Yanjie Dong

Fronthauling for 5G LTE-U Ultra Dense Cloud Small Cell Networks

Ultra dense cloud small cell network (UDCSNet), which combines cloud computing and massive deployment of small cells, is a promising technology for 5G LTE-U mobile communications because it can accommodate the anticipated explosive growth of mobile users’ data traffic. As a result, fronthauling becomes a challenging problem in 5G LTE-U UDCSNet. In this article, we present an overview of the challenges and requirements of the fronthaul technology in 5G LTE-U UDCSNets. We survey the advantages and challenges for various candidate fronthaul technologies such as optical fiber, millimeter- wave-based unlicensed spectrum, Wi-Fibased unlicensed spectrum, sub-6-GHz-based licensed spectrum, and free-space optical-based unlicensed spectrum.

Performance of Wireless Powered Amplify and Forward Relaying Over Nakagami-

Performance of wireless powered relay with amplify-and-forward protocol is studied for Nakagami-m fading channels. Different from the existing literature, we consider the nonlinearity of the energy harvester. An analytical expression is derived for the complementary cumulative distribution function (CCDF) of the end-to-end signal-to-noise ratio. Using the CCDF, outage capacity is calculated.

m

In this paper, we consider an orthogonal frequency division multiple access based full duplex distributed antenna system (FDDAS) with energy recycling capability and develop an energy efficient resource allocation scheme for such system. In particular, in order to optimize the energy efficiency of FDDAS, we formulate the problem of joint subchannel allocation and power control as a mixed integer nonlinear programming problem. Since the formulated optimization problem is a NP-hard problem whose computation complexity will rise exponentially with larger network scale, we develop a low complexity subchannel allocation and power control algorithm. The convergence property of the proposed algorithm is established. Simulation results show that the proposed algorithm for FDDAS results in a higher system energy efficiency than the existing algorithm for distributed antenna systems without energy recycling capability.

Fading Channels With Nonlinear Energy Harvester

The problem of proportional fairness-based beamforming and power splitting is investigated for simultaneous wireless information and power transferring systems. Due to the non-convexity of the formulated problem, a suboptimal solution is proposed based on the classical zero-forcing beamforming (ZFBF) and maximal ratio transmission techniques. To overcome the limitation of the ZFBF technique, a successive approximation-based algorithm is proposed. Simulation results verify the effectiveness of the proposed algorithms.

Energy Efficient Resource Allocation for OFDMA Full Duplex Distributed Antenna Systems with Energy Recycling

The application of renewable energy is a promising solution for realizing green communications. However, if the cellular systems are solely powered by the renewable energy, the weather dependence of the renewable energy arrival makes the systems unstable. On the other hand, the proliferation of the smart grid facilitates the loads with two-way energy trading capability. Hence, a hybrid powered cellular system, which combines the smart grid with the base stations, can reduce the grid energy expenditure and improve the utilization efficiency of the renewable energy. In this paper, the long-term grid energy expenditure minimization problem is formulated as a stochastic optimization model. By leveraging the stochastic optimization theory, we reformulate the stochastic optimization problem as a per-frame grid energy plus weighted penalized packet rate minimization problem, which is NP-hard. As a result, two suboptimal algorithms, which jointly consider the effects of the channel quality and the packet reception failure, are proposed based on the successive approximation beamforming (SABF) technique and the zero-forcing beamforming (ZFBF) technique. The convergence properties of the proposed suboptimal algorithms are established, and the corresponding computational complexities are analyzed. Simulation results show that the proposed SABF algorithm outperforms the ZFBF algorithm in both grid energy expenditure and packet delay. By tuning a control parameter, the grid energy expenditure can be traded for the packet delay under the proposed stochastic optimization model.

Proportional Fairness-Based Beamforming and Signal Splitting for MISO-SWIPT Systems

A coalition based joint subchannel and power allocation approach is studied to improve the performance of device-to-device (D2D) communication underlaying cellular networks with uplink spectrum sharing. To exploit the spectrum reuse gain, we formulate the problem as a coalition formation game. Furthermore, a distributed coalition formation algorithm is devised to assist D2D pairs in joining or leaving a coalition. During the coalition formation process, we introduce an iterative power control method. By using this method, D2D pairs can evaluate their current coalition with D2D sum rate maximization and cellular user equipment protection. Numerical results are provided to corroborate the proposed studies.

Dynamic Cross-Layer Beamforming in Hybrid Powered Communication Systems With Harvest-Use-Trade Strategy

Radio frequency energy harvesting (RF-EH) is a promising technology to increase the lifetime of the wireless nodes, and there are many use cases for this emerging technology. Despite a number of advantages of the RF-EH technology, several challenges remain to be solved in order to fully exploit its potential. For example, the impact of integrating the RFEH technology into transmitters of the wireless communication systems remains unknown. Besides, heterogeneous quality-of-service (QoS) and the nonlinearity of the energy harvester require fundamental investigation from both resource allocation and performance analysis perspective. Failing to address these issues can wipe out the advantages that the RF-EH technology brings. In this thesis, we consider some of these challenges and develop solutions as described below. First, an energy efficiency (EE) maximization problem is studied in a distributed antenna system with RF-EH capability. The energy harvester on each radio remote head can scavenge energy over all frequency band for practical amount of energy. A low-complexity semi-distributed algorithm is proposed to maximize the system EE via subchannel allocation and power control. Next, an innovative optimization framework is proposed to formulate the long-term power minimization problem in a simultaneous wireless information and power transferring system. The formulated problem contains both long-term and short-term QoS constraints, which is difficult to solve via standard optimization methods. Thus, the stochastic optimization theory is used to propose a dynamic power control and time switching algorithm for suboptimal solution. By tuning a control parameter, the power consumption can approach its optimal value at the expense of the delay of the wireless nodes with best effort traffic. Finally, the performance of the wireless powered relaying systems with nonlinear energy harvester is investigated. We derive an analytical expression of the complementary cumulative distribution function (CCDF) for the end-to-end signal-to-noise ratio. Compared with our analytical results, the linear energy harvester overestimates the CCDF of the end-to-end signal-to-noise ratio when the relay is placed closer to the source node.