Yuzhou Li

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.

Energy Efficiency and Delay Tradeoff in Device-to-Device Communications Underlaying Cellular Networks

This paper investigates the problem of revealing the tradeoff between energy efficiency (EE) and delay in device-to-device (D2D) communications underlaying cellular networks. Considering both stochastic traffic arrivals and time-varying channel conditions, we formulate it as a stochastic optimization problem, which optimizes EE subject to the average power, interference-control, and network stability constraints. With the help of fractional programming and the Lyapunov optimization technique, we develop an algorithm, referred to as the TRADEOFF, to solve the problem. To deal with the nonconvex and NP-hard power allocation subproblem in the TRADEOFF, we adopt the prismatic branch and bound algorithm to find its globally optimal solution, where only a linear programming needs to be solved in each iteration. Thus, the TRADEOFF serves as an important benchmark to evaluate performance of other heuristic algorithms and is usually cost-efficient. The theoretical analysis and simulation results show that the TRADEOFF achieves an EE-delay tradeoff of [O(1/V),O(V)] with V being a control parameter and can strike a flexible balance between them by simply tuning V.

Energy-Efficient Subcarrier Assignment and Power Allocation in OFDMA Systems With Max-Min Fairness Guarantees

In next-generation wireless networks, energy efficiency optimization needs to take individual link fairness into account. In this paper, we investigate a max-min energy efficiency-optimal problem (MEP) to ensure fairness among links in terms of energy efficiency in OFDMA systems. In particular, we maximize the energy efficiency of the worst-case link subject to the rate requirements, transmit power, and subcarrier assignment constraints. Due to the nonsmooth and mixed combinatorial features of the formulation, we focus on low-complexity suboptimal algorithms design. Using a generalized fractional programming theory and the Lagrangian dual decomposition, we first propose an iterative algorithm to solve the problem. We then devise algorithms to separate the subcarrier assignment and power allocation to further reduce the computational cost. Our simulation results verify the convergence performance and the fairness achieved among links, and particularly reveal a new tradeoff between the network energy efficiency and fairness by comparing the MEP with the existing algorithms.

Energy Efficiency and Delay Tradeoff for Time-Varying and Interference-Free Wireless Networks

In this paper, we investigate the fundamental tradeoff between energy efficiency (EE) and delay for time-varying and interference-free wireless networks. We formulate the problem as a stochastic optimization model, which optimizes the system EE subject to network stability and the average and peak transmit power constraints. By adopting the fractional programming theory and Lyapunov optimization technique, a general and effective algorithm, referred to as the EE-based dynamic power allocation algorithm (EE-DPAA), is proposed. The EE-DPAA does not require any prior knowledge of traffic arrival rates and channel statistics, yet yields an EE that can arbitrarily approach the theoretical optimum achieved by a system with complete knowledge of future events. Most importantly, we quantitatively derive the EE-delay tradeoff as [O(1/V),O(V)] with V as a control parameter for the first time. This result provides an important method for controlling the EE-delay performance on demand. Simulation results validate the theoretical analysis on the EE-delay tradeoff, as well as show the adaptiveness of the EE-DPAA.

Max–Min Energy-Efficient Power Allocation in Interference-Limited Wireless Networks

The widely studied network energy efficiency (EE)-optimal problems (NEPs) emphasize optimality of system EE without taking fairness into account. In this paper, we focus on the max-min EE-optimal problem (MEP) by means of power allocation in interference-limited wireless networks. The MEP offers fairness assurance for users in terms of EE by maximizing the EE of the worst-case user. We show that the MEP is NP-hard. Based on generalized fractional programming, we propose a general EE-based update algorithm (EEUA) to tackle the MEP. One key step in the EEUA involves a nonconvex optimization and NP-hard power allocation problem, and we solve it by devising an iterative power allocation algorithm using sequential convex programming. Simulation results exhibit fast convergence, low complexity, and insensitivity to initial values of the proposed algorithms and verify that the MEP guarantees EE fairness among users, as well as reveal the differences between the MEP and the NEP.

Throughput–Delay Tradeoff in Interference-Free Wireless Networks With Guaranteed Energy Efficiency

Existing works have addressed the tradeoffs between any two of the three performance metrics: throughput, energy efficiency (EE), and delay. In this paper, we unveil the intertwined relations among these three metrics under a unifying framework and particularly investigate the problem of EE-guaranteed throughput-delay tradeoff in interference-free wireless networks. We first propose two admission control schemes, referred to as the first-out and first-in schemes. We then formulate it as two stochastic optimization problems, aiming at throughput maximization (in the first-out scheme) or dropping rate minimization (in the first-in scheme) subject to requirement of EE (RoE), stability, admission control, and transmit power. To solve the problems, the EE-Guaranteed algorithm for throUghput-delAy tRaDeoff (eGuard), respectively called eGuard-I and eGuard-II in the first-out and first-in schemes, is devised. Moreover, with guaranteed RoE, we theoretically show that the eGuard (I and II) can not only push the throughput arbitrarily close to the optimal with tradeoffs in delay but also quantitatively control the throughput-delay performance on demand. Simulation results consolidate the theoretical analysis and particularly show the pros and cons of the two schemes.

Energy-Efficient Antenna selection and power allocation in downlink distributed antenna systems: A stochastic optimization approach

In this paper, by jointly considering antenna selection and power allocation, we address the energy efficiency (EE) maximization problem with delay performance taken into account in downlink distributed antenna systems (DAS). To characterise system EE, we first define a revenue-cost (RC) function as the weighted difference between sum transmit rate and total energy consumption. We then formulate the problem as a stochastic optimization model, which maximizes the long-term average RC value subject to network stability (used to depict delay performance) and average power constraints. An Energy-Efficient Antenna selection and Power allocation Algorithm (EE-APA) is proposed based on Lyapunov optimization technique. The EE-APA adapts to time-varying channel conditions and stochastic traffic arrivals without requiring any corresponding prior-knowledge. Moreover, the theoretical analysis shows that the EE-APA can not only push the EE arbitrarily close to the optimal at the cost of delay performance, but also quantitatively control the EE-delay performance. Numerical results validate the adaptiveness of the EE-APA and the correctness of the theoretical analysis.

Rate and Energy Maximization in SCMA Networks With Wireless Information and Power Transfer

In this letter, we investigate the fundamental tradeoff between rate and energy for sparse code multiple access (SCMA) networks with wireless power transfer. A weighted rate and energy maximization problem by jointly considering power allocation, codebook assignment, and power splitting, is formulated. To solve the hard problem, an iterative algorithm based on the univariate search technique is proposed, which has good performance with low complexity. Specifically, we analyze the special structure of the problem and exploit it to obtain the optimal power splitting ratio and resource allocation strategy when one of them is fixed. Simulation results indicate that our algorithm achieves a better rate-energy tradeoff compared to other schemes.

Globally optimal antenna selection and power allocation for energy efficiency maximization in downlink distributed antenna systems

Green communications are becoming an inevitable trend for future wireless network design, meanwhile, as a promising technique, distributed antenna systems (DAS) cater for this evolution. In this paper, we focus on the problem of devising globally optimal antenna selection and power allocation algorithm in downlink DAS to achieve energy efficiency (EE) maximization. We formulate it as a mixed-integer nonlinear programming (MINLP), which maximizes EE subject to rate requirements, transmit power, and antenna selection constraints. By equivalent transformation, an iterative antenna selection and power allocation algorithm is proposed based on nonlinear fractional programming theory, and branch and bound methods. Our algorithm ensures global optimality and thus, it provides an important benchmark for performance evaluation of other heuristic algorithms targeting the same problem. Simulation results show that the computation complexity can be dramatically reduced comparing with exhaustive search, as well as demonstrate that a significant gain can be obtained in terms of EE against the schemes without antenna selection.

Energy-Efficient Transmission in Heterogeneous Wireless Networks: A Delay-Aware Approach

In this paper, we investigate the delay-aware energy-efficient transmission problem in dynamic heterogeneous wireless networks (HWNs) with time-variant channel conditions, random traffic loads, and user mobility. By jointly considering subcarrier assignment, power allocation, and time fraction determination, we formulate it as a stochastic optimization problem to maximize the system energy efficiency (EE) and to ensure network stability. By leveraging the fractional programming theory and the Lyapunov optimization technique, we first propose a general algorithm framework, referred to as the eTrans, to solve the formulation. Further, we exploit the special structure of the subproblem embedded in the eTrans to develop the extremely simple and low complexity but optimal algorithms for subcarrier assignment, power allocation, and time fraction determination. In particular, all of them have closed-form solutions, and no iteration is required, which paves the way for employing the eTrans to practical applications. The theoretical analysis and simulation results exhibit that eTrans can flexibly strike a balance between EE and average delay by simply tuning an introduced control parameter.