Hybrid millimeter wave heterogeneous networks with spatially correlated user equipment

In this paper,we analyze a hybrid Heterogeneous Cellular Network(HCNet)framework by deploying millimeter Wave(mmWave)small cells with coexisting traditional sub-6GHz macro cells to achieve improved coverage and high data rate.We consider randomly-deployed macro base stations throughout the network whereas mmWave Small Base Stations(SBSs)are deployed in the areas with high User Equipment(UE)density.Such user centric deployment of mmWave SBSs inevitably incurs correlation between UE and SBSs.For a realistic scenario where the UEs are distributed according to Poisson cluster process and directional beamforming with line-of-sight and non-line-of-sight transmissions is adopted for mmWave communication.By using tools from stochastic geometry,we develop an analytical framework to analyze various performance metrics in the downlink hybrid HCNets under biased received power association.For UE clustering we considered Thomas cluster process and derive expressions for the association probability,coverage probability,area spectral efficiency,and energy efficiency.We also provide Monte Carlo simulation results to validate the accuracy of the derived expressions.Furthermore,we analyze the impact of mmWave operating frequency,antenna gain,small cell biasing,and BSs density to get useful engineering insights into the performance of hybrid mmWave HCNets.Our results show that network performance is significantly improved by deploying millimeter wave SBS instead of microwave BS in hot spots.

Joint User Association and Fair Scheduling for Dual Connectivity Heterogeneous Networks

Heterogeneous small cell network is one of the most effective solutions to overcome spectrum scarcity for the next generation of mobile networks.Dual connectivity(DC)can improve the throughput for each individual user by allowing concurrent access to two heterogeneous radio networks.In this paper,we propose a joint user association and fair scheduling algorithm(JUAFS)to deal with the resource allocation and load balancing issues for DC heterogeneous small cell networks.Considering different coverage sizes,numbers of users,and quality of experience characteristics of heterogeneous cells,we present a proportional fair scheduling for user association among cells and utilize interference graph to minimize the transmission conflict probability.Simulation results show the performance improvement of the proposed algorithm in spectrum efficiency and fairness comparing to the existing schemes.

Analysis on D2D Heterogeneous Networks with State-Dependent Priority Traffic

In this work,we consider the performance analysis of state dependent priority traffic and scheduling in device to device(D2D)heterogeneous networks.There are two priority transmission types of data in wireless communication,such as video or telephone,which always meet the requirements of high priority(HP)data transmission first.If there is a large amount of low priority(LP)data,there will be a large amount of LP data that cannot be sent.This situation will cause excessive delay of LP data and packet dropping probability.In order to solve this problem,the data transmission process of high priority queue and low priority queue is studied.Considering the priority jump strategy to the priority queuing model,the queuing process with two priority data is modeled as a two-dimensionalMarkov chain.A state dependent priority jump queuing strategy is proposed,which can improve the discarding performance of low priority data.The quasi birth and death process method(QBD)and fixed point iterationmethod are used to solve the causality,and the steady-state probability distribution is further obtained.Then,performance parameters such as average queue length,average throughput,average delay and packet dropping probability for both high and low priority data can be expressed.The simulation results verify the correctness of the theoretical derivation.Meanwhile,the proposed priority jump queuing strategy can significantly improve the drop performance of low-priority data.

Adaptive Learning Video Streaming with QoE in Multi-Home Heterogeneous Networks

In recent years,real-time video streaming has grown in popularity.The growing popularity of the Internet of Things(IoT)and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience(QoE)and performance objectives.Most researchers focused on Forward Error Correction(FEC)techniques when attempting to strike a balance between QoE and performance.However,as network capacity increases,the performance degrades,impacting the live visual experience.Recently,Deep Learning(DL)algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks.But these algorithms need to be changed to make the experience better without sacrificing packet loss and delay time.To address the previous challenge,this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric characteristics.The proposed framework contains modules such as Intelligent Content Extraction Module(ICEM),Channel Status Monitor(CSM),and Adaptive FEC(AFEC).This framework adopts the Cognitive Learning-based Scheduling(CLS)Module,which works on the deep Reinforced Gated Recurrent Networks(RGRN)principle and embeds them along with the FEC to achieve better performances.The complete framework was developed using the Objective Modular Network Testbed in C++(OMNET++),Internet networking(INET),and Python 3.10,with Keras as the front end and Tensorflow 2.10 as the back end.With extensive experimentation,the proposed model outperforms the other existing intelligentmodels in terms of improving the QoE,minimizing the End-to-End Delay(EED),and maintaining the highest accuracy(98%)and a lower Root Mean Square Error(RMSE)value of 0.001.

Stability and optimal control for delayed rumor-spreading model with nonlinear incidence over heterogeneous networks

On the multilingual online social networks of global information sharing,the wanton spread of rumors has an enormous negative impact on people's lives.Thus,it is essential to explore the rumor-spreading rules in multilingual environment and formulate corresponding control strategies to reduce the harm caused by rumor propagation.In this paper,considering the multilingual environment and intervention mechanism in the rumor-spreading process,an improved ignorants–spreaders-1–spreaders-2–removers(I2SR)rumor-spreading model with time delay and the nonlinear incidence is established in heterogeneous networks.Firstly,based on the mean-field equations corresponding to the model,the basic reproduction number is derived to ensure the existence of rumor-spreading equilibrium.Secondly,by applying Lyapunov stability theory and graph theory,the global stability of rumor-spreading equilibrium is analyzed in detail.In particular,aiming at the lowest control cost,the optimal control scheme is designed to optimize the intervention mechanism,and the optimal control conditions are derived using the Pontryagin's minimum principle.Finally,some illustrative examples are provided to verify the effectiveness of the theoretical results.The results show that optimizing the intervention mechanism can effectively reduce the densities of spreaders-1 and spreaders-2 within the expected time,which provides guiding insights for public opinion managers to control rumors.

Adaptive Load Balancing for Parameter Servers in Distributed Machine Learning over Heterogeneous Networks

In distributed machine learning(DML)based on the parameter server(PS)architecture,unbalanced communication load distribution of PSs will lead to a significant slowdown of model synchronization in heterogeneous networks due to low utilization of bandwidth.To address this problem,a network-aware adaptive PS load distribution scheme is proposed,which accelerates model synchronization by proactively adjusting the communication load on PSs according to network states.We evaluate the proposed scheme on MXNet,known as a realworld distributed training platform,and results show that our scheme achieves up to 2.68 times speed-up of model training in the dynamic and heterogeneous network environment.

A Novel Resource Allocation Algorithm based on Downlink and Uplink Decouple Access Scheme in Heterogeneous Networks

Traditional cellular network requires that a user equipment(UE) should associate to the same base station(BS) in both the downlink(DL) and the uplink(UL). Based on dual connectivity(DC) introduced in LTE-Advanced R12, DL/UL decouple access scheme has been proposed, which is especially suitable for heterogeneous networks(Het Nets). This paper is the pioneer to take the DL/UL decouple access scheme into consideration and develop a novel resource allocation algorithm in a two-tier Het Net to improve the total system throughput in the UL and ease the load imbalance between macro base stations(MBSs) and pico base stations(PBSs). A model is formulated as a nonlinear integer programming, and the proposed algorithm is a sub-optimal algorithm based on the graph theory. First, an undirected and weighted interference graph is obtained. Next, the users are grouped to let users with large mutual interferences to be assigned to different clusters. Then, the users in different clusters are allocated to different resource blocks(RBs) by using the Hungarian algorithm. Simulation results show that the proposed algorithm can provide great promotions for both the total system throughput and the average cell edge user throughput and successfully ease the load imbalance between MBSs and PBSs.

A Novel Vertical Handoff Algorithm with Dynamic Weights in Wireless Heterogeneous Networks

In order to enhance the quality of vertical handoff in an overlay wireless network, multiple attributes are taken into account when optimizing the vertical handoff decision including user-based and network-based QoS factors. In this paper, we develop a novel vertical handoff algorithm in an integrated 3G cellular and Wireless LAN networks. The proposed algorithm can adjust the weight of each QoS attribute dynamically as the networks change, trace the network condition and choose the optimal access point at transient regions. Simulation results show that this algorithm is able to provide accurate handoff decision, resulting in small unnecessary handoff numbers, good performance of throughput and handoff delay in heterogeneous environments.

Interference management via access control and mobility prediction in two-tier heterogeneous networks

Abstract： Two-tier heterogeneous networks （HetNets）, where the current cellular networks, i.e., macrocells, are overlapped with a large number of randomly distributed femtocells, can potentially bring significant benefits to spectral utilization and system capacity. The interference management and access control for open and closed femtocells in two-tier HetNets were focused. The contributions consist of two parts. Firstly, in order to reduce the uplink interference caused by MUEs （macrocell user equipments） at closed femtocells, an incentive mechanism to implement interference mitigation was proposed. It encourages femtoeells that work with closed-subscriber-group （CSG） to allow the interfering MUEs access in but only via uplink, which can reduce the interference significantly and also benefit the marco-tier. The interference issue was then studied in open-subscriber-group （OSG） femtocells from the perspective of handover and mobility prediction. Inbound handover provides an alternative solution for open femtocells when interference turns up, while this accompanies with PCI （physical cell identity） confusion during inbound handover. To reduce the PCI confusion, a dynamic PCI allocation scheme was proposed, by which the high handin femtocells have the dedicated PCI while the others share the reuse PCIs. A Markov chain based mobility prediction algorithm was designed to decide whether the femtoeell status is with high handover requests. Numerical analysis reveals that the UL interference is managed well for the CSG femtocell and the PCI confusion issue is mitigated greatly in OSG femtocell compared to the conventional approaches.

Context-Oriented Multi-RAT User Association and Resource Allocation with Triple Decision in 5G Heterogeneous Networks

In the upcoming 5 G heterogeneous networks, leveraging multiple radio access technologies(RATs) shows to be a crucial issue in achieving RAT multiplexing gain to meet the explosive traffic demand. For always best connection(ABC), users tend to activate parallel transmission across all available RATs. However from a system-wide perspective, this might not be optimal given the context of network load, interference and diverse service requirements. To intelligently determine how to use these multi-RAT access resources concurrently, this paper proposes a joint multi-RAT user association and resource allocation strategy with triple decision and integrated context awareness of users and networks. A dynamic game based ant colony algorithm(GACA) is designed to simultaneously maximize the system utility and the fairness of resource allocation. Simulation results show that it's more reasonable to make multi-RAT association decision from a system-wide viewpoint than from an individual one. Compared to max-SNR based and ABC based strategies, the proposed method alleviates network congestion and optimizes resource allocation. It obtains 39%~70% performance improvement.

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 Io T 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 Het Nets 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.

A Resource Scheduling Scheme for Spectrum Aggregation in Cognitive Radio Based Heterogeneous Networks

In spectrum aggregation(SA), two or more component carriers(CCs) of different bandwidths in different bands can be aggregated to support wider transmission bandwidth. The current resource scheduling schemes for spectrum aggregation are not optimal or suitable for CR based heterogeneous networks(Het Nets). Consequently, the authors propose a novel resource scheduling scheme for spectrum aggregation in CR based Het Nets, termed as cognitive radio based resource scheduling(CR-RS) scheme. CR-RS has a three-level structure. Under a dynamic traffic model, an equivalent throughput of the CCs based on the knowledge of primary users(PUs) is given. On this basis, the CR users data transmission time of each CC is equal in CR-RS. The simulation results show that CR-RS has the better performance than the current resource scheduling schemes in the CR based Het Nets. Meanwhile, CR-RS is also effective in other spectrum aggregation systems which are not CR based HetNets.

Clustering-based adaptive low-power subframe configuration with load-aware offsetting in dense heterogeneous networks

Heterogeneous Networks(HetNets)and cell densification represent promising solutions for the surging data traffic demand in wireless networks.In dense HetNets,user traffic is steered toward the Low-Power Node(LPN)when possible to enhance the user throughput and system capacity by increasing the area spectral efficiency.However,because of the transmit power differences in different tiers of HetNets and irregular service demand,a load imbalance typically exists among different serving nodes.To offload more traffic to LPNs and coordinate the Inter-Cell Interference(ICI),Third-Generation Partnership Project(3GPP)has facilitated the development of the Cell Range Expansion(CRE),enhanced Inter-Cell Interference Coordination(eICIC)and Further enhanced ICIC(FeICIC).In this paper,we develop a cell clustering-based load-aware offsetting and an adaptive Low-Power Subframe(LPS)approach.Our solution allows the separation of User Association(UA)functions at the User Equipment(UE)and network server such that users can make a simple cell-selection decision similar to that in the maximum Received Signal Strength(max-RSS)based UA scheme,where the network server computes the load-aware offsetting and required LPS periods based on the load conditions of the system.The proposed solution is evaluated using system-level simulations wherein the results correspond to performance changes in different service regions.Results show that our method effectively solves the offloading and interference coordination problems in dense HetNets.

Mobile Social Networking Aided Content Dissemination in Heterogeneous Networks

Since more and more mobile applications are based on the proliferation of social information, the study of Mobile Social Networks （MSNs） combines social sciences and wireless communications. Operating wireless networks more efficiently by exploiting social relationships between MSN users is an appealing but challenging option for network operators. An MSN-aided content dissemination technique is presented as a potential extension of conventional cellular wireless networks in order to satisfy growing data traffic. By allowing the MSN users to create a self-organized ad hoc network for spontaneously disseminating contents, the network operator may be able to reduce the operational costs and simultaneously achieve an improved network performance. In this paper, we first summarize the basic features of the MSN architecture, followed by a survey of the factors which may affect MSN-aided content dissemination. Using a case study, we demonstrate that one can save resources of the Base Station （BS） while substantially lowering content dissemination delay. Finally, other potential applications of MSN-aided content dissemination are introduced, and a range of lustre challenges are summarized.

Resource Control Architecture in Heterogeneous Networks

TISPAN,from a fixed access perspective,proposes Resource and Admission Control Subsystem[0](RACS) as a solution to Quality of Service(QoS) problem for NGN bearer network.In contrast,3GPP has an approach to this from the perspective of mobile access.In the latest 3GPP R7 draft,integration of Policy Control Function(PCF) with Flow Based Charging(FBC) function of the R6 brought forward policy control and charging.With the development of fixed mobile convergence,the inconsistence in architectures and interfaces of different resource and admission control[0] solutions will have a huge impact on manufacture and network implementation of NGN related equipment.To solve this problem,both 3GPP and TISPAN have been working on the convergence of Gq’/Rx reference points.Harmonized Policy Control and Charging(PCC) proposed by the Next Generation Mobile Network(NGMN) forum,i.e.cooperative resource control architecture for heterogeneous networks,represents an evolutional sign post for resource control technology for heterogeneous network architecture.

TCP Upload and Download Fairness over IEEE 802.11 Wired-cum-Wireless Heterogeneous Networks

A scheme of setting a limit to the TCP sending window size is proposed to improve the TCP fairness between upload and/or download flows in wired-cum-wireless networks. The goodput and delay of the upload and download TCP flows arc compared to evaluate the TCP fairness for different schemes, which are the different combinations of setting a limit （64 or 4） to the sending window size and using the delayed acknowledgement （ACK） scheme or not. Extensive simulation results and analysis show that （ 1 ） for TCP download flows, setting the limit of sending window size to 4 can improve the fairness; （2） for TCP upload flows, limiting the sending window size and using the delayed ACK strategy are both beneficial to fairness; （3） for TCP download and upload mixture flows, limiting the sending window size to a small value （ e. g. , 4） rather than using the delayed ACK strategy, is the solution to improvement of the fairness ; （4） a large delay interval （200 ms or 300 ms） does not result in improvement in fairness and performance; （ 5 ） a larger TCP packet size （ 1400 B） can improve the TCP upload goodput and decrease the download goodput; in contrast, a smaller TCP packet size （560 B） can increase the download goodput and decrease the upload goodput.

Robust signal recognition algorithm based on machine learning in heterogeneous networks

There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio（SNR） circumstances or under time-varying multipath channels, the majority of the existing algorithms for signal recognition are already facing limitations. In this series, we present a robust signal recognition method based upon the original and latest updated version of the extreme learning machine（ELM） to help users to switch between networks. The ELM utilizes signal characteristics to distinguish systems. The superiority of this algorithm lies in the random choices of hidden nodes and in the fact that it determines the output weights analytically, which result in lower complexity. Theoretically, the algorithm tends to offer a good generalization performance at an extremely fast speed of learning. Moreover, we implement the GSM/WCDMA/LTE models in the Matlab environment by using the Simulink tools. The simulations reveal that the signals can be recognized successfully to achieve a 95% accuracy in a low SNR（0 dB） environment in the time-varying multipath Rayleigh fading channel.

A novel modeling approach for vertical handover based on dynamic k-partite graph in heterogeneous networks

The future network world will be embedded with different generations of wireless technologies,such as 3G,4G and 5G.At the same time,the development of new devices equipped with multiple interfaces is growing rapidly in recent years.As a consequence,the vertical handover protocol is developed in order to provide ubiquitous connectivity in the heterogeneous wireless environment.Indeed,by using this protocol,the users have opportunities to be connected to the Internet through a variety of wireless technologies at any time and anywhere.The main challenge of this protocol is how to select the best access network in terms of Quality of Service(QoS)for users.For that,many algorithms have been proposed and developed to deal with the issue in recent studies.However,all existing algorithms permit only the selection of one access network from the available networks during the vertical handover process.To cope with this problem,in this paper we propose a new approach based on k-partite graph.Firstly,we introduce k-partite graph theory to model the vertical handover problem.Secondly,the selection of the best path is performed by a robust and lightweight mechanism based on cost function and Dijkstra’s algorithm.The experimental results show that the proposed approach can achieve better performance of QoS than the existing algorithms for FTP traffic and video streaming.

Vertical handoff algorithm in heterogeneous networks to maximize system benefit

A new vertical handoff decision algorithm is proposed to maximize the system benefit in heterogeneous wireless networks which comprise cellular networks and wireless local area networks （WLANs）. Firstly the block probability, the drop probability and the number of users in the heterogeneous networks are calculated in the channel-guard call admission method, and a function of the system benefit which is based on the new call arrival rate and the handoff call arrival rate is proposed. Then the optimal radius of WLAN is obtained by using simulation annealing （SA） method to maximize the benefit. All the nodes should handoff from cellular network to WLAN if they enter WLAN＇s scope and handoff from WLAN to cellular network if they leave the scope. Finally, the algorithm in different new call arrival rates and handoff call arrival rates is analyzed and results show that it can achieve good effects.

Robust resource allocation for NOMA-assisted heterogeneous networks

As a promising technology to improve spectrum efficiency and transmission coverage,Heterogeneous Network(HetNet)has attracted the attention of many scholars in recent years.Additionally,with the introduction of the Non-Orthogonal Multiple Access(NOMA)technology,the NOMA-assisted HetNet cannot only improve the system capacity but also allow more users to utilize the same frequency band resource,which makes the NOMA-assisted HetNet a hot topic.However,traditional resource allocation schemes assume that base stations can exactly estimate direct link gains and cross-tier link gains,which is impractical for practical HetNets due to the impact of channel delays and random perturbation.To further improve energy utilization and system robustness,in this paper,we investigate a robust resource allocation problem to maximize the total Energy Efficiency(EE)of Small-Cell Users(SCUs)in NOMA-assisted HetNets under imperfect channel state information.By considering bounded channel uncertainties,the robust resource optimization problem is formulated as a mixed-integer and nonlinear programming problem under the constraints of the cross-tier interference power of macrocell users,the maximum transmit power of small base station,the Resource Block(RB)assignment,and the quality of service requirement of each SCU.The original problem is converted into an equivalent convex optimization problem by using Dinkelbach's method and the successive convex approximation method.A robust Dinkelbach-based iteration algorithm is designed by jointly optimizing the transmit power and the RB allocation.Simulation results verify that the proposed algorithm has better EE and robustness than the existing algorithms.