Performance of symmetric and asymmetric links in wireless networks

<p>Wireless networks are designed to provide the enabling infrastructure for emerging technological advancements. The main characteristics of wireless networks are: Mobility, power constraints, high packet loss, and lower bandwidth. Nodes’ mobility is a crucial consideration for wireless networks, as nodes are moving all the time, and this may result in loss of connectivity in the network. The goal of this work is to explore the effect of replacing the generally held assumption of symmetric radii for wireless networks with asymmetric radii. This replacement may have a direct impact on the connectivity, throughput, and collision avoidance mechanism of mobile networks. The proposed replacement may also impact other mobile protocol’s functionality. In this work, we are mainly concerned with building and maintaining fully connected wireless network with the asymmetric assumption. For this extent, we propose to study the effect of the asymmetric links assumption on the network performance using extensive simulation experiments. Extensive simulation experiments were performed to measure the impact of these parameters. Finally, a resource allocation scheme for wireless networks is proposed for the dual rate scenario. The performance of the proposed framework is evaluated using simulation.</p>

An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks

The high data rates detail that internet-connected devices have been increasing exponentially. Cognitive radio (CR) is an auspicious technology used to address the resource shortage issue in wireless IoT networks. Resource optimization is considered a non-convex and nondeterministic polynomial (NP) complete problem within CR-based Internet of Things (IoT) networks (CR-IoT). Moreover, the combined optimization of conflicting objectives is a challenging issue in CR-IoT networks. In this paper, energy efficiency (EE) and spectral efficiency (SE) are considered as conflicting optimization objectives. This research work proposed a hybrid tabu search-based stimulated algorithm (HTSA) in order to achieve Pareto optimality between EE and SE. In addition, the fuzzy-based decision is employed to achieve better Pareto optimality. The performance of the proposed HTSA approach is analyzed using different resource allocation parameters and validated through simulation results.

GCN based virtual resource allocation scheme for power internet of things

With the construction of smart grid, increasing number of smart devices will be connected to the power communication network. Therefore, how to allocate the resources of access devices has become an urgent problem to be solved in smart grid. However, due to the diversity and time-variability of access devices at the edge of the power grid, such dynamic changes may lead to untimely and unbalanced resource allocation of the power grid and additional system overhead, resulting in reducing the efficiency of power grid operation, unbalanced workload and other problems. In this paper, a grid resource allocation scheme based on Gauss optimization is proposed. The grid virtualization application resources are managed through three main steps: decomposition, combination and exchange, so as to realize the reasonable allocation of grid resources. Considering the time-variability of the grid topology and the diversity of the access device, the computational complexity of the traditional data analysis model is too high to be suitable for time-sensitive power network structure. This paper proposes an MPNN framework combined with the Graph Convolutional Network (GCN) to enhance the calculation efficiency and realize the rapid allocation of network resources. Since the smart gateway connected by the grid terminal has certain computation ability, the cloud computing used in distribution model in deep learning to find the optimal solution can be distributed in the cloud and edge computing gateway. In this way, The entire electricity network can efficiently manage and orchestrate virtual services to maximize the utility of grid virtual resources. Furthermore, this paper also adopt the GG-NN (Gated Graph Neural Network) which is based on the MPNN framework in the training. Finally, we carry out simulation for the Gauss optimization scheme and the MPNN-based scheme to verify that the convolutional diagram neural network is suitable for virtual resource allocating in multi-access power Internet-of –Things (IoTs).

Resource Allocation Scheme for Fog-Enabled Wireless Access Networks under the QoS of Users

With the complexity of the network architecture, the diversity of network slicing, and the introduction of advanced techniques such as device to device (D2D), it is difficult for the next-generation (5G+ or 6G) networks to comprehensively consider the requirements of users from different slices and jointly allocate wireless resources to improve network energy efficiency. This paper studies the energy efficiency optimization problem for D2D-enabled fog radio access networks (FRANs). A resource allocation algorithm is proposed to maximize the network energy efficiency by jointly optimizing the beamforming vector, resource block allocation, and transmission power of the remote radio heads (RRHs), fog access point (FAP), and D2D users. The developed algorithm is based on nonlinear programming, convex optimization, and Lagrangian duality. Simulation results show that, by applying the proposed algorithm, the system throughput is significantly improved, and the network energy consumption is greatly reduced, which can ultimately improve the network energy efficiency obviously.

Throughput Fairness in Cognitive Backscatter Networks With Residual Hardware Impairments and a Nonlinear EH Model

This paper is to design a throughput fairness-aware resource allocation scheme for a cognitive backscatter network (CBN), where multiple backscatter devices (BDs) take turns to modulate information on the primary signals and backscatter the modulated signals to a cooperative receiver (C-Rx), while harvesting energy to sustain their operations. The nonlinear energy harvesting (EH) circuits at the BDs and the residual hardware impairments (HWIs) at the transceivers are considered to better reflect the properties of the practical energy harvesters and transceivers, respectively. To ensure the throughput fairness among BDs, we formulate an optimization problem to maximize the minimum throughput of BDs by jointly optimizing the transmit power of the primary transmitter, the backscattering time and reflection coefficient for each BD, subject to the primary user&rsquo;s quality of service (QoS) and BDs&rsquo; energy-causality constraints. We introduce the variable slack and decoupling methods to transform the formulated non-convex problem, and propose an iterative algorithm based on block coordinate descent (BCD) technique to solve the transformation problem. We also investigate a special CBN with a single BD and derive the optimal solution in the closed form to maximize the BD&#39;s throughput. Numerical results validate the quick convergence of the proposed iterative algorithm and that the proposed scheme ensures much fairness than the existing schemes.

A Deep Learning Based Automatic Interference Avoidance Resource Allocation Scheme for SCMA Systems

Sparse code multiple access (SCMA) is able to provide high spectral efficiency and massive connectivity, hence it is considered as a promising scheme for the fifth generation (5G) systems. This paper proposed a radio resource allocation scheme based on deep learning for SCMA systems, with the aim to automatically avoid the inter-cell interference. A long short term memory (LSTM) network is adopted to learn the past interference characteristics and predict the interference power in the current subframe. Radio resource blocks with less predicted interference power are then selected for users to transmit signals. Simulation results show that the proposed scheme outperforms the moving average prediction method and has significant gains over the random radio resource block allocation in terms of achievable bit error rate in SCMA systems.