Improved Clustering and Resource Allocation for Ultra-Dense Networks

To reduce the interference among small cells of Ultra-Dense Networks(UDN),an improved Clustering-Assisted Resource Allocation(CARA)scheme is proposed in this paper.The proposed scheme is divided into three steps.First,an Interference-Limited Clustering Algorithm(ILCA)based on interference graph corresponding to the interference relationship between Femtocell Base Stations(FBSs),is proposed to group FBSs into disjoint clusters,in which a pre-threshold is set to constrain the sum of interference in each cluster,and a Cluster Head(CH)is selected for each cluster.Then,CH performs a twostage sub-channel allocation within its associated cluster,where the first stage assigns one sub-channel to each user of the cluster and the second stage assigns a second sub-channel to some users.Finally,a power allocation method is designed to maximize throughput for a given clustering and sub-channel configuration.Simulation results indicate that the proposed scheme distributes FBSs into each cluster more evenly,and significantly improves the system throughput compared with the existing schemes in the same scenario.

Joint User Association and Resource Allocation for Self-Backhaul Ultra-Dense Networks

In order to meet the exponentially increasing demand on mobile data traffic, self-backhaul ultra-dense networks(UDNs) combined with millimeter wave(mm Wave) communications are expected to provide high spatial multiplexing gain and wide bandwidths for multi-gigabit peak data rates. In selfbackhaul UDNs, how to make the radio access rates of small cells match their backhaul rates by user association and how to dynamically allocate bandwidth for the access links and backhaul links to balance two-hop link resources are two key problems on improving the overall throughputs. Based on this, a joint scheme of user association and resource allocation is proposed in self-backhaul ultra-dense networks. Because of the combinatorial and nonconvex features of the original optimization problem, it has been divided into two subproblems. Firstly, to make the radio access rates of small base stations match their backhaul rates and maximize sum access rates per Hz of all small cells, a proportional constraint is introduced, and immune optimization algorithm(IOA) is adopted to optimize the association indicator variables and the boresight angles of between users and base stations. Then, the optimal backhaul and access bandwidths are calculated by differentiating the general expression of overall throughput. Simulation results indicatethat the proposed scheme increases the overall throughputs significantly compared to the traditional minimum-distance based association scheme.

Two-Stage Resource Allocation Scheme for Three-Tier Ultra-Dense Network

In 5 G Ultra-dense Network(UDN), resource allocation is an efficient method to manage inter-small-cell interference. In this paper, a two-stage resource allocation scheme is proposed to supervise interference and resource allocation while establishing a realistic scenario of three-tier heterogeneous network architecture. The scheme consists of two stages: in stage I, a two-level sub-channel allocation algorithm and a power control method based on the logarithmic function are applied to allocate resource for Macrocell and Picocells, guaranteeing the minimum system capacity by considering the power limitation and interference coordination; in stage II, an interference management approach based on K-means clustering is introduced to divide Femtocells into different clusters. Then, a prior sub-channel allocation algorithm is employed for Femtocells in diverse clusters to mitigate the interference and promote system performance. Simulation results show that the proposed scheme contributes to the enhancement of system throughput and spectrum efficiency while ensuring the system energy efficiency.

Centralization and collaboration in 5G ultra-dense network architecture

An ultra-dense network scenario is a scene where a large number of people assemble in a limited area to generate centralized broadband data traffic requirements.Because ultra-dense networks generate enormous traffic pressure,traditional network capabilities are not enough to accommodate the user s needs.Based on the description of ultra-dense network architecture,we analyze millimeter wave radio spectrum,high gain beam forming,physical layer frame structure,resource concentration and edge computing technology.In addition,the cooperative technology required by overlay and interference symbiosis in the dense network architecture as well as the access control technology of centralized access is analyzed and discussed comprehensively.

Mobility-driven user-centric AP clustering in mobile edge computing-based ultra-dense networks

ultra-Dense Network(UDN)has been envisioned as a promising technology to provide high-quality wireless connectivity in dense urban areas,in which the density of Access Points(APs)is increased up to the point where it is comparable with or surpasses the density of active mobile users.In order to mitigate inter-AP interference and improve spectrum efficiency,APs in UDNs are usually clustered into multiple groups to serve different mobile users,respectively.However,as the number of APs increases,the computational capability within an AP group has become the bottleneck of AP clustering.In this paper,we first propose a novel UDN architecture based on Mobile Edge Computing(MEC),in which each MEC server is associated with a user-centric AP cluster to act as a mobile agent.In addition,in the context of MEC-based UDN,we leverage mobility prediction techniques to achieve a dynamic AP clustering scheme,in which the cluster structure can automatically adapt to the dynamic distribution of user traffic in a specific area.Simulation results show that the proposed scheme can highly increase the average user throughput compared with the baseline algorithm using max-SINR user association and equal bandwidth allocation,while it guarantees at the same time low transmission delay.

Two-Codebook-Based Cooperative Precoding for TDD-CoMP in 5G Ultra-Dense Networks

In ultra-dense networks （UDN）, the local precoding scheme for time-division duplex coordinated multiple point transmission （TDD-CoMP） can have a good performance with no feedback by using reciprocity between uplink and dovallink. However, if channel is time-varying, the channel difference would cause codeword mismatch between transmitter and receiver, which leads to performance degradation. In this paper, a linear interpolation method is proposed for TDD-CoMP system to estimate the uplink channel at the receiver, which would reduce the channel difference caused by time delay and decrease the probability of codeword mismatch between both sides. Moreover, to mitigate severe inter-cell interference and increase the coverage and throughput of celledge users in UDN, a two-codebook scheme is used to strengthen cooperation between base stations （BSs）, which can outperform the global precoding scheme with less overhead. Simulations show that the proposed scheme can significantly improve the link performance compared to the global precoding scheme.

Ultra-Dense Networking Architectures and Technologies for 5G

With the continuous enrichment of mobile communication application scenarios in the future, the traditional macro-cellular-based mobile communication network architecture will be difficult to meet the explosive growth in demand for communications services.

Anti-jamming channel access in 5G ultra-dense networks: a game-theoretic learning approach

This paper investigates the Quality of Experience(QoE)oriented channel access anti-jamming problem in 5th Generation Mobile Communication(5G)ultra-dense networks.Firstly,considering that the 5G base station adopts beamforming technology,an anti-jamming model under Space Division Multiple Access(SDMA)conditions is proposed.Secondly,the confrontational relationship between users and the jammer is formulated as a Stackelberg game.Besides,to achieve global optimization,we design a local cooperation mechanism for users and formulate the cooperation and competition among users as a local altruistic game.By proving that the local altruistic game is an Exact Potential Game(EPG),we further prove the existence of pure strategy Nash Equilibrium(NE)among users and Stackelberg Equilibrium(SE)between users and jammer.Thirdly,to obtain the equilibrium solutions of the proposed games,we propose an anti-jamming channel selection algorithm and improve its convergence speed through heterogeneous learning parameters.The simulation results validate the convergence and effectiveness of the proposed algorithm.Compared with the throughput optimization scheme,our proposed scheme obtain a greater network satisfaction rate.Finally,we also analyze user fairness changes during the algorithm convergence process and get some interesting conclusions.

5G Ultra-Dense Networks

In future 5G networks,a key scenario is the dense user distribution over some area,such as office,urban apartments,shopping mall,stadium,etc.,where the requirement for user-experienced rate at cell-edge can be

Coverage Analyses on Directional Transmissions Ultra-Dense Networks with Imperfect Beam Alignment

Blockage and imperfect beam alignment are two principal difficulties in high-frequency bands directional transmissions.In this paper,the coverage performance of downlink directional transmissions in ultra-dense networks is analyzed,with the consideration of beam alignment error and link blockage through stochastic geometry.Numerical experiments demonstrate that narrower beam leads to higher coverage probability with perfect beam alignment,but it is not the case with imperfect beam alignment.Therefore,the optimal beamwidth that maximize the coverage probability is characterized and a closed-form approximation of the optimal beamwidth is derived under imperfect beam alignment,accordingly.Furthermore,the optimal beamwidth is a monotonically increasing function of the standard deviation of the beam alignment error,and a monotonically decreasing function of the beamwidth of correspondent communication end,indicating that the beamwidth of the communication pairs ought to be jointly designed.

A Pilot Contamination Avoidance Based on Pilot Pattern Design for Ultra-Dense Network

With the ever-growing number of base stations(BSs)and user equipments(UEs)in ultra-dense networks(UDN),reusing the same pilot sequences among the cells is inevitable.With pilot reuse scheme,the channel estimation obtained at a BS contains not only the desired channel-state information(CSI)but also interference from neighboring cells,which can severely degrade CSI estimation performance and adversely affect communication performance.In this paper we consider a pilot contamination avoidance based on pilot pattern design for UDN where the pilot reuse employed and the interfering users from neighboring cells may be not at lower power levels at the BS compared to the in-cell users.We present a novel statistical interference model of sub-carriers to describe the non-deterministic interference from neighboring cells.Then,we provide a pilot pattern design model with non-uniform pilot distribution.Based on this,a pilot contamination avoidance based on pilot pattern design is proposed where pilot reuse scheme and the non-deterministic interference from neighboring cells are taken into consideration.Unlike existing interference mitigation approaches,the proposed method eliminates interference through the method of interference avoidance and can be applied to different kinds of channel estimation algorithms.Simulation results showed that the proposed approach can effectively avoid the interference and ensure the accuracy of channel estimation.

The analysis of coverage probability,ASE and EE in heterogeneous ultra-dense networks with power control

The ultra-dense network is a promising technology to increase the network capacity in the forthcoming fifthgeneration(5G)mobile communication networks by deploying lots of low power Small Base Stations(SBSs)which overlap with Macro Base Stations(MBSs).The interference and energy consumption increase rapidly with the number of SBSs although each SBS transmits with small power.In this paper,we model a downlink heterogeneous ultra-dense network where a lot of SBSs are randomly deployed with MBSs based on the Poisson point process.We derive the coverage probability and its variance,and analyze the area spectral efficiency and energy efficiency of the network considering three Fractional Power Control(FPC)strategies.The numerical results and Monte Carlo simulation results show that power control can mitigate the interference and balance the performances of inner-user and edge-user equipments.Especially,a great improvement of energy efficiency is archived with a little loss of area spectral efficiency when FPC is adopted.Finally,we analyze the effect of base stations’(BSs’)sleeping on the performance of the network when it is partially loaded.

Cross-Layer Framework for Capacity Analysis in Multiuser Ultra-Dense Networks with Cell DTx

Cell discontinuous transmission(Cell DTx)is a key technology to mitigate inter-cell interference(ICI)in ultra-dense networks(UDNs).The aim of this work is to understand the impact of Cell DTx on physical-layer sum rates of SBSs and link-layer quality-of-service(QoS)performance in multiuser UDNs.In this work,we develop a cross-layer framework for capacity analysis in multiuser UDNs with Cell DTx.In particular,we first extend the traditional one-dimensional effective capacity model to a new multidimensional effective capacity model to derive the sum rate and the effective capacity.Moreover,we propose a new iterative bisection search algorithm that is capable of approximating QoS performance.The convergence of this new algorithm to a unique QoS exponent vector is later proved.Finally,we apply this framework to the round-robin and the max-C/I scheduling policies.Simulation results show that our framework is accurate in approximating 1)queue length distribution,2)delay distribution and 3)sum rates under the above two scheduling policies,and further show that with the Cell DTx,systems have approximately 30% higher sum rate and 35% smaller average delay than those in full-buffer scenarios.

Distributed Chunk-Based Optimization for MultiCarrier Ultra-Dense Networks

In this paper,a distributed chunkbased optimization algorithm is proposed for the resource allocation in broadband ultra-dense small cell networks.Based on the proposed algorithm,the power and subcarrier allocation problems are jointly optimized.In order to make the resource allocation suitable for large scale networks,the optimization problem is decomposed first based on an effective decomposition algorithm named optimal condition decomposition(OCD) algorithm.Furthermore,aiming at reducing implementation complexity,the subcarriers are divided into chunks and are allocated chunk by chunk.The simulation results show that the proposed algorithm achieves more superior performance than uniform power allocation scheme and Lagrange relaxation method,and then the proposed algorithm can strike a balance between the complexity and performance of the multi-carrier Ultra-Dense Networks.

Distributed Resource Allocation in Ultra-Dense Networks via Belief Propagation

Ultra-dense networking is widely accepted as a promising enabling technology to realize high power and spectrum efficient communications in future 5G communication systems. Although joint resource allocation schemes promise huge performance improvement at the cost of cooperation among base stations,the large numbers of user equipment and base station make jointly optimizing the available resource very challenging and even prohibitive. How to decompose the resource allocation problem is a critical issue. In this paper,we exploit factor graphs to design a distributed resource allocation algorithm for ultra dense networks,which consists of power allocation,subcarrier allocation and cell association. The proposed factor graph based distributed algorithm can decompose the joint optimization problem of resource allocation into a series of low complexity subproblems with much lower dimensionality,and the original optimization problem can be efficiently solved via solving these subproblems iteratively. In addition,based on the proposed algorithm the amounts of exchanging information overhead between the resulting subprob-lems are also reduced. The proposed distributed algorithm can be understood as solving largely dimensional optimization problem in a soft manner,which is much preferred in practical scenarios. Finally,the performance of the proposed low complexity distributed algorithm is evaluated by several numerical results.

Reinforcement Learning-Based Optimization for Drone Mobility in 5G and Beyond Ultra-Dense Networks

Drone applications in 5th generation(5G)networks mainly focus on services and use cases such as providing connectivity during crowded events,human-instigated disasters,unmanned aerial vehicle traffic management,internet of things in the sky,and situation awareness.4G and 5G cellular networks face various challenges to ensure dynamic control and safe mobility of the drone when it is tasked with delivering these services.The drone can fly in three-dimensional space.The drone connectivity can suffer from increased handover cost due to several reasons,including variations in the received signal strength indicator,co-channel interference offered to the drone by neighboring cells,and abrupt drop in lobe edge signals due to antenna nulls.The baseline greedy handover algorithm only ensures the strongest connection between the drone and small cells so that the drone may experience several handovers.Intended for fast environment learning,machine learning techniques such as Q-learning help the drone fly with minimum handover cost along with robust connectivity.In this study,we propose a Q-learning-based approach evaluated in three different scenarios.The handover decision is optimized gradually using Q-learning to provide efficient mobility support with high data rate in time-sensitive applications,tactile internet,and haptics communication.Simulation results demonstrate that the proposed algorithm can effectively minimize the handover cost in a learning environment.This work presents a notable contribution to determine the optimal route of drones for researchers who are exploring UAV use cases in cellular networks where a large testing site comprised of several cells with multiple UAVs is under consideration.

Novel Path Counting-Based Method for Fractal Dimension Estimation of the Ultra-Dense Networks

Next-generation networks,including the Internet of Things(IoT),fifth-generation cellular systems(5G),and sixth-generation cellular systems(6G),suf-fer from the dramatic increase of the number of deployed devices.This puts high constraints and challenges on the design of such networks.Structural changing of the network is one of such challenges that affect the network performance,includ-ing the required quality of service(QoS).The fractal dimension(FD)is consid-ered one of the main indicators used to represent the structure of the communication network.To this end,this work analyzes the FD of the network and its use for telecommunication networks investigation and planning.The clus-ter growing method for assessing the FD is introduced and analyzed.The article proposes a novel method for estimating the FD of a communication network,based on assessing the network’s connectivity,by searching for the shortest routes.Unlike the cluster growing method,the proposed method does not require multiple iterations,which reduces the number of calculations,and increases the stability of the results obtained.Thus,the proposed method requires less compu-tational cost than the cluster growing method and achieves higher stability.The method is quite simple to implement and can be used in the tasks of research and planning of modern and promising communication networks.The developed method is evaluated for two different network structures and compared with the cluster growing method.Results validate the developed method.

Joint Computation Offloading and Resource Allocation for Mobile-Edge Computing Assisted Ultra-Dense Networks

Mobile-edge computing(MEC),enabling to offload computing tasks on mobile devices towards edge servers,can reduce the terminals cost.However,a single MEC sever usually has limited computing capabilities,which can not meet a large number of terminals’requirements.In this paper,we consider an ultra-dense networks(UDN)scenario where the macro base stations(MBSs)are assisted by MEC severs.In particular,we first construct system model for MEC assisted UDN,and build the system overhead minimization.Next,in order to solve the problem,we transform the problem into three sub-problems,i.e.,offloading strategies subproblem,channel assignments subproblem,and power allocation subproblem.Then,employing joint offloading and resource allocation algorithms,we obtain the optimal joint strategy for the MEC assisted UDNs.Finally,simulations are conducted to evaluate the performance of our proposed algorithms.Numerical results show that obtained algorithms can effectively reduce the energy consumption of the system and improve the overall performance of the system.

Spectrum allocation for wireless backhaul in heterogeneous ultra-dense networks

Wireless ultra-dense network （UDN） is one of the important technologies to solve the burst of throughput demand in the forthcoming fifth generation （SG） cellular networks. Reusing spectrum resource for the backhaul of small base stations （SBSs） is a hotspot research because of lower cost and rapid implementation with macro base stations （MBSs） in recent years. In heterogeneous UDN, the problem of spectrum allocation for wireless backhaul is investigated. In particular, two different spectrum resource reusing strategies for wireless backhaul are proposed in heterogeneous UDN with the limited bandwidth condition. Using a stochastic geometry-based heterogeneous UDN model, the success probabilities that mobile users communicate with SBSs or MBSs are derived under two different spectrum resource reusing strategies. In addition, the network throughput＇s analytical expressions and the optimal ratio of spectrum allocation are derived. Numeral results are provided to evaluate the performance of the proposed strategies at throughput. Thus, the effectiveness of the strategy that mobile users can only communicate with SBSs is validated.

Dynamic computation offloading in time-varying environment for ultra-dense networks:a stochastic game approach

To meet the demands of large-scale user access with computation-intensive and delay-sensitive applications,combining ultra-dense networks(UDNs)and mobile edge computing(MEC)are considered as important solutions.In the MEC enabled UDNs,one of the most important issues is computation offloading.Although a number of work have been done toward this issue,the problem of dynamic computation offloading in time-varying environment,especially the dynamic computation offloading problem for multi-user,has not been fully considered.Therefore,in order to fill this gap,the dynamic computation offloading problem in time-varying environment for multi-user is considered in this paper.By considering the dynamic changes of channel state and users’queue state,the dynamic computation offloading problem for multi-user is formulated as a stochastic game,which aims to optimize the delay and packet loss rate of users.To find the optimal solution of the formulated optimization problem,Nash Q-learning(NQLN)algorithm is proposed which can be quickly converged to a Nash equilibrium solution.Finally,extensive simulation results are presented to demonstrate the superiority of NQLN algorithm.It is shown that NQLN algorithm has better optimization performance than the benchmark schemes.