User Association In Heterogeneous Networks Research Articles

Variable Hybrid Action Space Deep Q-Networks for Optimal Power Allocation and User Association in Heterogeneous Networks

Variable Hybrid Action Space Deep Q-Networks for Optimal Power Allocation and User Association in Heterogeneous Networks

A New Effective Strategy for User Association in Heterogeneous Networks

Introduction: Heterogeneous networks (HetNet) represent a promising technology that satisfies the needs of mobile users. However, several problems have influenced the performance of wireless communication, such as the maximization of energy efficiency and the problem of interferences due to the uncontrolled association of the user equipment (UE). Methods: Solving the problem of maximizing energy efficiency has captured the attention of several researchers. In this work, we propose an effective user association based on K-nearest Neighbors (KNN) approach considering a large dataset. The major novelty of this work is that the supervised learning perspective is applied to a dataset regrouped from an optimal user association, where the most valuable parameters are considered. Results: Additionally, it allows for mitigating the problem of interferences using individual user association. Simulation results have proven the efficiency of the proposed methodology. Conclusion: The suggested results have outperformed the two works in terms of accuracy, where the proposed method presents a better accuracy of 95%.

Semi‐dynamic Markov approximation‐based base station sleep with user association for heterogeneous networks

AbstractEnergy efficiency (EE) in the communication industry is the general trend of green communication development, and the base station sleep (BSS) strategy is crucial to improving EE. However, how to maximize the cell's EE and optimize the performance of users in the sleeping area after the BSS is a challenging problem. The sub‐6GHz and millimeter‐wave (mmWave) integrated networks are considered in this paper. The users’ backhaul of the cell is jointly provided by the sub‐6 GHz band and the mmWave band. User reaccess algorithm based on multiple parameters access criterion is adopted when their severed base stations (BSs) are shut down. The authors also consider a resource allocation algorithm based on the semi‐dynamic Markov approximation (MA) algorithm, which is proposed to quickly allocate sub‐channels to reaccessed users and ensure the maximum throughput. The simulation results show that the proposed algorithm can improve the EE of the heterogeneous network and promptly meet the users’ needs when some BSs are shut down.

Energy-Efficient Power Allocation and User Association in Heterogeneous Networks with Deep Reinforcement Learning

This paper studies the problem of joint power allocation and user association in wireless heterogeneous networks (HetNets) with a deep reinforcement learning (DRL)-based approach. This is a challenging problem since the action space is hybrid, consisting of continuous actions (power allocation) and discrete actions (device association). Instead of quantizing the continuous space (i.e., possible values of powers) into a set of discrete alternatives and applying traditional deep reinforcement approaches such as deep Q learning, we propose working on the hybrid space directly by using the novel parameterized deep Q-network (P-DQN) to update the learning policy and maximize the average cumulative reward. Furthermore, we incorporate the constraints of limited wireless backhaul capacity and the quality-of-service (QoS) of each user equipment (UE) into the learning process. Simulation results show that the proposed P-DQN outperforms the traditional approaches, such as the DQN and distance-based association, in terms of energy efficiency while satisfying the QoS and backhaul capacity constraints. The improvement in the energy efficiency of the proposed P-DQN on average may reach 77.6% and 140.6% over the traditional DQN and distance-based association approaches, respectively, in a HetNet with three SBS and five UEs.

Network Capacity Enhancement in HetNets Using Incentivized Offloading Mechanism

This paper investigates distributed algorithms for joint power allocation and user association in heterogeneous networks. We propose auction-based algorithms for offloading macrocell users ( MUs ) from the macrocell base station to privately owned small-cell access points (SCAs). We first propose a simultaneous multiple-round ascending auction (SMRA) for allocating MUs to SCAs. Taking into account the overheads incurred by SCAs during valuation in the SMRA, further improvements are proposed using techniques known as sub-optimal altered SMRA, the combinatorial auction with item bidding (CAIB), and its variations; the sequential CAIB and the repetitive CAIB. The proof for existence of the Walrasian equilibrium is demonstrated through establishing that the valuation function used by the SCAs is a gross substitute. Finally, we show that truthful bidding is individual rational for all of our proposed algorithms.

Delay-based user association in heterogeneous networks with Backhaul

The Internet of things (IoT) as an important application of future communication networks puts a high premium on delay issues. Thus when IoT applications meet heterogeneous networks (HetNets) where macro cells are overlaid with small cells, some traditional problems need rethinking. In this paper, we investigate the delay-addressed association problem in two-tier HetNets considering different backhaul technologies. Specifically, millimeter wave and fiber links are used to provide high-capacity backhaul for small cells. We first formulate the user association problem to minimize the total delay which depends on the probability of successful transmission, the number of user terminals (UTs), and the number of base stations (BSs). And then two algorithms for active mode and mixed mode are proposed to minimize the network delay. Simulation results show that algorithms based on mutual selection between UTs and BSs have better performance than those based on distance. And algorithms for mixed modes have less delay than those for active mode when the number of BSs is large enough, compared to the number of UTs.

Delay-Optimal Biased User Association in Heterogeneous Networks

In heterogeneous networks (HetNets), load balancing among different tiers can be effectively achieved by a biased user association scheme with which each user chooses to associate with one base station (BS) based on the biased received power. In contrast to previous studies, where a BS always has packets to transmit, we assume in this paper that incoming packets intended for all the associated users form a queue in the BS. In order to find the delay limit of the network to support real-time service, we focus on the delay optimization problem by properly tuning the biasing factor of each tier. By adopting a thinned Poisson point process model to characterize the locations of BSs in the busy state, an explicit expression of the average traffic intensity of each tier is obtained. On that basis, an optimization problem is formulated to minimize a lower bound of the network mean queuing delay. By showing that the optimization problem is convex, the optimal biasing factor of each tier can be obtained numerically. When the mean packet arrival rate of each user is small, a closed-form solution is derived. The simulation results demonstrate that the network queuing performance can be significantly improved by properly tuning the biasing factor. It is further shown that the network mean queuing delay might be improved at the cost of a deterioration of the network signal-to-interference ratio coverage, which indicates a performance tradeoff between real-time and non-real-time traffic in HetNets.

A distributed learning–based user association for heterogeneous networks

AbstractThe coexistence of various base stations (BSs) in heterogeneous networks (HetNets) has emerged as a promising approach to meet the ever increasing network capacity. In these networks, one of the important issues is the problem of associating user equipments (UEs) to BSs. In this paper, we investigate the UE association (UEA) problem in heterogeneous networks and propose a load‐aware UEA mechanism based on the BSs' estimated load and signal‐to‐interference‐and‐noise ratio. The proposed mechanism can capture the trade‐off between UE's quality‐of‐service requirement and delay. We model this strategic UE‐BS association as a noncooperative game. To solve the game, we develop a fully distributed algorithm inspired by machine learning techniques, whereby the proposed UEA scheme corresponds to a Markov chain. In the proposed scheme, each UE senses its environment and decides which BS to select based on the satisfaction technique. Therefore, it achieves a high level of satisfaction for UEs. Furthermore, use of historical information helps UEs select BSs with better long‐term performance. Simulation results show that the proposed mechanism reduces fractional transfer time and the number of unsatisfied UEs, respectively, up to about 46% and 52.3% and improves BS throughput up to about 15.4% compared to a benchmark algorithm that is based on the received signal strength and the BS's estimated load.

Distributed Cache Placement and User Association in Multicast-Aided Heterogeneous Networks

The small cells have been equipped with caching and multicast capabilities to save energy and ease backhaul burden. However, given the increasing diversity of user association in heterogeneous networks, traditional schemes may fail to exploit the energy-saving potential of caching and multicast. In this paper, we propose the model to minimize total power consumption by jointly optimizing the user association and cache deployment. The formulated joint optimization problem is decomposed and reformulated as a NP-complete set partition problem. Motivated by the idea of the tactile networks, the devices can find the candidate user grouping based on its own utility. The utility function is judiciously designed to reduce the searching space without compromising optimality. Then, a heuristic caching algorithm is proposed by rigorously deriving the upper and lower bounds of cache placement. Simulation results show that our proposed scheme outperforms the other existing multicast and caching algorithms in terms of power consumption by up to 28%, while keeping the load among base stations balanced.

User Association in Heterogeneous Networks: A Social Interaction Approach

This paper deals with the problem of user association in heterogeneous networks (HetNets). With the existence of small cells, to better associate user equipment (UE) with base stations (BSs), we propose a novel collaborative filtering (CF)-based wireless network recommendation system, which involves social interactions among UEs. Different from traditional user association schemes, the UEs in the proposed system interact with each other by rating the BSs to improve recommendation qualities. Similarity between UEs' preferences to the quality of service (QoS) and historical information on the QoS of networks are taken into consideration. The core of the network recommendation system is a rating matrix maintained by the operator. A UE can automatically rate the connected BS according to real-time measured parameters (interference, packet loss rate, time delay, etc.) that it experiences during service. Taking the Voice over Internet Protocol (VoIP) service as an example, the measured parameters can be mapped to certain QoS levels with the E-model, which is used as the ratings. During the process of handover, the operator recommends the BSs to the UEs based on the rating matrix. While the ratings help the UEs gain better recommendation qualities, they tend to avoid the costs, e.g., computational and bandwidth resources. Therefore, the UEs have to trade off between the profits and costs brought about by the rating procedure. A satisfaction game is formulated to deal with this problem. We use a utility function to measure a UE's satisfaction. When every UE's utility comes to a preset level, the game is considered to reach the satisfaction equilibrium (SE). An algorithm is designed to learn the SE, and the convergence is analyzed. The advantages of the proposed system lie in two aspects. First, it comprehensively considers multiple factors that influence the QoS, instead of signal strength only. Second, the historical information helps UEs select BSs with better long-term QoS but not immediate QoS. Simulation results show the effectiveness of the learning algorithm on both learning the SE and avoiding severe congestions in each BS.

Energy-efficient user association in heterogeneous networks with M2M/H2H coexistence under QoS guarantees

In this paper, we propose an energy efficient user association scheme for uplink heterogeneous networks with machine-to-machine (M2M) and human-to-human (H2H) coexistence. A group based random access protocol is designed for massive number of machine-type-communications (MTC) user equipments' (UEs) transmissions. A user association problem for UEs' energy efficiency maximization is formulated considering the HTC UEs' quality of service (QoS) guarantees and load balance among multiple BSs, simultaneously. A distributed iterative algorithm is developed to solve the optimization problem. In addition, the convergence of the proposed algorithm is proved. Simulation results show that our proposed scheme outperforms other schemes in terms of energy efficiency and QoS guarantees.

Low Complexity Distributed Max-Throughput Algorithm for User Association in Heterogeneous Network

This letter presents a distributed user association algorithm for the load balancing in heterogeneous networks. In this new method, we formulate the load balancing problem as a max min throughput problem. To solve this problem, we propose a distributed max-throughput algorithm. The main advantages of this algorithm include low computational complexity and few free parameters. Analysis and simulation results show that the proposed algorithm is very practical due to its simplicity.

A Joint Algorithm for Base Station Operation and User Association in Heterogeneous Networks

In this paper, we present an algorithm that performs base station (BS) switching-on/off and user terminal (UT) association jointly in heterogeneous networks composed of cellular networks and wireless local area networks (WLANs). We first formulate a general problem which minimizes a total cost function, which is designed to balance the energy consumption of overall network and the revenue of cellular networks. Given that the time scale for determining the set of active BSs is much larger than that for UT association, we decompose the problem into a UT association algorithm and a BS switching-on/off algorithm, and then obtain an optimal UT association policy for the UT association problem. Since BS switching-on/off problem is a challenging combinatorial problem, we propose two heuristic algorithms based on the total cost function and the density of access points of WLANs within the coverage of each BS, respectively. Numerical results demonstrate that the proposed algorithms can considerably reduce energy consumption compared with the case where all the BSs are always turned on.

Resource Allocation, Transmission Coordination and User Association in Heterogeneous Networks: A Flow-Based Unified Approach

In this paper, we formulate a flow-based framework for the joint optimization of resource allocation, transmission coordination, and user association in a heterogeneous network comprising of a macro base station and a set of pico base stations and/or relay nodes. By incorporating these three important network processes together and by unifying the analysis of pico base stations and relay nodes, our framework can act as an important engineering tool for understanding the performance of different configurations. We use the resulting formulations to characterize the performance of different combinations of resource allocation schemes and transmission coordination mechanisms. We obtained important engineering insights regarding the interplay of these network processes. In particular, under the deployment of pico base stations, we find that partially shared deployment outperforms the co-channel deployment, with or without transmission coordination. In contrast, the results also show that the deployment of relay nodes does not offer meaningful throughput gains for any choice of resource allocation scheme or transmission coordination mechanism.