, we present an energy-efficient joint collaborative and passive beamforming design for an IRS-assisted WSN, which jointly optimizes the beamforming vector for all nodes and phase shifts for IRS’s reflecting elements. To efficiently resolve the joint optimization problem, we develop a penalty dual-decomposition (PDD)-based algorithm combined with the augmented Lagrangian method, penalty-based method and block coordinate descent algorithm, which decomposes the joint optimization problem into four simplified subproblems. Especially, in solving IRS’s phase-shift optimization subproblem, we formulate this subproblem as a quadratically constrained quadratic programming (QCQP) problem and further propose a low computational complexity approximate iteration algorithm to resolve this QCQP problem, which iteratively linearizes this QCQP problem to a sequence of approximate programming with linear equality constraints, whose closed-form solution is presented. Theoretical analysis indicates the proposed algorithm can converge to a KKT solution. Simulation results indicate that the IRS-assisted design and proposed algorithm improve the energy efficiency and prolong the network lifetime as compared to alternative algorithms.

Network slicing is a fundamental architectural technology for the fifth generation mobile network. It is challenging to design a robust end-to-end network slice spanning overall networks, where a slice is constituted by a set of virtual network functions (VNFs) and links. Bugs may accidentally occur in some VNFs, invalidating some slices, and triggering slice recovery processes. Besides, the traffic demands in each slice can be stochastic, and drastic changes of traffic demands may trigger slice reconfiguration. In this paper, we investigate robust network slicing mechanisms by addressing the slice recovery and reconfiguration in a unified framework. We first develop an optimal slice recovery mechanism for deterministic traffic demands. This optimal solution is used as a benchmark for evaluating other robust slicing algorithms. Then, we design an optimal joint slice recovery and reconfiguration algorithm for stochastic traffic demands by exploiting robust optimization. To tackle the slow convergence issue in the robust optimization algorithm, we propose a heuristic algorithm based on variable neighborhood search. Numerical results reveal that our proposed robust network slicing algorithms can provide adjustable tolerance of traffic uncertainties compared with the deterministic algorithm.

Network slicing enables diversified services to be accommodated by isolated slices in network function virtualization-enabled software-defined networks. To maintain satisfactory user experience and high profit for service providers in a dynamic environment, a slice may need to be reconfigured according to the varying traffic demand and resource availability. However, frequent reconfigurations incur certain cost and might cause service interruption. In this paper, we propose a hybrid slice reconfiguration (HSR) framework, where a fast slice reconfiguration (FSR) scheme reconfigures flows for individual slices at the time scale of flow arrival/departure, while a dimensioning slices with reconfiguration (DSR) scheme is occasionally performed to adjust allocated resources according to the time-varying traffic demand. In order to optimize the slice’s profit, i.e., the total utility minus the resource consumption and reconfiguration cost, we formulate the problems for FSR and DSR, which are difficult to solve due to the discontinuity and non-convexity of the reconfiguration cost function. Hence, we approximate the reconfiguration cost function with${L} _{1}$norm, which preserves the sparsity of the solution, thus facilitating restricting reconfigurations. Besides, we design an algorithm to schedule FSR and DSR, so that DSR is timely triggered according to the traffic dynamics and resource availability to improve the profit of slice. Furthermore, we extend HSR with a resource reservation mechanism, which reserves partial resources for near future traffic to reduce potential reconfigurations. Numerical results validate that our reconfiguration framework is effective in reducing reconfiguration overhead and achieving high profit for slices.

In this paper, we propose software-defined protocol (SDP) technique to facilitate flexible service-oriented protocol stack deployment for providing high-throughput, low-latency and elastic mobile services based on platform virtualization and functionality modularization. We first elaborate the principle of SDP and then address one of the most important issues in SDP, namely SDP request mapping (SDPM), where an SDP request is fulfilled by mapping a set of required SDP function blocks and virtual links onto underlying SDP servers. We formulate the SDPM problem as a mixed integer programming (MIP). To address the NP-hardness and scalability of SDPM problem, we propose a decomposition algorithm which breaks down the SDPM problem into inter-block link and block mapping problems to accomplish the upper bound (UB) and lower bound (LB) of the MIP solution, respectively. The optimality can be achieved when the UB and the LB converges by using iterations. We employ LTE Layer-2 data-plane processing as a benchmark for validating the effectiveness of the SDP technique and evaluate the performance of SDPM algorithm. Numerical results show that SDP is effective to provide elastic low-latency mobile services and the proposed SDPM algorithm significantly outperforms the benchmark in stack processing delay, mapping cost, and resource utilization.

End-to-end network slicing has been viewed as a key enabler for the next generation mobile network (5G), where a Slice Provider (SP) creates various network slices for Slice Customers (SCs) to accommodate diverse services. Due to resource isolation, effective resource allocation for coexisted multiple network slices, \textit{i.e.} network slice dimensioning, is essential to maximize network resource efficiency. From the perspective of operators, both SP and SC pursue a profit-earning business model. However, the relationship between resource efficiency and profit maximization is not clear so far. In this paper, we study network slice dimensioning with resource pricing policy, by exploring this relationship. We first develop an optimization framework for network slice dimensioning, in which the Slice Customer's Problem (SCP) maximizes the SC's profit and the Slice Provider's Problem (SPP) maximizes net social welfare (resource efficiency). We find that maximization of net social welfare and SP's profit are two consistent objectives when resources are scarce; otherwise, there is a tradeoff. Based on this finding, we propose a low-complexity distributed algorithm to achieve near-optimal net social welfare with profit guarantee for SP/SCs. Simulations and numerical results verify the effectiveness of our proposed slice dimensioning strategy, which can help fully exploiting the capability of network slicing.

Network slicing is an emerging terminology that enables operators to partition a shared substrate network into multiple logically isolated and on- demand virtual networks to support diverse communication cases. However, the performance of network slices can be heavily deteriorated due to unexpected software or hardware malfunctions in the substrate network. Furthermore, the traffic demand is usually considered as a deterministic parameter during the network slice deployment, while it could be stochastically varied in reality, and such stochasticality may invalidate some network slices. Therefore, it is imperative to develop a robust network slicing algorithm. In this paper, we first formulate the failure recovery problem of network slicing as a mixed integer programming (MIP) and then model the robust MIP (RMIP) to capture the stochastic traffic demand. We solve the RMIP by using the robust optimization approach. Numerical results reveal that the proposed the robust network slicing algorithm can provide adjustable tolerance of traffic uncertainty in comparison with the nonrobust algorithm. In the meanwhile, the trade-off between robustness of requests and the load of substrate links can be hence efficiently managed and controlled.

Virtualization of network functions and centralized management are anticipated to provide 5G mobile networks with flexibility, lower end-to-end latency and reduced cost. Based on the concept of emerging Software Defined Network (SDN) and Network Function Virtualization (NFV) techniques, we propose Software Defined Protocol (SDP) technique to facilitate a flexible service-oriented protocol stack deployment under centralized network control. The design objective of SDP is to provide high-throughput, low-latency and elastic mobile services by making data-plane protocol programmable. In this paper, we first elaborate the SDP mechanisms and then address one of the most important issues in SDP, namely protocol stack mapping (PSM). We formulate the PSM problem as a 0-1 quadratic programming for selecting the optimal SDP servers to balance network load. We employ the legacy LTE data-plane processing as a benchmark for validating the effectiveness of the SDP and PSM algorithm. Numerical results show that SDP is effective to provide elastic low-latency mobile services and the proposed PSM algorithm significantly outperforms the benchmark in stack processing delay, mapping cost and resource utilization.