This paper investigates joint bandwidth and power allocation for energy-efficient uplink communication in cellular networks. Instead of overall system energy efficiency (EE), we focus on maximizing the EE for each individual user while guaranteeing the quality-of-service. Therefore, a multi-objective optimization problem is formulated. To find its optimal solutions, we first utilize two different methods to convert the multi-objective optimization problem into single objective optimization problems. They are the scalarization method to maximize the weighted summation of users' EE and the max-min method to maximize the minimum EE among users. Then, we develop an effective algorithm based on the sum-of-ratios optimization to solve the scalarization problem, and an algorithm based on the generalized fractional programming to solve the max-min optimization. We also discuss a simple scenario with equivalent bandwidth allocation as a benchmark. Numerical results are provided to validate the effectiveness of the proposed algorithms.

Heterogeneous network(HetNet) integrated with multipleradio access technologies(RATs) is a promising technique for satisfying the exponentially increasing traffic demand of future cellular systems. In this paper, we investigate energy-efficient resource allocation in a multi-RAT HetNet, aimed at maximizing theenergy efficiency(EE) for each individual user while guaranteeing thequality-of-service(QoS) requirement. Since the EE cannot be simultaneously maximized for every user, amultiple-objective optimization problem(MOOP) is formulated. To find its Pareto optimal solution, we first introduce the concept of Utopia EE, defined as the maximum achievable EE, for each user. Then, using the weighted Tchebycheff method, asingle-objective optimization problem(SOOP) is formulated, which can achieve Pareto optimal solution of the original MOOP. The SOOP is a generalized fractional programming problem that aims to minimize the maximum of several quasiconvex fractional functions. We further transform the problem into an equivalent but better tractable one, and develop an iterative algorithm to effectively solve it. Numerical results demonstrate that the proposed algorithm yields fast convergence, high system EE, and flexible EE tradeoff.

In this paper, we investigate the energy-efficient resource allocation problem in a single-cell orthogonal frequency division multiple access (OFDMA) system to achieve the energy efficiency (EE) tradeoff among users. Rather than overall system EE, our objective is to maximize the EE for each individual user. Therefore, a multiple-objective optimization problem is formulated, which in general has many Pareto optimal solutions and is hard to solve. To find its solution, we first convert it into two different single-objective optimization problems using the weighted-sum approach and the max-min approach, respectively. The single-objective optimization problems are non-convex due to the combinatorial channel allocation variables. Therefore, for both problems, we first provide an upper bound algorithm by relaxing the combinatorial variables and then develop a suboptimal heuristic algorithm. The sum-of-ratios optimization and the generalized fractional programming are utilized for the weighted-sum problem and the max-min problem, respectively. Numerical results demonstrate that both the weighted-sum and the max-min approaches can effectively solve the EE maximization problem, and the suboptimal heuristic algorithms can achieve a close performance to the corresponding upper bound algorithm.

Device-to-device (D2D) communications have been recently proposed as an effective way to increase both spectrum and energy efficiency for future cellular systems. In this paper, joint mode selection, channel assignment, and power control in D2D communications are addressed. We aim at maximizing the overall system throughput while guaranteeing the signal-to-noise-and-interference ratio of both D2D and cellular links. Three communication modes are considered for D2D users: cellular mode, dedicated mode, and reuse mode. The optimization problem could be decomposed into two subproblems: power control and joint mode selection and channel assignment. The joint mode selection and channel assignment problem is NP-hard, whose optimal solution can be found by the branch-and-bound method, but is very complicated. Therefore, we develop low-complexity algorithms according to the network load. Through comparing different algorithms under different network loads, proximity gain, hop gain, and reuse gain could be demonstrated in D2D communications.

In this paper, we propose a novel power control scheme for small cells deployed within macro cells. Our aim is to find the optimal power level for each small cell according to the energy consumption tradeoff between network and User Equipments (UEs). Two different small cell deployment scenarios are considered: the non-dense scenario and the dense scenario. The multiagent decentralized Reinforcement Learning (RL) technique is applied to deal with the dense deployment scenario where the coverage of different small cells are overlapped. In the proposed multiagent RL algorithm, each small cell is modeled as an agent to learn the optimal policy from interaction with environment to dynamically change its transmit power. Simulation results are presented to validate the proposed method and show that the RL based algorithm could provide a satisfactory performance.