Yifei Wei

Distributed Optimal Relay Selection in Wireless Cooperative Networks With Finite-State Markov Channels

Relay selection is crucial in improving the performance of wireless cooperative networks. Most previous works for relay selection use the current observed channel conditions to make the relay-selection decision for the subsequent frame. However, this memoryless channel assumption is often not realistic given the time-varying nature of some mobile environments. In this paper, we consider finite-state Markov channels in the relay-selection problem. Moreover, we also incorporate adaptive modulation and coding, as well as residual relay energy in the relay-selection process. The objectives of the proposed scheme are to increase spectral efficiency, mitigate error propagation, and maximize the network lifetime. The formulation of the proposed relay-selection scheme is based on recent advances in stochastic control algorithms. The obtained relay-selection policy has an indexability property that dramatically reduces the computation and implementation complexity. In addition, there is no need for a centralized control point in the network, and relays can freely join and leave from the set of potential relays. Simulation results are presented to show the effectiveness of the proposed scheme.

Reinforcement-Learning-Based Double Auction Design for Dynamic Spectrum Access in Cognitive Radio Networks

In cognitive radio networks, an important issue is to share the detected available spectrum among different secondary users to improve the network performance. Although some work has been done for dynamic spectrum access, the learning capability of cognitive radio networks is largely ignored in the previous work. In this paper, we propose a reinforcement-learning-based double auction algorithm aiming to improve the performance of dynamic spectrum access in cognitive radio networks. The dynamic spectrum access process is modeled as a double auction game. Based on the spectrum access history information, both primary users and secondary users can estimate the impact on their future rewards and then adapt their spectrum access or release strategies effectively to compete for channel opportunities. Simulation results show that the proposed reinforcement-learning-based double auction algorithm can significantly improve secondary users’ performance in terms of packet loss, bidding efficiency and transmission rate or opportunity access.

Energy Efficient Distributed Relay Selection in Wireless Cooperative Networks with Finite State Markov Channels

Relay selection is crucial in improving the performance of wireless cooperative networks. Most of previous works for relay selection use the current observed channel conditions to make the relay selection decision for the subsequent frame. However, this assumption is often not realistic given the time-varying nature of some mobile environments. In this paper, we consider finite state Markov channels in the relay selection problem. Moreover, we also incorporate adaptive modulation and coding, as well as residual relay energy in the relay selection process. The objectives of the proposed scheme are not only to increase spectral efficiency, mitigate error propagation, but also to maximize the network lifetime. Simulation results are presented to show the effectiveness of the proposed scheme.

The offloading model for green base stations in hybrid energy networks with multiple objectives

Summary Based on green energy prediction and storage, a novel green base station (GBS) offloading model is proposed and can be employed with multiple objectives in this paper to save energy. By predicting the value of green energy collected by GBS and updating the residual energy of each GBS, we can obtain the maximum number of users that each GBS can offload theoretically. Then, the optimum number of users should be calculated in order to achieve different network performance. Eventually, under the restrictions of the maximum number of users and the optimum number of users, we can finish offloading for traditional base station in the network. Simulation results demonstrate that through the proposed GBS offloading model, we can fulfill compromise between maximizing green energy utilization and load balancing in the offloading process, and the effect of energy saving is remarkable. Copyright

Simulation and analysis of node throughput using smart antenna in wireless mesh networks

Smart antenna technology is introduced to wireless mesh networks. Smart antennas based wider-range access medium access control (MAC) protocol (SWAMP) is used as MAC protocol for IEEE 802.11 mesh networks in this study. The calculation method of node throughput in chain and arbitrary topology is proposed under nodes fairness guarantee. Network scale and interference among nodes are key factors that influence node throughput. Node distribution pattern near the gateway also affects the node throughput. Experiment based on network simulator-2 (NS-2) simulation platform compares node throughput between smart antenna scenario and omni-antenna scenario. As smart antenna technology reduces the bottle collision domain, node throughput increases observably.

Hierarchical power management architecture and optimal local control policy for energy efficient networks

Since energy efficiency has become a significant concern for network infrastructure, next-generation network devices are expected to have embedded advanced power management capabilities. However, how to effectively exploit the green capabilities is still a big challenge, especially given the high heterogeneity of devices and their internal architectures. In this paper, we introduce a hierarchical power management architecture (HPMA) which represents physical components whose power can be monitored and controlled at various levels of a device as entities. We use energy aware state (EAS) as the power management setting mode of each device entity. The power policy controller is capable of getting information on how many EASes of the entity are manageable inside a device, and setting a certain EAS configuration for the entity. We propose the optimal local control policy which aims to minimize the router power consumption while meeting the performance constraints. A first-order Markov chain is used to model the statistical features of the network traffic load. The dynamic EAS configuration problem is formulated as a Markov decision process and solved using a dynamic programming algorithm. In addition, we demonstrate a reference implementation of the HPMA and EAS concept in a NetFPGA frequency scaled router which has the ability of toggling among five operating frequency options and/or turning off unused Ethernet ports.

High throughput route selection in multi-rate wireless mesh networks

Most existing Ad-hoc routing protocols use the shortest path algorithm with a hop count metric to select paths. It is appropriate in single-rate wireless networks, but has a tendency to select paths containing long-distance links that have low data rates and reduced reliability in multi-rate networks. This article introduces a high throughput routing algorithm utilizing the multi-rate capability and some mesh characteristics in wireless fidelity (WiFi) mesh networks. It uses the medium access control (MAC) transmission time as the routing metric, which is estimated by the information passed up from the physical layer. When the proposed algorithm is adopted, the Ad-hoc on-demand distance vector (AODV) routing can be improved as high throughput AODV (HT-AODV). Simulation results show that HT-AODV is capable of establishing a route that has high data-rate, short end-to-end delay and great network throughput.

Energy saving local control policy for Green Reconfigurable Routers

Energy efficiency is now a significant concern for network infrastructure and next-generation network devices are expected to embed advanced power management capabilities. However, the effective exploitation of advanced power management capabilities in network devices which adaptively meet network load and operational constraints is still a considerable challenge due to the stochastic properties of the actual traffic load. In this paper, a statistical optimal local control policy for dynamic control of power state configurations according to the actual traffic load is proposed to minimize the power consumption while meeting the performance constraints. The packet-level statistical features of network traffic load is modeled as a first-order Markov chain and the dynamic power state selection problem is formulated as a Markov decision process, which can be solved using dynamic programming. In addition, we discuss the possibility of implementing the proposed scheme in real network devices, and design a case study in an NetFPGA frequency scaled router. Simulation results are presented to show the effectiveness of the proposed scheme.

Research on secrecy capacity oriented relay selection for mobile cooperative networks

In order to improve the performance of security and QoS provisioning of mobile cooperative network, this paper proposes an efficient approach for optimal secure relay selection, by considering secrecy capacity (SC). Firstly, the channel states are divided into several levels due to their received signal-to-noise ratio (SNR), in the meanwhile, first order finite-state Markov model is employed to indicate the time-varying Rayleigh fading channel. Secondly, Restless Multi-armed Bandit model is involved to demonstrate the secure relay selection problem according to the correlated channel states and the state transition probabilities. Finally, simulation results witnessed the efficiency of the proposed approach.

Distributed Optimal Relay Selection for QoS Provisioning in Wireless Multi-Hop Cooperative Networks

This paper proposes a distributed optimal relay selection scheme in wireless multi-hop cooperative networks where the wireless channels are modeled as first-order finite-state Markov channels (FSMCs) and adaptive modulation and coding (AMC) is applied. The FSMC model is used to approximate the time variations of the average received signal-to-noise ratio (SNR). The state of a relay consists of the channel states of both source-to-relay and relay-to-destination links. In this scheme, a stochastic decision making approach is taken to select the optimal relay according to the states of all available relays with the quality of service (QoS) optimization goals of mitigating error propagation and increasing spectral efficiency. Simulation results show that the proposed scheme outperforms the existing scheme.