Green resource allocation for mobile edge computing

We investigate the green resource allocation to minimize the energy consumption of the users in mobile edge computing systems,where task offloading decisions,transmit power,and computation resource allocation are jointly optimized.The considered energy consumption minimization problem is a non-convex mixed-integer nonlinear programming problem,which is challenging to solve.Therefore,we develop a joint search and Successive Convex Approximation(SCA)scheme to optimize the non-integer variables and integer variables in the inner loop and outer loop,respectively.Specifically,in the inner loop,we solve the optimization problem with fixed task offloading decisions.Due to the non-convex objective function and constraints,this optimization problem is still non-convex,and thus we employ the SCA method to obtain a solution satisfying the Karush-Kuhn-Tucker conditions.In the outer loop,we optimize the offloading decisions through exhaustive search.However,the computational complexity of the exhaustive search method is greatly high.To reduce the complexity,a heuristic scheme is proposed to obtain a sub-optimal solution.Simulation results demonstrate the effectiveness of the developed schemes.

Energy Efficiency Optimization for D2D Communications Based on SCA and GP Method

In this paper, we propose an energy-efficient power control scheme for device-to-device(D2D) communications underlaying cellular networks, where multiple D2D pairs reuse the same resource blocks allocated to one cellular user. Taking the maximum allowed transmit power and the minimum data rate requirement into consideration, we formulate the energy efficiency maximization problem as a non-concave fractional programming(FP) problem and then develop a two-loop iterative algorithm to solve it. In the outer loop, we adopt Dinkelbach method to equivalently transform the FP problem into a series of parametric subtractive-form problems, and in the inner loop we solve the parametric subtractive problems based on successive convex approximation and geometric programming method to obtain the solutions satisfying the KarushKuhn-Tucker conditions. Simulation results demonstrate the validity and efficiency of the proposed scheme, and illustrate the impact of different parameters on system performance.

Coverage and Area Spectral Efficiency Analysis of Dense Terahertz Networks in Finite Region

This paper develops a general and tractable framework for the finite-sized downlink terahertz(THz)network.Specifically,the molecular absorption loss,receiver locations,directional antennas,and dynamic blockage are taken into account.Using the tools from stochastic geometry,the exact expressions of the blind probability,signal-to-interference-plus-noise ratio(SINR)coverage probability,and area spectral efficiency(ASE)for the reference receivers and random receivers are derived.The upper bounds of the SINR coverage probability are also obtained by using the generalized dominant interferers approach.Numerical results validate the accuracy of our theoretical analysis and suggest that two or more dominant interferers are required to provide sufficiently tight approximations for the SINR coverage probability.We also show that densifying the finite terahertz networks over a certain density threshold will degrade the coverage probability while the ASE keeps increasing.Moreover,deploying more obstructions appropriately in ultra-dense THz networks will benefit both the coverage probability and ASE.