Wenpeng Jing

Resource management based on security satisfaction ratio with fairness-aware in two-way relay networks

Information security has been received more and more attention for next-generation wireless sensor networks. In this paper, we consider the problem of resource management based on security satisfaction ratio with fairness-aware in two-way relay networks. Multiple source nodes exchange information with the help of relay node in the presence of an eavesdropper, and diverse security requirements are taken into account with coexistence of security users and normal users. The joint problem of power allocation, and subchannel pairing and allocation aims to maximize the security satisfaction ratio for legitimate users subject to limited power and subchannel constraints. We model the security resource management problem as a mixed integer programming problem, which is decomposed into three subproblems, distributed power allocation, distributed subchannel allocation, and distributed subchannel pairing, and then solved it in constraint particle swarm optimization (CPSO), binary CPSO (B_CPSO), and classic Hungarian algorithm (CHA) method, respectively. Moreover, a suboptimal subchannel pairing algorithm is proposed to reduce the computational complexity compared with the CHA. Simulations are conducted to evaluate the effectiveness of the proposed algorithms.

Exact potential game based power control with QoS provisioning in two-tier femtocell networks

In this paper, we investigate the power control problem in the uplink of spectrum-sharing femtocell networks with delay quality of service (QoS) guarantees. Due to the time-varying nature of wireless channels, deterministic QoS is hard to be guaranteed in wireless networks. By integrating information theory with the principle of effective capacity (EC), we formulate an interference-aware power control problem with statistical QoS provisioning. To solve the problem, we firstly model the optimization problem as an exact potential game. Specially, a channel quality related pricing mechanism is presented to mitigate co-tier interference. Then the existence and uniqueness of Nash Equilibrium (NE) point is studied. Finally, a distributed optimal power control algorithm is proposed to implement the game. Simulation results indicate that the proposed algorithm greatly improves EC of all femtocell user equipments (FUE).

Energy-efficient power allocation with QoS provisioning in OFDMA femtocell networks

This paper addresses the energy-efficient power allocation problem of downlink transmission with delay quality of service (QoS) constraint in the femtocell networks. Particularly, in order to provide statistical delay guarantee, the effective capacity (EC) is employed as the network performance measure instead of the conventional Shannon capacity. As a result, the energy efficiency (EE) metric of the femtocell is defined to be the total-EC-to-the-overall-power-consumption ratio of the femtocell base station (FBS). The optimization problem is firstly modeled as a supermodular game. Then the existence and characteristics of the Nash Equilibrium (NE) are investigated. A distributed energy-efficient power allocation algorithm is also designed to implement the game. Simulation results demonstrate that, our proposed algorithm delivers substantial energy efficiency improvement while satisfying a wide range of delay requirements.

Cooperation-enabled energy efficient base station management for dense small cell networks

Dense small cell networks are deployed for future wireless communication to meet the ever-increasing mobile traffic demand. However, network densification will significantly increase the energy budget and lead to energy inefficiency due to the constant operation of network hardware. In this paper, we consider cooperation-enabled dynamic base station (BS) management for downlink dense small cell networks. By introducing two traffic-aware sleep modes, i.e., deep sleep mode and opportunistic sleep mode which are operating in different time and energy consumption scales, the network hardwares are turned to be the resources that can be occupied and released dynamically. Small cell BSs (SBSs) with zero or low load are completely switched off and reside in deep sleep mode during a predefined time interval. At each time slot, SBS dynamically turn some antennas and associated physical components into opportunistic sleep mode according to the short term traffic distribution, and the users are jointly served by the remaining antennas via cooperative transmission. The corresponding sleep mode decision making are presented respectively to find the optimal number of SBS and antennas that should be switched off. Numerical results are then presented to illustrate the superior performance in terms of energy efficiency gain. In summary, the proposed cooperation-aided sleep strategies for dense small cell networks take both traffic features and optimal cooperative transmission into account, and can achieve great energy saving while maintaining required quality of service.

Simultaneous information and energy transfer in large-scale cellular networks with sleep mode

Energy harvesting from ambient radio frequency (RF) radiation is an efficient approach to provide wireless energy replenishment for mobile devices. At the same time, introducing active/sleep modes in base stations (BSs) is an effective method to save the energy consumption of cellular networks. However, the performance of the two techniques would be dependent and interact with each other if both of them were employed in cellular networks. In this paper, we analyze the performance and investigate the sleeping strategy design problem in the cellular network, in which the mobile devices have the capability of energy harvesting from the ambient RF signals. Specifically, using tools from stochastic geometry theory, we derive analytical expressions of the coverage probability and the harvested energy and show that they are both affected by the sleeping strategy. Then, we formulate a BSs power consumption minimization problem under the coverage probability and harvesting performance constraints. Finally, the optimal operating regime of the sleeping strategy is derived. Numerical results confirm the correctness of the analysis and show the effect of switching off BSs on both performance metrics.

Clustering-based low-complexity resource allocation in two-tier femtocell networks with QoS provisioning

Summary In this paper, we study the resource allocation problem of the uplink transmission with delay quality-of-service constraints in two-tier femtocell networks. Particularly, to provide statistical delay guarantees, the effective capacity is employed as the network performance measure instead of the conventional Shannon capacity. To make the problem computationally efficient and numerically tractable, we decompose the problem into three subproblems, namely, cluster configuration subproblem, intra-cluster subchannel allocation subproblem and inter-cluster power control subproblem. Firstly, we develop a low-complexity heuristic semi-dynamic clustering scheme, where the delay of the channel state information feedback via backhaul is considered. We model such system in the framework of networked partial observation Markov decision process and derive a strategy to reduce the search range for the best cluster configuration. Then, for a given cluster configuration, the cluster heads deal with subchannel allocation and power control within each cluster. We propose a subchannel allocation scheme with proportional fairness. Thereafter, the inter-cluster power control subproblem is modeled as a set of exact potential games, and a channel quality related pricing mechanism is presented to mitigate inter-cluster interference. The existence and uniqueness of Nash equilibriums for the proposed game are investigated, and an effective decentralized algorithm with guaranteed convergence is designed. Simulation results demonstrate that the proposed algorithms not only have much lower computational complexity but also perform close to the exhaustive search solutions and other existing schemes. Copyright

Handoff management scheme based on frame loss rate and RSSI prediction for IEEE 802.11 networks.

This paper proposes a frame loss rate (FLR) and received signal strength indicator (RSSI) prediction based handoff management scheme to reduce the ping-pong effect caused by the purely RSSI based handoff decision schemes in IEEE 802.11 networks. The proposed scheme is performed by each mobile station (MS) in a decentralized manner. In the proposed scheme, the FLR information is used as handoff trigger criterion. To perceive the change trends of wireless link thus reducing the target AP selection time, the proposed scheme adopts polynomial regression-based RSSI prediction approach. The predicted RSSI together with FLR information is used for target access point (AP) selection. Moreover, we construct neighboring AP information list (NAIL) for MSs to store the above information. Therefore, the proposed scheme can also be called NAIL-based handoff management scheme. The results indicate that the proposed scheme can effectively reduce the invalid handoffs and avoid the ping-pong effect.

A modified Matérn hard core point process for modeling and analysis of dense IEEE 802.11 networks.

Stochastic geometry is a powerful tool for modeling wireless networks employing various MAC protocols with random topologies. For wireless networks operating carrier sense multiple access/collision avoidance (CSMA/CA) protocol, there are distance constraints between simultaneous transmitters, and only one node is allowed to transmit within its carrier sensing range. Matérn hard core point process is generally used to model and analyze the performance of this type of networks, because it is a repulsive point process that also defines a minimum distance between any two points. However, the traditional Matérn HCPP has the defect of underestimating the intensity of concurrent transmission nodes, which will affect the accuracy of analysis results, when wireless nodes are densely deployed. In this paper, we propose a modified Matérn HCPP called TM-HCPP which can mitigate the intensity underestimation problem to model dense IEEE 802.11 networks. Then, we simulate the network coverage probability using the traditional Matérn HCPP and the proposed TM-HCPP, and compare the simulated results with the theoretical analyzed results. Simulation results validate the accuracy of the proposed TM-HCPP.

Dynamic user-centric clustering algorithm based on energy efficiency in Cloud-RAN

This paper studies user-centric cluster scheme in cloud radio access network (Cloud-RAN), where distributed Radio Remote Heads (RRHs) are connected to a centralized Based Band Units (BBUs) pool and all baseband processing is performed via high-bandwidth low-latency backhaul network. In user-centric cluster scheme, BBU schedules multiple RRHs for each user to form the serving cluster, and then BBU pool distributes the users data to the cluster via backhaul link. By taking into account signal-to-interference-and-noise ratio (SINR) constraint for each user and transmit power constraint for per-RRH, we design a cluster scheme aiming at maximizing network energy efficiency (EE). And then weighted minimal mean square error (WMMSE) method are adopted to transform the original problem into a tractable form. Specially, a sparsity-based dynamic user-centric cluster algorithm is proposed, where the cluster for each user can change over time according to network EE performance. Simulation results show that the proposed algorithm can effectively form the serving cluster according to users QoS demand and greatly improve the network energy efficiency.

Performance analysis of delayed mobile data offloading with multi-level priority

WiFi offloading is a cost-effective and practical solution to alleviate the problem of highly congested cellular networks. Recent theoretical and experimental studies show that delayed WiFi offloading where traffic can be delayed to increase the chance of meeting WiFi can significantly improve offloading efficiency. Nevertheless, there is no exact analytic model to analyze the offloading benefits with different types of traffic. In this paper, we propose a preemptive priority queuing analytic model for delayed offloading with multi-level priority traffic and derive expressions for the average delay and offloading efficiency of traffic with different priorities as a function of the WiFi availability, deadlines, and other key parameters. At last, we validate the accuracy of our queuing model by simulation in different scenarios and clarify how to choose a suitable deadline for traffic of different priorities.