Heterogeneous Networks in 5g using Joint Path Selection and Rate Allocation Framework

Abstract

One of the major challenges in evolving wireless cellular networks whose return network is constrained by capability and heterogeneity (wired, wireless, and hybrid) is the design of distributed management mechanism. This study proposes a new method of managing wireless network interference with the ability to detect return network. The proposed approach helps Macro-cell User Equipment (MUE) to maximize its output using adjacent small cell base stations to maximize its uplink. Considering the radio access network and backhaul (possibly heterogeneous), this issue is represented as a non-cooperative game between MUE that attempts to maximize its compensation for delay levels. A new distributed learning algorithm is proposed to solve this problem, which uses the algorithm to autonomously choose the best uplink transmission strategy, provided a limited amount of available knowledge. The algorithm's convergence is demonstrated, and its output is studied. The simulation results show that, in comparison to the existing reference algorithm, The proposed approach has substantial efficiency benefits for various forms of backhaul in terms of average output and delay in MUE.