Chungang Yang

Energy Efficiency and Spectral Efficiency Tradeoff in Interference-Limited Wireless Networks

This letter investigates the fundamental tradeoff between energy efficiency (EE) and spectral efficiency (SE) for interference-limited wireless networks (IWNs), which is a nonconvex optimization and NP-hard problem. A general algorithm procedure (GAP) embedded with an iterative power allocation algorithm (IPAA) is proposed, which has good performance with the advantages of fast convergence, low complexity and insensitivity to initial values. Simulation results explicitly depict the EE-SE tradeoff curve for IWNs.

Cooperation for spectral and energy efficiency in ultra-dense small cell networks

Extensive deployment of small cells in heterogenous cellular networks introduces both challenges and opportunities. Challenges come with reuse of limited frequency resources, which always introduce both intra- and inter-interference among small cells and macrocells. The opportunities refer to more potential inter- and intra-tier cooperation gains, in particular for ultra-dense heterogeneous and small cell networks. In addition to current spectral efficiency optimization, energy efficiency will also be a critical performance requirement for future green communications, especially when small cells are densely deployed to enhance the users quality of experience. In this article, potential cooperation gains are explored via a cooperative bargaining game to counter challenges of mitigating interference and saving energy, thus improving both spectral and energy efficiency. We survey the current optimization and trade-offs of spectral and energy efficiency, and introduce the basics of cooperative game theory. Then a utility function is presented with spectral and energy efficiency coupled together. Furthermore, we present the bargaining cooperative game theoretic framework to explore potential cooperation gains. Moreover, two applications are investigated for the dedicated and co-channel deployment cases, including cooperative relay with spectrum leasing and cooperative capacity offload. Finally, we conclude the article with potential challenges of the presented cooperative framework and some thoughts for future research directions.

Joint Power Coordination for Spectral-and-Energy Efficiency in Heterogeneous Small Cell Networks: A Bargaining Game-Theoretic Perspective

Extensive deployment of small cells in heterogenous cellular networks introduces both challenges and opportunities. Challenges come with the reuse of the limited frequency resource for improving spectral efficiency, which always introduces serious mutual inter- and intracell interference between or among small cells and macrocells. The opportunities refer to more potential chances of inter- and intratier cooperations among small cells and macrocells. Energy efficiency will be a critical performance requirement for future green communications, especially when small cells are densely deployed to enhance the quality of users experience. We exploit the potential cooperation diversities to combat the interference and energy management challenges. To capture the complicated interference interaction and also the possible coordination behavior among small cells and macrocells, this paper proposes a novel bargaining cooperative game (BCG) framework for energy efficient and interference-aware power coordination in a dense small cell network. In particular, a new adjustable utility function is employed in the BCG framework to jointly address both the spectral efficiency and energy efficiency issues. Using the BCG framework, we then derive the closed-form power coordination solutions and further propose a joint interference-aware power coordination scheme (Joint) with the considerations of both interference mitigation and energy saving. Moreover, a simplified algorithm (Simplified) is presented to combat the heavy signaling overhead, which is one of the significant challenges in the scenario of extensive deployment of small cells. Finally, numerical results are provided to illustrate the effectiveness of the proposed Joint and Simplified schemes.

Pricing-Based Multiresource Allocation in OFDMA Cognitive Radio Networks: An Energy Efficiency Perspective

Both the orthogonal frequency-division multiple access (OFDMA) and cognitive radio (CR) technologies offer great flexibility and feasibility for future green wireless communications. In this paper, an energy-efficient multiresource-allocation scheme is proposed for OFDMA CR networks (CRNs) with multiple secondary transmitters (STs). To maximize the energy efficiency (EE) and guarantee the primary transmitters (PTs) quality-of-service (QoS) requirement, a linear pricing technique is employed to handle both the inter-ST coupling (the spectrum competition among STs) and intra-ST coupling (the correlation between the available transmit power and assigned subchannels for each ST). Furthermore, a distributed algorithm is devised to harvest the multiresource and multiuser gains, and the multiresource allocation is transformed to 1-D pricing-factor profile searching. Simulation results demonstrate that the proposed strategy brings higher EE. Additionally, by adjusting the pricing factors, a different performance can be achieved by the STs with different priorities.

Advanced spectrum sharing in 5G cognitive heterogeneous networks

Spectrum utilization, energy consumption, and cost efficiency are three key performance metrics that should be jointly investigated in developing a sustainable 5G system. Advanced spectrum sharing can enhance both the spectral efficiency and energy efficiency in a cost-effective manner, which is expected to perform much better than conventional networks. In this article, we survey cognitive and cooperative spectrum sharing, and classify a multi-level spectrum exploitation, coordination, and utilization framework from both technical and economic perspectives. We specifically concentrate on spectrum trading and leasing, spectrum mobility, relaying, routing, and harvesting. Finally, a spectrum flowing scheme is proposed for 5G cognitive heterogeneous cellular networks, which improves both spectral and energy efficiency.

Interference-Aware Energy Efficiency Maximization in 5G Ultra-Dense Networks

Ultra-dense networks can further improve the spectrum efficiency (SE) and the energy efficiency (EE). However, the interference avoidance and the green design are becoming more complex due to the intrinsic densification and scalability. It is known that the much denser small cells are deployed, the more cooperation opportunities exist among them. In this paper, we characterize the cooperative behaviors in the Nash bargaining cooperative game-theoretic framework, where we maximize the EE performance with a certain sacrifice of SE performance. We first analyze the relationship between the EE and the SE, based on which we formulate the Nash-product EE maximization problem. We achieve the closed-form sub-optimal SE equilibria to maximize the EE performance with and without the minimum SE constraints. We finally propose a CE2MG algorithm, and numerical results verify the improved EE and fairness of the presented CE2MG algorithm compared with the non-cooperative scheme.

Tradeoff between energy-efficiency and spectral-efficiency by cooperative rate splitting

The trend of an increasing demand for a high-quality user experience, coupled with a shortage of radio resources, has necessitated more advanced wireless techniques to cooperatively achieve the required quality-of-experience enhancement. In this study, we investigate the critical problem of rate splitting in heterogeneous cellular networks, where concurrent transmission, for instance, the coordinated multipoint transmission and reception of LTE-A systems, shows promise for improvement of network-wide capacity and the user experience. Unlike most current studies, which only deal with spectral efficiency enhancement, we implement an optimal rate splitting strategy to improve both spectral efficiency and energy efficiency by exploring and exploiting cooperation diversity. First, we introduce the motivation for our proposed algorithm, and then employ the typical cooperative bargaining game to formulate the problem. Next, we derive the best response function by analyzing the dual problem of the defined primal problem. The existence and uniqueness of the proposed cooperative bargaining equilibrium are proved, and more importantly, a distributed algorithm is designed to approach the optimal unique solution under mild conditions. Finally, numerical results show a performance improvement for our proposed distributed cooperative rate splitting algorithm.

Hierarchical Decision-Making With Information Asymmetry for Spectrum Sharing Systems

In this paper, observing the information asymmetry phenomenon among multiple secondary users (SUs) spectrum sharing, we analyze the hierarchical decision-making and the strategic interaction of information-poor and information-rich SUs. A Stackelberg capacity-maximization game is formulated with leaders and followers, and closed-form solutions are mathematically derived for the optimal Stackelberg equilibrium solution. Moreover, the existence and uniqueness of equilibrium solutions are investigated via the quasi-variational inequality method. Finally, the distributed algorithm with partial asymmetric information awareness is designed to reach the solution. Numerical results demonstrate that the proposed algorithm achieves improved individual and system performance with mild condition on the ratio of information-poor leaders and information-rich followers.

Green heterogeneous networks: a cognitive radio idea

From an energy-saving perspective, the authors investigate the downlink power control issue of the two-tier heterogeneous networks (HetNets) using a cognitive radio train of thought. The authors consider the HetNets scenario of one macro-cell evolved-NodeB (eNB) and multiple femto-cell Home evolved NodeBs (HeNBs) cooperatively coexisting to provide better services. A specific HeNB allows macro-mobile station (macro-MS) previously associated with eNB to access to it for better signal-to-interference plus noise ratio (SINR) guarantee. As a reward, the macro-MS pays a certain of revenue to HeNB as the incentive mechanism for this HeNBs downlink extra power consumption, which is manifested in the design of the price function. The throughput bound of Macro-MSs in HeNB cell is given. Then, the authors select the SINR as the performance measure and formulate the power control of selected HeNBs as multi-constrained optimisation problem. Meanwhile, the authors derive the sub-optimal and closed-form power control of individual HeNB, based on which the authors design the distributed algorithm with the aid of eNB and HeNBs cooperatively to implement the pricing information exchange. Simulation results show the improved performance of the convergence, the energy-efficiency measured by ‘energy-per-bit’ and the throughput of the ‘Proposed-Cognitive-x’ power control algorithm.

Interference-aware spectral-and-energy efficiency tradeoff in heterogeneous networks

Heterogeneous networks (HetNets), where multiple low power small cell eNodeBs (SeNBs) are overlaid on the coverage of a high power macrocell eNodeB (MeNB), serve as promising paradigm to enhance spectral efficiency of future cellular wireless networks. To capture the complicated interference interaction and also the coordination behavior among MeNB and SeNBs, this paper proposes a bargaining cooperative game (BCG) framework for interference-aware power coordination in a HetNet. In particular, a new adjustable utility function is employed in the BCG framework to jointly address the spectral and energy efficiencies as well as to achieve the optimal tradeoff between them. We then derive the closed-form power coordination solutions and further propose an interference-aware power coordination scheme with the considerations of both interference mitigation and energy saving. Finally, the numerical results are provided to illustrate the convergence property and efficiency of the proposed power coordination scheme.