Online Distributed User Association for Heterogeneous Radio Access Network

Abstract

Future-generation radio access networks (RAN) are projected to fulfill the diverse requirements of user equipment (UE) by adopting a heterogeneous network (HetNet) environment. Necessary integration of different radio access technologies (RAT), such as 2G, 3G, 4G, wireless local area network (WLAN), and visible light communication (VLC) is inevitable. Moreover, UEs equipped with diverse requirements will be capable of accessing some or all the RATs. The complex HetNet environment with diverse requirements of UEs will present many challenges. The HetNet is likely to suffer severely from load imbalance among the base stations (BSs) from inheriting the traditional user association scheme such as max-SINR (signal-to-interference-plus-noise ratio)/max-RSSI (received signal strength indicator), unless some sophisticated schemes are invented. In this paper, a novel scheme is devised for a joint-user association for load balancing, where BSs are densely deployed and UEs typically have a certain degree of mobility. Unlike most of the present works, a dynamic network is considered where the position and channel condition of the UEs are not fixed. We develop two complex and distributed association schemes based on probability and d-choices, while carefully considering both loads of the BSs and SINR experienced by the UEs. Numerical results validate the efficiency of the proposed schemes by showing a received data-rate fairness among UEs and an improvement in the UE’s minimum received data rate.

Discontinuous Reception Based Energy-Efficient User Association for 5G Heterogeneous Networks

“…Hence, the objective is to allocate RBs to the number of UEs, with different rates to maximize the utility function (ψ) as shown in Eq. (7). It also provides an upper limit to the nominal bit-rate of the network.…”

“…LB can be solved by a combination of good user association (UA) and user-scheduling. The traditional UA, i.e., max-SINR (signal-to-interference-plus-noise ratio) makes UE prefer to connect to the macro base station (MBS) even though UE is within the coverage of small base station (SBSs) which causes load imbalance in HetNet [5]- [7]. This cause the MBSs to be overloaded and the SBSs to be too underloaded.…”

A Multi-Layer Multi-Timescale Network Utility Maximization Framework for the SDN-Based LayBack Architecture Enabling Wireless Backhaul Resource Sharing

“…We model the upstream packet traffic generation at a given eNB (which is due to upstream packet arrivals from associated user end devices [91]) as an independent Poisson process. We set the eNB Poisson process rates such that the aggregate load from the eNBs at a given operator o results in a base packet traffic load of 5 Mbps, whereby each of the 30 eNBs at a given operator o contributes equally to the aggregate operator load.…”

5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network

“…Mollel et al (2020) presented an offline network selection scheme based on the double deep reinforcement learning (DDRL) approach to reduce number of handovers and alleviate the adverse QoS in 5G mm-wave networks. A distributed optimisation method based on multi-agent reinforcement learning (MARL) is developed in Kumar et al (2019) to guarantee user specific requirements and maximum long term network utility in 5G heterogeneous network. Authors in (Ding et al 2019), leveraged an energy efficient algorithm based on multi-agent deep Q network (DQN) network to ensure user satisfaction and maximum network utility in OFDMA based uplink HetNets.…”