Achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices. This paper presents a novel solution that seamlessly integrates two technologies, mobile cloud computing and microwave power transfer (MPT), to enable computation in passive low-complexity devices such as sensors and wearable computing devices. Specifically, considering a single-user system, a base station (BS) either transfers power to or offloads computation from a mobile to the cloud; the mobile uses harvested energy to compute given data either locally or by offloading. A framework for energy efficient computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local computing, time division between MPT and offloading for the other mode of offloading, and mode selection. Given the CPU-cycle statistics information and channel state information (CSI), the policies aim at maximizing the probability of successfully computing given data, called computing probability, under the energy harvesting and deadline constraints. The policy optimization is translated into the equivalent problems of minimizing the mobile energy consumption for local computing and maximizing the mobile energy savings for offloading which are solved using convex optimization theory. The structures of the resultant policies are characterized in closed form. Furthermore, given non-causal CSI, the said analytical framework is further developed to support computation load allocation over multiple channel realizations, which further increases computing probability. Last, simulation demonstrates the feasibility of wirelessly powered mobile cloud computing and the gain of its optimal control.

Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer

Network virtualization enables the creation of multiple instances of virtual networks on top of a single physical infrastructure. Given its wide applicability, this technique has attracted a lot of interest both from academic researchers and major companies within the segment of computer networks. Although recent efforts (motivated mainly by the search for mechanisms to evaluate Future Internet proposals) have contributed substantially to materialize this concept, none of them has attempted to combine efficient resource allocation with fulfillment of security requirements (e.g., confidentiality). It is important to note that, in the context of virtual networks, the protection of shared network infrastructures constitutes a fundamental condition to enable its use in large scale. To address this problem, in this paper we propose a virtual network embedding model that satisfies security requirements and, at the same time, optimizes physical resource usage. The results obtained demonstrate that the model is able to correctly and optimally map virtual networks to a physical substrate, minimizing bandwidth costs for infrastructure providers.

Security-aware optimal resource allocation for virtual network embedding

Singular value decomposition is highly essential to achieve a higher performance in signal processing using massive multiple-input multiple-output (MIMO) systems. This paper aims to provide a solution to control power allocation problem identified as an essential metric in a massive MIMO system that maximizes energy efficiency (EE). The network performance was evaluated by measuring circuit power consumption to maximize EE. The computational efficiency to maximize EE power allocation is very important to fifth generation networks (5G). The study aims to maximize the non-convex EE in a downlink (DL) massive MIMO system using a proposed energy-efficient low-complexity algorithm (EELCA) that guarantees optimal power allocation solution based on Newton's methods and joint user's association based on the Lagrange's decomposition method. An optimal power allocation solution in closed form to decrease the complexity of the power subject to both the maximum power and minimum data rate constrained systems was derived. Then, the unconstrained EE power allocation to solve the unconstrained optimal power was used to select the optimal power allocation by computing a root of the first derivative of the EE based on differentiating the instantaneous power allocation to maximize EE was formulated. Simulation results showed that the proposed EELCA with a total transmitted power allocation provided maximum EE for a large number of antennas at the base station (BS), Generally, non-linear schemes outperformed linear schemes. Finally, the large cost of circuit power consumption increased at the BS due to the large loss of radio frequency (RF) chains at every antenna when the signals were transmitted to all users. The maximum EE = 5.9 Mbits/joule when the number of distributed users, K = 33, with (pc, M) = (1000mW, 200). The proposed low complexity algorithm provides the better result EE based on a training channel for a number of distributed users.

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Energy-efficient power allocation and joint user association in multiuser-downlink massive MIMO system

Nowadays, heterogeneous networks (HetNets) aims at achieving a huge amount of data rates and implementing customized service demands. Virtualization technology is the most promising approach for implementing HetNets. In the virtualization environment, physical network resources (e.g. CPU, storage, caching) are virtualized and abstracted in order to be managed and allocated by telecommunication providers flexibly. The virtual resource allocation issue is generally called as virtual network embedding (VNE), attracting extensive attention from academic community. Correspondingly, multiple resource allocation algorithms are proposed. However, existing allocation algorithms adopt ‘one fits all’ mode to serve all requested VN services. All VN services are different and customized, in terms of resource and QoS demands. Adopting the ‘one fits all’ mode to serve all different VNs will eventually lead to inefficient resource utilization and low revenues of telecommunication providers. Hence, it is essential to research the service oriented virtual resource allocation issue in virtualization environment, aiming at efficiently mapping each customized VN service. In this paper, we research the service oriented virtual resource allocation problem. A novel framework, labeled as SerOri , is proposed. The SerOri framework mainly consists of two parts: the VN service classification part SerOri-Class and the VN embedding part SerOri-Embed . By adopting the SerOri , each requested VN service can be implemented efficiently. In order to highlight our SerOri framework, we conduct the experiments. Existing latest or typical embedding algorithms are selected for performance comparison. Experiment results are plotted and discussed.

On Virtual Resource Allocation of Heterogeneous Networks in Virtualization Environment: A Service Oriented Perspective

Virtual Network Embedding (VNE) is an essential component of network virtualization technology. Prior works on VNE mainly focused on resource efficiency and did not address the scalability as a first-grade objective. Consequently, the ever-increasing demand and size render them less-practical. The few existing designs for mitigating this problem either do not extend to multi-resource settings or do not consider the physical servers and network simultaneously. In this work, we develop GraphViNE, a parallelizable VNE solution based on spatial Graph Neural Networks (GNN) that clusters the servers to guide the embedding process towards an improved runtime and performance. Our experiments using simulations show that the parallelism of GraphViNE reduces its runtime by a factor of 8. Also, GraphViNE improves the revenue-to-cost ratio by about 18%, compared to other simulated algorithms.

Accelerating Virtual Network Embedding with Graph Neural Networks

This paper considers the problem of renewable energy allocation for a cellular backhaul network where each backhaul node can request renewable energy from a microgrid to forward the traffic. Each microgrid includes multiple renewable power suppliers (RPSs), which directly compete with each other to be the sole supplier of the node. The operator needs to specify the energy storage level for each RPS along the backhaul and the wireless service price for the end users. Then, the RPSs will determine their individual energy prices. The problem is formulated as a Stackelberg game between the leader (the wireless operator) and multiple followers (RPSs). We will identify the RPSs’ unique equilibrium pricing scheme as well as the operator’s optimal energy storage and wireless service price decisions. Our analysis also provides the important observation that direct competition between RPSs on the same backhaul node will induce the operator to store more energy and can thus improve the revenues of both the operator and the RPSs. Furthermore, we consider that RPSs along the backhaul may form coalitions to better coordinate their pricing decisions when they sell energy to the wireless operator. Such a study leads to a hybrid two-level game whose the first level is similar to the Stackelberg game for the non-cooperative scenario. We will show that a coalition formation depends crucially on how the wireless traffic will be affected by the energy price reduction due to the coalition formation. In a nutshell, the proposed techniques provide insights for operating a green cellular backhaul by using the distribute energy sources in a decentralized market setting. Finally, simulation results are presented to compare the solutions of the competitive model with those of the cooperative model. We show that forming coalitions between RPSs benefits both the RPSs and the operator.

Integrating Distributed Grids With Green Cellular Backhaul: From Competition to Cooperation

In this paper, a location-fair based mmWave stable beamforming scheme under interlaced redundant coverage architecture is proposed to improve the stability and reliability of high speed railway communications. In the proposed scheme, an adaptive searching algorithm is presented to calculate the transmitting (TX) beamwidth and positions for beam switching. Thus, optimal TX beams with different beamwidth are formed by base station to balance the data rate. Theoretical analysis and simulations are conducted to verify the improved stability and reliability performance.

Location-Fair Based mmWave Stable Beamforming Scheme for High Speed Railway

Summary
To achieve the energy efficiency (EE) for multi-user multiple-input multiple-output (MU-MIMO) systems with GZI precoding, based on an actual power consumption model, the paper investigates the power allocation (PA) optimization problem with guaranteeing the sum rate for all downlink users and the different rate requirements for different downlink users. For the case of guaranteeing the sum rate for all downlink users, the highly complex optimization problem is firstly reformulated into an equivalent pseudo-convex form to be handled by the Lagrange Multiplier method. Then, it is simplified with reducing 2-dimension PA parameter into 1 dimension. Finally, the closed-form optimal solution is obtained through deriving of complex equations involving the log function. And for the case of guaranteeing the different rate requirements for different downlink users, the concrete numerical solution to the optimization problem is iteratively obtained through convex optimization toolbox in MATLAB. Simulation results validate the efficacy of the proposed optimization scheme. Furthermore, it is illustrated that the proposed optimization algorithm shows more optimal than average PA and antenna selection algorithms.

Energy-efficient power allocation for multi-user MIMO systems with GZI precoding

Data offloading is a promising technique for mobile terminals (MTs) for conserving energy in wireless networks. This technique enables MTs to support heavy data traffic by offloading computation-intensive tasks to the cloud. However, due to the long transmission up-link, data offloading may lead to unnecessary energy usage. In this paper, we propose a joint collaborative data offloading framework with channel selection for MTs, in which MTs can first form a coalition and then execute data offloading in a cooperative way. We adopt game theory to formulate the problem as a cooperation game. We then prove that the formulated game is a potential game and can achieve Nash equilibrium. A Markov approximation approach is applied to design a distributed channel selection algorithm for the game so that each MT can self-organize into stability without information exchange across the whole network. Analytical and numerical results show that the algorithm is feasible and efficient in comparison to traditional centralized optimization solutions.

The join collaborative data offloading and distributed channel selection in wireless networks

Network softwarization technologies, such as software defined networking (SDN), network virtualization (NV), and network function virtualization (NFV), emerge and have been greatly promoted by their supporters This softwarization scheme contributes to isolating network control from data panel, and allocating virtualized physical network resources flexibly However, physical switches and links, supporting the softwarization scheme, may be attacked by malicious attackers Even if these switches and links are softwarized and virtualized successfully, the problem of mapping various virtual network requests, known as virtual network embedding, is difficult to solve directly Each customized VN request must be allocated and implemented in the appropriate manner To deal with these issues, we research the virtual network mapping for secure SDN-enabled networks Firstly, a hierarchical virtualization architecture for SDN- enabled networks is presented Then, the security model for different malicious attacks is presented Thirdly, we propose the novel and secure virtual network mapping framework, labeled as \textit{NoViSec}, for supporting SDN-enabled networks Preliminary experiment results are plotted and discussed so as to validate the effectiveness of our secure \textit{NoViSec} framework

Feng Tian

Papers

Integrating Distributed Grids With Green Cellular Backhaul: From Competition to Cooperation

Location-Fair Based mmWave Stable Beamforming Scheme for High Speed Railway

Energy-efficient power allocation for multi-user MIMO systems with GZI precoding

The join collaborative data offloading and distributed channel selection in wireless networks

NoViSec: Novel Virtual Network Mapping Framework for Secure Software-Defined Networking