Xinyu Gu

An Enhanced Mobility State Estimation Based Handover Optimization Algorithm in LTE-A Self-organizing Network☆

Abstract Heterogeneous network (HetNet) is considered as a prime way to solve the limits of system capacity and broadband service coverage in traditional network. However, the deployments of small cells with varied sizes make the network topology more complicated. Self-organizing network (SON) technology, aiming to reduce the operational costs, is a significant technology in HetNet. One of the common use cases is to improve handover performance. In this paper, a handover optimization algorithm based on enhanced mobility state estimation (EMSE) is proposed. Considering both user equipment (UE) speed and handover types, the optimization algorithm based on EMSE combines selective Time-to-Trigger (TTT) and dynamic handover margin (HM)-adjusting in SON. Furthermore, the algorithm performance is compared with two different reference cases. Simulation results show that total handover failure has an obvious decline with our self-optimizing algorithm. Therefore, handover performance gets improved and UEs have better mobility robustness in HetNet through our algorithm.

A two-stages relay selection and resource allocation joint method for d2d communication system

Introducing relay node to Device-to-Device (D2D) technology can extend the cell coverage range and improve the system performance, and the relay selection is a key point of relay-assisted D2D communication. In this paper, the relay selection problem is discussed for D2D communication underlaying cellular network. We propose a two-stages relay selection and resource allocation joint method for relay-assisted D2D communication. The method maximizes the total throughput of cellular uplink (UL) and D2D link, and it can guarantee the quality of service (QoS) of the two links simultaneously. Through this method, the optimal relay UE and its transmission power can be obtained. This method also determines which spectrum resource of cellular user equipments(CUs) are reused by D2D source node to relay node link and relay node to D2D destination node link. Simulation results show that the proposed two-stages method can improve total throughput and decrease the complexity comparing with random selection method and one-stage method.

Q-learning based power control algorithm for D2D communication

In this paper, reinforcement learning (RL) based power control algorithm in underlay D2D communication is studied. The approach we use regards D2D communication as a multi-agents system, and power control is achieved by maximizing system capacity while maintaining the requirement of quality of service(QoS) from cellular users. We propose two RL based power control methods for D2D users, i.e., team-Q learning and distributed-Q learning. The former is a centralized method in which only one Q-value table needs to be maintained, while the latter enables D2D users to learn independently and reduces the complexity of Q-value table. Simulation results show the difference of the two Q-learning algorithm in terms of convergence and reward function. In addition, it is shown that through our distributed-Q learning, D2D users not only are able to learn their power in a self-organized way, but also achieve better system performance than that using traditional method in LTE(Long Term Evolution).

A Novel Dynamic Adjusting Algorithm for Load Balancing and Handover Co-Optimization in LTE SON

With the development of mobile internet and multi-media service, advanced techniques need to be applied in wireless network to improve user experience. Long term evolution (LTE) systems, which can offer up to 100Mbps downlink date rates, have been deployed in USA and Korea. However, because plenty of complex physical layer algorithms are utilized, network planning and optimization become heavy burdens for LTE network operators. Self-organizing network (SON) is a promising method to overcome this problem by automatically selecting and adjusting key parameters in LTE systems. In this paper, we present a dynamic adjusting algorithm to improve both handover and load balancing performance by introducing a weighted co-satisfaction factor (CSF). Analysis and system level simulation are conducted to exhibit the performance improvement of the proposed scheme. Results show that the proposed method outperforms the conventional solutions in terms of the network handover success ratio and load balancing gains significantly.

A Two-Stages Relay Selection and Resource Allocation with Throughput Balance Scheme in Relay-Assisted D2D System

Introducing relay node to Device-to-Device (D2D) technology can extend the cell coverage range and improve the system performance. In this paper, we discuss a relay-assisted D2D communication underlaying cellular network from two aspects. Firstly, we discuss relay selection problem. A two-stages relay selection and resource allocation method is proposed. The stage 1 determines the range of candidate relay UEs through an area division method. Through this way, signaling overhead and measurement overhead can be reduced. The stage 2 selects an optimal relay UE from candidate relay UEs and determines the resource allocation method using an optimal bipartite matching theory with a pretty low complexity. Secondly, we find that the two-stages method causes a throughput unbalance and resource waste problem. Therefore, throughput balance is investigated in this paper. Eight throughput balance options are discussed in details. Then, three throughput balance schemes are proposed. The two-stages method and three throughput balance schemes are evaluated by simulation. Simulation results show that the proposed two-stages method can improve the throughput of relay-assisted D2D communication with a low signaling overhead and measurement overhead. Three proposed throughput balance schemes can improve system throughput performance further.

User average data rate analysis in future dense small cells

In the fifth generation (5G) mobile communication system, the experienced user data rate is expected to achieve at least 0.1Gbps. And the connection density may be one million connections per square kilometers. To satisfy this requirement, deploying dense small cell networks as an overlay to conventional macro cell is regarded as an effective method. In this paper, we analyze the deployment of dense small cells to meet with 5G data rate requirement using stochastic geometry theory. A two-tier heterogeneous cellular network (HCN) which contains both macro cells and small cells is used as the cellular network model. And base stations (BSs) in each tier are randomly located following spatial Poisson point process (PPP) distribution. Both macro BSs and small cells deployed in a separate frequency band and in the same frequency band are analyzed as a comparison. Also other factors which have impacts on the user data rate are considered for a comprehensive assessment of the small cell densification degree, such as the bandwidth, the interference elimination factor and the transmission power ratio.

Green heterogeneous network with load balancing in LTE-A systems

Heterogeneous networks (HetNets) have been considered as a promising technique for improving spectral efficiency, but the energy efficiency influenced by the fluctuation of user numbers should not be neglected to achieve green communications. We introduce the energy-efficient switch-off mode algorithm for pico cells to reduce power consumption in the self-organizing network (SON). Once the switch-off mode is initiated in some picked pico cells, the users connecting to these cells would face the problem of redistribution. In this paper, we introduce the energy saving (ES) strategy based on the prediction of load capability to select pico cells to enter switchoff mode. Furthermore, network flow based load balancing (LB) is proposed to provide solution of uneven load status caused by ES. System level simulations are conducted to exhibit the performance enhancement that the proposed algorithm can reduce energy consumption as well as improve the balanced degree of resource allocation significantly.

A two-step resource allocation algorithm for D2D communication in full duplex cellular network

In this paper, a scenario was considered that cellular user equipments (UEs) and device to device (D2D) communication pairs exist in a full duplex cell. Limited frequency resources are shared by a large number of UEs, and complex interference is introduced due to resource sharing between uplink, downlink UEs and D2D links. To manage the resource and achieve the maximum total system capacity, a non-deterministic polynomial (NP) hard optimization problem is formulated. To efficiently solve the problem, a Graph based Two-step Resource Allocation (GTRA) algorithm is proposed. In the first step, a full duplex base station (BS) allocates resources to half duplex uplink and downlink UEs by a graph coloring based algorithm. The second step is a bipartite graph based Multi-Stage Matching (MSM) algorithm to allocate resources to D2D pairs. The GTRA algorithm can effectively allocate the spectrum resources with low complexity while guaranteeing the Quality of Service (QoS) of cellular UEs as well. Simulation results show that our proposed algorithm performs better than the reference algorithms.

Future network deployment strategy based on user behavior analysis and stochastic geometry

In the future Ultra Dense Network (UDN), deploying new small cells within macro cells is a solution to improve the User Data Rate (UDR). How and where to deploy the small cells are the questions network operator is facing. The paper presents strategies based on stochastic geometry and user behavior analysis to solve the problems separately. A smartphone dataset that reflects real-time network conditions is analyzed, based on which a process is conducted and results are demonstrated to give advice for network suppliers.

Algorithm to reduce impact of LTE-A CSI-RS on LTE legacy UE in CoMP scenario

In LTE-A (Long Term Evolution-Advanced) network, CSI-RS (Channel State Information Reference Signal) is substituted for CRS (Common Reference signal) to perform higher order MIMO (Multiple Input Multiple Output). In CoMP (Coordinated Multiple Point Transmission) scenario, the number of REs (Resource Element) occupied by CSI-RS would be multiplied. However, LTE (Long Term Evolution) legacy UEs (User Equipment) cannot distinguish CSI-RS from data, which may deteriorate the LTE UEs demodulation performance. Therefore it could be troublesome for LTE legacy UEs scheduled in LTE-A network with higher order MIMO equipped, and it is even more serious when CoMP technology is used. Investigation results by both link-level and system-level simulation reveal that LTE legacy UEs performance is seriously degraded due to the impact of CSI-RS when coding rate and modulation order go higher. To reduce the performance degeneration, an improved scheduling algorithm named AUP-AMCS (Advanced UE Prioritization combined with Adaptive Modulation Coding Scheme) is proposed in this paper. Compared to existing algorithm, AUP-AMCS algorithm takes UE priority into consideration and is compatible with the established specification. Simulation results indicate that the proposed algorithm can improve system performance obviously.