Yegui Cai

When the Smart Grid Meets Energy-Efficient Communications: Green Wireless Cellular Networks Powered by the Smart Grid

Recently, there is great interest in considering the energy efficiency aspect of cellular networks. On the other hand, the power grid infrastructure, which provides electricity to cellular networks, is experiencing a significant shift from the traditional electricity grid to the smart grid. When a cellular network is powered by the smart grid, only considering energy efficiency in the cellular network may not be enough. In this paper, we consider not only energy-efficient communications but also the dynamics of the smart grid in designing green wireless cellular networks. Specifically, the dynamic operation of cellular base stations depends on the traffic, real-time electricity price, and the pollutant level associated with electricity generation. Coordinated multipoint (CoMP) is used to ensure acceptable service quality in the cells whose base stations have been shut down. The active base stations decide on which retailers to procure electricity from and how much electricity to procure. We formulate the system as a Stackelberg game, which has two levels: a cellular network level and a smart grid level. Simulation results show that the smart grid has significant impacts on green wireless cellular networks, and our proposed scheme can significantly reduce operational expenditure and CO_2 emissions in green wireless cellular networks.

Software-Defined Device-to-Device (D2D) Communications in Virtual Wireless Networks With Imperfect Network State Information (NSI)

Software-defined networking (SDN) and network function virtualization (NFV) are a promising system architecture and control mechanism for future networks. Although some works have been done on wireless SDN and NFV, recent advancements in device-to-device (D2D) communications are largely ignored in this novel framework. In this paper, we study the integration of D2D communication in the framework of SDN and NFV. An inherent challenge in supporting software-defined D2D is the imperfectness of network state information, including channel state information (CSI) and queuing state information, in virtual wireless (QSI) networks. To address this challenge, we formulate the resource sharing problem in this framework as a discrete stochastic optimization problem and develop discrete stochastic approximation algorithms to solve this problem. Such algorithms can reduce the computational complexity compared with exhaustive search while achieving satisfactory performance. Both the static wireless channel and time-varying channels are considered. Extensive simulations show that users can benefit from both wireless network virtualization and software-defined D2D communications, and our proposed scheme can achieve considerable performance gains in both system throughput and user utility under practical network settings.

Medium Access Control for Unmanned Aerial Vehicle (UAV) Ad-Hoc Networks With Full-Duplex Radios and Multipacket Reception Capability

Recent advances in interference cancellation and signal-processing techniques can enable full-duplex radios and multipacket reception (MPR), which will have significant impact on medium access control (MAC) schemes. In this paper, we propose a MAC scheme in unmanned aerial vehicle (UAV) ad-hoc networks with full-duplex radios and MPR. To efficiently handle the highly mobile environment of a UAV ad-hoc network, a token-based technique is used to update information in the network as well. The MAC schemes in the presence of perfect and imperfect channel state information (CSI) are formulated as a combinatorial optimization problem and a discrete stochastic optimization problem, respectively. Simulation results show the effectiveness of the proposed MAC.

Cloud computing meets mobile wireless communications in next generation cellular networks

In next generation cellular networks, cloud computing will have profound impacts on mobile wireless communications. On the one hand, the integration of cloud computing into the mobile environment enables MCC systems. On the other hand, the powerful computing platforms in the cloud for radio access networks lead to a novel concept of C-RAN. In this article we study the topology configuration and rate allocation problem in C-RAN with the objective of optimizing the end-to-end performance of MCC users in next generation cellular networks. We use a decision theoretical approach to tackle the delayed channel state information problem in C-RAN. Simulation results show that the design and operation of future mobile wireless networks can be significantly affected by cloud computing, and the proposed scheme is capable of achieving substantial performance gains over existing schemes.

Cloud radio access networks (C-RAN) in mobile cloud computing systems

Cloud computing will have profound impacts on wireless networks. On one hand, the integration of cloud computing into the mobile environment enables mobile cloud computing (MCC) systems; on the other hand, the powerful computing platforms in the cloud for radio access networks lead to a novel concept of cloud radio access networks (C-RAN). In this paper, we study the topology configuration and rate allocation problem in C-RAN with the objective of optimizing the end-to-end performance of MCC users in next generation wireless networks. An intrinsic issue related to such system is that only sub-optimal decisions can be made due to the fact that the channel state information is outdated. We employ a decision-theoretic framework to tackle this issue, and maximize the system throughput with constraints on the response latency experienced by each MCC user. Using simulation results, we show that, with the emergence of MCC and C-RAN technologies, the design and operation of future mobile wireless networks can be significantly affected by cloud computing, and the proposed scheme is capable of achieving substantial performance gains over existing schemes.

Decoupling congestion control from TCP (semi-TCP) for multi-hop wireless networks

Although many problems for transmission control protocol (TCP) in multi-hop wireless networks have been studied with many proposals in the literature, they are not solved completely yet. Different from the existing proposals to mitigate the limitation of TCP in multi-hop wireless networks, we propose a framework of semi-TCP which decouples two functionalities of traditional TCP, i.e., congestion control and reliability control, in order to get rid of the constraint of TCP’s congestion window on performance enhancement. Specifically, we employ hop-by-hop congestion control which is more efficient than its end-to-end counterpart since the control efficiency of the later relies on the availability of end-to-end connectivity which is difficult to sustain in wireless networks. We implement hop-by-hop congestion control via intra-node and inter-node congestion control, and propose a distributed hop-by-hop congestion control algorithm based on the widely used request-to-send/clear-to-send protocol. Such a semi-TCP retains the reliability control in original TCP. Extensive simulations based on network simulator-2 show the promising performance of semi-TCP over traditional schemes.

Optimal clustering and rate allocation for uplink coordinated multi-point (CoMP) systems with delayed channel state information (CSI)

Coordinated multi-point (CoMP) is a promising technology in next generation cellular networks to enhance the cell-edge performance under universal frequency reuse. In uplink CoMP, the transmitted signals from user equipments are cooperatively processed by multiple base stations assuming there is perfect channel state information (CSI) shared by those base stations. However, practical systems can only obtain imperfect CSI which suffers from quantization errors and especially delay introduced by channel estimation and signalling over backhaul networks. In this paper, we consider the optimal clustering and rate allocation problem in CoMP systems where the CSI feedback via backhaul is delayed. We model such system in the framework of networked Markov decision process (networked-MDP), which is equivalent to a partial observable Markov decision process (POMDP). We find out an optimal policy for such POMDP, which is computational inexpensive. Extensive simulations show promising performance gain of the proposed scheme over existing scheme which makes the decisions merely based on the current observation.

MAC performance improvement in UAV ad-hoc networks with full-duplex radios and multi-packet reception capability

Recent advances in interference cancellation and signal processing techniques can enable full-duplex radios and multi-packet reception (MPR) capability, which will have significant impacts on the medium access control (MAC) design. In this paper, we study the MAC design in UAV ad-hoc networks with full-duplex radios and MPR. To efficiently handle the highly mobile environment of a UAV ad-hoc network, a token-based technique is used for updating information in the network. The MAC scheme in the presence of perfect and imperfect channel state information are formulated as a combinatorial optimization problem and a discrete stochastic optimization problem, respectively. Simulation results are presented to show the effectiveness of the proposed MAC.

Dynamic operation of BSs in green wireless cellular networks powered by the smart grid

There is great interest in considering the energy efficiency aspect of wireless cellular networks. When a wireless cellular network is powered by the smart grid, only considering energy efficiency in the cellular network is not enough. In this paper, we consider not only energy-efficient communications but also the dynamics of the smart grid in operating green wireless cellular networks. We formulate the system as a two-level Stackelberg game. A backward induction method is used to analyze the proposed scheme. we prove that the Stackelberg equilibrium (SE) of the proposed game exists and is unique. An iteration algorithm is proposed to obtain the SE. Simulation results show that the smart grid has a significant impact on green wireless cellular networks, and our proposed scheme can significantly reduce operational expenditure and CO2 emissions in green wireless cellular networks.

A decision theoretic approach for clustering and rate allocation in coordinated multi-point (CoMP) networks with delayed channel state information

Coordinated multi-point (CoMP) is a promising technique in next generation cellular networks. Compared with traditional mobile networks, one of the important design problems in CoMP is clustering, which decides how the base stations cooperate with each other. Channel state information (CSI) is needed in clustering decisions in CoMP. Most previous works assume that perfect CSI is available. However, practical systems suffer from constraints imposed by backhaul networks, which are used for CSI exchange. In this paper, we study the clustering and rate allocation problem in CoMP with delayed CSI. We present a decision theoretic approach to this problem. Specifically, we model such a system in the framework of networked Markov decision process (networked-MDP) with delays, which is equivalent to a partial observable Markov decision process (POMDP). We derive an optimal policy for such POMDP with low computation complexity. Simulation results are provided to show promising gain achieved in the proposed scheme over existing schemes especially when the delay is large and the channel coherence time is small. Copyright