In this paper, we consider Internet of Things (IoT) based mobile edge computing (MEC) system. IoT devices are controlled by access points (APs), which do not have access to any licensed spectrum. This puts limitations on IoT devices’ offloading capability and hence effectively reduces the number of computed bits. Under such scenario, we consider spectrum sharing, where IoT networks get access to licensed spectrum by helping a primary network (owns licensed spectrum) in cooperative relaying. We aim to maximize sum of the primary network’s communication rate and IoT networks’ total number of computed bits by jointly optimizing relay selection, licensed spectrum allocation, local computation, and offloading sequence selection. Moreover, we consider important constraints, e.g., the guaranteed rate for the primary network, IoT devices’ available energy, and available licensed spectrum resource blocks (RBs). We observe that the optimization problem is combinatoric in nature, which becomes more complicated when IoT devices’ scheduling order comes into the formulation. From our analysis, we devise an optimal and computationally efficient algorithm. In the result section, we compare with relevant benchmark systems and show the efficacy of our proposed model. Moreover, we also show how IoT and primary networks benefit from spectrum sharing.

This paper considers uplink multiple access (MA) transmissions, where the MA technique is adaptively selected between Non Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA). Two types of users, namely Internet of Things (IoT) and enhanced mobile broadband (eMBB) coexist with different metrics to be optimized, energy efficiency (EE) for IoT and spectral efficiency (SE) for eMBB. The corresponding multi-objective power allocation problems aiming at maximizing a weighted sum of EE and SE are solved for both NOMA and OMA. Based on the identification of the best MA strategy, a clustering algorithm is then proposed to maximize the multi-objective metric per cluster as well as NOMA use. The proposed clustering, power allocation and MA selection algorithm is shown to outperform other clustering solutions and non-adaptive MA techniques.

In this paper, a sum inverse energy efficiency (SIEE) minimization problem for multi-link systems is studied. Compared with conventional sum energy efficiency (EE) maximization problems, minimizing SIEE achieves a better fairness. Based on a framework for solving sum-fraction minimization (SFMin) problems, this paper investigates the resource (time fraction and power) allocation problem where links are separated in the time domain. Closed-form solutions are found for the reformulated problem. By using Newton's method for system of equations, iterative algorithms are proposed to find the solution thanks to the closed-form expressions. Numerical results confirm the fast convergence of the algorithm for SFMin problems. Simulation results show that the algorithm convergences fast with practical parameter when each subproblem uses Newton's method. Impacts of system parameters on the resource allocation are also investigated.

A sum inverse energy efficiency (SIEE) minimization problem is solved. Compared with conventional sum energy efficiency (EE) maximization problems, minimizing SIEE achieves a better fairness. The paper begins by proposing a framework for solving sum-fraction minimization (SFMin) problems, then uses a novel transform to solve the SIEE minimization problem in a multiple base station (BS) system. After the reformulation into a multi-convex problem, the alternating direction method of multipliers (ADMM) is used to further simplify the problem. Numerical results confirm the efficiency of the transform and the fairness improvement of the SIEE minimization. Simulation results show that the algorithm convergences fast and the ADMM method is efficient.

In this paper, the problem of subcarrier allocation and power control for uplink orthogonal frequency division multiple access (OFDMA) channels is studied. Each user may have a different strategy of power control due to his/her own limits of battery, quality-of-service (QoS) requirement, and the amount of information to transmit. The problem is first formulated as a minimization problem, and the alternating direction method of multipliers (ADMM) is then introduced to solve the problem in a distributed manner. Necessary conditions of each subproblem are derived to obtain a deeper understanding in the structure of the solutions. Numerical results show that the algorithm converges fast and obtains a near-optimal solution within only a few iterations.

An energy efficiency (EE) maximization problem with multiple transmit antennas (multiple-input-single-output or distributed antennas setups) is considered in this paper. Besides the constraint on the total transmit power, per-antenna transmit power constraints, and a total weighted power constraint are jointly considered. Moreover, a quality-of-service constraint is also considered. Both linear and nonlinear high power amplifier (HPA) cases are studied. Because the original problem for the linear HPA case is a fractional convex problem, Dinkelbach's algorithm is implemented to reformulate the problem. By investigating the Karush-Kuhn-Tucker conditions, the mathematical properties of the globally optimal solution are studied and proved, which provides a deeper understanding of the structure of power allocation among antennas. These properties also lead to a reduction of the complexity of the algorithms proposed to find the global optimum. For the nonlinear HPA cases, the reformulated problem after successive convex approximation is shown to be exactly in the same form as that of the previous problem with linear HPA with different weights of power consumption at each antenna. On the basis of numerical results comparing our EE maximization algorithms against different benchmarks, the efficiency and the superiority of our algorithms can be concluded.

This paper studies the subcarrier allocation and precoder design problem for downlink multiple-input-multiple-output-orthogonal frequency-division multiple access systems. The criterion is to maximize the energy efficiency (EE) of the system. We adopt the time-sharing concept to allocate each subcarrier to one user, which results in a criterion similar with that of rate maximization. However, this approach does not make it possible to solve the problem for any value of the transmit power. We found that there exist gaps along the direction of total transmit power when the user selection switches for each subcarrier, which leads to a discontinuous EE function, for which quasi-concavity has to be checked. We first show that the sufficient conditions obtained for the subcarrier allocation approach are the optimal ones among all possibilities with the same transmit power. Then, we show that the proposed subcarrier allocation results in a discontinuous and quasi-concave EE function. We also give an upper bound of the EE function when the proposed sufficient conditions are not met. Finally, we propose an algorithm to find the maximal EE and its performance is illustrated by means of numerical results.

This paper investigates the optimal design of multiple-input-multiple-output (MIMO) precoders for orthogonal frequency-division multiplexing (OFDM) systems in the presence of nonlinear distortions due to the high power amplifier (HPA). In traditional approaches, the designer lets the input backoff (IBO) of the HPA be large enough to neglect the unavoidable nonlinear effects of the amplifier. However, this hypothesis may severely hinder the efficiency of the HPA and, consequently, the global energy efficiency of the link. On the other hand, making full use of the available power gives rise to unbearable nonlinear effects with consequent strong rate degradations. In this work, we take advantage of the Bussgang theorem to unveil the impact of the nonlinear distortion noise on the optimal precoding and to derive a power allocation algorithm that achieves the optimal tradeoff between HPA efficiency and rate degradation. We also provide a sufficient condition for the concavity of the information rate objective function in this nonlinear scenario. Finally, numerical results shows that the proposed algorithm outperforms conventional fixed-IBO precoding strategies.

In this paper, we consider the energy efficiency (EE) maximization problem for MIMO-relay channels with relay selection and individual power constraints for each node. We implement Dinkelbach's method for the whole system to iteratively select the relays. Closed-form expressions of the precoding matrix at the source and the beamforming matrix at the selected relay are obtained thanks to the concavity of the objective function. We also give the mathematical property of monotonicity which guarantees that the values of Lagrangian multipliers can always be found. Finally, we give the numerical results illustrating the performance gain due to relay selection.

This paper studies an energy efficient design of precoders for point-to-point multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. Differently from traditional approaches, the optimal power allocation strategy is studied by modelling the circuit power as a rate-dependent function. We show that if the circuit power is a constant plus an increasing and convex function of the transmission rate, the problem of minimizing the consumed energy per bit received can be reformulated as a convex fractional program and solved by means of a bisection algorithm. The impact of the some system parameters is investigated either analytically or by means of computational results.