Joint Optimization of Device to Device Resource and Power Allocation Based on Genetic Algorithm

Device-to-device (D2D) communication plays an important role in the next generation ofcommunication systems. Enabling D2D communication decreases latency and expands the coverage of acell in cellular networks. In addition, D2D underlaying cellular users benefit from high spectral efficiency.However, it creates interference to cellular communications. In this paper, we propose a genetic algorithmbased method to minimize the interference and maximize the spectral efficiency. One of the advantagesof genetic algorithm is that it escapes from local maximums and evolves toward global maximum bysearching different parts of search space simultaneously. Since D2D underlay cellular network degrades thesignal-to-interference plus noise ratio (SINR), a minimum SINR is considered for cellular users. Numericalevaluations demonstrate the superior performance of the proposed technique in terms of spectral efficiencyand interference mitigation

Bio-Inspired Resource Allocation for Relay-Aided Device-to-Device Communications

The Device-to-Device (D2D) communication principle is a key enabler of direct localized communication between mobile nodes and is expected to propel a plethora of novel multimedia services. However, even though it offers a wide set of capabilities mainly due to the proximity and resource reuse gains, interference must be carefully controlled to maximize the achievable rate for coexisting cellular and D2D users. The scope of this work is to provide an interference-aware real-time resource allocation (RA) framework for relay-aided D2D communications that underlay cellular networks. The main objective is to maximize the overall network throughput by guaranteeing a minimum rate threshold for cellular and D2D links. To this direction, genetic algorithms (GAs) are proven to be powerful and versatile methodologies that account for not only enhanced performance but also reduced computational complexity in emerging wireless networks. Numerical investigations highlight the performance gains compared to baseline RA methods and especially in highly dense scenarios which will be the case in future 5G networks.Comment: 6 pages, 6 figure

VirtFogSim: A parallel toolbox for dynamic energy-delay performance testing and optimization of 5G Mobile-Fog-Cloud virtualized platforms

It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudtechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands the optimization and performance evaluation of several parts of the underlying infrastructures. However, field trials are challenging due to their operational costs, and in every case, the obtained results could be difficult to repeat and customize. These emergingMobile-Fog-Cloud ecosystems still lack, indeed, customizable software tools for the performance simulation of their computing-networking building blocks. Motivated by these considerations, in this contribution, we present VirtFogSim. It is aMATLAB-supported software toolbox that allows the dynamic joint optimization and tracking of the energy and delay performance of Mobile-Fog-Cloud systems for the execution of applications described by general Directed Application Graphs (DAGs). In a nutshell, the main peculiar features of the proposed VirtFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the placement of the application tasks and the allocation of the needed computing-networking resources under hard constraints on acceptable overall execution times, (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall system; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operational environments, as those typically featuring mobile applications; (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering, and (v) itsMATLAB code is optimized for running atop multi-core parallel execution platforms. To check both the actual optimization and scalability capabilities of the VirtFogSim toolbox, a number of experimental setups featuring different use cases and operational environments are simulated, and their performances are compared

Perfomance Comparison of Genetic and Greedy Algorithms in Underlay Device-to-Device Communication

The number of cellular users (CU) continues to increase in Indonesia. This impacts a large network load for the number of devices connected to the main network so it will have an impact on the quality of service. Device-to-Device (D2D) communication as components for LTE-A technology enabling a direct wireless link between the CUs without routing the data via the evolved Node B (eNB) signal or the core network. The need for algorithm and power control used to allocate radio resources so it can get a good quality of service because of communications technology D2D. In this study, we analyze and compare the performance parameters of D2D communication systems, including system interference, system sum-rate, system spectral efficiency, total energy system, and system energy efficiency based on Genetic and Greedy Algorithms in allocating radio resources and controlling the power of users. The genetic algorithm works with three operators in allocating resource block (RB), including proportional selection, crossover, and mutation. This process is repeated many times to produce several generations so that the best allocation can be got. The genetic algorithm has a flexible number of D2D and cellular communications in several RBs, minimum signal to interference plus noise ratio (SINR) also considered for mobile communication in ensuring the quality of its services. Numerical evaluations demonstrate the superior performance of the Genetic Algorithm in terms of system power, energy efficiency, and interference mitigation. As repetition gets larger, the Genetic algorithm results in better spectral efficiency