On Evolutionary Game of Dynamic Devices in NOMA-based IoT Networks

Abstract : In this paper, we consider an Internet-of-Things (IoT) network that supports two types of devices, namely static devices (SDs) and dynamic devices (DDs), with multiple channels. The notion of power-domain non-orthogonal multiple access (NOMA) is applied so that DDs can not only dynamically change channels, but also transmit signals with a high power to reduce collisions with SDs. Thus, learning algorithms are needed for DDs to select channels. To this end, multiarmed bandit (MAB) algorithms can be used. However, in general, it is difficult to analyze the performance of MAB algorithms in the setting due to the interaction of DDs. Thus, in this paper, instead of analyzing MAB algorithms, we formulate an evolutionary game of DDs in NOMA-based IoT networks and analyze the evolutionary game. It is shown that the game has a unique evolutionary stable state (ESS) in terms of the channel selection probability. The performance according to the ESS can be regarded as a baseline performance, because it is the best performance achieved by the DDs competing for multiple channel resources. Thus, the resulting baseline performance can be used to see if a learning algorithm including MAB can achieve a better performance by avoiding competition between DDs.

 EXISTING SYSTEM :

 ? The existing literature on NOMA schemes majorly considers centralized scheduling based HTC, where users are already connected, and various system parameters like spreading sequences, interleaving patterns, power control, etc., are predefined. ? The situation is exacerbated by the fact that radio resource allocation in existing multiple access (MA) techniques, orthogonal MA (OMA), is non-overlapping in nature i.e., a radio resource can be allocated to only a single device/user. ? In existing wireless networks, radio resources (e.g., time, frequency) are allocated orthogonally to connected devices. ? To enable massive connectivity and to deal with the limitations of existing MA schemes explained earlier, need for the design of new wireless technologies is inevitable.

 DISADVANTAGE :

 ? Game theory is a set of applied mathematical tools aiming to understand and solve decision-making problems, such as competing and independent actors during conflicts. ? It has been extensively used in wireless networks, and more specifically in solving cooperation and competition problems between devices over limited resources . ? Also, reducing transmit power allows us to lower the impact of cross-interference, which helps to improve the network performance, enhance the QoS (improved throughput, reduced latency, and increased reliability). ? Consider a two-stage decision problem of a fixed number n of devices inside a single cell.

 PROPOSED SYSTEM :

 • In order to provide massive connectivity, a contention based/grant-free SCMA scheme is proposed in. • Moreover, by considering the use of CTUs and massive number of MTCD transmissions using grant-free access, a blind detection based receiver is proposed in. • An improved detection-based group orthogonal matching pursuit (DGOMP) MUD is proposed in to facilitate massive grant-free UL SCMA transmission and reception. • Some noncoherent detection techniques have recently been proposed in literature for massive NOMA in grant-free access. • Moreover, a slice-based virtual resource scheduling scheme with NOMA to enhance the QoS of the system is proposed in.

 ADVANTAGE :

 ? Device-to-Device (D2D) relaying has been proposed as an efficient solution to lower energy consumption and extend the battery life of the mobile device, while expanding the network coverage and improving local performance in a rapid and cost-effective way. ? However, decentralizing decision making sometimes may induce some paradoxical outcomes resulting, therefore, in a performance drop, which sustains the design of self-organizing, yet efficient systems. ? Structureless communications such as Device-to-Device (D2D) relaying are undeniably of paramount importance to improving the performance of today’s mobile networks. ? To cope with this neverseen demand, substantial research effort is being conducted to enhance the performance of next generation mobile networks.