Ultra-dense networks (UDNs) have been known as an efficient method to satisfy tremendous traffic demands in future mobile networks. With high density deployment of small base stations (SBSs), mobile user equipments (UEs) will suffer frequent and unnecessary handovers (HOs) which severely degrade the quality of service (QoS) and/or quality of experience (QoE). To solve this problem, we propose a novel multi-attribute mobility management strategy according to link reliability level and throughput requirement. In particularly, the reference signal received quality (RSRQ) and speed of UE are taken into account for the link reliability index. Besides, the minimum throughput is considered for guaranteeing QoS requirement of users. The UE reconnects the target small cell with the highest priority, which are calculated according to link index, QoS index, and corresponding weights. The goal of proposed mobility management scheme is to improve HO performance and QoS of UEs. Extensive simulation experiences with different traffic types are evaluated and the simulation results indicate the effectiveness of our mobility management strategy in terms of throughput and HO delay.

A Multi-Attribute Handover Algorithm for QoS Enhancement in Ultra Dense Network

Reaching a flat network is the main target of future evolved packet core for the 5G mobile networks. The current 4th generation core network is centralized architecture, including Serving Gateway and Packet-data-network Gateway; both act as mobility and IP anchors. However, this architecture suffers from non-optimal routing and intolerable latency due to many control messages. To overcome these challenges, we propose a partially distributed architecture for 5th generation networks, such that the control plane and data plane are fully decoupled. The proposed architecture is based on including a node Multi-session Gateway to merge the mobility and IP anchor gateway functionality. This work presented a control entity with the full implementation of the control plane to achieve an optimal flat network architecture. The impact of the proposed evolved packet Core structure in attachment, data delivery, and mobility procedures is validated through simulation. Several experiments were carried out by using NS-3 simulation to validate the results of the proposed architecture. The Numerical analysis is evaluated in terms of total transmission delay, inter and intra handover delay, queuing delay, and total attachment time. Simulation results show that the proposed architecture performance-enhanced end-to-end latency over the legacy architecture.

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An Efficient 5G Data Plan Approach Based on Partially Distributed Mobility Architecture

The uplink (UL) throughput prediction is indispensable for a sustainable and reliable cellular network due to the enormous amounts of mobile data used by interconnecting devices, cloud services, and social media. Therefore, network service providers implement highly complex mobile network systems with a large number of parameters and feature add-ons. In addition to the increased complexity, old-fashioned methods have become insufficient for network management, requiring an autonomous calibration to minimize utilization of the system parameter and the processing time. Many machine learning algorithms utilize the Long-Term Evolution (LTE) parameters for channel throughput prediction, mainly in favor of downlink (DL). However, these algorithms have not achieved the desired results because UL traffic prediction has become more critical due to the channel asymmetry in favor of DL throughput closing rapidly. The environment (urban, suburban, rural areas) affect should also be taken into account to improve the accuracy of the machine learning algorithm. Thus, in this research, we propose a machine learning-based UL data rate prediction solution by comparing several machine learning algorithms for three locations (Houston, Texas, Melbourne, Florida, and Batman, Turkey) and determine the best accuracy among all. We first performed an extensive LTE data collection in proposed locations and determined the LTE lower layer parameters correlated with UL throughput. The selected LTE parameters, which are highly correlated with UL throughput (RSRP, RSRQ, and SNR), are trained in five different learning algorithms for estimating UL data rates. The results show that decision tree and k-nearest neighbor algorithms outperform the other algorithms at throughput estimation. The prediction accuracy with the R2 determination coefficient of 92%, 85%, and 69% is obtained from Melbourne, Florida, Batman, Turkey, and Houston, Texas, respectively.

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Machine-Learning-Based Uplink Throughput Prediction from Physical Layer Measurements

Increasing the deployment density of small base stations (SBS) is a key method designed to satisfy high data traffic in 5th generation mobile network (5G). However, a large number of SBSs in such ultra-dense network (UDN) may cause ping-pong handovers (HOs), accompanied by increased delay and HO failure. In addition, because of the separation of control and data signaling in 5G, the HO procedure must be performed in both layers. In this paper, we introduce an SDN-based intelligent dynamic HO parameter optimization strategy to minimize both HO failures and ping-pong HOs together. The goal of the proposed strategy is to reduce the HO failure rate and redundant HO (i.e. ping-pong HO) while enabling user equipment (UE) to make full use of the benefits of dense deployment of BSs. Simulation results present that the method proposed in this paper effectively suppresses the ping-pong effect and keeps it at a low level in all of the investigated scenes. In addition, compared with the other algorithms, the HO failure rate is significantly reduced and the throughput of UE is greatly increased, especially in the case of high BS density. Therefore, the benefits of intensive BS deployment are retained.

Self-Adapting Handover Parameters Optimization for SDN-Enabled UDN

The user-centric cooperative transmission provides a compelling way to alleviate frequent handovers caused by an ever-increasing number of randomly deployed base stations (BSs) in ultra-dense networks (UDNs). This paper proposes a new user-centric cooperative transmissions-based handover scheme, i.e., the group-cell handover (GCHO) scheme, with the aim of reducing the handover rate in UDNs. In the proposed scheme, the boundary of the cooperating cluster depends on the distance among the user equipment (UE) and cooperating BSs. The new scheme captures the dynamicity and irregularity of the cooperating cluster topology resulting from randomly distributed BSs. Based on stochastic-geometry tools where BSs locations are modeled as the Poison point process (PPP), we derive an analytical expression of the handover rate for the UE with an arbitrary movement trajectory. Furthermore, a GCHO skipping (GCHO-S) scheme is proposed to minimize the handover cost, i.e., the percentage of time wasted in handover signaling in user-centric cooperative transmissions scenarios. The numerical results show that the GCHO scheme decreases the handover rate by 42.3&#x0025; and 72.7&#x0025; compared with the traditional single BS association and fixed-region cooperative network topology handover approaches, respectively. Moreover, under the same group-cell size and constant velocity, the GCHO-S scheme diminishes the handover cost by 50&#x0025; against the GCHO scheme. 

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User-Centric Cooperative Transmissions-Enabled Handover for Ultra-Dense Networks

In the heterogeneous networks (HetNets), the downlink-uplink decoupled (DUDE) user association is a very promising technology that can employ different association strategies for uplink and downlink. The DUDE mode has great potential for improving system capacity and frequency efficiency. However, interference between the DUDE user equipment (DUDE UE), macro user equipment (MUE) and small user equipment (SUE) still needs to be considered and properly tackled. Therefore, this paper presents an optimization scheme of frequency allocation and power control to improve the communication quality of users. Firstly, the multiple region frequency allocation (MRFA) scheme is proposed for non-uniform user distribution to alleviate interference among users and allocate spectrum resources equally to dense users. This scheme is modelled as a sub-region partition problem with maximum sum rate, which can disperse densely distributed users to different sub-bands for transmission. Secondly, the convergent power control (CPC) algorithm is considered to improve the transmission performance of each user. It can converge the transmit power to the optimal transmit power for reducing interference among co-channel users when there is no disturbance in the network. The CPC algorithm can also make the signal-to-interference-plus-noise-ratio (SINR) of each active user always greater than the target threshold until the end of communication by dynamically adjusting the transmit power of users. Simulation results show that the joint scheme of MRFA and CPC achieves a higher system throughput and better user performance than existing frequency allocation schemes or power control schemes.

Uplink Performance Improvement for Downlink-Uplink Decoupled HetNets With Non-Uniform User Distribution

Software defined network (SDN) is considered as one of the most promising network architectures in the next generation mobile networks. SDN-enabled ultra dense network (UDN) has a simpler and more flexible network architecture, but its mobility management is still a challenging task. The major problem is the occurrence of frequent handover (FHO). Therefore, a SDN-enabled UDN architecture is firstly proposed to make the network more agile. Then, a deep Q-learning (DQN) method is used to control the handover (HO) procedure of the user equipments (UEs) by well capturing the characteristics of wireless signals/interferences and network load. In details, we use the SINR and the access rate per node to characterize the state of the UE. Thanks to the generalization ability of deep neural network (DNN), newly arrived UEs can use the trained neural network to avoid possible bad initial points. Experimental results show that the proposed scheme can reduce HO rate and guarantee the system throughput, which is better than the traditional HO scheme.

A DQN-Based Handover Management for SDN-Enabled Ultra-Dense Networks

With the exponential explosion of high traffic demand in wireless networks, the heterogeneous network (HetNet) is considered as one promoting method for improving the throughput of network and quality of service (QoS) of users. The HetNet consists of different cells, which operate at different transmit power. The small cells can be used to offload traffic from macro cells. Nevertheless, the uses of small cells also bring some problems. One problem is how efficiently an handover (HO) of small cells is performed in mobility management, when small cells are deployed in coverage area of a macro cell. In this condition, the various problems faced by users include frequent and unnecessary HO, ping-pong effect, which severely degrade the quality of traffic and users experience. Many existing works propose optimal solutions to improve the performance of long-term evolution (LTE) HetNets, which only consider the one of users’ speeds and traffic classifications. In this paper, we propose an optimal HO algorithm to meet the margin coverage problems. Our proposed algorithm considers the different conditions of user speed and traffic classification. Our proposed scheme avoids users with high speed in handing off to home-evolved nodeB (HeNB) and makes them limited to evolved nodeB (eNB). Simulation results show that our proposed algorithm improves the performance in in terms of the downlink throughput, physical downlink shared channel (PDSCH) utilization, system delay, voice end-to-end delay and voice jitter.

Wei Huang

Papers

Uplink Performance Improvement for Downlink-Uplink Decoupled HetNets With Non-Uniform User Distribution

A DQN-Based Handover Management for SDN-Enabled Ultra-Dense Networks

A Multi-Attribute Handover Algorithm for QoS Enhancement in Ultra Dense Network

A Speed-Based and Traffic-Based Handover Algorithm in LTE Heterogeneous Networks