Aggregated Throughput Prediction for Collated Massive Machine-Type Communications in 5G Wireless Networks

Abstract

The demand for extensive data rates in dense-traffic wireless networks has expanded and needs proper controlling schemes. The fifth generation of mobile communications (5G) will accommodate these massive communications, such as massive Machine Type Communications (mMTC), which is considered to be one of its top services. To achieve optimal throughput, which is considered a mandatory quality of service (QoS) metric, the carrier sense multiple access (CSMA) transmission attempt rate needs optimization. As the gradient descent algorithms consume a long time to converge, an approximation technique that distributes a dense global network into local neighborhoods that are less complex than the global ones is presented in this paper. Newton’s method of optimization was used to achieve fast convergence rates, thus, obtaining optimal throughput. The convergence rate depended only on the size of the local networks instead of global dense ones. Additionally, polynomial interpolation was used to estimate the average throughput of the network as a function of the number of nodes and target service rates. Three-dimensional planes of the average throughput were presented to give a profound description to network’s performance. The fast convergence time of the proposed model and its lower complexity are more practical than the previous gradient descent algorithm.

An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios

“…• D2D communication has shown to enable multihop communication for delivering a high-quality video stream, contributing to an increased network capacity at the expense of a higher end-to-end delay [4]. Each node can access both cellular channels (in which the node is the PU) and cognitive channels (in which the node is the SU) to improve spectrum efficiency and quality of service (QoS) [6,7]. • Route selection enables a source node to select a route to a destination node while garnering the benefits of traffic offloading from a MC BS to SCs [27].…”

“…Each node can access both cellular channels (in which the node is the PU) and cognitive channels (in which the node is the SU). D2D enables direct communication between two nodes without traversing the base station (BS), particularly the macrocell (MC) BS (or a central controller) that has the largest coverage in 5G networks [5] using D2D communication, a node can access both cellular and cognitive channels; as a result, it improves spectrum efficiency and QoS [6,7].…”

D2D Matching Techniques for Resource Allocation Management and Power Control Management in B5G/6G

“…The impact of resource allocation and clustering techniques on network scalability, robustness, and resilience as discussed in [6,7].…”